Resolving brain circuit function and dysfunction by computational modeling and optogenetic functional magnetic resonance imaging

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are provided for optimizing neurostimulation therapy for the treatment of nervous system diseases and neurodegenerative diseases. Combined dynamic causal modeling and biophysics modeling are used for optimization of stimulation targets and parameters. In particular, methods of performing neuromodulation to inhibit b-band oscillations in the brain of a subject are provided.

Description

[0001] Cross - reference to related applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 377,960, filed Sep. 30, 2022, which is incorporated herein by reference in its entirety.

[0003] Statement regarding federally - sponsored research or development

[0004] This invention was made with government support under Contracts MH114227, NS116783, NS091461, EB030884, and AG064051 awarded by the National Institutes of Health. The government has certain rights in this invention. Background of the invention

[0006] Neuronal firing rate and patterns are both crucial in unraveling the dynamics of brain function and dysfunction (Engel et al., Nat Rev Neurosci 2, 704 - 716 (2001); Cannon et al., Eur J Neurosci 39, 705 - 719 (2014); Schoffelen et al., Science 308, 111 - 113 (2005)). For example, in Parkinson's disease (PD), altered firing rates and patterns in the form of abnormal beta - band (12 - 30 Hz) oscillations across the cortico - basal ganglia - thalamus (CBT) network have been observed (Albin et al., Trends Neurosci 12, 366 - 375 (1989); DeLong, Trends Neurosci 13, 281 - 285 (1990); Kühn et al., Brain 127, 735 - 746 (2004)). Understanding how the CBT network mediates neural oscillations and how neural oscillations relate to the firing rates of individual cell populations can help decode the circuit mechanisms underlying PD pathology and improve neuromodulation therapies. Summary of the invention

[0007] Methods, systems, and devices are provided, including computer programs encoded on computer storage media, for optimizing neuromodulation therapies for treating neurological and neurodegenerative diseases. Joint dynamic causal modeling and biophysical modeling are used for the optimization of stimulation targets and parameters. In particular, methods for performing neuromodulation to inhibit beta - band oscillations in a subject's brain are provided.

[0008] In one aspect, a method for suppressing beta-band oscillations in a subject's brain is provided, wherein optogenetic neuromodulation of medium spiny neurons is performed according to a method comprising the following steps: (a) optogenetically inhibiting D1-medium spiny neurons (D1-MSNs) in the globus pallidus internus (GPi) region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are suppressed; (b) optogenetically inhibiting D2-medium spiny neurons (D2-MSNs) in the globus pallidus externus (Gpe) region of the subject's brain, wherein D2-MSN-mediated beta-band oscillations are suppressed; (c) periodically optogenetically stimulating D1-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are suppressed; or (d) randomly optogenetically stimulating D1-MSNs and D2-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations and D2-MSN-mediated beta-band oscillations are suppressed; or any combination of (a)-(d).

[0009] In certain embodiments, the method comprises: (a) optogenetically inhibiting D1-MSNs in the GPi region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are suppressed; (b) optogenetically inhibiting D2-MSNs in the Gpe region of the subject's brain, wherein D2-MSN-mediated beta-band oscillations are suppressed; (c) periodically optogenetically stimulating D1-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are suppressed; and (d) randomly optogenetically stimulating D1-MSNs and D2-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations and D2-MSN-mediated beta-band oscillations are suppressed.

[0010] In certain embodiments, optogenetically inhibiting D1-MSNs or D2-MSNs in the GPi region or Gpe region comprises: introducing a recombinant polynucleotide encoding a light-responsive ion channel into D1-MSNs or D2-MSNs in the GPi region or Gpe region, wherein the light-responsive ion channel is expressed in the D1-MSNs or D2-MSNs; and irradiating the light-responsive ion channel with light of a wavelength that activates the light-responsive ion channel, wherein ion conduction through the light-responsive ion channel in response to light absorption results in hyperpolarization and inhibition of the D1-MSNs or D2-MSNs.

[0011] In certain embodiments, the light-responsive ion channel is a light-responsive anion-conducting opsin or a light-responsive proton conductance regulator.

[0012] In certain embodiments, a light-responsive anion-conducting opsin conducts chloride ions (Cl - -).

[0013] In certain embodiments, the anion-conducting opsin is anion-conducting channelrhodopsin or halorhodopsin.

[0014] In certain embodiments, the halorhodopsin is Natronomonas pharaonis halorhodopsin (NpHR), enhanced NpHR (eNpHR) 1.0, eNpHR 2.0, or eNpHR 3.0.

[0015] In certain embodiments, the anion-conducting channelrhodopsin is iC1C2, SwiChR, SwiChR++, or iC++.

[0016] In certain embodiments, the light-responsive proton-conducting modulator is bacteriorhodopsin or archaeorhodopsin.

[0017] In certain embodiments, the light-responsive proton-conducting modulator is Arch from Halorubrum sodomense, ArchT from Halorubrum sp., TP009 from Leptosphaeria maculans, or Mac from Leptosphaeria maculans.

[0018] In certain embodiments, optogenetic stimulation of D1-MSNs or D2-MSNs in the Gpi region or Gpe region comprises: introducing a recombinant polynucleotide encoding a light-responsive ion channel into D1-MSNs or D2-MSNs in the Gpi region or Gpe region, wherein the light-responsive ion channel is expressed in the D1-MSNs or D2-MSNs; and irradiating the light-responsive ion channel with light of a wavelength that activates the light-responsive ion channel, wherein ion conduction through the light-responsive ion channel in response to light absorption results in depolarization and activation of the D1-MSNs or D2-MSNs.

[0019] In certain embodiments, the light-responsive ion channel is a light-responsive cation-conducting opsin.

[0020] In certain embodiments, the light-responsive cation-conducting opsin conducts calcium cations (Ca 2+ 2+).

[0021] In certain embodiments, the light-responsive cation-conducting opsin is light-responsive cation-conducting channelrhodopsin.

[0022] In certain embodiments, the light-responsive cation-conducting channelrhodopsin is Chlamydomonas reinhardtii channelrhodopsin or Volvox carteri channelrhodopsin.

[0023] In certain embodiments, the light-responsive cation-conducting channelrhodopsin is Chlamydomonas reinhardtii channelrhodopsin-1 (ChR1), Chlamydomonas reinhardtii channelrhodopsin-2 (ChR2), Volvox carteri channelrhodopsin-1 (VChR1), or chimeric ChR1-VChR1 channelrhodopsin.

[0024] In certain embodiments, the polynucleotide encoding the light-responsive ion channel is provided by a viral vector.

[0025] In certain embodiments, the viral vector is a lentiviral vector or an adeno-associated virus (AAV) vector.

[0026] In certain embodiments, the viral vector is stereotactically injected into the retrosplenial cortex.

[0027] In certain embodiments, the vector further comprises a neuron-specific promoter operably linked to the polynucleotide encoding the light-responsive ion channel.

[0028] In certain embodiments, the expression of the light-responsive ion channel is inducible.

[0029] In certain embodiments, irradiating the light-responsive ion channel comprises delivering light from a light source to the light-responsive ion channel using a fiber-optic based optical neural interface.

[0030] In certain embodiments, the light source is a solid-state diode laser.

[0031] In certain embodiments, the subject has Parkinson's disease.

[0032] In certain embodiments, optogenetic inhibition of D1-MSNs comprises sustained shunting inhibition of D1-MSNs in the GPi region of the subject's brain.

[0033] In certain embodiments, optogenetic inhibition of D2-MSNs comprises sustained shunting inhibition of D2-MSNs in the GPe region of the subject's brain.

[0034] In certain embodiments, periodic optogenetic stimulation of D1-MSNs in the Gpe region of the subject's brain comprises periodic stimulation at a frequency of 130 Hz.

[0035] In certain embodiments, optogenetic stimulation of D1-MSNs and D2-MSNs randomly in the Gpe region of the subject's brain includes using a plurality of stimulation sequences with pulses having a random interval of 130 Hz, wherein each neuron is stimulated with one of the stimulation sequences in the stimulation sequence such that synchronized neurons are decoupled.

[0036] In certain embodiments, optogenetic stimulation of D1-MSNs or D2-MSNs includes directly activating D1-MSNs or D2-MSNs with an intensity of 500 pA, 700 pA, or 900 pA.

[0037] In another aspect, a method of treating Parkinson's disease in a subject is provided, the method comprising: positioning a first electrode at a first location in the medial globus pallidus (Gpi) region of the subject's brain to deliver electrical stimulation to D1-medium spiny neurons in the GPi region; positioning a second electrode at a second location in the lateral globus pallidus (Gpe) region of the subject's brain to deliver electrical stimulation to D1-medium spiny neurons and D2-medium spiny neurons in the Gpe region; and using the first electrode to apply electrical stimulation to the Gpi region of the subject's brain and using the second electrode to apply electrical stimulation to the Gpe region of the subject's brain in a manner effective to suppress beta band oscillations to treat Parkinson's disease.

[0038] In certain embodiments, electrical stimulation is applied unilaterally or bilaterally with the first electrode or the second electrode.

[0039] In certain embodiments, the first electrode or the second electrode is a depth electrode or a surface electrode.

[0040] In certain embodiments, the first electrode or the second electrode is a non-brain-penetrating surface electrode array or a brain-penetrating electrode array.

[0041] In certain embodiments, the first electrode is placed on the surface of the Gpi region.

[0042] In certain embodiments, the second electrode is placed on the surface of the Gpe region.

[0043] In certain embodiments, the method further comprises using a visual analogue scale, a speech rating scale, the Movement Disorder Society-sponsored Unified Parkinson's Disease Rating Scale Revision (MDS-UPDRS), the Hoehn and Yahr (HnY) scale, or the Montreal Cognitive Assessment (MoCA) scale to evaluate the effectiveness of the treatment of the subject.

[0044] On the other hand, a computer-implemented method is provided for modeling the propagation of beta-band oscillations in a subject's brain and the response to neuromodulation, the computer performing the following steps, including: a) receiving functional magnetic resonance imaging data of the neural activity of D1-medium spiny neurons (D1-MSNs) and D2-medium spiny neurons (D2-MSNs) in the caudate putamen (CPu) region, globus pallidus externa (GPe) region, globus pallidus interna (GPi) region, subthalamic nucleus (STN) region, substantia nigra reticulata (SNr) region, thalamus (THL) region, and motor cortex (MCX) region of the subject's brain before and during optogenetic stimulation; b) performing spectral dynamic causal modeling on the effective connectivity strengths between the CPu region, GPe region, GPi region, STN region, SNr region, THL region, and MCX region; c) receiving experimental electrophysiological data of the CPu region; d) using the experimental electrophysiological data of the CPu region to estimate the effective connectivity strength of GABAergic connections; e) using dynamic causal modeling of the functional magnetic resonance imaging data to estimate the effective connectivity strength of glutamatergic connections; f) performing biophysical modeling using the Hodgkin-Huxley model to generate simulated electrophysiological data using the effective connectivity strength estimates; g) iteratively optimizing the GABAergic projections until the simulated electrophysiological data matches the experimental electrophysiological data of the CPu region; h) calculating the temporal profile of the average power of the beta-band frequency of each neuron in the CPu region, GPe region, GPi region, STN region, SNr region, THL region, and MCX region; and i) comparing the total amount of co-occurring beta-band oscillation power before optogenetic stimulation with the co-occurring beta-band oscillation power during optogenetic stimulation to model the propagation of beta-band oscillations in the subject's brain.

[0045] In certain embodiments, the D1-MSNs are Huxley-Hudgkin neurons.

[0046] In certain embodiments, the experimental electrophysiological data includes single neuron recordings.

[0047] In certain embodiments, the single neuron recordings are from GABAergic neurons in the CPu region.

[0048] In certain embodiments, the glutamatergic connections are modeled as 1-to-1 connections, where the connection strength is proportional to the effective connectivity estimated by DCM.

[0049] In certain embodiments, the CPU-GPi / GPe and GPi / SNr-thalamic GABAergic projections are modeled as a one-to-n diffuse projection with a connectivity strength wGABA, where n and wGABA are free parameters, and where n and wGABA are searched across the parameter space until the simulated spike rate statistically matches the single neuron recordings.

[0050] In certain embodiments, optogenetic stimulation includes optogenetic inhibition of D1-MSNs in the GPi region of the subject's brain.

[0051] In certain embodiments, optogenetic inhibition of D1-MSNs includes continuous shunting inhibition of D1-MSNs in the GPi region of the subject's brain.

[0052] In certain embodiments, optogenetic stimulation includes optogenetic inhibition of D2-MSNs in the GPe region of the subject's brain.

[0053] In certain embodiments, optogenetic inhibition of D2-MSNs includes continuous shunting inhibition of D2-MSNs in the GPe region of the subject's brain.

[0054] In certain embodiments, optogenetic stimulation includes direct activation of D1-MSNs or D2-MSNs at intensities of 500 pA, 700 pA, or 900 pA.

[0055] In certain embodiments, optogenetic stimulation includes periodic optogenetic stimulation of D1-MSNs in the Gpe region of the subject's brain.

[0056] In certain embodiments, optogenetic stimulation includes randomly optogenetically stimulating D1-MSNs and D2-MSNs in the Gpe region of the subject's brain using multiple stimulation trains with randomly spaced pulses, where each neuron is stimulated with one of the stimulation trains in the train such that synchronous neurons are decoupled.

[0057] In certain embodiments, optogenetic stimulation is performed by periodic stimulation at a frequency of 130 Hz.

[0058] In another aspect, there is provided a system for modeling the propagation of beta band oscillations in a subject's brain using the computer-implemented methods described herein. The system includes: a) a storage component for storing data, the storage component having instructions for modeling the propagation of beta band oscillations based on an analysis of functional magnetic resonance imaging data and experimental electrophysiology data stored therein; b) a computer processor for processing the functional magnetic resonance imaging data and the experimental electrophysiology data using one or more algorithms, the computer processor being coupled to the storage component and configured to execute the instructions stored in the storage component to receive the input functional magnetic resonance imaging data and experimental electrophysiology data and analyze the data according to the computer-implemented methods described herein; and c) a display component for displaying information regarding the propagation of beta band oscillations in the subject's brain.

[0059] In another aspect, there is provided a non-transitory computer-readable medium including program instructions that, when executed by a processor in a computer, cause the processor to perform the methods described herein.

[0060] In another aspect, there is provided a kit including a non-transitory computer-readable medium and instructions for modeling the propagation of beta band oscillations based on functional magnetic resonance imaging data and experimental electrophysiology data, as described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figures 1A - 1E . In vivo electrophysiology data show that the direct pathway can mediate beta band oscillations. ( Figure 1A ) Schematic illustration of in vivo extracellular recording sites in the CPu, GPi, thalamus, and STN, respectively. ( Figure 1B ) Histograms of representative neurons in the CPu, GPi, thalamus, and STN, respectively. During D1-MSN stimulation, neuronal activity increased in all four ROIs. To the right of each histogram is the corresponding firing rate of the selected neurons before, during, and after stimulation (***p < 0.001, one-tailed paired t-test, mean ± SEM). ( Figure 1C ) SPIKE synchronization profiles of ROIs during D1-MSN stimulation and D2-MSN stimulation, respectively. D1-MSN stimulation induced above-baseline synchronization in all 4 ROIs. To the right of each time profile is the corresponding mean SPIKE synchronization before, during, and after stimulation (***p < 0.001, one-tailed paired t-test, mean ± SEM). ( Figure 1D)Power spectral profiles of the CPu, GPi, thalamus, and STN during D1-MSN stimulation, respectively. 20 Hz optogenetic stimulation in D1-MSN drives oscillations of the same frequency in the GPi and thalamus, but not in the STN.( Figure 1E )When aligned with the laser pulse period, the spike trains of the CPu, GPi, and thalamus exhibit phase-locked activity during D1-MSN stimulation. During each phase cycle, the spikes of the CPu coincide with the onset of the laser. The spikes of the GPi and thalamus exhibit phase delays of approximately 30 ms and 60 ms, respectively, relative to the onset of the laser.

[0062] Figures 2A - 2F .In vivo electrophysiological data indicate that the indirect pathway can partially mediate β-band oscillations.( Figure 2A )Diagrams of the in vivo extracellular recording locations in the CPu, GPi, thalamus, and STN, respectively.( Figure 2B )Histograms of representative neurons in the CPu, GPi, thalamus, and STN, respectively. During D2-MSN stimulation, the neuronal activities of the GPi, thalamus, and STN decrease. On the right side of each histogram is the corresponding firing frequency of the selected neurons before, during, and after stimulation (***p < 0.001, one-tailed paired t-test, mean ± SEM).( Figure 2C )SPIKE synchronization profiles of the ROI during D2-MSN stimulation. D2-MSN stimulation induces higher synchronization in the CPu, but the synchronization levels in the GPi and STN are lower than the baseline. On the right side of each time profile is the corresponding mean SPIKE synchronization before, during, and after stimulation (***p < 0.001, one-tailed paired t-test, mean ± SEM).( Figure 2D )Power spectral density of the ROI during D2-MSN. A 20 Hz peak is observed in the CPu and STN, but the synchronization does not propagate to the thalamus.( Figure 2E )During D2-MSN stimulation, the CPu and STN exhibit in-phase activity. During each phase cycle, the spikes of the CPu coincide with the onset of the laser. During D2-MSN stimulation, the spikes of the GPi and thalamus do not have a preferred phase.( Figure 2F )During D2-MSN stimulation, STN neurons simultaneously exhibit hypoactivity and 20 Hz synchronization. The histogram and power spectral profile of a representative STN neuron are shown in the two plots on the right.

[0063] Figures 3A - 3C .A biophysical model of action potential firing level established and optimized based on ofMRI and single-neuron recording data.( Figure 3A)Graph and matrix representations of the structural network of the biophysical model. 20 Hz optogenetic stimulation of D1-MSNs or D2-MSNs in the CPu. Glutamatergic connections are modeled as 1-to-1 projections, where the strength is optimized by a DCM model based on fMRI time series. GABAergic connections are modeled as 1-to-n projections, where the parameters are optimized using single-neuron recordings.( Figure 3B )The left panel shows the average PSD profiles of 118 neurons identified from our recordings in a single trial during the baseline and stimulation periods. We used the PSD profiles from these recordings to simulate the true input to our model. The middle panel shows the resulting average PSD of 100 simulated CPu neurons. In each trial, we simulated a 20 s baseline, a 20 s D1- / D2-MSN stimulation period, and an additional 20 s of no stimulation.( Figure 3C )A flowchart showing the experimental data and its relationship to the modeling process. We used optogenetic fMRI data and DCM to evaluate glutamatergic connection strength. The parameters, fan-out ratio, and strength of GABAergic connections were optimized by matching synthetic action potential sequences to single-neuron recordings. Using this approach, we developed a single-cell action potential firing model whose parameters were optimized using fMRI and electrophysiology data, and the model converged when the model action potential sequence matched the experimental data action potential firing rate.

[0064] Figures 4A - 4I .The model successfully replicated the experimental results of D1-MSN and D2-MSN stimulation across the entire CBT network.( Figure 4A )Schematic diagrams of optogenetic stimulation and simulated single-neuron recording locations in the CPu, GPi, thalamus, and STN, respectively.( Figure 4B and Figure 4E )Are histograms of simulated neuronal activity in the CPu, GPi, thalamus, and STN during D1-MSN stimulation and D2-MSN stimulation, respectively. During D1-MSN stimulation, an increase in neuronal activity was observed in all four ROIs( Figure 4B ). During D2-MSN stimulation, a decrease in neuronal activity was observed in the GPi, thalamus, and STN( Figure 4E ). To the right of each histogram are the corresponding firing frequencies before, during, and after stimulation (***p < 0.001, one-tailed paired t-test, mean ± SEM).( Figure 4C and Figure 4F )Are the power spectral profiles in the CPu, GPi, thalamus, and STN during simulated D1-MSN stimulation and D2-MSN stimulation, respectively. 20 Hz optogenetic D1-MSN stimulation drives oscillations at the same frequency in the CPu and synchronizes the downstream GPi and thalamus( Figure 4C) During D2-MSN stimulation, 20 Hz synchronization was observed in the CPu and STN, but not in the thalamus ( Figure 4F ). ( Figure 4D And Figure 4G ) The comparison between the experimental and simulated action potential firing rates for D1-MSN ( Figure 4D ) and D2-MSN ( Figure 4G ) stimulation showed that the simulated action potential firing rates matched the experimental recordings. ( Figure 4H ) The simulation results suggest that β-band oscillations are mediated by phase-locked rebound firing along the direct and indirect pathways. ( Figure 4I ) The parameter space of connectivity strength and axon density in the target regions predicts that the GPe-STN connection may have higher axonal branching than other β-oscillation-mediated connections.

[0065] Figures 5A - 5E . Optogenetic enhancement of spontaneous activity verified the propagation pathway of β-band oscillations. ( Figure 5A ) Activation of SSFOD1-MSN induced a broad-spectrum increase in activity in the CPu, SNr, thalamus, and GPi. ( Figure 5B ) Activation of SSFO·D2-MSN induced a broad-spectrum decrease in activity in the CPu and thalamus. ( Figure 5C ) We identified co-occurrence periods of high β power from simultaneous extracellular recordings of two-region pairs. An exemplary pair from a selected time window of simultaneous CPu-GPi recording during D1-MSN activation is shown. Co-occurring above-threshold β power peaks are marked with gray arrows. ( Figure 5D ) D1-MSN activation significantly increased the number of co-occurring β periods / 10 s between the CPu-GPi, CPu-SNr, and CPu-thalamus pairs (***p < 0.001, two-tailed paired t-test). ( Figure 5E ) D2-MSN activation significantly decreased the number of co-occurring β periods / 10 s between the CPu-SNr and CPu-thalamus pairs (***p < 0.001, two-tailed paired t-test).

[0066] Figures 6A - 6F . Simulated neuromodulation experiments successfully predicted the optimal neuromodulation targets for interrupting β-band oscillations. ( Figure 6A ) Schematic of the experimental design for interrupting direct-pathway β-band oscillations using optoinhibition (left) and optostimulation (right). The STN, motor cortex, GPi, and GPe were selected as candidate targets for neuromodulation. ( Figure 6B ) Simulated optoinhibition showed that the GPi is the optimal site for optoinhibition to interrupt β-band oscillations along the direct pathway. ( Figure 6C ) Simulated optostimulation showed that both periodic and random stimulation of the GPe can inhibit β-band oscillations along the direct pathway.Figure 6D ) Schematic of the experimental design for disrupting β-band oscillations mediated by the indirect pathway using optogenetic inhibition (left) and optogenetic stimulation (right). The subthalamic nucleus (STN), motor cortex, globus pallidus interna (GPi), and globus pallidus externa (GPe) were selected as candidate targets for neuromodulation.( Figure 6E ) Simulated optogenetic inhibition showed that the GPe was the optimal site for optogenetic inhibition to disrupt β-band oscillations along the indirect pathway.( Figure 6F ) Simulated optogenetic stimulation showed that only random stimulation of the GPe could inhibit β-band oscillations along the indirect pathway. The error bars represent the 95% confidence interval.

[0067] Figures 7A - 7F . Optogenetic functional magnetic resonance imaging (fMRI) bridges multiple scales.( Figure 7A ) Optogenetics enables cell-type specific stimulation, such as selectively targeting D1 medium spiny neurons (MSNs) or D2-MSNs in the striatum.( Figure 7B ) In the case of selectively stimulating D1-MSNs or D2-MSNs in the striatum, mice showed reverse rotation or ipsilateral rotation, respectively (D1-MSN stimulation: n = 12 animals, mean ± SEM, ***p < 0.001, two-tailed paired t-test; D2-MSN stimulation: n = 11 animals, mean ± SEM, *p < 0.05, **p < 0.005, two-tailed paired t-test).( Figure 7C ) Optogenetic fMRI technology combines optogenetic stimulation with fMRI readout.( Figure 7D ) Optogenetic fMRI of selectively stimulating D1-MSNs and D2-MSNs resulted in different whole-brain activities associated with different behaviors (n = 12 animals). Only the group phase maps of the activated voxels in the brain were retained after thresholding, depicting the heterogeneity in the temporal dynamics of the evoked responses.( Figure 7E ) Time series of any region can be extracted from four-dimensional fMRI data.( Figure 7F ) Electrophysiological recordings reflected the fMRI responses of the polarity changes in neural activity. The figure is based on Lee et al. (2).

[0068] Figures 8A - 8G . Computational modeling of optogenetic fMRI data reveals whole-brain functional interaction dynamics.( Figure 8A ) The cortico-basal ganglia-thalamus network involves a large number of network nodes on a whole-brain scale.( Figure 8B ) The anatomical connectivity of the direct and indirect pathways involves a large number of common anatomical regions with different cell types in the caudate putamen (CPu).( Figure 8C ) The anatomical connectivity was used as a prior to generate a network model. In addition to the direct and indirect pathways shown in (B), other established anatomical connections, such as the hyperdirect pathway, were also included.( Figure 8D)The fMRI time series generated by DCM matched well with the experimental ofMRI time series.( Figure 8E )DCM used ofMRI data to estimate the causal influence (effective connectivity) between regions of interest during D1-MSN stimulation and D2-MSN stimulation, respectively.( Figure 8F and Figure 8G )Graphical and matrix representations of the effective connectivity networks for (F) D1-MSN stimulation and (G) D2-MSN stimulation, respectively. Significant and near-significant represent parameters with p < 0.05 and 0.05 ≤ p < 0.10, respectively (one-sample t-test, multiple comparison correction across connectivities, FDR p < 0.10). CPu: caudate putamen; GPe: globus pallidus externa; GPi: globus pallidus interna; STN: subthalamic nucleus; SNr: substantia nigra; THL: thalamus; CTX: cortex. This figure is based on Bernal-casas et al. (4).

[0069] Figures 9A - 9D .Through a multiscale approach, brain circuit function can be modeled at the single-cell action potential firing level.( Figure 9A )Schematically shows the positions of optogenetic stimulation and in vivo extracellular recordings for corticobasal ganglia-thalamus network studies.( Figure 9B )Illustrates single-cell action potential firing level modeling using ofMRI and single-unit recordings data. The large-scale model established using ofMRI data is extended to a biophysical model at the single-neuron level. Each ROI consists of many simulated single neurons. The model is validated by directly comparing the simulated action potential firing sequences with experimental data.( Figure 9C )The model allows for a direct comparison between the experimental single-unit recording data and the simulated data.( Figure 9D )The model should be designed such that the action potential firing rates of all ROIs simulated by the single-cell action potential firing level biophysical model match the experimental data statistically. The combination of ofMRI and biophysical modeling can enable the successful reproduction of the single-cell action potential firing level dynamics induced by cell type-specific optogenetic stimulation, such as D1-MSN stimulation and D2-MSN stimulation.

[0070] Figures 10A - 10F .To understand pathological functional interactions, whole-brain pathological dynamics can be modeled together with ofMRI.( Figure 10A) α - synuclein PFFs injected into the seed location induce pathology at different time points after injection, which can then be captured by iDISCO tissue clearing and light - sheet fluorescence microscopy (LSFM). Machine - learning - based automated segmentation and registration techniques can simplify the quantification of each pathological marker in the Allen Reference Atlas (ARA).( Figure 10B ) Comparison of averaged heatmaps across different cohorts can delineate whole - brain pathological changes over many months after injection.( Figure 10C ) Modeling based on longitudinal data of whole - brain anatomical connectivity can capture regional differences in pathology.( Figure 10D ) Re - weighting the connectivity matrix can allow encoding of genetic contributions within the model.( Figure 10E ) Whole - brain co - localization analysis between optogenetically induced α - synuclein pathology changes and optogenetic fMRI activity can reveal pathological - functional relationships. In this example, positive activity is co - localized with decreasing pathology, while negative activity is highly co - localized with increasing pathology.( Figure 10F ) Montage of the modulated α - synuclein pathology and ofMRI brain activity maps shows high co - localization with opposite polarities.

[0071] Figure 11 . Schematic of technical approaches. Optogenetic fMRI and single - neuron recordings are used together with dynamic causal modeling and biophysical modeling to optimize neuromodulation parameters.

[0072] Figures 12A - 12F . Simulated neuromodulation experiments successfully predicted the optimal neuromodulation targets for disrupting beta - band oscillations.( Figure 12A ) Schematic of experimental designs for disrupting direct - pathway beta - band oscillations using optoinhibition (left) and optostimulation (right). STN, motor cortex, GPi, and GPe are selected as candidate targets for neuromodulation.( Figure 12B ) Simulated optoinhibition shows that GPi is the optimal site for optoinhibition to disrupt beta - band oscillations along the direct pathway.( Figure 12C ) Simulated optostimulation shows that both periodic and random stimulation of GPe can inhibit beta - band oscillations along the direct pathway.( Figure 12D ) Schematic of experimental designs for disrupting indirect - pathway - mediated beta - band oscillations using optoinhibition (left) and optostimulation (right). STN, motor cortex, GPi, and GPe are selected as candidate targets for 5 neuromodulation.( Figure 12E ) Simulated optoinhibition shows that GPe is the optimal site for optoinhibition to disrupt beta - band oscillations along the indirect pathway.( Figure 12F ) Simulated optostimulation shows that only random stimulation of GPe can inhibit beta - band oscillations along the indirect pathway. Error bands represent 95% confidence intervals. Detailed implementation

[0073] Methods, systems, and devices are provided, including computer programs encoded on computer storage media, for optimizing neuromodulation therapies for treating neurological and neurodegenerative diseases. Joint dynamic causal modeling and biophysical modeling are used for optimizing stimulation targets and parameters. In particular, methods for performing neuromodulation to inhibit beta-band oscillations in a subject's brain are provided.

[0074] Before describing the methods and compositions of the present invention, it is to be understood that the invention is not limited to the specific methods or compositions described, as they may of course vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, since the scope of the invention will be defined only by the appended claims.

[0075] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limits of that range is also specifically disclosed. Every smaller range between any of the stated values or intervening values in a stated range is also encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded from the range, and every range that includes either, neither, or both of the limits is also encompassed within the invention, subject to any specific excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0076] All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated by reference herein to disclose and describe the methods and / or materials related to the cited publications. It is to be understood that to the extent there is any conflict, the present disclosure supersedes any disclosure of the incorporated publications.

[0077] As will be apparent to those skilled in the art upon reading this disclosure, each individual embodiment described and illustrated herein has discrete components and features that can be readily separated from or combined with the features of any of several other embodiments. Any recited method can be performed in the recited order of events or in any other order that is logically possible.

[0078] It must be noted that, as used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a neuron" includes a plurality of such neurons, and reference to "a measurement" includes reference to one or more measurements and equivalents thereof known to those skilled in the art, and so forth.

[0079] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed.

[0080] Definitions

[0081] The term "about", particularly when referring to a given quantity, is intended to encompass deviations of plus or minus 5%.

[0082] The term "connectivity" refers to causal, functional, or directed coupling between brain regions and / or neuronal populations. Neural processing may involve an integrated network of several regions of the brain and functional connectivity of spatially distant brain regions. Analysis of functional connectivity may involve detecting inter-regional and intra-regional neural interactions involved in brain activity during a particular cognitive or motor task, or from spontaneous activity during rest. The "strength of connectivity" between neural regions, both inter- and intra-, can be estimated by dynamic causal modeling of brain function.

[0083] As used herein, "neural activity" can refer to the electrical activity of neurons (e.g., changes in the membrane potential of neurons) and indirect measurements of the electrical activity of one or more neurons. Thus, neural activity can refer to changes in field potentials, changes in intracellular ion concentrations (e.g., intracellular calcium concentration), and / or changes in magnetic resonance caused by the electrical activity of neurons, as measured, for example, by the blood oxygenation level-dependent (BOLD) signal in functional magnetic resonance imaging.

[0084] The terms "individual", "subject", "host", and "patient" are used interchangeably herein and refer to any subject having a brain, including invertebrates and vertebrates, such as but not limited to arthropods (e.g., insects, crustaceans, arachnids), cephalopods (e.g., octopus, squid), amphibians (e.g., frog, salamander, caecilian), fish, reptiles (e.g., turtle, crocodilian, snake, amphisbaenian, lizard, tuatara), mammals, including humans and non-human mammals, such as non-human primates, including chimpanzees and other ape and monkey species; laboratory animals, such as mice, rats, rabbits, hamsters, guinea pigs, and chinchillas; domestic animals, such as dogs and cats; farm animals, such as sheep, goats, pigs, horses, and cattle; and birds, such as poultry, wild birds, and game birds, including chickens, turkeys, and other gallinaceous birds, ducks, and geese. In some cases, the methods of the present invention are used in experimental animals, in veterinary applications, and in the development of animal models of diseases, including but not limited to rodents, including mice, rats, and hamsters; primates, and transgenic animals.

[0085] The term "treatment", "treating", "treat", etc. are generally used herein to refer to obtaining the desired pharmacological and / or physiological effect. In terms of completely or partially preventing a disease or its symptoms, the effect may be prophylactic, and / or in terms of partially or completely stabilizing or curing a disease and / or an adverse reaction attributable to the disease, it may be therapeutic. The term "treatment" encompasses any treatment of a disease in a mammal (particularly a human) and includes: (a) preventing the disease and / or symptoms from occurring in a subject who may be susceptible to the disease or symptoms but has not been diagnosed as having the disease or symptoms; (b) inhibiting the disease and / or symptoms, i.e., preventing its development; or (c) alleviating the symptoms of the disease, i.e., causing the regression of the disease and / or symptoms. Patients in need of treatment include patients who have already suffered from the disease (e.g., patients with Parkinson's disease) and patients in need of prevention (e.g., patients with a genetic predisposition to develop Parkinson's disease).

[0086] A "therapeutically effective dose or amount" of electrical stimulation (e.g., delivered by optogenetics or an electrode) refers to an amount that produces a positive therapeutic response in the treatment of Parkinson's disease when the electrical stimulation is administered as described herein, such as an amount that inhibits beta band oscillations. In addition, a positive therapeutic response in the treatment of Parkinson's disease can include a reduction in the symptoms of Parkinson's disease, such as a reduction in tremor, stiffness, bradykinesia, and / or difficulty walking, an improvement in sleep, and / or an increase in neuronal survival. The exact amount required varies from subject to subject and depends on the species, age, and general condition of the subject, the severity of the condition being treated, the particular mode of administration, etc. Based on the information provided herein, one of ordinary skill in the art can use routine experimentation to determine the appropriate "effective" amount in any individual case.

[0087] Modeling Brain Function Using a Combination of Dynamic Causal Modeling and Biophysical Modeling

[0088] A method of using a combination of dynamic causal modeling and biophysical modeling is provided to generate improved models of brain function and response to neuromodulatory therapies. In particular, the subject method can be used to integrate brain function measurements by two or more techniques such as functional neuroimaging and electrophysiology. For example, functional neuroimaging can be performed to image neural activity in one or more regions of interest in a subject's brain. Additionally, electrophysiological measurements can be made on one or more neurons in the same brain region of interest. First, functional neuroimaging data of cerebral nerve activity is fitted to a dynamic causal model (DCM) that is used to calculate estimates of inter-regional and intra-regional connectivity strengths. Next, the inter-regional and intra-regional connectivity strength estimates calculated from the DCM analysis are used to construct a biophysical model. The biophysical model is used to simulate synthetic electrophysiological data that can be compared with experimental electrophysiological data collected for neurons in the brain region of interest. The biophysical model can be iteratively adjusted until model convergence is achieved with the experimental electrophysiological data. In this method, sequential model fitting is used to improve modeling accuracy to generate a more accurate and comprehensive model of the brain neuronal circuit.

[0089] Exemplary functional neuroimaging techniques that can be used to practice the subject method include, but are not limited to, functional magnetic resonance imaging (fMRI), positron emission tomography (PET), functional near-infrared spectroscopy (fNIRS), single photon emission computed tomography (SPECT), and functional ultrasound imaging (fUS). These functional neuroimaging techniques measure local changes in cerebral blood flow and changes in the composition of blood associated with neural activity. Functional neuroimaging is useful for non-invasively detecting patterns of brain activity related to specific stimuli or tasks.

[0090] In some embodiments, fMRI is used to monitor the temporal changes in blood flow associated with changes in the level of brain activity. When a region of the brain is in use, blood flow increases following neuronal activation. Changes in brain activity can be imaged using fMRI by detecting the blood oxygenation level-dependent (BOLD) signal. For descriptions of fMRI and methods of imaging brain activity using fMRI, see, for example, Ogawa et al., (1990) Magnetic Resonance in Medicine 14(1):68-78, Kim et al., (2002) Curr Opin Neurobiol. 12(5):607-15, Kim et al., (2012) J Cereb Blood Flow Metab. 32(7):1188-206, Bandettini (2012) Neuroimage 62(2):575-88; Zarghami et al., (2020) Neuroimage 207:116453; Logothetis, N.K. (June 12, 2008), Logothetis et al., (2008) Nature 453(7197):869-78, and Logothetis et al., (2001) Nature 412(6843):150-157; which are incorporated herein by reference.

[0091] Electrophysiological techniques are used to measure the electrical properties in the brain, typically voltage or current changes in neurons. Exemplary electrophysiological techniques that can be used to practice the subject methods include, but are not limited to, electroencephalography (EEG), magnetoencephalography (MEG), and patch clamp. These electrophysiological techniques are useful for identifying specific types of neurons involved in neural networks and measuring neuron-specific activity changes associated with brain responses.

[0092] In some embodiments, electroencephalogram (EEG) is used to record neuronal electrical activity in the brain. EEG can be performed non-invasively by placing electrodes on the scalp. EEG measurements have the advantage of high temporal resolution and can detect changes in electrical activity in the brain on a millisecond timescale. For a description of EEG and methods of using EEG to record electrical activity in the brain, see, for example, Niedermeyer et al., (2004) Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Lippincott Williams & Wilkins; Jackson et al., (2014) Psychophysiology 51(11):1061-71, Khanna et al., (2015) Neurosci Biobehav Rev. 49:105-13, Feyissa et al., (2019) Handb Clin Neurol. 160:103-124, Beres et al., (2017) Appl Psychophysiol Biofeedback 42(4):247-255; which are incorporated herein by reference.

[0093] In some embodiments, magnetoencephalogram (MEG) is used to record the magnetic fields generated by electrical currents in the brain. MEG detects weak magnetic fields generated by synchronous neuronal currents (i.e., ionic currents flowing within the dendrites of neurons during synaptic transmission). These weak magnetic fields can be detected using magnetometers such as superconducting quantum interference devices (SQUIDs) or spin-exchange relaxation-free (SERF) magnetometers. For a description of MEG and methods of using MEG to record magnetic fields related to neuronal activity in the brain, see, for example, Hamalainen et al., (1993) Reviews of Modern Physics. 65(2):413-497, Baillet et al., (2017) Nat Neurosci. 20(3):327-339, Gross et al., (2019) Neuron 104(2):189-204, Stapleton-Kotloski et al., (2018) Brain Sci. 8(8):157; which are incorporated herein by reference.

[0094] Dynamic causal modelling (DCM) is a Bayesian generative modelling paradigm that can consider neuronal dynamics, modulatory inputs and observed data (e.g., BOLD fMRI, EEG or MEG) to model causal relationships in the brain. DCM can be used to estimate the coupling between brain regions and to quantify the strength of effective connectivity between neuronal populations in one or more regions of the brain. Dynamic causal models of interacting neural populations can be used to estimate inter- and intra-regional connectivity strengths from functional neuroimaging data using Bayesian statistical methods. The model can include interactions between excitatory and inhibitory neuronal populations. Connectivity strength estimates can be computed using different types of DCM. Classical DCM assumes no state noise, while stochastic DCM (sDCM) exploits state noise to resolve uncertainty due to neighbouring regions. Spectral DCM is another paradigm that assumes state noise, but in this approach, model fitting is performed on the cross-spectrum of the system, which makes sDCM useful for resting-state modelling. Dynamic effective connectivity (or dynamic DCM) is another approach that is useful for studying temporal changes in connectivity strength. For descriptions of different types of DCM methods, see, e.g., Friston et al., (2003) Neuroimage 19(4):1273-1302, Friston et al., (2014) Neuroimage 94:396-407, Park et al., (2018) Neuroimage 180:594-608, Li et al., (2011) Neuroimage 58(2):442-457, Stephan et al., (2010) Neuroimage 49(4):3099-3109, Stephan et al., (2008) Neuroimage 42(2):649-662, Marreiros et al., Neuroimage 39(1):269-278, Razi et al., (2015) Neuroimage106:1-14 and Lee et al., (2006) Neuroimage 30(4):1243-1254; which are incorporated herein by reference.

[0095] Biophysical modeling is based on the biophysical properties of neurons and brain regions. Biophysical neuron models can include a variety of biophysical properties, including but not limited to the firing patterns of individual neurons, neuron types, receptors, inputs and outputs, topology, and self-connectivity. The methods of the present invention utilize biophysical models to simulate synthetic electrophysiological data using interregional and intraregional connectivity strength estimates from dynamic causal modeling. Next, the synthetic electrophysiological data is compared with experimental electrophysiological data obtained from a subject, and the model is refined by iteratively adjusting the model to convergence. For a description of biophysical modeling, see, for example, Example 5, Hill and Tononi (2005) Journal of Neurophysiology 93(3):1671-1698, and Murray et al., (2018) Biol Psychiatry Cogn Neurosci Neuroimaging 3(9):777-787; which are incorporated herein by reference.

[0096] In some embodiments, functional neuroimaging and electrophysiological techniques are used in combination to detect brain responses when a subject is exposed to a stimulus or performing a task. In addition, functional neuroimaging and electrophysiological measurements of brain activity can be made when the subject is at rest (e.g., no stimulus or no task) to allow comparison of brain activity to the subject's "baseline" brain state, i.e., to identify brain regions that exhibit changes in neural activity related to a particular stimulus or task.

[0097] In some embodiments, the methods described herein are used to evaluate changes in brain function in response to optogenetic perturbations of neural activity. In certain embodiments, optogenetics is used to induce cell-specific perturbations in the brain. For example, optogenetics can be used to excite or inhibit one or more selected neurons of interest using light. For a description of optogenetic techniques, see, for example, Abe et al., 2012; Desai et al., 2011; Duffy et al., 2015; Gerits et al., 2012; Kahn et al., 2013; Lee et al., 2010; Liu et al., 2015; Ohayon et al., 2013; Weitz et al., 2015; Weitz and Lee, 2013; which are incorporated herein by reference.

[0098] The methods described herein can also be used to evaluate changes in brain function in response to brain stimulation with an electric current or magnetic field applied to a selected brain region. For example, electro-brain stimulation (EBS) can be used to stimulate neurons or neural networks in the brain by directly or indirectly exciting their cell membranes by using an electric current. For a description of EBS techniques, see, e.g., Aum et al., (2018) Front Biosci (Landmark Ed) 23:162-182, Tellez-Zenteno et al., (2011) Neurosurg Clin N Am. 22(4):465-75, Padberg et al., (2009) Exp. Neurol. 219:2-13, Nahas et al., (2010) Biol. Psychiatry 67:101-109, Lefaucheur et al., (2010) Exp. Neurol. 223:609-614, Levy et al., (2008) J. Neurosurg. 108:707-714, Hanajima et al., (2002) Clin. Neurophysiol. 113:635-641, Picillo et al., (2015) Brain Stimul. 8:840-842., Canavero (2014) Textbook of Cortical Brain Stimulation, Berlin: De Gruyter Open; which is incorporated herein by reference. Alternatively, transcranial magnetic stimulation (TMS) can be used to electrically stimulate the brain by electromagnetic induction and can be used to non-invasively stimulate specific regions of the brain. For a description of TMS techniques, see, e.g., Klomjai et al., (2015) Ann Phys Rehabil Med. 58(4):208-213, Lefaucheur (2019) Handb Clin Neurol. 160:559-580, Burke et al., (2019) Handb Clin Neurol. 163:73-92; which is incorporated herein by reference.

[0099] The subject methods are applicable to modeling the effects of neuromodulation (e.g., delivered in a optogenetic manner or by electrical stimulation) to produce a single cell action potential firing level model that matches experimental data (see Examples). Using such models, virtual neuromodulation (deep brain stimulation, optogenetic stimulation, or other neuromodulation with different combinations of parameters and targets) can be tested to allow for efficient optimization of stimulation parameters and targets.

[0100] Systems and Computer - Implemented Methods for Modeling the Propagation of Beta - Band Oscillations

[0101] In one aspect, a computer-implemented method is provided for modeling the propagation of beta-band oscillations and the response to neuromodulation using a combination of dynamic causal modeling and biophysical modeling. The steps performed by the computer include: a) receiving functional magnetic resonance imaging data of the neural activity of D1-medium spiny neurons (D1-MSNs) and D2-medium spiny neurons (D2-MSNs) in the caudate putamen (CPu) region, external globus pallidus (GPe) region, internal globus pallidus (GPi) region, subthalamic nucleus (STN) region, substantia nigra pars reticulata (SNr) region, thalamus (THL) region, and motor cortex (MCX) region of a subject's brain before and during optogenetic stimulation; b) performing spectral dynamic causal modeling on the effective connectivity strengths between the CPu region, GPe region, GPi region, STN region, SNr region, THL region, and MCX region; c) receiving experimental electrophysiology data of the CPu region; d) using the experimental electrophysiology data of the CPu region to estimate the effective connectivity strength of GABAergic connections; e) using dynamic causal modeling of the functional magnetic resonance imaging data to estimate the effective connectivity strength of glutamatergic connections; f) performing biophysical modeling using the Hodgkin-Huxley model to generate simulated electrophysiology data using the effective connectivity strength estimates; g) iteratively optimizing the GABAergic projections until the simulated electrophysiology data matches the experimental electrophysiology data of the CPu region; h) calculating the temporal profile of the average power of the beta-band frequency for each neuron in the CPu region, GPe region, GPi region, STN region, SNr region, THL region, and MCX region; and i) comparing the total amount of co-occurring beta-band oscillation power before optogenetic stimulation with the co-occurring beta-band oscillation power during optogenetic stimulation to model the propagation of beta-band oscillations in the subject's brain.

[0102] In certain embodiments, the D1-MSNs are Huxley-Hudgkin neurons.

[0103] In certain embodiments, the experimental electrophysiology data includes single neuron recordings.

[0104] In certain embodiments, the single neuron recordings are from GABAergic neurons in the CPu region.

[0105] In certain embodiments, the glutamatergic connections are modeled as one-to-one connections, where the connection strength is proportional to the effective connectivity estimated by DCM.

[0106] In certain embodiments, the CPU-GPi / GPe and GPi / SNr-thalamic GABAergic projections are modeled as a one-to-n diffusive projection with connectivity strength wGABA, where n and wGABA are free parameters, and where n and wGABA are searched across the parameter space until the simulated action potential firing rate matches the single neuron recordings statistically.

[0107] In certain embodiments, optogenetic stimulation includes optogenetic inhibition of D1-MSNs in the GPi region of the subject's brain.

[0108] In certain embodiments, optogenetic inhibition of D1-MSNs includes tonic shunting inhibition of D1-MSNs in the GPi region of the subject's brain.

[0109] In certain embodiments, optogenetic stimulation includes optogenetic inhibition of D2-MSNs in the GPe region of the subject's brain.

[0110] In certain embodiments, optogenetic inhibition of D2-MSNs includes tonic shunting inhibition of D2-MSNs in the GPe region of the subject's brain.

[0111] In certain embodiments, optogenetic stimulation includes direct activation of D1-MSNs or D2-MSNs at intensities of 500 pA, 700 pA, or 900 pA.

[0112] In certain embodiments, optogenetic stimulation includes periodic optogenetic stimulation of D1-MSNs in the Gpe region of the subject's brain.

[0113] In certain embodiments, optogenetic stimulation includes randomly optogenetically stimulating D1-MSNs and D2-MSNs in the Gpe region of the subject's brain using multiple stimulation trains with randomly spaced pulses, where each neuron is stimulated with one of the stimulation trains in the train such that synchronous neurons are decoupled.

[0114] In certain embodiments, optogenetic stimulation is performed by periodic stimulation at a frequency of 130 Hz.

[0115] In some embodiments, non-human animals are used to develop simulations. Non-human animals can include, but are not limited to, non-human primates, including chimpanzees and other ape and monkey species; laboratory animals such as mice, rats, rabbits, hamsters, guinea pigs, and chinchillas; domestic animals such as dogs and cats; farm animals such as sheep, goats, pigs, horses, and cows; and birds such as poultry, wild birds, and game birds, including chickens, turkeys, and other gallinaceous birds, ducks, and geese. For example, by optogenetically stimulating D1-MSNs and / or D2-MSNs in the striatum at β-band frequencies in the range of 12.5 Hz to 30 Hz, β-band oscillations can be generated in the brains of non-human animals. In some embodiments, β-band oscillations are generated in the brains of non-human animals by optogenetically stimulating D1-MSNs and / or D2-MSNs in the striatum at a frequency of 20 Hz. Functional magnetic resonance imaging data and experimental electrophysiology data can be obtained from such animals for use in the methods described herein to simulate the propagation of β-band oscillations and the response to neuromodulation.

[0116] The method can be implemented in digital electronic circuitry or in computer software, firmware, or hardware. The disclosed embodiments and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, a data processing apparatus. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter implementing a machine-readable propagated signal, or any combination thereof.

[0117] A computer program (also referred to as a program, software, software application, script, or code) can be written in any form of programming language, including a compiled or interpreted language, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program being discussed, or in multiple cooperating files (e.g., files that store one or more modules, subroutines, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0118] In another aspect, a system for performing the computer-implemented method as described may include a processor, a storage component (i.e., memory), a display component, and other components typically present in a general-purpose computer. In some embodiments, the processor is provided by a computer or a handheld device (e.g., a cell phone or a tablet). The storage component stores information accessible by the processor, including instructions that can be executed by the processor and data that can be retrieved, manipulated, or stored by the processor.

[0119] The storage component includes instructions. For example, the storage component includes instructions for modeling the propagation of beta-band oscillations and the response to neuromodulation according to the methods described herein. The computer processor is coupled to the storage component and is configured to execute the instructions stored in the storage component to receive functional magnetic resonance imaging data and experimental electrophysiology data and analyze the data according to one or more algorithms as described herein.

[0120] The processor and / or the memory may be operatively connected to a display device, e.g., operatively connected to the display device via a wired connection (such as a Universal Serial Bus (USB) connection) or a wireless connection (such as a Bluetooth connection). Any convenient display device may be used, such as a liquid crystal display (LCD), a light-emitting diode (LED) display, a plasma (PDP) display, a quantum dot (QLED) display, or a cathode ray tube display device. The display component displays information about the location of pathological protein aggregates in a subject's brain. In some embodiments, the display displays information about the propagation of beta-band oscillations (or the inhibition thereof by neuromodulation) in a subject's brain as determined by the computer-implemented method.

[0121] The storage component can be of any type capable of storing information accessible by a processor, such as a hard disk drive, memory card, ROM, RAM, DVD, CD-ROM, USB flash drive, writable memory, and read-only memory. The processor can be a general-purpose processor, a graphics processing unit, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. The general-purpose processor can be a microprocessor, but in the alternative, the processor can be a controller, microcontroller, or state machine, combinations thereof, etc. The processor can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, the combination of one or more microprocessors and a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, the processor can also include primarily analog components. The computing environment can include any type of computer system, including but not limited to microprocessor-based computer systems, graphics processing units, mainframe computers, digital signal processors, portable computing devices, personal organizers, device controllers, and computing engines within devices, to name just a few examples.

[0122] The steps of a method, process, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in software modules executed by a processor, or in a combination of both. Software modules, engines, and associated databases can reside in memory resources, such as in RAM memory, FRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of non-transitory computer-readable storage medium, medium, or physical computer memory known in the art. The exemplary storage medium can be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integrated into the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0123] Instructions can be any set of instructions to be executed directly (such as machine code) or indirectly (such as a script) by the processor. In this regard, the terms "instructions", "steps", and "program" can be used interchangeably herein. Instructions can be stored in the form of object code for direct processing by the processor, or in any other computer language (including scripts or collections of independent source code modules that are interpreted or pre-compiled as needed).

[0124] Data can be retrieved, stored, or modified by a processor according to instructions. For example, although the system is not restricted to any particular data structure, data can be stored in computer registers, in a relational database as a table with multiple different fields and records, in an XML document, or in a flat file. Data can also be formatted in any computer-readable format such as, but not limited to, binary values, ASCII, or Unicode. Additionally, data can include any information sufficient to identify relevant information such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memories (including other network locations), or information used by a function to compute relevant data.

[0125] In some embodiments, the processor and storage components can include multiple processors and storage components, which may or may not be stored within the same physical enclosure. For example, some instructions and data can be stored on a removable CD-ROM, while other instructions and data can be stored in a read-only computer chip. Some or all of the instructions and data can be stored in a location physically remote from the processor but still accessible by the processor. Similarly, the processor can include a collection of processors that may or may not operate in parallel.

[0126] In some embodiments, a cloud computing system can be used to perform the method. In these embodiments, an image of a subject's brain can be output to a cloud computer, which runs a program and returns the output to the user.

[0127] The components of the system for implementing the methods disclosed in the present invention are further described in the following examples.

[0128] Neuromodulation Therapies for Treating Parkinson's Disease

[0129] In some embodiments, neuromodulation is used to inhibit beta-band oscillations in a subject's brain to treat Parkinson's disease. Electrical stimulation (e.g., from implanted, clinically approved electrodes), transcranial magnetic stimulation, transcranial electrical stimulation, or focused ultrasound, among other techniques, can be used to achieve neuromodulation. For electrical stimulation, a deep brain stimulator can be used. In some embodiments, cortical layers or cell types are specifically targeted by genetically encodable modulation techniques such as optogenetics.

[0130] Optogenetics

[0131] In some embodiments, optogenetics is used to treat Parkinson's disease in a manner effective to suppress beta band oscillations in a subject's brain. Optogenetics is used to permit optical control of the activation (i.e., depolarization) or inhibition (i.e., hyperpolarization) of neurons that have been genetically modified to express light-responsive ion channels. In some embodiments, the light-responsive ion channels are naturally occurring or synthetic opsins that use a retinylidene-based cofactor (e.g., all-trans-retinal for microbial opsins) to respond to light. For example, light-responsive cation-conducting opsins (e.g., conducting Ca 2+Channelrhodopsins can be used to activate or depolarize neurons. Light-responsive anion-conducting opsins (e.g., chloride-conducting channelrhodopsins or halorhodopsins) or light-responsive proton-conducting modulators (e.g., bacteriorhodopsin or archaerhodopsin) can be used to inhibit neurons or hyperpolarize the neurons. The levels of retinoids present in the mammalian brain are generally sufficient to enable the expressed opsins to function without cofactor supplementation.For a description of optogenetics and its use in controlling neural activity, see, e.g., Aravanis et al., (2007) J Neural Eng 4:S143-S156, Arenkiel et al., (2007) Neuron 54:205-218, Boyden et al., (2005) Nat Neurosci 8:1263-1268, Chow et al., (2010) Nature 463:98-10, Gradinaru et al., (2007) J Neurosci 27:14231-14238, Gradinaru et al., (2008) Brain Cell Biol 36:129-139, Gradinaru et al., (2010) Cell 141:1-12, Li et al., (2005) Proc Natl Acad Sci 102:17816-17821, Lin et al., 2009. “Characterization of engineered channelrhodopsin variants with improved properties and kinetics”. Biophys J 96:1803-1814, Yizhar et al., (2011) “Microbial opsins: A family of single-component tools for optical control of neural activity”, Cold Spring Harbor Protoc, Zhang et al., (2007) Nat Methods 4:139-141, Zhang et al., (2006) Nat Methods 3:785-792, Zhang et al., (2007) Nature 446:633-639, Zhang et al., (2008) Nat Neurosci 11:631-633; and U.S. Patent Nos. 10,914,803; 10,589,123; 10,583,309; 10,568,516; 10,568,307; 10,538,560; 10,478,499; 10,220,092; 10,196,431; 10,087,223; 10,052,383; 9,969,783; 9,878,176; 9,855,442; 9,757,587; 9,458,208; and 8,834,546; which are incorporated herein by reference in their entirety.

[0132] In some embodiments, the target neurons are genetically modified to express a light-responsive ion channel that, when stimulated by an appropriate light stimulus, hyperpolarizes or depolarizes the stimulated target neurons. The term "genetically modified" refers to a permanent or transient genetic change induced in a cell after introducing a heterologous nucleic acid (i.e., a nucleic acid that is exogenous to the cell) into the cell. The genetic change ("modification") can be achieved by incorporating the heterologous nucleic acid into the genome of the host cell or by transiently or stably maintaining the heterologous nucleic acid as an extrachromosomal element. In the case where the cell is a eukaryotic cell, a permanent genetic change can be achieved by introducing the nucleic acid into the genome of the cell. Suitable methods of genetic modification include using viral infection, transfection, conjugation, protoplast fusion, electroporation, particle gun technology, calcium phosphate precipitation, direct microinjection, etc.

[0133] In some cases, target cells expressing a light-responsive polypeptide can be activated or inhibited upon exposure to light of different wavelengths. In some cases, the target cells expressing a light-responsive polypeptide are neuronal cells expressing a light-responsive polypeptide, and exposure to light of different wavelengths results in depolarization or polarization of the neurons.

[0134] In some instances, the light-responsive polypeptide is a light-responsive ion channel polypeptide. When irradiated with light of an activation wavelength, the light-responsive ion channel polypeptide is adapted to allow one or more ions to cross the plasma membrane of the target cell. The light-responsive protein can be characterized as an ion pump protein that facilitates the passage of a small amount of ions per photon of light across the plasma membrane; or as an ion channel protein that allows the flow of ions to freely pass across the plasma membrane when the channel is open. In some embodiments, when activated by light of an activation wavelength, the light-responsive polypeptide depolarizes an excitable cell. In some embodiments, when activated by light of an activation wavelength, the light-responsive polypeptide hyperpolarizes an excitable cell.

[0135] In some cases, when the cell is irradiated with light, the light-responsive polypeptide mediates a hyperpolarizing current in the target cell in which it is expressed. Non-limiting examples of light-responsive polypeptides capable of mediating a hyperpolarizing current can be found, for example, in U.S. Patent No. 9,359,449 and U.S. Patent No. 9,175,095. Non-limiting examples of hyperpolarizing light-responsive polypeptides include NpHr, eNpHr2.0, eNpHr3.0, eNpHr3.1, or GtR3. In some cases, when the cell is irradiated with light, the light-responsive polypeptide mediates a depolarizing current in the target cell in which it is expressed. Non-limiting examples of depolarizing light-responsive polypeptides include "C1V1", ChR1, VChR1, ChR2. Additional information regarding other light-responsive cation channels, anion pumps, and proton pumps can be found in U.S. Patent Application Publication No. 2009 / 0093403 and U.S. Patent No. 9,359,449.

[0136] In some embodiments, the light-responsive polypeptide can be activated by blue light (e.g., in the range of 490 nm to 450 nm). In one embodiment, the light-responsive polypeptide can be activated by light having a wavelength of about 473 nm. In some embodiments, the light-responsive polypeptide can be activated by yellow light (e.g., in the range of 590 nm to 560 nm). In another embodiment, the light-responsive polypeptide can be activated by light having a wavelength of about 560 nm. In another embodiment, the light-responsive polypeptide can be activated by red light (e.g., in the range of 700 nm to 635 nm). In another embodiment, the light-responsive polypeptide can be activated by light having a wavelength of about 630 nm. In other embodiments, the light-responsive polypeptide can be activated by violet light (e.g., in the range of 450 nm to 400 nm). In one embodiment, the light-responsive polypeptide can be activated by light having a wavelength of about 405 nm. In other embodiments, the light-responsive polypeptide can be activated by green light (e.g., in the range of 560 nm to 520 nm). In other embodiments, the light-responsive polypeptide can be activated by cyan light (e.g., in the range of 520 nm to 490 nm). In other embodiments, the light-responsive polypeptide can be activated by orange light (e.g., in the range of 635 nm to 590 nm). Those skilled in the art will recognize that each light-responsive polypeptide will have its own activation wavelength range.

[0137] In some cases, one or more optical fibers are used to irradiate the region of the brain containing neurons with the light-responsive polypeptide. The optical fiber(s) can be configured in any suitable manner to direct light emitted from a suitable light source (e.g., a laser or a light-emitting diode (LED) light source) to the region of the brain. The optical fiber can be any suitable optical fiber. In some cases, the optical fiber is a multimode optical fiber. The optical fiber can include a core defining a core diameter, through which light from the light source passes. The optical fiber can have any suitable core diameter. In some cases, the core diameter of the optical fiber is 10 mm or greater, such as 20 mm or greater, 30 mm or greater, 40 mm or greater, 50 mm or greater, 60 mm or greater, including 80 mm or greater, and 1,000 mm or less, such as 500 mm or less, 200 mm or less, 100 mm or less, including 70 mm or less. In some embodiments, the core diameter of the optical fiber is in the range of 10 to 1,000 mm, such as 20 to 500 mm, 30 to 200 mm, including 40 to 100 mm.

[0138] The distal end of the optical fiber implanted into the target region of the brain can have any suitable configuration adapted to irradiate the region of the brain using light stimulation delivered through the optical fiber. In some cases, the optical fiber includes an attachment device at or near the distal end of the optical fiber, where the distal end of the optical fiber corresponds to the end inserted into the subject. In some cases, the attachment device is configured to connect to the optical fiber and facilitate attachment of the optical fiber to the subject, such as attachment to the subject's skull. Any suitable attachment device can be used. In some cases, the attachment device includes a ferrule, such as a metal ferrule, a ceramic ferrule, or a plastic ferrule. The ferrule can have any suitable size for holding and attaching the optical fiber.

[0139] In certain embodiments, the methods of the present disclosure can be performed using any suitable electronic components to control and / or coordinate the various optical components for irradiating the region of the brain. The optical components (e.g., light source, optical fiber, lens, objective lens, mirror, etc.) can be controlled by a controller, e.g., to coordinate the light source for irradiating the region of the brain with light pulses. The controller can include a driver for the light source that controls one or more parameters associated with the light pulses, such as, but not limited to, the frequency, pulse width, duty cycle, wavelength, intensity, etc. of the light pulses. The controller can communicate with components of the light source (e.g., collimator, shutter, filter wheel, movable mirror, lens, etc.).

[0140] In some embodiments, the light-responsive polypeptide is activated by a light pulse that can have a duration of any one of about 1 millisecond (ms), about 2 ms, about 3 ms, about 4 ms, about 5 ms, about 6 ms, about 7 ms, about 8 ms, about 9 ms, about 10 ms, about 15 ms, about 20 ms, about 25 ms, about 30 ms, about 35 ms, about 40 ms, about 45 ms, about 50 ms, about 60 ms, about 70 ms, about 80 ms, about 90 ms, about 100 ms, about 200 ms, about 300 ms, about 400 ms, about 500 ms, about 600 ms, about 700 ms, about 800 ms, about 900 ms, about 1 second, about 1.25 seconds, about 1.5 seconds, or about 2 seconds (including themselves), including any time between these numbers. In some embodiments, the light-responsive polypeptide is activated by a light pulse that can have about 0.05 mW / mm 2 、about 0.1 mW / mm 2 、about 0.25 mW / mm 2 、about 0.5 mW / mm 2 、about 0.75 mW / mm 2 、about 1 mW / mm 2 、about 2 mW / mm 2 、about 3 mW / mm2 , about 4 mW / mm 2 , about 5 mW / mm 2 , about 6 mW / mm 2 , about 7 mW / mm 2 , about 8 mW / mm 2 , about 9 mW / mm 2 , about 10 mW / mm 2 , about 20 mW / mm 2 , about 50 mW / mm 2 , about 100 mW / mm 2 , about 250 mW / mm 2 , about 500 mW / mm 2 , about 750 mW / mm 2 , about 1000 mW / mm 2 , about 1100 mW / mm 2 , about 1200 mW / mm 2 , about 1300 mW / mm 2 , about 1400 mW / mm 2 , about 1500 mW / mm 2 , about 1600 mW / mm 2 , about 1700 mW / mm 2 , about 1800 mW / mm 2 , about 1900 mW / mm 2 , about 2000 mW / mm 2 , about 2100 mW / mm 2 , about 2200 mW / mm 2 , about 2300 mW / mm 2 , about 2400 mW / mm 2 , about 2500 mW / mm 2 , about 2600 mW / mm 2 , about 2700 mW / mm 2 , about 2800 mW / mm 2 , about 2900 mW / mm 2 , about 3000 mW / mm 2 , about 3100 mW / mm 2 , about 3100 mW / mm 2 , about 3300 mW / mm 2 , about 3400 mW / mm 2 or about 3500 mW / mm 2 (including itself), including any value between these numbers.

[0141] The light stimulation for activating the light-responsive polypeptide may include light pulses characterized by, for example, frequency, pulse width, duty cycle, wavelength, intensity, etc. In some cases, the light stimulation includes two or more different sets of light pulses, where each set of light pulses is characterized by a different temporal pattern of the light pulses. The temporal pattern may be characterized by any suitable parameter, including but not limited to frequency, time period (i.e., the total duration of the light stimulation), pulse width, duty cycle, etc.

[0142] The light pulses can have any suitable frequency. In some cases, the set of light pulses includes a single light pulse that persists throughout the entire duration of the light stimulation. In some cases, a set of light pulses has a frequency of 0.1 Hz or higher, such as 0.5 Hz or higher, 1 Hz or higher, 5 Hz or higher, 10 Hz or higher, 20 Hz or higher, 30 Hz or higher, 40 Hz or higher, including 50 Hz or higher, or 60 Hz or higher, or 70 Hz or higher, or 80 Hz or higher, or 90 Hz or higher, or 100 Hz or higher, and has a frequency of 100,000 Hz or lower, such as 10,000 Hz or lower, 1,000 Hz or lower, 500 Hz or lower, 400 Hz or lower, 300 Hz or lower, 200 Hz or lower, including 100 Hz or lower. In some embodiments, the light pulses have a frequency in the range of 0.1 to 100,000 Hz, such as 1 to 10,000 Hz, 1 to 1,000 Hz, including 5 to 500 Hz or 10 to 100 Hz.

[0143] In some cases, two sets of light pulses are characterized by having different parameter values, such as different pulse widths, e.g., short or long pulse widths. The light pulses can have any suitable pulse width. In some cases, the pulse width is 0.1 ms or longer, such as 0.5 ms or longer, 1 ms or longer, 3 ms or longer, 5 ms or longer, 7.5 ms or longer, 10 ms or longer, including 15 ms or longer, or 20 ms or longer, or 25 ms or longer, or 30 ms or longer, or 35 ms or longer, or 40 ms or longer, or 45 ms or longer, or 50 ms or longer, and is 500 ms or shorter, such as 100 ms or shorter, 90 ms or shorter, 80 ms or shorter, 70 ms or shorter, 60 ms or shorter, 50 ms or shorter, 45 ms or shorter, 40 ms or shorter, 35 ms or shorter, 30 ms or shorter, 25 ms or shorter, including 20 ms or shorter. In some embodiments, the pulse width is in the range of 0.1 to 500 ms, such as 0.5 to 100 ms, 1 to 80 ms, including 1 to 60 ms, or 1 to 50 ms, or 1 to 30 ms.

[0144] The average power of the light pulse measured at the tip of the optical fiber delivering the light pulse to the region of the brain can be any suitable power. In some cases, the power is 0.1 mW or greater, such as 0.5 mW or greater, 1 mW or greater, 1.5 mW or greater, including 2 mW or greater, or 2.5 mW or greater, or 3 mW or greater, or 3.5 mW or greater, or 4 mW or greater, or 4.5 mW or greater, or 5 mW or greater, and can be 1,000 mW or less, such as 500 mW or less, 250 mW or less, 100 mW or less, 50 mW or less, 40 mW or less, 30 mW or less, 20 mW or less, 15 mW or less, including 10 mW or less, or 5 mW or less. In some embodiments, the power ranges from 0.1 to 1,000 mW, such as 0.5 to 100 mW, 0.5 to 50 mW, 1 to 20 mW, including 1 to 10 mW or 1 to 5 mW.

[0145] The wavelength and intensity of the light pulse can vary and can depend on the activation wavelength of the light-responsive polypeptide, the optical transparency of the region of the brain, the desired volume of the brain to be irradiated, and the like.

[0146] The volume of the brain region irradiated by the light pulse can be any suitable volume. In some cases, the irradiated volume is 0.001 mm 3 or greater, such as 0.005 mm 3 or greater, 0.001 mm 3 or greater, 0.005 mm 3 or greater, 0.01 mm 3 or greater, 0.05 mm 3 or greater, including 0.1 mm 3 or greater, and is 100 mm 3 or less, such as 50 mm 3 or less, 20 mm 3 or less, 10 mm 3 or less, 5 mm 3 or less, 1 mm 3 or less, including 0.1 mm 3 or less. In certain cases, the irradiated volume ranges from 0.001 to 100 mm 3 such as 0.005 to 20 mm 3 、0.01 to 10 mm 3 、0.01 to 5 mm 3 ,including 0.05 to 1 mm 3 .

[0147] In some embodiments, a light-responsive polypeptide expressed in a cell can be fused to one or more amino acid sequence motifs selected from the group consisting of a signal peptide, an endoplasmic reticulum (ER) export signal, a membrane trafficking signal, and / or an N-terminal Golgi export signal. One or more amino acid sequence motifs that enhance the trafficking of the light-responsive protein to the plasma membrane of mammalian cells can be fused to the N-terminus, C-terminus, or both the N-terminus and C-terminus of the light-responsive polypeptide. In some cases, one or more amino acid sequence motifs that enhance the trafficking of the light-responsive polypeptide to the plasma membrane of mammalian cells are fused within the light-responsive polypeptide. Optionally, the light-responsive polypeptide and one or more amino acid sequence motifs can be separated by a linker. In some embodiments, the light-responsive polypeptide can be modified by addition of a trafficking signal (ts) that enhances the trafficking of the protein to the cytoplasmic membrane. In some embodiments, the trafficking signal can be derived from the amino acid sequence of the human inward rectifier potassium channel Kir2.1. In some embodiments, the signal peptide sequence in the protein can be deleted or replaced with a signal peptide sequence from a different protein.

[0148] Exemplary light-responsive polypeptides and amino acid sequence motifs that can be used in the systems and methods of the invention are disclosed, for example, in U.S. Patent Nos. 10,538,560; 10,568,307; 9,284,353; 9,359,449; and 9,365,628, which are incorporated herein by reference.

[0149] Light-responsive polypeptides of interest include, for example, step function opsin (SFO)6 protein or stabilized step function opsin (SSFO) protein, which may have specific amino acid substitutions at key positions in the retinal-binding pocket of the protein. See, for example, WO 2010 / 056970, the disclosure of which is hereby incorporated by reference in its entirety. The polypeptide may be a cation channel derived from Volvox carteri (VChR1), optionally comprising one or more amino acid substitutions, such as C123A; C123S; D151A, etc. The light-responsive cation channel protein may be a C1V1 chimeric protein of the VChR1 protein derived from Volvox carteri and the ChR1 protein from Chlamydomonas reinhardtii, wherein the protein comprises the amino acid sequence of VChR1, said amino acid sequence having at least the first transmembrane helix and the second transmembrane helix replaced by the first transmembrane helix and the second transmembrane helix of ChR1, optionally having an amino acid substitution at amino acid residue E122 or E162. In other embodiments, the light-responsive cation channel protein is a C1C2 chimeric protein derived from the ChR1 and ChR2 proteins from Chlamydomonas reinhardtii, wherein the protein is responsive to light and is capable of mediating a depolarizing current in the cell when the cell is irradiated with light. In some embodiments, the depolarizing light-responsive polypeptide is a red-shifted variant of the depolarizing light-responsive polypeptide derived from Chlamydomonas reinhardtii; referred to as "ReaChR polypeptide" or "ReaChR protein" or "ReaChR". In some embodiments, the depolarizing light-responsive polypeptide is the SdChR polypeptide derived from Scherffelia dubia, wherein the SdChR polypeptide is capable of transporting cations across the cell membrane when the cell is irradiated with light. In some embodiments, the depolarizing light-responsive polypeptide is CnChR1, which is derived from Chlamydomonas noctigama, wherein the CnChR1 polypeptide is capable of transporting cations across the cell membrane when the cell is irradiated with light. In some embodiments, the light-responsive cation channel protein is a CsChrimson chimeric protein derived from the CsChR protein of Chloromonas subdivisa and the CnChR1 protein from Chlamydomonas noctigama, wherein the N-terminus of the protein comprises the amino acid sequence of residues 1-73 of CsChR, followed by the amino acid sequence of residues 79-350 of CnChR1; is responsive to light; and is capable of mediating a depolarizing current in the cell when the cell is irradiated with light. In some embodiments, the depolarizing light-responsive polypeptide may be, for example, ShChR1 derived from Stigeoclonium helveticum, wherein the ShChR1 polypeptide is capable of transporting cations across the cell membrane when the cell is irradiated with light.

[0150] In some embodiments, the depolarizing light-responsive polypeptide is derived from Chlamydomonas reinhardtii (CHR1, and in particular CHR2), wherein the polypeptide is capable of transporting cations across the cell membrane when the cell is illuminated with light; and is capable of mediating a depolarizing current in the cell when the cell is illuminated with light. In some embodiments, a CaMKIIa-driven humanized channelrhodopsin CHR2 H134R mutant fused to EYFP is used for optogenetic activation. The light used to activate the light-responsive cation channel protein derived from Chlamydomonas reinhardtii can have a wavelength between about 460 nm and about 495 nm, or can have a wavelength of about 480 nm. The light-responsive cation channel protein can additionally comprise substitutions, deletions, and / or insertions introduced into the native amino acid sequence to increase or decrease sensitivity to light, increase or decrease sensitivity to a particular light wavelength, and / or increase or decrease the ability of the light-responsive cation channel protein to regulate the polarization state of the plasma membrane of the cell. Additionally, the light-responsive cation channel protein can comprise one or more conservative amino acid substitutions and / or one or more non-conservative amino acid substitutions. The light-responsive proton pump protein containing substitutions, deletions, and / or insertions introduced into the native amino acid sequence suitably retains the ability to transport cations across the cell membrane. The protein can comprise various amino acid substitutions, such as one or more of H134R; T159C; L132C; E123A, etc. The protein can further comprise a fluorescent protein, such as but not limited to yellow fluorescent protein, red fluorescent protein, green fluorescent protein, or cyan fluorescent protein.

[0151] Neurons can be optogenetically selectively activated or inhibited by engineering the neurons to express one or more light-responsive polypeptides configured to hyperpolarize or depolarize the neurons. Suitable light-responsive polypeptides and methods of their use are further described below.

[0152] The light-responsive polypeptides for the present disclosure can be any suitable light-responsive polypeptides for selectively activating subtypes of neurons by irradiating the neurons with activating light stimulation. In some instances, the light-responsive polypeptide is a light-responsive ion channel polypeptide. When irradiated with light of an activating wavelength, the light-responsive ion channel polypeptide is adapted to allow one or more ions to cross the plasma membrane of the target cell. The light-responsive protein can be characterized as an ion pump protein that facilitates the passage of a small amount of ions per photon of light across the plasma membrane; or as an ion channel protein that allows the flow of ions to freely flow across the plasma membrane when the channel is open. In some embodiments, the light-responsive polypeptide depolarizes the cell when activated by light of an activating wavelength. In some embodiments, the light-responsive polypeptide hyperpolarizes the cell when activated by light of an activating wavelength. Suitable hyperpolarizing and depolarizing polypeptides are known in the art and include, for example, channelrhodopsin (e.g., ChR2), variants of ChR2 (e.g., C128S, D156A, C128S+D156A, E123A, E123T), iC1C2, C1C2, GtACR2, NpHR, eNpHR3.0, C1V1, VChR1, VChR2, SwiChR, Arch, ArchT, KR2, ReaChR, ChiEF, Chronos, ChRGR, CsChrimson, etc. In some cases, the light-responsive polypeptide includes bReaCh ES, as described, for example, in Rajasethupathy et al., Nature. October 29, 2015; 526(7575):653, which is incorporated herein by reference. Hyperpolarizing and depolarizing opsins have been described in various publications; see, for example, Berndt and Deisseroth (2015) Science 349:590; Berndt et al., (2014) Science 344:420; and Guru et al., (July 25, 2015) Intl. J. Neuropsychopharmacol., pp. 1-8 (PMID 26209858).

[0153] Any suitable method can be used to introduce the light-responsive polypeptide into the neurons. In some cases, the neurons of the subtype of interest are genetically modified to express the light-responsive polypeptide. In some cases, viral vectors (e.g., adeno-associated viral vectors) can be used to genetically modify the neurons, and the viral vectors contain nucleic acids having nucleotide sequences encoding the light-responsive polypeptides. The viral vectors can include any suitable control elements (e.g., promoters, enhancers, recombination sites, etc.) to control the expression of the light-responsive polypeptide according to the neuron subtype, timing, presence of an inducer, etc.

[0154] "Operably linked" means juxtaposition wherein the components so described are in a relationship that permits them to function in their intended manner. For example, if a promoter affects transcription and / or expression of a nucleotide sequence, the promoter is operably linked to the nucleotide sequence (e.g., a protein-coding sequence, e.g., a sequence encoding mRNA; a non-protein-coding sequence, e.g., a sequence encoding a photoreactive protein; etc.).

[0155] Neuronal-specific promoters and other control elements (e.g., enhancers) are known in the art. Suitable neuronal-specific control sequences include, but are not limited to, the neuronal-specific enolase (NSE) promoter (see, e.g., EMBL HSENO2, X51956; also see, e.g., U.S. Patent No. 6,649,811, U.S. Patent No. 5,387,742); the aromatic amino acid decarboxylase (AADC) promoter; the neurofilament promoter (see, e.g., GenBank HUMNFL, L04147); the synapsin promoter (see, e.g., GenBank HUMSYNIB, M55301); the thy-1 promoter (see, e.g., Chen et al., (1987) Cell 51:7-19; and Llewellyn et al., (2010) Nat. Med. 16:1161); the serotonin receptor promoter (see, e.g., GenBank S62283); the tyrosine hydroxylase promoter (TH) (see, e.g., Nucl. Acids Res. 15:2363-2384 (1987) and Neuron 6:583-594 (1991)); the GnRH promoter (see, e.g., Radovick et al., Proc. Natl. Acad. Sci. USA 88:3402-3406 (1991)); the L7 promoter (see, e.g., Oberdick et al., Science 248:223-226 (1990)); the DNMT promoter (see, e.g., Bartge et al., Proc. Natl. Acad. Sci. USA 85:3648-3652 (1988)); the enkephalin promoter (see, e.g., Comb et al., EMBO J. 17:3793-3805 (1988)); the myelin basic protein (MBP) promoter; the CMV enhancer / platelet-derived growth factor-β promoter (see, e.g., Liu et al., (2620) Gene Therapy 11:52-60); the motor neuron-specific gene Hb9 promoter (see, e.g., U.S. Patent No. 7,632,679; and Lee et al., (2620) Development 131:3295-3306); and Ca 2+- Promoter of the α subunit of calmodulin-dependent protein kinase II (CaMKII) (see, e.g., Mayford et al., (1996) Proc. Natl. Acad. Sci. USA 93:13250). Other suitable promoters include elongation factor (EF) 1 and dopamine transporter (DAT) promoters.

[0156] In some cases, neuron subtype-specific expression of the light-responsive polypeptide can be achieved by using a recombination system (e.g., Cre-Lox recombination, Flp-FRT recombination, etc.). Cell type-specific expression of recombinant genes has been described, for example, in Fenno et al., Nat Methods, July 2014; 11(7):763; and Gompf et al., Front Behav Neurosci., July 2, 2015; 9:152, which are incorporated herein by reference.

[0157] In some embodiments, the vector is a recombinant adeno-associated virus (AAV) vector. AAV vectors are DNA viruses of relatively small size that can integrate into the genome of the cells they infect in a stable and site-specific manner. They are able to infect a broad spectrum of cells without inducing any effect on cell growth, morphology, or differentiation, and they do not appear to be involved in human pathology. The genome of AAV has been cloned, sequenced, and characterized. It encompasses approximately 4700 bases and contains inverted terminal repeat (ITR) regions of approximately 145 bases at each end, which serve as the origin of replication of the virus. The remaining part of the genome is divided into two essential regions carrying capsidization functions: the left part of the genome, which contains the rep gene involved in viral replication and expression of viral genes; and the right part of the genome, which contains the cap gene encoding the capsid protein of the virus.

[0158] In recent years, the application of AAV as a vector for gene therapy has developed rapidly. Wild-type AAV can infect dividing or non-dividing cells or tissues of mammals including humans at relatively high titers, and can also integrate into human cells at a specific site (on the long arm of chromosome 19) (Kotin et al., Proc. Natl. Acad. Sci. U.S.A., 1990. 87:2211-2215; Samulski et al., EMBO J., 1991. 10:3941-3950, the disclosures of which are hereby incorporated by reference in their entirety). The AAV vector without the rep and cap genes loses the specificity of site-specific integration but can still mediate the long-term and stable expression of foreign genes. The AAV vector exists in cells in two forms, one of which is an episome outside the chromosome; the other is integrated into the chromosome, with the former as the main form. In addition, AAV has not been found to be associated with any human diseases so far, and no changes in any biological characteristics caused by integration have been observed. There are 16 serotypes of AAV reported in the literature, named AAV1, AAV2, AAV3, AAV4, AAV5, AAV6, AAV7, AAV8, AAV9, AAV10, AAV11, AAV12, AAV13, AAV14, AAV15 and AAV16 respectively, among which AAV5 was initially isolated from humans (Bantel-Schaal and H. zur Hausen. Virology, 1984. 134:52-63), while AAV1-4 and AAV6 were all discovered in the study of adenoviruses (Ursula Bantel-Schaal, Hajo Delius and Harald zur Hausen. J. Virol., 1999. 73:939-947).

[0159] Any convenient method can be used to prepare the AAV vector. Adeno-associated viruses of any serotype are suitable (see, e.g., “Parvoviruses and Human Disease” by Blacklow, pages 165 - 174, edited by J.R. Pattison (1988); Rose, Comprehensive Virology 3:1, 1974; P. Tattersall “The Evolution of Parvovirus Taxonomy” In Parvoviruses (J.R. Kerr, S.F. Cotmore, M.E. Bloom, R.M. Linden, C.R. Parrish editors) pages 5 - 14, Hudder Arnold, London, UK (2006); and D.E. Bowles, J.E. Rabinowitz, R.J. Samulski “The Genus Dependovirus” (J.R. Kerr, S.F. Cotmore, M.E. Bloom, R.M. Linden, C.R. Parrish editors) pages 15 - 23, Hudder Arnold, London, UK (2006), the disclosures of which are hereby incorporated by reference in their entirety into this text). Methods for purifying the vector can be found, for example, in U.S. Patent Nos. 6,566,118, 6,989,264, and 6,995,006, and WO / 1999 / 011764 (entitled “Methods for Generating High Titer Helper-free Preparation of Recombinant AAV Vectors”), the disclosures of which are incorporated by reference in their entirety into this text. The preparation of hybrid vectors is described, for example, in PCT Application No. PCT / US2005 / 027091, the disclosure of which is incorporated by reference in its entirety into this text. The use of vectors derived from AAV for gene transfer in vitro and in vivo has been described (see, e.g., International Patent Application Publication Nos. 91 / 18088 and WO 93 / 09239; U.S. Patent Nos. 4,797,368, 6,596,535, and 5,139,941; and European Patent No. 0488528, all of which are incorporated by reference in their entirety into this text). These publications describe various AAV-derived constructs in which the rep and / or cap genes are deleted and replaced with a gene of interest; and the use of these constructs for transferring a gene of interest in vitro (into cultured cells) or in vivo (directly into an organism).The replication-deficient recombinant AAV according to the present invention can be prepared by co-transfecting a plasmid containing a nucleic acid sequence of interest flanked by two AAV inverted terminal repeat (ITR) regions and a plasmid carrying AAV encapsidation genes (rep and cap genes) into a cell line infected with a human helper virus (e.g., adenovirus). The resulting AAV recombinants are then purified by standard techniques.

[0160] In some embodiments, the vectors for use in the methods of the present invention are encapsidated into viral particles (e.g., AAV viral particles, including but not limited to AAV1, AAV2, AAV3, AAV4, AAV5, AAV6, AAV7, AAV8, AAV9, AAV10, AAV11, AAV12, AAV13, AAV14, AAV15, and AAV16). Accordingly, the present invention includes recombinant viral particles (recombinant in that it contains a recombinant polynucleotide) containing any of the vectors described herein. Methods for producing such particles are known in the art and are described in U.S. Patent No. 6,596,535.

[0161] It should be understood that one or more vectors can be administered to the nerve cells. If more than one vector is used, it is understood that they can be administered at the same time or at different times.

[0162] In certain embodiments, optogenetic neuromodulation of medium spiny neurons is performed according to a method comprising the steps of: (a) optogenetically inhibiting D1-medium spiny neurons (D1-MSNs) in the medial globus pallidus (GPi) region of the subject's brain, wherein D1-MSN-mediated beta band oscillations are inhibited; (b) optogenetically inhibiting D2-medium spiny neurons (D2-MSNs) in the lateral globus pallidus (Gpe) region of the subject's brain, wherein D2-MSN-mediated beta band oscillations are inhibited; (c) periodically optogenetically stimulating D1-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta band oscillations are inhibited; or (d) randomly optogenetically stimulating D1-MSNs and D2-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta band oscillations and D2-MSN-mediated beta band oscillations are inhibited; or any combination of (a)-(d).

[0163] In certain embodiments, the method comprises: (a) optogenetically inhibiting D1-MSNs in the GPi region of the subject's brain, wherein D1-MSN-mediated β-band oscillations are inhibited; (b) optogenetically inhibiting D2-MSNs in the Gpe region of the subject's brain, wherein D2-MSN-mediated β-band oscillations are inhibited; (c) periodically optogenetically stimulating D1-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated β-band oscillations are inhibited; and (d) randomly optogenetically stimulating D1-MSNs and D2-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated β-band oscillations and D2-MSN-mediated β-band oscillations are inhibited.

[0164] In certain embodiments, optogenetically inhibiting D1-MSNs or D2-MSNs in the GPi region or Gpe region comprises: introducing a recombinant polynucleotide encoding a light-responsive ion channel into D1-MSNs or D2-MSNs in the GPi region or Gpe region, wherein the light-responsive ion channel is expressed in the D1-MSNs or D2-MSNs; and irradiating the light-responsive ion channel with light of a wavelength that activates the light-responsive ion channel, wherein ion conduction through the light-responsive ion channel in response to light absorption results in hyperpolarization and inhibition of the D1-MSNs or D2-MSNs.

[0165] In certain embodiments, the light-responsive ion channel is a light-responsive anion-conducting rhodopsin or a light-responsive proton-conducting modulator. In certain embodiments, the light-responsive anion-conducting rhodopsin conducts chloride ions (Cl - −). In certain embodiments, the anion-conducting rhodopsin is an anion-conducting channelrhodopsin or halorhodopsin. In certain embodiments, the halorhodopsin is Natronomonas pharaonis halorhodopsin (NpHR), enhanced NpHR (eNpHR) 1.0, eNpHR2.0, or eNpHR 3.0. In certain embodiments, the anion-conducting channelrhodopsin is iC1C2, SwiChR, SwiChR++, or iC++. In certain embodiments, the light-responsive proton-conducting modulator is bacteriorhodopsin or archaerhodopsin. In certain embodiments, the light-responsive proton-conducting modulator is Arch from Halorubrum sodomense, ArchT from Halorubrum, TP009 from Leptosphaeria maculans, or Mac from Leptosphaeria maculans.

[0166] In certain embodiments, optogenetic stimulation of D1-MSNs or D2-MSNs in the Gpi region or Gpe region comprises: introducing a recombinant polynucleotide encoding a light-responsive ion channel into D1-MSNs or D2-MSNs in the Gpi region or Gpe region, wherein the light-responsive ion channel is expressed in the D1-MSNs or D2-MSNs; and irradiating the light-responsive ion channel with light of a wavelength that activates the light-responsive ion channel, wherein ion conduction through the light-responsive ion channel in response to light absorption results in depolarization and activation of the D1-MSNs or D2-MSNs. In certain embodiments, the light-responsive ion channel is a light-responsive cation-conducting rhodopsin. In certain embodiments, the light-responsive cation-conducting rhodopsin conducts calcium cations (Ca 2+ ). In certain embodiments, the light-responsive cation-conducting rhodopsin is a light-responsive cation-conducting channelrhodopsin. In certain embodiments, the light-responsive cation-conducting channelrhodopsin is Chlamydomonas channelrhodopsin or Volvox channelrhodopsin. In certain embodiments, the light-responsive cation-conducting channelrhodopsin is Chlamydomonas channelrhodopsin-1 (ChR1), Chlamydomonas channelrhodopsin-2 (ChR2), Volvox channelrhodopsin-1 (VChR1), or a chimeric ChR1-VChR1 channelrhodopsin.

[0167] Electrical Stimulation

[0168] In certain embodiments, electrical stimulation is applied to a subject's brain using an electrode to treat Parkinson's disease. The method comprises positioning the electrode in a region of the subject's brain to deliver electrical stimulation to the brain to inhibit beta band oscillations. In some embodiments, the method comprises positioning a first electrode at a first location in the medial globus pallidus (Gpi) region of the subject's brain to deliver electrical stimulation to D1-medium spiny neurons in the GPi region; positioning a second electrode at a second location in the lateral globus pallidus (Gpe) region of the subject's brain to deliver electrical stimulation to D1-medium spiny neurons and D2-medium spiny neurons in the Gpe region; and applying electrical stimulation to the Gpi region of the subject's brain using the first electrode and applying electrical stimulation to the Gpe region of the subject's brain using the second electrode in a manner effective to inhibit beta band oscillations to treat Parkinson's disease.

[0169] As used herein, the phrase "an electrode" or "the electrode" refers to a single electrode or multiple electrodes, such as an electrode array. As used herein, the term "contact" as used in the context of an electrode contacting a region of the brain refers to a physical association between the electrode and the region. The electrode can conduct an electric current to a specific target in the brain. The electrodes used in the methods disclosed herein can be monopolar (cathode or anode) or bipolar (e.g., having an anode and a cathode). The electrode can be a non-brain-penetrating surface electrode, an extracranial electrode (e.g., mounted subgaleally or on the skull (in a drilled cap or in the case of a skull-mounted nerve stimulator)), or a brain-penetrating depth electrode.

[0170] Standard surgical procedures for placing intracranial electrodes can be used to position the electrodes. In some cases, placing the electrodes can involve positioning the electrodes on the surface of a designated region of the brain (e.g., the Gpi and / or Gpe regions). The electrode can contact at least a portion of the surface of the brain at the designated region. In some embodiments, the electrode can contact substantially the entire surface area at the designated region. In some embodiments, the electrode can additionally contact a region adjacent to the designated region.

[0171] In some embodiments, an electrode array disposed on a planar support substrate can be used. The surface area of the electrode array can be determined by the desired contact area between the electrode array and the brain. Electrodes for implantation on the brain surface (such as surface electrodes or surface electrode arrays) can be obtained from commercial suppliers. Commercially obtained electrodes / electrode arrays can be modified to obtain the desired contact area. In some cases, non-brain-penetrating electrodes (also referred to as surface electrodes) that can be used in the methods disclosed herein can be electrocorticogram (ECoG) electrodes, subgaleal electrodes, or electroencephalogram (EEG) electrodes. In certain embodiments, multiple electrodes are positioned in one or more designated brain regions (e.g., the Gpi and / or Gpe regions).

[0172] In some cases, placing the electrode in the target region or site may involve positioning a brain-penetrating electrode (also referred to as a depth electrode) in a designated region of the brain. For example, the electrode can be placed in a region of the brain (e.g., the Gpi and / or Gpe). In some embodiments, the electrode can additionally contact a region adjacent to the designated region of the brain. In some embodiments, one or more electrodes or electrode arrays are used to target D1-MSNs and / or D2-MSNs in one or more regions of the brain (e.g., the Gpi and / or Gpe).

[0173] The depth at which the electrodes are inserted into the brain can be determined by the desired level of contact between the electrode array and the brain. Brain-penetrating electrode arrays are available from commercial suppliers. Commercially available electrode arrays can be modified to achieve the desired depth of insertion into brain tissue.

[0174] Standard surgical procedures for placing electrodes for deep brain stimulation can be used to position the electrodes for delivering electrical stimulation to the brain. For example, the electrodes can be placed in target regions of the brain (e.g., the Gpi and / or Gpe regions). Medical imaging such as, for example, magnetic resonance imaging (MRI) or computed tomography (CT) can be used to provide guidance for the placement of the DBS electrodes and to verify the correct placement of the electrodes in the brain. Additionally, a neurostimulator that generates electrical pulses is placed under the skin of the chest, typically below the collarbone or in the abdomen. In some embodiments, the neurostimulator is mounted on the skull. The surgical procedure may involve placing the electrodes into the brain through small holes in the skull. The electrode leads tunnel under the skin of the neck and under the skin of the chest to connect to the neurostimulator implanted in the chest.

[0175] Current is supplied from the neurostimulator to the electrodes. Parameters such as pulse width, shape, frequency, amplitude, pattern, and temporal profile can be adjusted to modulate neural activity and neuronal circuits to suppress beta-band oscillations. In some embodiments, a closed-loop system is used to adjust the DBS settings based on optimization of the neurostimulation parameters and targets using the methods described herein. In other embodiments, an open-loop system is used, where the DBS settings are adjusted by the user or a medical practitioner.

[0176] Electrical stimulation can be applied to the GPi and / or Gpe regions using a single electrode, an electrode pair, or an electrode array. In some embodiments, the number of electrodes for delivering electrical stimulation to the brain ranges from 8 to 32, including any number of electrodes within that range, such as 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, or 32 electrodes. In some embodiments, electrical stimulation is applied to more than one site. The sites to which the electrical stimulation is applied can be alternated, or otherwise patterned spatially or temporally. The electrical stimulation can be applied to the site simultaneously or sequentially. For different subjects, the sites selected for stimulation may be different and will depend on the location where pathological protein aggregates are known to be present (e.g., by medical imaging) or predicted to be present.

[0177] In some embodiments, an electrode array disposed on a planar support substrate can be used to electrically stimulate regions of the brain. The surface area of the electrode array can be determined by the desired contact area between the electrode array and the brain. In some cases, a cylindrical electrode array, paddle electrode array, or plate electrode array can be used in the methods for deep brain stimulation disclosed herein. Such electrode arrays for implantation into the brain can be obtained from commercial suppliers. Commercially available electrodes / electrode arrays can be modified to obtain the desired contact area.

[0178] The exact number of electrodes included in an electrode array (e.g., for electrical stimulation) can vary. In certain aspects, the electrode array can include two or more electrodes, such as three or more, including four or more, e.g., about 3 to 6 electrodes, about 6 to 12 electrodes, about 12 to 18 electrodes, about 18 to 24 electrodes, about 24 to 30 electrodes, about 30 to 48 electrodes, about 48 to 72 electrodes, about 72 to 96 electrodes, or about 96 or more electrodes. The electrodes can be arranged in a regular repeating pattern (e.g., a grid, such as a grid with a spacing of about 1 cm between the electrodes), or without a pattern. Electrodes that conform to the target site can be used for optimal delivery of electrical stimulation. One such example is a single multi-contact electrode having eight contacts spaced 2 1 / 2 mm apart. Each contact will have a span of approximately 2 mm. Another example is an electrode having two 1 cm contacts and a 2 mm insertion gap. Additionally, another example of an electrode that can be used in the methods of the present invention is a 2- or 3-branched electrode that covers the target site. Each of these trident electrodes has four contacts of 1 to 2 mm, with a center-to-center spacing of 2 to 2.5 mm and a span of 1.5 mm.

[0179] The size of each electrode can also vary depending on factors such as the number of electrodes in the array, the location of the electrodes, the material, the age of the patient, and other factors. In certain aspects, the electrode array has a size (e.g., diameter) of about 5 mm or less, such as about 4 mm or less, including 4 mm to 0.25 mm, 3 mm to 0.25 mm, 2 mm to 0.25 mm, 1 mm to 0.25 mm, or about 3 mm, about 2 mm, about 1 mm, about 0.5 mm, or about 0.25 mm.

[0180] In certain embodiments, the method further includes mapping the subject's brain to optimize the positioning of the electrodes for applying electrical stimulation. The positioning of the electrodes is optimized to maximize the clinical response to electrical stimulation to suppress beta band oscillations. In some embodiments, DBS is optimized to achieve neurophysiologically defined changes, e.g., reducing beta band synchronization and / or improving brain function.

[0181] Any standard method can be used to evaluate the effectiveness of electrical stimulation at a specific site for treating a neurological disorder or a neurodegenerative disorder. In some embodiments, the effectiveness of the electrical stimulation is evaluated by performing functional imaging of the subject's brain to determine the effectiveness of the nerve stimulation in suppressing beta-band oscillations. In some embodiments, the effectiveness of the electrical stimulation is evaluated by measuring the subject's brain function after the nerve stimulation. For example, brain function can be measured by performing electroencephalogram (EEG), stereoelectroencephalogram (sEEG), electrocorticogram (ECoG), magnetoencephalogram (MEG), single photon emission computed tomography (SPECT), functional magnetic resonance imaging (fMRI), optogenetic functional magnetic resonance imaging, or positron emission tomography (PET).

[0182] In some cases, the severity of the symptoms of Parkinson's disease can be further evaluated using a visual analogue scale or a verbal rating scale. In certain embodiments, the method further comprises using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), the Hoehn and Yahr (HnY) scale, or the Montreal Cognitive Assessment (MoCA) scale to evaluate one or more motor and / or non-motor symptoms of the subject.

[0183] As described herein, the subject method involves applying electrical stimulation to the GPi and / or Gpe regions. The parameters for applying electrical stimulation to the brain can be determined empirically during treatment or can be predefined, such as predefined from a pilot study of the subject. For example, different stimulation settings can be applied, including baseline (stimulus off), optimal treatment stimulation, modified and ineffective stimulation, and maximum tolerated stimulation, to determine the optimal treatment stimulation parameters for treating Parkinson's disease according to the methods described herein.

[0184] The parameters of the electrical stimulation can include one or more of frequency, pulse width / duration, duty cycle, intensity / amplitude, pulse pattern, program duration, program frequency, etc. In certain embodiments, the parameters are adjusted to target D1-MSNs and D2-MSNs in the GPi and / or GPe regions.

[0185] Frequency refers to the number of pulses generated per second during stimulation and is stated in Hertz (Hz, e.g., 60 Hz = 60 pulses per second). The frequency of the electrical stimulation used in the methods of the present invention can vary widely depending on a number of factors and can be determined empirically during treatment of a subject or can be predefined. In certain embodiments, the method can involve applying electrical stimulation to the brain at a frequency of from 2 Hz to 250 Hz, such as from 25 Hz to 200 Hz, from 50 Hz to 250 Hz, from 50 Hz to 185 Hz, from 50 Hz to 150 Hz, from 75 Hz to 200 Hz, from 100 Hz to 200 Hz, from 100 Hz to 180 Hz, from 100 Hz to 160 Hz, or from 130 Hz to 150 Hz. In some embodiments, electrical stimulation is applied to the brain at a frequency of from about 120 Hz to about 160 Hz, including any pulse frequency within that range, such as 120 Hz, 122 Hz, 124 Hz, 126 Hz, 128 Hz, 130 Hz, 132 Hz, 134 Hz, 136 Hz, 138 Hz, 140 Hz, 142 Hz, 144 Hz, 146 Hz, 148 Hz, 150 Hz, 152 Hz, 154 Hz, 156 Hz, 158 Hz, or 160 Hz. In some embodiments, non-integer pulse frequencies (e.g., 130.2 Hz, 130.4 Hz, etc.) are used.

[0186] The electrical stimulation can be applied as pulses (such as monopolar or bipolar pulses). The time span of a single pulse is referred to as the pulse width or pulse duration. The pulse width used in the methods of the present invention can vary widely depending on a number of factors (e.g., severity of the disease, condition of the patient, etc.) and can be determined empirically or can be predefined. In certain embodiments, the method can involve applying electrical stimulation at a pulse width of from about 10 microseconds to 500 microseconds, such as from 20 microseconds to 450 microseconds, from 40 microseconds to 450 microseconds, from 60 microseconds to 450 microseconds, from 60 microseconds to 220 microseconds, from 60 microseconds to 120 microseconds, or from 60 microseconds to 90 microseconds. In some embodiments, electrical stimulation is applied to the brain at a pulse width of from about 60 microseconds to about 210 microseconds, including any pulse width within that range, such as 60 microseconds, 65 microseconds, 70 microseconds, 75 microseconds, 80 microseconds, 85 microseconds, 90 microseconds, 95 microseconds, 100 microseconds, 105 microseconds, 110 microseconds, 115 microseconds, 120 microseconds, 125 microseconds, 130 microseconds, 135 microseconds, 140 microseconds, 145 microseconds, 150 microseconds, 155 microseconds, 160 microseconds, 165 microseconds, 170 microseconds, 175 microseconds, 180 microseconds, 185 microseconds, 190 microseconds, 195 microseconds, 200 microseconds, 205 microseconds, 210 microseconds, 215 microseconds, or 220 microseconds.

[0187] Electrical stimulation can be applied for a stimulation period lasting from 0.1 seconds to 1 month, during which there may be periods of rest (i.e., no electrical stimulation). In some cases, the period of electrical stimulation can be from 0.1 seconds to 1 week, from 1 second to 1 day, from 10 seconds to 12 hours, from 1 minute to 6 hours, from 10 minutes to 1 hour, and so on. In some cases, the period of electrical stimulation can be from 1 second to 1 minute, from 1 second to 30 seconds, from 1 second to 15 seconds, from 1 second to 10 seconds, from 1 second to 6 seconds, from 1 second to 3 seconds, from 1 second to 2 seconds, or from 6 seconds to 10 seconds. The period of rest between each stimulation period can be 60 seconds or less, 30 seconds or less, 20 seconds or less, or 10 seconds. In some embodiments, electrical stimulation can be applied for a year or longer, 2 years or longer, 3 years or longer, 5 years or longer, or 10 years or longer. In some embodiments, as part of a long-term DBS therapy regimen, electrical stimulation can be applied indefinitely.

[0188] Electrical stimulation can be applied at an amplitude of current from 0.1 mA to 30 mA, such as from 0.1 mA to 25 mA, such as from 0.1 mA to 20 mA, 0.1 mA to 15 mA, 0.1 mA to 10 mA, 0.1 mA to 2 mA, 0.1 mA to 1 mA, 1 mA to 20 mA, 1 mA to 10 mA, 2 mA to 30 mA, 2 mA to 15 mA, 2 mA to 10 mA, or 1 mA to 3 mA. In some embodiments, the amplitude of the current is from 0.1 mA to 3.5 mA, or any amplitude of current within that range, such as 0.1 mA, 0.2 mA, 0.3 mA, 0.4 mA, 0.5 mA, 0.6 mA, 0.7 mA, 0.8 mA, 0.9 mA, 1.0 mA, 1.1 mA, 1.2 mA, 1.3 mA, 1.4 mA, 1.5 mA, 1.6 mA, 1.7 mA, 1.8 mA, 1.9 mA, 2.0 mA, 2.1 mA, 2.2 mA, 2.3 mA, 2.4 mA, 2.5 mA, 2.6 mA, 2.7 mA, 2.8 mA, 2.9 mA, 3.0 mA, 3.1 mA, 3.2 mA, 3.3 mA, 3.4 mA, or 3.5 mA.

[0189] Electrical stimulation can be applied at an amplitude of voltage from 0.1V to 15V, such as 0.1V to 10V, 0.1V to 5V, 1V to 10V, 1V to 5V, or 1V to 3.5V. In some embodiments, the amplitude of the voltage is 1V to 3.5V, or any amplitude of voltage within this range, such as 1V, 1.1V, 1.2V, 1.3V, 1.4V, 1.5V, 1.6V, 1.7V, 1.8V, 1.9V, 2.0V, 2.1V, 2.2V, 2.3V, 2.4V, 2.5V, 2.6V, 2.7V, 2.8V, 2.9V, 3.0V, 3.1V, 3.2V, 3.3V, 3.4V, or 3.5V.

[0190] Electrical stimulation having the parameters as described above can be applied within a program duration of about 1 day or less, such as 18 hours, 6 hours, 3 hours, 2 hours, 1 hour, 45 minutes, 30 minutes, 20 minutes, 10 minutes, or 5 minutes or less, for example, 1 minute to 5 minutes, 2 minutes to 10 minutes, 2 minutes to 20 minutes, 2 minutes to 30 minutes, 5 minutes to 10 minutes, 5 minutes to 30 minutes, or 5 minutes to 15 minutes, 10 minutes to 400 minutes, 25 minutes to 300 minutes, 50 minutes to 200 minutes, or 75 minutes to 150 minutes. This time period will include the application of pulses and the intermediate rest periods. The program can be repeated at a desired program frequency to reduce or prevent the aggregation and spread of pathological protein aggregates and relieve the symptoms of a neurological disorder or neurodegenerative disorder in a subject. Thus, a treatment regimen can include a program for electrical stimulation at a desired program frequency and program duration. In some embodiments, the treatment regimen is controlled by a control unit that communicates with a pulse generator connected to one or more DBS electrodes in a closed-loop treatment regimen.

[0191] After completion of the treatment regimen, the effectiveness of the treatment of the patient can be evaluated and, if necessary, the treatment regimen can be repeated. In certain cases, the treatment regimen may be changed before repetition. For example, one or more of the frequency, pulse width, current amplitude, period of electrical stimulation, program duration, program frequency, and / or placement of DBS or sensing electrodes can be changed before starting a second treatment regimen.

[0192] The application of the method can include a previous step of selecting a patient for treatment based on a need determined, e.g., by a clinical assessment, which can include an assessment of the severity of a neurological disorder or neurodegenerative disorder (e.g., a neurological disorder or neurodegenerative disorder lasting at least 3 months), physical condition, medication regimen, cognitive assessment, anatomical assessment, behavioral assessment, and / or neurophysiological assessment. In some cases, a subject can be further evaluated to determine whether a nerve stimulation will completely or partially (e.g., at least 50%) relieve the neurological disorder or neurodegenerative disorder. Such a patient can undergo nerve stimulation on a trial basis to determine whether the nerve stimulation inhibits beta band oscillations or reduces the severity of the symptoms experienced by the patient.

[0193] Kit

[0194] Also provided are kits containing software for performing the computer-implemented methods described herein for modeling the propagation of beta band oscillations in a subject's brain and the response to neuromodulation. In some embodiments, the kit includes a non-transitory computer-readable medium and instructions for optimizing nerve stimulation parameters and targets based on modeling the propagation of beta band oscillations in a subject's brain and the response to neuromodulation using the computer-implemented methods described herein. In some embodiments, the kit includes a system that includes a processor programmed according to the computer-implemented methods described herein; and a display component for displaying information regarding the propagation of beta band oscillations in a subject's brain and the response to neuromodulation.

[0195] In addition to the above components, the subject kit can further include (in some embodiments) instructions for practicing the subject method. These instructions can exist in a variety of forms in the subject kit, and one or more of these forms can be present in the kit. One form in which these instructions can exist is as printed information on a suitable medium or substrate, such as one or more sheets of paper on which the information is printed, in the packaging of the kit, in a package insert, and so forth. Another form of these instructions is a computer-readable medium on which the information is recorded, such as a disk, a compact disc (CD), a flash drive, and the like. Yet another form in which these instructions can exist is a website address that can be used via the Internet to access information on a remote site.

[0196] Examples of non-limiting aspects of the present disclosure

[0197] Aspects (including embodiments) of the subject matter of the present invention described above may be beneficial, either alone or in combination with one or more other aspects or embodiments. Without limiting the foregoing description, certain non-limiting aspects of the present disclosure numbered 1-53 are provided below. As will be understood by those of ordinary skill in the art upon reading this disclosure, each of the separately numbered aspects may be used or combined with any preceding or following separately numbered aspect. This is intended to provide support for all such combinations of aspects and is not limited to the combinations of aspects explicitly provided below.

[0198] 1. A method for inhibiting beta-band oscillations in a subject's brain by optogenetic neuromodulation of medium spiny neurons according to a method comprising the following steps:

[0199] (a) Optogenetically inhibiting D1-medium spiny neurons (D1-MSNs) in the globus pallidus internus (GPi) region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are inhibited;

[0200] (b) Optogenetically inhibiting D2-medium spiny neurons (D2-MSNs) in the globus pallidus externus (Gpe) region of the subject's brain, wherein D2-MSN-mediated beta-band oscillations are inhibited;

[0201] (c) Periodically optogenetically stimulating the D1-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are inhibited; or

[0202] (d) Randomly optogenetically stimulating the D1-MSNs and the D2-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations and D2-MSN-mediated beta-band oscillations are inhibited; or

[0203] (a)-(d) Any combination.

[0204] 2. The method according to aspect 1, wherein the method comprises:

[0205] (a) Optogenetically inhibiting the D1-MSNs in the GPi region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are inhibited;

[0206] (b) Optogenetically inhibiting the D2-MSNs in the Gpe region of the subject's brain, wherein D2-MSN-mediated beta-band oscillations are inhibited;

[0207] (c) Periodically performing optogenetic stimulation on the D1-MSNs in the Gpe region of the brain of the subject, wherein the β-band oscillations mediated by the D1-MSNs are suppressed; and

[0208] (d) Randomly performing optogenetic stimulation on the D1-MSNs and D2-MSNs in the Gpe region of the brain of the subject, wherein the β-band oscillations mediated by the D1-MSNs and the β-band oscillations mediated by the D2-MSNs are suppressed.

[0209] 3. The method according to aspect 1 or 2, wherein the optogenetic inhibition of the D1-MSNs or D2-MSNs in the Gpi region or the Gpe region comprises:

[0210] Introducing a recombinant polynucleotide encoding a light-responsive ion channel into the D1-MSNs or D2-MSNs in the Gpi region or the Gpe region, wherein the light-responsive ion channel is expressed in the D1-MSNs or D2-MSNs; and

[0211] Irradiating the light-responsive ion channel with light of a wavelength that activates the light-responsive ion channel, wherein ion conduction through the light-responsive ion channel in response to light absorption results in hyperpolarization and inhibition of the D1-MSNs or D2-MSNs.

[0212] 4. The method according to aspect 3, wherein the light-responsive ion channel is a light-responsive anion-conducting rhodopsin or a light-responsive proton-conducting modulator.

[0213] 5. The method according to aspect 4, wherein the light-responsive anion-conducting rhodopsin conducts chloride ions (Cl - ).

[0214] 6. The method according to aspect 4 or 5, wherein the anion-conducting rhodopsin is an anion-conducting channelrhodopsin or halorhodopsin.

[0215] 7. The method according to aspect 6, wherein the halorhodopsin is Natronomonas pharaonis halorhodopsin (NpHR), enhanced NpHR (eNpHR) 1.0, eNpHR 2.0, or eNpHR 3.0.

[0216] 8. The method according to aspect 6, wherein the anion-conducting channelrhodopsin is iC1C2, SwiChR, SwiChR++, or iC++.

[0217] 9. The method according to aspect 4, wherein the light-responsive proton-conducting regulator is bacteriorhodopsin or archaerhodopsin.

[0218] 10. The method according to aspect 9, wherein the light-responsive proton-conducting regulator is Arch from Halorubrum sodomense, ArchT from Halorubrum sp., TP009 from Leptosphaeria maculans, or Mac from Leptosphaeria maculans.

[0219] 11. The method according to aspect 1 or 2, wherein the optogenetic stimulation of the D1-MSN or the D2-MSN in the Gpi region or the Gpe region comprises:

[0220] introducing a recombinant polynucleotide encoding a light-responsive ion channel into the D1-MSN or the D2-MSN in the Gpi region or the Gpe region, wherein the light-responsive ion channel is expressed in the D1-MSN or the D2-MSN; and

[0221] irradiating the light-responsive ion channel with light of a wavelength that activates the light-responsive ion channel, wherein ion conduction through the light-responsive ion channel in response to light absorption causes depolarization and activation of the D1-MSN or the D2-MSN.

[0222] 12. The method according to aspect 11, wherein the light-responsive ion channel is a light-responsive cation-conducting opsin.

[0223] 13. The method according to aspect 12, wherein the light-responsive cation-conducting opsin conducts calcium cations (Ca 2+ ).

[0224] 14. The method according to aspect 12 or 13, wherein the light-responsive cation-conducting opsin is a light-responsive cation-conducting channelrhodopsin.

[0225] 15. The method according to aspect 14, wherein the light-responsive cation-conducting channelrhodopsin is Chlamydomonas reinhardtii channelrhodopsin or Volvox carteri channelrhodopsin.

[0226] 16. The method according to aspect 15, wherein the light-responsive cation-conducting channelrhodopsin is Chlamydomonas reinhardtii channelrhodopsin-1 (ChR1), Chlamydomonas reinhardtii channelrhodopsin-2 (ChR2), Volvox carteri channelrhodopsin-1 (VChR1), or chimeric ChR1-VChR1 channelrhodopsin.

[0227] 17. The method according to any one of aspects 1-16, wherein the polynucleotide encoding the light-responsive ion channel is provided by a viral vector.

[0228] 18. The method according to aspect 17, wherein the viral vector is a lentiviral vector or an adeno-associated virus (AAV) vector.

[0229] 19. The method according to aspect 17 or 18, wherein the viral vector is stereotactically injected into the posterior cingulate cortex.

[0230] 20. The method according to any one of aspects 17-19, wherein the vector further comprises a neuron-specific promoter operably linked to the polynucleotide encoding the light-responsive ion channel.

[0231] 21. The method according to any one of aspects 17-20, wherein the expression of the light-responsive ion channel is inducible.

[0232] 22. The method according to any one of aspects 1-21, wherein irradiating the light-responsive ion channel comprises delivering light from a light source to the light-responsive ion channel using a fiber-optic based optical neural interface.

[0233] 23. The method according to aspect 22, wherein the light source is a solid-state diode laser.

[0234] 24. The method according to any one of aspects 1-23, wherein the subject has Parkinson's disease.

[0235] 25. The method according to any one of aspects 1-24, wherein the optogenetic inhibition of the D1-MSNs comprises continuous shunting inhibition of the D1-MSNs in the GPi region of the subject's brain.

[0236] 26. The method according to any one of aspects 1-25, wherein the optogenetic inhibition of the D2-MSNs comprises continuous shunting inhibition of the D2-MSNs in the GPe region of the subject's brain.

[0237] 27. The method according to any one of aspects 1-26, wherein the periodic optogenetic stimulation of the D1-MSNs in the Gpe region of the subject's brain comprises periodic stimulation at a frequency of 130 Hz.

[0238] 28. The method according to any one of aspects 1-27, wherein the random optogenetic stimulation of the D1-MSNs and the D2-MSNs in the Gpe region of the subject's brain comprises using a plurality of stimulation sequences with pulses having a random interval of 130 Hz, wherein each neuron is stimulated with one of the stimulation sequences such that synchronized neurons are decoupled.

[0239] 29. The method according to any one of aspects 1-28, wherein the optogenetic stimulation of the D1-MSNs or the D2-MSNs comprises directly activating the D1-MSNs or the D2-MSNs with an intensity of 500 pA, 700 pA, or 900 pA.

[0240] 30. A method of treating Parkinson's disease in a subject, the method comprising:

[0241] Positioning a first electrode at a first location in the globus pallidus internus (Gpi) region of the subject's brain to deliver electrical stimulation to D1-medium spiny neurons in the GPi region;

[0242] Positioning a second electrode at a second location in the globus pallidus externus (Gpe) region of the subject's brain to deliver electrical stimulation to D1-medium spiny neurons and D2-medium spiny neurons in the Gpe region; and

[0243] Using the first electrode to apply electrical stimulation to the Gpi region of the subject's brain and using the second electrode to apply electrical stimulation to the Gpe region of the subject's brain in a manner effective to suppress beta-band oscillations to treat Parkinson's disease.

[0244] 31. The method according to aspect 30, wherein the electrical stimulation is applied unilaterally or bilaterally with the first electrode or the second electrode.

[0245] 32. The method according to aspect 30 or 31, wherein the first electrode or the second electrode is a depth electrode or a surface electrode.

[0246] 33. The method according to any one of aspects 30-32, wherein the first electrode or the second electrode is a non-brain-penetrating surface electrode array or a brain-penetrating electrode array.

[0247] 34. The method according to any one of aspects 30 - 33, wherein the first electrode is placed on the surface of the Gpi region.

[0248] 35. The method according to any one of aspects 30 - 34, wherein the second electrode is placed on the surface of the Gpe region.

[0249] 36. The method according to any one of aspects 30 - 35, wherein the method further comprises using a visual analogue scale, a verbal rating scale, the Movement Disorder Society - sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS - UPDRS), the Hoehn and Yahr (HnY) scale, or the Montreal Cognitive Assessment (MoCA) scale to evaluate the effectiveness of the treatment of the subject.

[0250] 37. A computer - implemented method for modeling the propagation of beta - band oscillations and the response to neuromodulation in a subject's brain, the computer performing the following steps, including:

[0251] a) Receiving functional magnetic resonance imaging data of the neural activity of D1 - medium spiny neurons (D1 - MSN) and D2 - medium spiny neurons (D2 - MSN) in the caudate putamen (CPu) region, external globus pallidus (GPe) region, internal globus pallidus (GPi) region, subthalamic nucleus (STN) region, substantia nigra reticulata (SNr) region, thalamus (THL) region, and motor cortex (MCX) region of the subject's brain before and during optogenetic stimulation;

[0252] b) Performing spectral dynamic causal modeling on the effective connectivity strength between the CPu region, the GPe region, the GPi region, the STN region, the SNr region, the THL region, and the MCX region;

[0253] c) Receiving experimental electrophysiological data of the CPu region;

[0254] d) Using the experimental electrophysiological data of the CPu region to estimate the effective connectivity strength of GABAergic connections;

[0255] e) Using the dynamic causal modeling of the functional magnetic resonance imaging data to estimate the effective connectivity strength of glutamatergic connections;

[0256] f) Performing biophysical modeling using the Hodgkin - Huxley model to generate simulated electrophysiological data using the estimated effective connectivity strength values;

[0257] g) iteratively optimize the GABAergic projections until the simulated electrophysiological data matches the experimental electrophysiological data of the CPu region;

[0258] h) calculate the temporal profile of the average power of the beta-band frequency for each neuron in the CPu region, the GPe region, the GPi region, the STN region, the SNr region, the THL region, and the MCX region; and

[0259] i) compare the total amount of co-occurring beta-band oscillation power before optogenetic stimulation with the co-occurring beta-band oscillation power during optogenetic stimulation to model the propagation of beta-band oscillations in the subject's brain.

[0260] 38. The computer-implemented method according to aspect 37, wherein the D1-MSN is a Huxley-Hudgkin neuron.

[0261] 39. The computer-implemented method according to aspect 37 or 38, wherein the experimental electrophysiological data includes single neuron recordings.

[0262] 40. The computer-implemented method according to aspect 39, wherein the single neuron recordings are from GABAergic neurons in the CPu region.

[0263] 41. The computer-implemented method according to any one of aspects 37-40, wherein glutamatergic connections are modeled as 1-to-1 connections, where the connection strength is proportional to the effective connectivity estimated by DCM.

[0264] 42. The computer-implemented method according to any one of aspects 39-41, wherein the CPu-GPi / GPe and GPi / SNr-thalamus GABAergic projections are modeled as 1-to-n diffusive projections with a connectivity strength wGABA, where n and wGABA are free parameters, and where N and wGABA are searched across the parameter space until the simulated action potential firing rate statistically matches the single neuron recordings.

[0265] 43. The computer-implemented method according to any one of aspects 37-42, wherein the optogenetic stimulation includes optogenetic inhibition of the D1-MSNs in the GPi region of the subject's brain.

[0266] 44. The computer-implemented method according to aspect 43, wherein the optogenetic inhibition of the D1-MSNs includes continuous shunting inhibition of the D1-MSNs in the GPi region of the subject's brain.

[0267] 45. The computer-implemented method according to any one of aspects 37-44, wherein the optogenetic stimulation comprises optogenetically inhibiting the D2-MSNs in the GPe region of the subject's brain.

[0268] 46. The computer-implemented method according to aspect 45, wherein the optogenetically inhibiting the D2-MSNs comprises continuously shunting inhibition of the D2-MSNs in the GPe region of the subject's brain.

[0269] 47. The method according to any one of aspects 37-46, wherein the optogenetic stimulation comprises directly activating the D1-MSNs or the D2-MSNs at an intensity of 500 pA, 700 pA or 900 pA.

[0270] 48. The computer-implemented method according to any one of aspects 37-47, wherein the optogenetic stimulation comprises periodically optogenetically stimulating the D1-MSNs in the Gpe region of the subject's brain.

[0271] 49. The computer-implemented method according to any one of aspects 37-48, wherein the optogenetic stimulation comprises randomly optogenetically stimulating the D1-MSNs and the D2-MSNs in the Gpe region of the subject's brain using a plurality of stimulation sequences having randomly spaced pulses, wherein each neuron is stimulated with one of the stimulation sequences in the stimulation sequence such that synchronized neurons are decoupled.

[0272] 50. The computer-implemented method according to any one of aspects 37-49, wherein the optogenetic stimulation is performed by periodic stimulation at a frequency of 130 Hz.

[0273] 51. A system for modeling the propagation of beta-band oscillations in a subject's brain using the computer-implemented method according to any one of aspects 37-50, the system comprising:

[0274] a) A storage component for storing data, wherein the storage component has instructions for modeling the propagation of beta-band oscillations based on an analysis of functional magnetic resonance imaging data and experimental electrophysiological data stored therein;

[0275] b) A computer processor for processing the functional magnetic resonance imaging data and the experimental electrophysiological data using one or more algorithms, wherein the computer processor is coupled to the storage component and is configured to execute instructions stored in the storage component to receive the input functional magnetic resonance imaging data and the experimental electrophysiological data, and analyze the data according to the computer-implemented method according to any one of aspects 37-50; and

[0276] c) A display component for displaying information about the propagation of beta-band oscillations in the brain of the subject.

[0277] 52. A non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, cause the processor to execute the method according to any one of aspects 37 to 50.

[0278] 53. A kit comprising the non-transitory computer-readable medium according to aspect 52 and instructions for modeling the propagation of beta-band oscillations from functional magnetic resonance imaging data and experimental electrophysiological data.

[0279] It will be apparent to those of ordinary skill in the art that various changes and modifications can be made without departing from the spirit or scope of the invention.

[0280] Experiment

[0281] The following examples are presented to provide a complete disclosure and description to those of ordinary skill in the art of how to make and use the invention, and are not intended to limit the scope that the inventors regard as their invention, nor are they intended to represent that the following experiments are all or the only experiments conducted. Efforts have been made to ensure the accuracy of the numbers used (e.g., amounts, temperatures, etc.) but some experimental errors and deviations should be considered. Unless otherwise indicated, parts are parts by weight, molecular weight is weight-average molecular weight, temperature is in degrees Celsius, and pressure is atmospheric or near atmospheric.

[0282] All publications and patent applications cited in this specification are incorporated herein by reference as if each individual publication or patent application was specifically and individually indicated to be incorporated herein by reference.

[0283] The invention has been described in terms of specific embodiments found or proposed by the inventors to include the preferred modes for practicing the invention. Those skilled in the art will understand that, in light of the present disclosure, many modifications and changes can be made to the illustrated specific embodiments without departing from the intended scope of the invention. All such modifications are intended to be included within the scope of the appended claims.

[0284] Example 1

[0285] Optogenetic - Driven Beta Synchrony Reveals Non - Classical Dynamics Along the Direct and Indirect Pathways of the Basal Ganglia Dynamics

[0286] Neuronal firing rates and patterns are both crucial in understanding the dynamics of brain function and dysfunction 1-3 For example, in Parkinson's disease (PD), altered firing rates and patterns in the form of abnormal beta-band (12 - 30 Hz) oscillations have been observed in the cortico-basal ganglia-thalamus (CBT) network 4-6 Understanding how the CBT network mediates neural oscillations and how neural oscillations are related to the firing rates of individual cell populations can help decode the circuit mechanisms underlying PD pathology and improve neuromodulation therapies

[0287] Pathological changes in firing rates in the case of PD are usually explained by the classical model of the basal ganglia circuit 4,5 The classical model attributes motor facilitation and inhibition to putative direct and indirect pathways mediated by medium spiny neurons (MSNs) expressing D1 and D2 receptors in the striatum 7 In PD, D2-MSNs are thought to be hyperactive, leading to persistent activation of the indirect pathway 4 Although this model is widely used to explain experimental results and disorders involving the basal ganglia, it cannot explain many recent experimental findings. For example, it has been found that the globus pallidus internus (GPi) and thalamus are simultaneously activated during voluntary actions and stimulation, while the classical model suggests that the activities of these two regions have opposite polarities 8-11

[0288] Excessive oscillations in the beta band are another hallmark of PD 6,12,13 The mechanism of which has not been explained by the classical basal ganglia model based on action potential firing rates. Under healthy conditions, beta-band oscillations in the CBT circuit are commonly observed and are hypothesized to play a role in maintaining the current state 14-17 Excessive beta-band oscillations in the CBT circuit in PD may reflect excessive stability, leading to abnormal persistence of the current motor state (bradykinesia) 16 Deep brain stimulation (DBS) can alleviate pathological synchronization in the subthalamic nucleus (STN), GPi 18,19 and cortical regions 20 Since the indirect pathway is thought to be involved in anti-kinetic control and its nodes have been shown to be hyperactive in PD 4,5 beta-band oscillations are thought to propagate along the indirect pathway 21,22 ​However, this hypothesis has not been examined at the scale of the entire CBT network, and the specific mechanism by which the basal ganglia circuitry facilitates the propagation of beta-band synchronization remains elusive. In this study, we utilized optogenetic-induced synchrony, optogenetic amplification of spontaneous activity, and computational modeling to dissect the contribution of the basal ganglia pathways to the propagation of beta-band oscillations. We show that the mechanisms underlying beta-band oscillations can also explain recent firing-rate-based observations that cannot be accounted for by canonical models.

[0289] Optogenetic stimulation and in vivo electrophysiology indicate that the direct pathway can mediate beta-band oscillations.

[0290] To investigate how the CBT network mediates beta-band oscillations and to understand previous findings that cannot be explained by classical models, we utilized optogenetics and in vivo extracellular recordings. To investigate the direct-pathway contribution to beta-band oscillations, D1-MSNs in the striatum were optogenetically stimulated at beta-band (20 Hz) (see "Methods"). During D1-MSN beta-band stimulation, extracellular recordings were collected from four ROIs, including the caudate putamen (CPu), GPi, thalamus, and subthalamic nucleus (STN) ( Figure 1A and Figure 1B ). Electrophysiological recordings targeting the ventrolateral nucleus in the thalamus showed increased firing during D1-MSN stimulation, which is consistent with the classical direct-pathway model. However, both GPi and STN showed increased activity inconsistent with the classical model.

[0291] We then employed SPIKE synchrony and power spectral density (PSD) profiles to measure changes in synchrony between neurons identified in the extracellular recordings within each ROI. SPIKE synchrony is a time-resolved measure of synchrony based on the relative number of simultaneous occurrences of action potentials 23 . Optogenetic stimulation of D1-MSNs successfully induced beta-band synchrony in the CPu ( Figure 1C and Figure 1D ), GPi, and thalamus, with an increase in PSD at approximately 20 Hz ( Figure 1D and Figure 5D ). STN neurons showed increased synchrony without any beta-band peaks ( Figure 1D ). When the neuronal action potentials in each ROI were aligned with the laser-pulse period (on for 8 ms every 50 ms), we found that neurons in the CPu, GPi, and thalamus fired with interlocked phases at 20 Hz ( Figure 1E ). Phase-locked oscillations involving GABAergic connections have been shown to result in post-inhibitory rebound firing 8,10 . Therefore, we hypothesize that the excitation of GPi and the thalamus may be driven by post-inhibitory rebound firing. These results together suggest that beta oscillations can propagate in a phase-locked manner via the direct pathway.

[0292] Optogenetic simulations and in vivo electrophysiology indicate that the indirect pathway mediates β-band oscillations in part.

[0293] To investigate the direct pathway contribution to β-band oscillations, D2-MSNs in the striatum were optogenetically stimulated at β-band (20 Hz) (see "Methods"), and in vivo extracellular recordings were collected from the CPu, GPi, thalamus, and STN ( Figure 2A and Figure 2B ). The increase in the discharge frequency of the CPu during D2-MSN stimulation confirmed the successful activation of D2-MSNs. The discharge frequency of the thalamus decreased during D2-MSN stimulation, which was consistent with the prediction of the classical indirect pathway model. However, the reduced discharges of the GPi and STN could not be explained by the classical model.

[0294] During D2-MSN stimulation, reduced levels of synchronization were found in the GPi and STN ( Figure 2C ). During stimulation, the PSDs of the CPu and STN peaked at 20 Hz, which confirmed successful D2-MSN stimulation, while the 20-Hz peak was absent in the thalamus ( Figure 2D ). Synchronized subpopulations of STN neurons showed in-phase firing with the CPu ( Figure 2E ). The PSDs of 22 / 90 (24.44%) of STN neurons showed the highest power at 20 Hz ( Figure 2F ). However, the synchronization in the STN did not propagate to the GPi and thalamus ( Figure 2C and Figure 2D ).

[0295] Modeling of the D1-MSN and D2-MSN stimulation networks revealed the mechanism of β-oscillation propagation.

[0296] To understand the new dynamics and how they relate to β-band oscillation propagation, we then constructed a large-scale neural network model that could directly simulate D1-MSN and D2-MSN β-band stimulation at the single-cell action potential firing level. We constructed 7 modules to represent 7 ROIs in the CBT network ( Figure 3A ). Given the different biophysical properties between GABAergic and glutamatergic projections, these two types of connections were modeled separately ( Figure 3A , for more details, see Methods). The model input was the sampled action potential sequence from the CPu extracellular recordings ( Figure 3B ). Then, we developed the model according to the experimental design and optimization procedure shown in Figure 3C . For the glutamatergic projection strength parameter, we utilized dynamic causal modeling from optogenetic fMRI BOLD time series 9,24Regional connectivity estimates (Figs. S2, S3, Tables S1 - S6; for more details, see Methods). The parameters of GABAergic projections were iteratively optimized until the simulated electrophysiological data matched the experimental results (Fig. 3c; also see "Methods").

[0297] The simulated action potential sequences successfully mimicked the firing and spectral patterns we measured in the CPu, GPi, thalamus, and STN ( Figures 4B - 4G and Fig. S5). Simulated D1 - MSN and D2 - MSN stimulations enabled us to comprehensively study the action potential firing activities of individual neurons and the population dynamics across ROIs. Based on the simulations, when the GPi and thalamus were stimulated ( Figure 4B ), we could clearly observe phase - locking during the stimulation of D1 - MSNs ( Figure 4H ). This indicates that the mechanism underlying the unexpected excitation observed in the GPi and thalamus is post - inhibitory rebound firing. During D2 - MSN stimulation, the firing frequencies of the GPi, thalamus, and STN decreased ( Figure 4E ), which matched our observations from electrophysiological recordings ( Figure 4G ). Simulated D2 - MSN stimulation synchronized the STN via phase - locked rebound firing along the indirect pathway ( Figure 4H ), but not the GPi and thalamus ( Figure 4F ). In both the measured data and the simulations, during D1 - MSN stimulation, the GPi and thalamus exhibited β - band peaks and increased firing frequencies. During D2 - MSN stimulation, the GPe exhibited β - band peaks and increased firing frequencies, but the STN exhibited β - band peaks ( Figure 2E and Figure 4F ), along with decreased activity ( Figure 2B and Figure 4E ). To understand this phenomenon, we searched the parameter space of projection strength and axon density in the target regions in our simulations. This revealed that the GPe - STN projection may have higher axon branching than other β - oscillation - mediated connections, which is consistent with anatomical studies 25 ( Figure 4I ).

[0298] Optogenetic enhancement of spontaneous activity verified the β - oscillation propagation pathway.

[0299] To further investigate whether the β - band oscillation propagation pathway revealed by optogenetically induced synchrony can be generalized to β - band oscillations during spontaneous activity, we used a stable - step - function opsin (SSFO) to selectively enhance the spontaneous activity of D1 - MSNs and D2 - MSNs, respectively. Activating D1 - MSNs led to a broadband increase in CPu activity without changing the general power - spectral distribution, which confirmed that SSFO amplified endogenous activity without inducing synchrony (Figure 5A )。D1-MSN activation also increased thalamic activity in a broadband manner, demonstrating enhanced spontaneous activity of the direct pathway. Activation of D2-MSN led to a broadband decrease in CPu activity, which may be due to lateral inhibition, and the decrease in broadband activity of the thalamus confirmed enhanced spontaneous activity of the indirect pathway( Figure 5B )。

[0300] During D1-MSN activation, simultaneous extracellular recordings were made of the CPu-GPi, CPu-SNr, and CPu-thalamus pairs. Similarly, during D2-MSN activation, simultaneous extracellular recordings of CPu-GPe, CPu-SNr, and CPu-thalamus were made. Using these simultaneously recorded single-neuron action potential sequences, we examined the co-occurrence of β-oscillation periods for all neuron pairs( Figure 5C )。Enhanced D1-MSN spontaneous activity significantly increased the amount of co-occurring β-oscillations in the CPu-GPi, CPu-SNr, and CPU-thalamus pairs( Figure 5D )。During D1-MSN activation, the co-occurrence of increased β-oscillations in the CPu-GPi and CPu-SNr pairs could not be explained by the classical model, which expected D1-MSN activation to inactivate the GPi and SNr. On the other hand, these results were consistent with our results from synchronous ChR2 D1-MSN stimulation experiments and biophysical models, which showed that D1-MSN could excite the GPi and SNr. This provided strong evidence that the direct pathway could mediate β-oscillations. Spontaneous SSFO D2-MSN activation led to a decrease in the co-occurring β-oscillation periods of the CPu-SNr and CPU-thalamus pairs( Figure 5E ), which confirmed that β-oscillations mediated by the indirect pathway could not propagate to the SNr and thalamus.

[0301] The simulated neuromodulation experiments successfully predicted the optimal neuromodulation targets for interrupting β-band oscillations.

[0302] We next sought to test whether our model could predict the optimal neuromodulation targets and parameters for interrupting β-band synchronization. We induced β-band oscillations along the direct and indirect pathways via D1-MSN and D2-MSN stimulation, respectively. GPi, GPe, STN, and motor cortex were selected as candidate neuromodulation targets. We simulated optogenetic inhibition, optogenetic periodic stimulation, and optogenetic random stimulation at each target( Figure 6A and Figure 6D ). The effects were evaluated based on the thalamic PSD profiles of D1-MSN-mediated β-band oscillations and the STN PSD profiles of D2-MSN-mediated β-band oscillations. In the case of optogenetic inhibition, the GPi was the only target that could eliminate β-band oscillations along the direct pathway( Figure 6B), and the GPe is the only target that can eliminate beta-band oscillations along the indirect pathway. Figure 6E ) Interestingly, this result matches the results from optogenetic inhibition studies 26 using a 6-OHDA rat PD model, which reported that inhibiting the globus pallidus instead of the STN and cortex could eliminate abnormal synchronization in the CBT network. In the case of periodic light stimulation, the GPe is the only effective target that can interrupt beta-band oscillations along the direct pathway at the highest stimulation intensity. Figure 6C ) In the case of random light stimulation, GPe stimulation reduced beta-band oscillations along both the direct and indirect pathways, but the interruption was broadband. Figure 6C and Figure 6F ). The complete simulation results are shown in Figs. S6 - S8. These results demonstrate the central role of the GPi and GPe in coordinating excessive beta-band synchronization in the CBT network.

[0303] Discussion

[0304] Through optogenetically driven synchronization and optogenetically modulated spontaneous activity experiments, we showed that beta-band oscillations can propagate via the direct pathway and partially via the indirect pathway. Traditionally, beta-band oscillations have been thought to propagate along the D2-MSN-mediated indirect pathway. This is because: 1) in both healthy and PD conditions, beta-band oscillations have been reported to be anti-kinetic, and the indirect pathway is considered anti-kinetic. 4,5,14 ; 2) in PD, hyperactivity of the indirect pathway (D2-MSN -> GPe -> STN) has been found, and beta-band oscillations have been observed in the GPe and STN. 6,27 However, this hypothesis has not been directly verified at the scale of the entire CBT circuit, and recent experimental findings related to the propagation of beta-band oscillations are inconsistent with the classical direct / indirect model. 9 We utilized optogenetic manipulations and single-unit recordings covering multiple key nodes along the direct and indirect pathways. Using this data, we constructed a large-scale action potential firing level model. We showed that this model has the ability to reveal the mechanisms underlying beta-band oscillations and optimize neuromodulation targets and parameters to inhibit beta-band oscillations.

[0305] The GPi / SNr-thalamus "paradoxical" co-activation reported by many studies is a major counterevidence against the classical firing frequency model of the basal ganglia. 8-11 For example, recent studies using 6-OHDA rats observed phase-locked beta-band oscillations in the SNr and motor thalamus, which is inconsistent with the reduced thalamic activity predicted by the classical model of PD. 11These results can be well explained by our experiments and models, suggesting that β-band oscillations mediated by D1-MSNs lead to co-activation of the GPi / SNr and thalamus in a phase-locked manner. Our computational modeling explains that GPi / SNr-thalamus co-activation is due to post-inhibitory rebound firing. These findings are supported by Kim et al. 10 who showed that GPi-thalamus co-activation depends on post-inhibitory rebound firing.

[0306] The anti-dynamical nature of β-band oscillations does not rule out the possibility of a direct pathway mediating β-band oscillations. Activation of D1-MSNs or the direct pathway is not always prokinetic. Yttri et al. 28 reported that both the direct and indirect pathways are capable of bidirectionally regulating movement, showing that optogenetic stimulation (15 Hz) of D1-MSNs decreased the speed of rapid movements. Studies also suggest that D1-MSNs and D2-MSNs work more synergistically rather than playing separate roles 29 . Recent studies have shown that under certain conditions, β-band oscillations increase with increasing striatal dopamine levels, which favors activation of D1-MSNs and inhibition of D2-MSNs.

[0307] The origin of excessive β-band oscillations in PD and whether the same circuits are involved as in healthy conditions remain controversial 21,22,31 The synchrony along the indirect pathway that we observed can be amplified via synaptic changes between the GPe and STN after the onset of PD 27 . Although PD pathological changes may preferentially activate the indirect pathway, we show that simply increasing the excitability of D2-MSNs cannot induce excessive β-band oscillations ( Figure 5B ). Hypoactivity of D1-MSNs in PD does not necessarily limit the possibility of β-band oscillations propagating via the direct pathway. We have shown that the STN mediates β-band oscillations during hypoactivity ( Figure 2F ), and D1-MSNs may mediate β-band oscillations in PD through a similar mechanism. Using 6-OHDA mice, Zemel et al. 32 found that although the mice rotated ipsilaterally most of the time, β-band oscillations in the striatum increased only during contralateral rotation. As shown in several previous experimental studies 7,9 , contralateral rotation corresponds to D1-MSN-dominated activity, while ipsilateral rotation corresponds to D2-MSN-dominated activity. These results suggest that PD mice are dominated by hyperactive D2-MSNs, while β-band oscillations are mediated by D1-MSN activity. The β-band oscillations observed in the SNr and motor thalamus in PD 11It also suggests that the direct pathway may still be related to PD, as we have shown that the beta-band oscillations in the indirect pathway cannot propagate to the SNR / thalamus, while the direct pathway can.

[0308] We demonstrated a new possibility that beta-band oscillations under healthy and potentially PD conditions are mediated by both the direct and indirect pathways: the beta-band oscillations observed in the GPi, SNr, and motor thalamus are propagated via D1-MSNs; the beta-band oscillations observed in the GPe and STN are mediated by D2-MSNs. We believe that the combination of optogenetics and computational modeling is a powerful tool for providing cell-type-specific mechanistic explanations for brain function / dysfunction and enabling therapeutic optimization.

[0309] Methods

[0310] Animals

[0311] Two bacterial artificial chromosome (BAC)-mediated transgenic mouse lines from the Gene Expression Nervous System Atlas (GENSAT)1 were used. Male mice weighing 15 g to 20 g (approximately 4 weeks old) were used as subjects in the experiments. The mice expressed Cre recombinase under the control of the dopamine D1 receptor (D1-Cre) or dopamine D2 receptor (D2-Cre) regulatory elements. The double-floxed inverted (DIO) recombinant AAV2 virus was used to express ChR2-YFP in Cre-expressing neurons. The recombinant AAV vector was serotyped with the AAV1 capsid protein and packaged by the University of North Carolina viral vector core (titer of 4 x 1012 particles / ml). Viral injections were performed in the dorsomedial striatum (+0.4 mm AP, -1.4 mm ML, at -2.6 mm DV). To verify the functional ChR2 expression in D1- and D2-expressing medium spiny neurons (MSNs) and the successful control of the direct and indirect pathways, a behavioral rotation test was performed during D1-MSN or D2-MSN stimulation. All experimental procedures and animal care were carried out in strict accordance with the NIH, UCLA Institutional Animal Care and Use Committee (IACUC), and Stanford University IACUC guidelines.

[0312] ofMRI Experiments and Data Analysis

[0313] ofMRI data were taken from Lee et al. 9Previous publications. Technical details of the ofMRI experiments and ofMRI data analysis were provided in previous publications and are provided here briefly. fMRI scans were performed on a 7 Tesla Bruker Bispec small animal MRI system at UCLA. Over the course of 14 days, mice were gradually introduced to fMRI-related phenomena and rewarded with peanut butter. A single ofMRI scan consisted of six cycles of a 20-second pre-stimulus baseline, a 20-second stimulus (20 Hz, 30% duty cycle), and a 20-second post-stimulus period. The fiber optic output power was calibrated to 2.5 mW. During the fMRI scan, the animals were very slightly anesthetized with isoflurane (0.5% to 0.7%) mixed with O2 and N2O. T2-weighted high-resolution anatomical images acquired prior to fMRI were used to examine brain damage and probe position. fMRI images were acquired during light stimulation using a gradient recalled echo (GRE) BOLD method. The fMRI image acquisition was designed to have an in-plane field of view (FOV) of 25×25 mm 2 and a spatial resolution of 0.36×0.36×0.5 mm 3 , and a sliding window reconstruction was employed to update the images at each repetition time (TR). A two-dimensional multi-slice gradient echo sequence used a four-interleaved spiral readout, 750 ms TR, and 12 ms echo time, resulting in 23 coronal slices. A custom-designed GPU-based system was used to perform real-time motion correction. fMRI data processing was performed using custom-written programs and mrVista (Stanford Vision and Imaging Science and Technology Laboratory, Stanford, California) in Matlab (MathWorks, Inc., Natick, Massachusetts). The motion-corrected images from scans of the same animal were first averaged together and then aligned to a single common coordinate frame using a six-degree-of-freedom rigid body transformation. Time series were calculated for each voxel as the percentage modulation of the BOLD signal relative to a 30-second baseline period collected prior to the stimulus. The average images from each animal were used to generate estimates of the mean and standard error of the active volumes for different ROIs. To visualize the spatial and temporal dynamics of the evoked activity, the images from all D1-Cre and D2-Cre animals were averaged together and divided into two groups.

[0314] In vivo electrophysiological recordings in ChR2 mice

[0315] In vivo single-unit recordings were made in selected sites (CPu, GPi, thalamus, and STN), which were modulated by optogenetic stimulation on D1-MSNs and D2-MSNs, respectively. Recordings were collected for 20 s without stimulation, followed by repeated stimulation cycles (20 s on, 40 s off) with light delivery at 20 Hz and 30% duty cycle. Animals were anesthetized with 0.8% to 1.0% isoflurane via a nosecone while fixed in a stereotaxic frame. An acute 16-channel microelectrode array (Neuronexus Technologies) was targeted to the recording sites using a stereotaxic instrument. Action potential waveform data were captured and sorted in real time using Plexon's omniplex system and Plexcontrol software. Threshold searches in the Plexon offline sorter application were used to automatically detect action potentials in multi-unit recordings, which were then verified by visual inspection and modified if necessary. For tracks with multiple action potential populations, thresholds were set to capture all action potentials. Thus, it is likely that multiple neurons were recorded simultaneously during bursts. Thalamic recordings were targeted at the ventrolateral (VL) motor nucleus. The mean recording locations across animals were: +0.52 mm AP, 1.64 mm ML, +3.33 mm DV (striatum, 10 out of 11 animals); 1.32 mm AP, 1.08 mm ML, +3.58 mm DV (thalamus, 9 out of 13 animals); 2.06 mm AP, 1.39 mm ML, +4.54 mm DV (STN, 4 out of 4 animals); and 1.34 mm AP, 1.73 mm ML, +4.37 mm DV (GPi, 5 out of 5 animals). Immunohistochemistry was performed to evaluate the specificity of ChR2-EYFP expression for dopaminergic D1-MSNs and D2-MSNs. Although we did not explicitly verify the neuronal types in the STN, GPi, and thalamus, the recorded neurons showed a firing pattern consistent with fMRI. Thus, we believe that most of the neurons we recorded belong to the same class.

[0316] In vivo electrophysiological recordings in SSFO mice

[0317] In vivo electrophysiology was performed to directly measure neuronal activity in various brain regions during caudate-putamen (CPu) stimulation. As with imaging, a mixture of O2 (35%), N2O (65%), and approximately 0.5% isoflurane was used to maintain anesthesia, with a Dex bolus of 0.025 mg / kg and an infusion of 0.05 mg / kg-h. Throughout the procedure, body temperature was maintained at 37 °C using a heat-resistant heating pad (FHC, Inc., Maine, USA). After the animal was fixed in a stereotaxic frame, a 16-channel microelectrode array (NeuroNexus Technologies, Michigan, USA; A1x16 standard model linear electrode array) was inserted at the desired recording site. For recordings at the stimulation site CPu (+0.86 mm AP, -1.50 mm ML, -3.40 mm DV), an optical fiber glued to the electrode tip was used to deliver light. Remote recordings were made at the following coordinates, averaged across animals: thalamus; THL (-1.34 mm AP, -1.00 mm ML, -3.40 mm DV), substantia nigra reticulata; SNr (-3.25 mm AP, +1.50 mm ML, -4.30 mm DV), medial globus pallidus; GPi (-1.22 mm AP, +2.20 mm ML, -4.10 mm DV), lateral globus pallidus; GPe (-1.34 mm AP, -1.90 mm ML, -3.50 mm DV), and. Light was delivered to the fiber implant at the CPu via 473 nm (blue) and 594 nm (yellow) laser sources (Laser Glow Technologie), which were calibrated to deliver 5 mW of power, with 2-second pulses at 473 nm and 10-second pulses at 594 nm.

[0318] For the optrode positioned in the CPu, continuous light was delivered from 473 nm and 594 nm laser sources using a 200-μm diameter optical fiber, which was calibrated to 5 mW at the tip of the implanted fiber. Recordings were made continuously for 538 seconds without stimulation, followed by a 2-second pulse of 473 nm light to activate the SSFO (stable step function opsin, AAV-EF1a-DIO-hChR2(C128S / D156A)-EYFP), and then the 594 nm yellow laser was turned on for 10 seconds to deactivate the SSFO.

[0319] Dynamic causal modeling

[0320] Dynamic causal modeling (DCM) is a Bayesian optimization method for estimating effective connectivity between brain regions. Spectral DCM is a type of DCM that also models endogenous fluctuations in addition to causal relationships between regions. 33 . The DCM implemented in the current study follows previous publications24 the same procedure in and is briefly described in this section. We improved the segmentation (for more details, see Fig. S7), and the updated DCM results are presented in Figs. 1 and 2. Classical DCM in the time domain uses a state-space system to model brain dynamics, which is described by:

[0321]

[0322] y(t) = g(x(t), u(t); θ g ) + e(t; θ ∈ )

[0323] The time series x(t) is neuronal activity, u(t) is a deterministic input stimulus (i.e., optogenetic stimulus), and y(t) is the observed signal, which in our case is the fMRI BOLD signal. w(t) is the state noise, and ε(t) is the observation noise. Classical DCM is the first DCM method developed for task-based fMRI. A major limitation of classical DCM is that the intrinsic neuronal fluctuations and noise are not modeled. Although our model directly covers 7 ipsilateral ROIs, we considered other regions ipsilateral and contralateral to the modeled ROIs as sources of noise and fluctuations for our model. In addition, the basal ganglia and thalamus are known to have various types of tonic-active neurons, and these intrinsic activities are crucial for both the classical direct / indirect pathway model and our own hypothesized mechanism. Therefore, classical DCM is not suitable for the purposes of the current study.

[0324] Spectral DCM was developed based on classical DCM, which predicts the cross-spectrum by modeling the coupled endogenous fluctuations 33 . Compared with classical DCM, the main advantage of spectral DCM is that it models the intrinsic neuronal fluctuations and noise, which represent the intrinsic fluctuations of the 7 nodes in our model and the noise input from other nodes not covered by our model. The SPM12 toolbox has two options for the form of noise in the function spm_csd_fmri_mtf.m, and the default neuronal fluctuations and noise in the SPM12 toolbox are in the 1 / f form. In the main text, we present the spectral DCM results with fluctuations and noise in the autoregressive form, which is consistent with Bernal-Casas et al., 2017. Based on the electrophysiological recordings of the baseline discharges of different ROIs we collected, the intrinsic fluctuations and noise are not in the form of pink noise (1 / f noise), but are close to white noise (see the power spectra of the experimental CPu baseline discharges shown in Figs. 1 and 2). The results of classical DCM, spectral DCM with 1 / f form fluctuations, and spectral DCM with autoregressive form fluctuations (selected) are shown in Fig. S3 for comparison.

[0325] Using optogenetic fMRI (ofMRI) BOLD time series from D1-MSN and D2-MSN β-band (20 Hz) stimulation experiments (Figs. S1a, S1d, and S1d), we implemented spectral dynamic causal modeling (DCM) to analyze the strength of effective connectivity between regions of interest (ROIs) using methods from previous publications 24,33 Seven ROIs were selected in the CBT network, and structural connectivity was used as a prior for spectral DCM analysis (Fig. S1b). The ROIs included the caudate putamen (CPu), globus pallidus externa (GPe), globus pallidus interna (GPi), subthalamic nucleus (STN), substantia nigra reticulata (SNr), thalamus (THL), and motor cortex (MCX). The seven ROIs were selected based on recent experimental and modeling literature 9, 34 ,35 The thalamus in our model was aligned with data collected from the ventral lateral thalamus to determine its role in motor control. OfMRI BOLD time series were extracted for the seven ROIs during D1-MSN and D2-MSN stimulation, as shown in Figs. S1D and S1E, respectively. Low-pass filtered fMRI time series were used to estimate the strength of effective connectivity for each subject (D1: n = 12; D2: n = 10), and then averaged across subjects.

[0326] Large-scale biophysical modeling

[0327] Large-scale neural network modeling based on the biophysical properties of neurons and brain regions illustrates neuronal function from the level of single neuron action potential firing to neural mass 36,37 . We constructed seven modules to represent the seven ROIs in the CBT network. The CPu module consisted of 100 Huxley-Hudgkin neurons representing D1-MSNs and 100 neurons representing D2-MSNs. Each of the remaining six modules consisted of 100 Hodgkin-Huxley neurons. Considering the different biophysical properties between GABAergic and glutamatergic projections, these two types of connections were modeled separately ( Figure 3A ).

[0328] As shown in Figure 3, only single neuron recordings were used to optimize the connections (GABAergic connections) along the direct / indirect pathways. As a “closed-loop” model, we only used DCM to close the loop by connecting the basal ganglia-thalamus pathway to the cortex and forwarding the cortical feedback to the basal ganglia-thalamus (glutamatergic connections). For these connections, we did not further investigate the synaptic properties, but only studied their effective effects on the direct / indirect pathways. The parameters of the GABAergic connections were the same for the D1-MSN and D2-MSN networks. According to the DCM results, the glutamatergic connections were separately connected for D1-MSN and D2-MSN stimulation. We only considered the connections estimated by DCM to be significant or nearly significant (p ∼ [0.05, 0.1]). The glutamatergic connections were modeled as one-to-one connections, where the connection strength was proportional to the effective connectivity estimated by DCM. The ratio of the proportion was determined as the minimum value at which Hodgkin-Huxley neurons could respond to all DCM significant projections. The GABAergic projections along the direct and indirect pathways were highly diffusive 25 . We modeled the CPu-GPi / GPe and GPi / SNr-thalamus GABAergic projections as 1-to-n diffusive projections with connectivity strength wGABA, where n and wGABA were free parameters. The parameter space of N and wGABA was searched until the simulated action potential firing rate matched the results of extracellular recordings statistically. The model input at the CPu was determined based on experimental single neuron recordings ( Figure 3B ). For more mathematical details, the configuration of DCM, and how the DCM results affected the results of biophysical modeling, see Figure S3. For CPu-GPi, CPu-GPe, and GPi-THL, n was 25, and wGABA was chosen to be -120. For GPe-STN / GPi, n was 75, and wGABA was chosen to be -140. The parameters were chosen according to Figure 4I because the firing frequency using this combination was able to reproduce the empirical data from single neuron recordings and was biologically realistic at the same time. We sampled the action potential sequence from the extracellular recordings of the CPu and used it as the input to the model ( Figure 3B ).

[0329] We chose the Hodgkin-Huxley neuron model as the basic unit of our biophysical model 38。Since we use fMRI and single-neuron recordings to model experimental data from different scales, the biophysical model needs to generate data comparable to our single-neuron recordings. Therefore, we restricted the choice of neuron models to electro-input-output membrane voltage models that can produce simulated action potential sequences. In this category, we selected the original Hodgkin-Huxley model (implemented with the hh_psc_alph node using the NEST simulator) to best replicate biological activity while limiting the size of the parameter space. Integrate-and-fire models are more simplified but ignore many aspects of neuron characteristics, including ion channel parameters.

[0330] Hodgkin-Huxley model 39 is a conductance-based model that describes the relationship between ionic currents and the membrane voltage of a neuron. The behavior of the model is governed by the following differential equations:

[0331]

[0332] where C m is the capacitance representing the lipid bilayer, V is the membrane voltage of the cell, and I i is the current of ion channel i. I i can be described as follows.

[0333] I i (t,V) = g i (t,V)(V - V m ),

[0334] where g is the conductance of each channel. The g value of each channel is determined by multiplying the maximum conductance by the fractions of activation and inactivation:

[0335]

[0336] n, m, and h are dimensionless fractions between 0 and 1 that represent potassium channel activation, sodium activation, and sodium inactivation, respectively. The impact of DCM results on the simulation results of the biophysical model

[0337] The same fMRI data and DCM modeling process were utilized in previous publications on DCM modeling 24To further improve the DCM results, we modified the segmentation scheme that mainly affects the STN (for details, see Methods). The fMRI BOLD time series extracted under the new segmentation scheme better aligns with the electrophysiological data, which was not considered in Bernal-Casas et al. Except for one connection (GPe to SNr), we replicated the connectivity estimates that match the results in Bernal-Casas et al. in terms of polarity and statistical significance level. The GPe-SNr connection in Bernal-Casas et al. was positive and significant (p < 0.05), while in our replication attempt, it was positive but not significant.

[0338] In Bernal-Casas et al., all fMRI BOLD time series were detrended using third-order non-linear regression. In our current study, we used the raw time series of DCM because we were interested in the excitatory / inhibitory trends caused by D1-MSN / D2-MSN stimulation. Detrending the time series affects the DCM amplitude but does not affect the polarity or the statistical significance of the connectivity estimates. All fMRI BOLD time series presented in our current study and in Bernal-Casas et al. were mean-centered. Mean-centering the time series does not affect the DCM results because the SPM12 toolbox automatically mean-centers all time series before model inversion (version of January 13, 2020). The DCM results from our current study and from Bernal-Casas et al. will lead to different simulated electrophysiological data from the biophysical model. This difference mainly occurs in the STN action potential firing rate, which is consistent with the change in the segmentation of the STN. The simulated action potential firing rate of the STN better matches the experimental data with the new segmentation (Figure S3c), which demonstrates the validity of our DCM results.

[0339] Simulated experiment

[0340] We first simulated a 20-second baseline and then applied 20 Hz stimulation to D1-MSN / D2-MSN for 20 seconds, followed by another 20-second simulation without stimulation. We further tested the generality of D1-MSN stimulation in different frequency bands (Figure S5e). Along with the β band, low γ band (30 Hz to 60 Hz) stimulation on striatal D1-MSN showed the highest generality in the basal ganglia, which matches the experimental observations. 31 。

[0341] We simulated how neuromodulation on various ROIs interrupts β-oscillations propagating along the direct and indirect pathways. The experimental design is in Figure 6A and Figure 6DShown in. Optogenetic stimulation was implemented as direct activation with different intensities (500 pA, 700 pA, and 900 pA), while optogenetic inhibition was implemented as continuous shunting inhibition of each neuron in the target area. 40 . Two types of optogenetic stimulation were implemented: periodic stimulation and random stimulation. For periodic stimulation, regular stimulation pulses of 130 Hz were applied to the target ROI. Inspired by the latest progress in random reset deep brain stimulation 41 , we also simulated random light stimulation on various targets. Forty stimulation sequences of pulses with a random interval of 130 Hz were used. Each neuron in the target ROI was stimulated with one of the forty stimulation sequences. Such a design means decoupling synchronous neurons in the target ROI.

[0342] Calculate SPIKE synchronization

[0343] Use the Python package PySpike 23 to calculate the SPIKE synchronization profile. The values were averaged over a time window (bin) of 200 ms to show the temporal variation of synchronization. A time window width of 200 ms was chosen to smooth the curve and still be able to reflect the temporal dynamics at the same time. SPIKE synchronization is a time-resolved measurement, so the effect of the firing frequency on the counted synchronous events has been corrected. For mathematical details, see Kreuz et al., 2015.

[0344] Calculate effective connectivity

[0345] Estimates of effective connectivity were calculated according to the procedure described in Bernal-Casas et al., 2017. Briefly, low-pass filtered fMRI time series were used to estimate the strength of effective connectivity for each individual subject (D1: n = 12; D2: n = 10), and then averaged across subjects. The cut-off frequency of the low-pass filter was chosen in such a way that it maximized the number of statistically significant connections and near-significant connections (see Figure S2). Connections with a one-sample t-test against zero and a p-value less than 0.05 were considered significant, and connections with a p-value between 0.05 and 0.1 were considered near-significant.

[0346] Detect co-occurrence in the beta period

[0347] We calculated the temporal profile of the average power of the beta-band frequency for each neuron at a temporal resolution of 350 ms. Then, we made pairwise comparisons of the beta power temporal profiles of all neurons in the simultaneously recorded ROI. Peaks of co-occurrence of beta power were calculated for each pair. The total amount of co-occurring beta-band oscillation periods in the baseline and stimulation time periods was compared statistically using a two-sided t-test.

[0348] Statistical data

[0349] A one-sample t-test was used to determine the significance of the effective connectivity estimates. For the selected cut-off frequencies, the mean, p-value, and 95% confidence intervals are shown in Tables S1 - S6. The number of subjects used for statistical tests throughout the study was n = 12 for D1-MSN stimulation and n = 10 for D2-MSN stimulation. One subject from the D2-MSN stimulation group was considered an outlier and excluded from the statistical tests.

[0350] In the results of the simulations shown in Figures 1, 2, and 4, a one-tailed paired t-test was performed when quantifying the differences in the firing frequency, SPIKE synchronization profiles, and corresponding metrics caused by optogenetic stimulation. A two-tailed paired t-test was performed when comparing the firing frequencies of the experiments in Figures 5 and 6 with the simulated firing frequencies. The number of neurons identified in single-neuron recordings was: in the CPu, n = 118 for D1-MSN stimulation and n = 143 for D2-MSN stimulation; in the GPi, n = 78 for D1-MSN stimulation and n = 266 for D2-MSN stimulation; in the thalamus, n = 31 for D1-MSN stimulation and n = 55 for D2-MSN stimulation; in the STN, n = 95 for D1-MSN stimulation and n = 90 for D2-MSN stimulation. We used the Lilliefors test in MATLAB to test the normality of our data. Specifically, we tested the Gaussianity of the connectivity estimates calculated in the DCM analysis and the firing frequencies of the neurons identified in single-neuron recordings. In both cases, the null hypothesis that the connectivity estimates are normally distributed was not rejected (p > 0.05). Therefore, it was reasonable for us to choose the classical t-test to test for statistical significance in the DCM and firing frequency analyses.

[0351] Software

[0352] All the main software tools used in this study are available. Dynamic causal modeling was performed using the SPM12 toolbox (fil.ion.ucl.ac.uk / spm). Large-scale biophysical modeling was performed on the NEST simulator (nest-simulator.org).

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[0395] Using Computational Modeling and Optogenetic fMRI to Address Brain Circuit Function and Dysfunction

[0396] Can we systematically design treatments for brain disorders such as Parkinson's disease? To be able to do so, we need complete algorithmic descriptions, rather than just simple correlations, of how specific cells or brain regions cooperate to promote behavior or symptoms in the context of the whole-brain network. What is needed to generate such algorithms? Cell types cannot be ignored, because even neurons in the same location in the brain can drive completely opposite functions and resulting behaviors (1, 2). Neurons often interact with large networks across the whole brain. Therefore, a limited view in the brain is insufficient to understand these algorithms. Thus, to obtain the data needed to reconstruct these behavior control algorithms, we need imaging systems that can measure cell type-specific whole-brain function. Optogenetic functional magnetic resonance imaging (ofMRI) (3) has started to achieve this goal. Using ofMRI, we can select cell type-specific regulatory targets while monitoring the consequences of such regulation across the whole brain in vivo with high spatio-temporal resolution. This opens a new window for the study of brain function. We can see how modulating specific elements of the brain leads to specific behaviors of interest, while also directly observing the internal workings of the brain that result in such behaviors. By computational modeling (4) of the ofMRI signals measured across the whole brain, cell type-specific large-scale brain function can be quantitatively described at the regional level. Once the regional interaction map is reconstructed, we envision that biophysical modeling can be combined to enable cell type-specific large-scale modeling of brain function at the single-cell action potential firing level. In addition, while restoring brain function is the ultimate goal of treating neurological diseases, it is also crucial to understand how significant pathologies are related to brain function. In this review, we will discuss the methods employed so far, as well as the methods that can be adopted in the future to achieve these goals.

[0397] Bridging multi-scale and optogenetic fMRI.

[0398] Optical fMRI (ofMRI) is a technique that combines optogenetic stimulation with fMRI readout. Optogenetics (5, 6) enables cell type-specific millisecond-scale regulation of activity using light, while high-field fMRI measures the hemodynamic response obtained from the entire brain of a living subject. In the initial proof-of-concept study (3), excitatory neurons in the motor cortex triggered an fMRI response that could be measured throughout the brain with sub-second temporal resolution. To accelerate scientific discovery through ofMRI, several technological innovations have been made. Real-time imaging robust to the motion of living subjects has been developed, which enables data acquisition, reconstruction, motion correction (7), and analysis of 3D images with high precision within approximately 12 ms. To address the specific responses of cortical layers and subnuclei, new fMRI techniques based on compressed sensing (8 - 10) and machine learning have been developed, which achieve a significant reduction in voxel volume. MR-compatible optrodes and electrodes have also been developed for simultaneous electrophysiological recordings to verify the neural basis of the ofMRI hemodynamic signal (11, 12). They enable simultaneous acquisition of electrophysiological recordings during the fMRI session and provide information with higher temporal resolution in regions of interest identified by ofMRI.

[0399] Using these advanced ofMRI techniques, the capabilities and precision of ofMRI have been widely tested. It has been shown that the location, polarity, and temporal shape of neural activity can be accurately inferred from the ofMRI signal (3, 13, 14), and neural activity can be measured by ofMRI across multiple synapses (11, 15). The stimulation cell type, location, frequency (14, 15), and intensity (11) have been shown to significantly alter the location and shape of activity throughout the brain. It is also clear that whole-brain neural dynamics, as measured by ofMRI, can accurately predict different behaviors (2, 14, 15).

[0400] Many studies have used ofMRI to improve our understanding of the basic circuits related to behavior, memory, and cognition. For example, ofMRI studies have found that frequency-dependent thalamic activity drives unique whole-brain functions in circuits related to arousal, attention, and somatosensory function (16 - 18). OfMRI studies have revealed distinct dorsal and ventral hippocampal control of whole-brain function (15, 19), circuits related to cell type-specific targeting of the somatosensory cortex (20), and functional control of the cerebellar cortex over the forebrain and midbrain (21). Another study has revealed whole-brain dynamics that control how the medial prefrontal cortex regulates reward-related behavior through distant regions such as the striatum (22). Recent studies have attempted fMRI of cell type-specific activation of astrocytes (23).

[0401] These studies have shown that using simultaneous or subsequent electrophysiology, the observed hemodynamic activity is closely related to neuronal activity. In addition, although most of the fMRI studies have been conducted in rodents, it has also been applied to non-human primates, in which both saccade latency and whole-brain activity have been found to depend on specific neuronal targets in the motor cortex (24).

[0402] The ability to detect and read out whole-brain activity using fMRI has also advanced our understanding of the dysfunctional circuits associated with neurological disorders. To study epilepsy, fMRI offers unique advantages in that it can induce seizures optogenetically with precise origins as needed while measuring the resulting whole-brain activity through simultaneous electrophysiological recordings. This enables studies to generate models using its early activity markers to predict and classify seizures (15, 25). In addition, the longitudinal effects of seizures on overall brain function can be measured to understand how the disease progresses and how seizures are generated and maintained (26). These advances help us understand the circuit mechanisms underlying seizures and assist in designing intervention parameters (such as stimulation location and frequency) to effectively suppress seizures. Optogenetic fMRI can also elucidate the mechanisms underlying existing therapies, such as post-stroke recovery. Activation measured by fMRI highly predicts the extent of recovery and identifies the sensory circuits involved in the process (27). We can now begin to reveal detailed circuit mechanisms that were previously difficult to understand. As an example, we will review studies that have revealed how medium spiny neurons (MSNs) expressing D1 receptors and D2 receptors dynamically regulate overall brain function and dysfunction (Figure 7). The cortico-basal ganglia-thalamic circuit is associated with many important brain functions, including motor control and reward mechanisms. Neurological disorders such as Parkinson's disease, Huntington's disease, addiction, and autism involve this network. The caudate putamen lies at the D1-MSNs and D2-MSNs ( Figure 7A ), which are key nodes separating the direct and indirect pathways ( Figure 7B ). To evaluate the whole-brain dynamics driven by inhibitory D1-MSNs and D2-MSNs, Lee et al. performed whole-brain fMRI during repeated 20-second periods of optogenetic D1-MSN stimulation or D2-MSN stimulation (2)( Figure 7C ). Active voxels were identified as those significantly synchronized with the repeated stimulation ( Figure 7D ). The local signal at the site of stimulation was positive for both inhibitory D1-MSN stimulation and D2-MSN stimulation ( Figure 7E) clarifies the widely debated issue of whether the activity of inhibitory neurons induces positive or negative fMRI signals. In most regions of the ipsilateral cortico-basal ganglia-thalamic network, the evoked responses in a given region exhibit qualitatively different temporal profiles between D1-MSN stimulation and D2-MSN stimulation ( Figure 7D and Figure 7E ). Given the diversity of BOLD responses evoked by D1-MSN stimulation and D2-MSN stimulation, we sought to use single-unit recordings to verify whether the responses reflected underlying neuronal activity. For example, the fMRI time series in the thalamus showed robust and reliable increases and decreases, respectively, after D1-MSN stimulation and D2-MSN stimulation ( Figure 7D and Figure 7E ). Indeed, single units showed an increase in firing frequency during D1-MSN stimulation and a decrease in firing frequency during D2-MSN stimulation ( Figure 7F ). These results suggest that ofMRI can detect cell-type-specific multisynaptic activity changes across the whole brain with high spatiotemporal precision, and electrophysiological recordings together with ofMRI can support these findings.

[0403] As with any technology, ofMRI warns of future improvements and potential pitfalls to avoid. Channelrhodopsin (ChR2) is known to induce synchronous neuronal activity after light stimulation. Therefore, it is important to first investigate the behavioral effects of optogenetic stimulation before initiating an ofMRI investigation, which is a means of ensuring that the resulting behavior is meaningful within a normal physiological or pathological context. For example, in our D1-MSN and D2-MSN stimulation ofMRI experiments, increased contralateral rotation and ipsilateral rotation were observed, respectively (2)( Figure 7B ). This indicates that these two separate stimulations led to opposite behaviors associated with movement disorders. The fact that ChR2 induces synchronization after light stimulation also makes it suitable for studying pathological oscillations in many disease models / contexts. Excessive beta-band oscillations in Parkinson's disease have been extensively explored by ChR2-induced oscillations (28, 29). Some newer opsins, such as the stable step function opsin (SSFO) (30), regulate target neurons by increasing excitability to amplify existing spontaneous activity, which extends the application of ofMRI to more physiological conditions. There are many aspects of ofMRI that can be further improved. For example, ofMRI would greatly benefit from higher spatiotemporal resolution, real-time feedback-based stimulation control, and imaging during behavior (7, 31).

[0404] It is also important to note other technologies developed for studying brain circuits, including high-speed volumetric calcium imaging (32, 33), probes for recording with high-density electrophysiology (34, 35), and wide-field calcium and voltage imaging (36). Compared to fMRI, these new advances offer higher spatio-temporal resolution, although limited by recording depth and field of view. For example, wide-field "whole-cortex" calcium imaging enables simultaneous recordings of up to 30 Hz in cortical regions, but its depth coverage is limited to the superficial layers of the cortex. Combining fMRI and wide-field calcium imaging has been shown to mitigate the limitations of both methods (36). Thus, future fMRI studies can be integrated with other technologies for complementary advantages.

[0405] Cell-type specific modeling of large-scale brain function.

[0406] Large-scale neural network models (37 - 41) that utilize experimental data from PET (42), fMRI (43), EEG / MEG (44) have made significant contributions to understanding brain function. However, although these modeling efforts are based on data from carefully designed experiments attempting to isolate specific brain functions, multiple networks and pathways mediated by different cell types are simultaneously involved in orchestrating brain function. Thus, without the ability to disentangle the contributions from different cell types across the whole brain, the models are limited in their capabilities. The development of fMRI technology has opened up new opportunities in whole-brain computational modeling because it uniquely measures cell-type specific whole-brain dynamics.

[0407] For example, the cortico-basal ganglia-thalamus network is characterized by a large number of network nodes ( Figure 8A ) and different cell types with distinct long-range projections. According to the classical functional model of the basal ganglia (45, 46), movement initiation and inhibition are mediated by the direct and indirect pathways respectively ( Figure 8B ). The direct pathway promotes movement through the excitation of D1-MSNs in the striatum, followed by excitation in the thalamus and thus excitation of the motor cortex. On the other hand, the indirect pathway is thought to transfer the activation of D2-MSNs in the striatum via complex interactions within the basal ganglia, resulting in inhibition of the thalamus and thus inhibition of the motor cortex. However, although the classical model has been widely applied and successful in explaining experimental results, recent experimental evidence contradicts the model, and the interaction between the two pathways remains puzzling. For example, the direct and indirect pathways operate simultaneously during movement onset (47).

[0408] As demonstrated earlier with ofMRI, we can dissect the operation of the cortico-basal ganglia-thalamo network by selectively optogenetically activating D1-MSNs and D2-MSNs and directly observing the corresponding whole-brain dynamics (2) (Figure 7). However, the degree of connectivity in the cortico-basal ganglia-thalamo network is too high ( Figure 8B and Figure 8C ) to identify regional causal influences based solely on time series ( Figure 8D ). Through computational modeling such as dynamic causal modeling (DCM), leveraging the precision provided by ofMRI data, cell-type-specific causal connections can be identified in regions of interest (ROIs) within the cortico-basal ganglia-thalamo network.

[0409] DCM is a modeling scheme for estimating causal coupling (effective connectivity) in multi-regional networks based on neuroimaging data (fMRI, MEG / EEG) (48 - 50). Bayesian techniques are used to fit the estimates to empirical results. A major advantage of DCM is that the estimated regional connectivity is directional, which is particularly valuable for networks with a large number of reciprocal connections and feedback, such as the cortico-basal ganglia-thalamo network. ofMRI can also be combined with other modeling schemes. For example, Salvan et al. (51) performed optogenetic modulation of the entorhinal cortex and combined a hidden Markov model with ofMRI data to study how the entorhinal cortex drives frequency-dependent whole-brain dynamic states. In another study, multivariate dynamical systems (MDS) causal modeling was used in conjunction with ofMRI to estimate causal brain interactions (52). Like DCM, MDS models both intrinsic causal coupling and experimentally induced causal coupling in large-scale brain networks.

[0410] In our previous study (4), spectral DCM (38), a variant of DCM capable of computationally efficient modeling of large-scale networks, was used to study the interactions within the cortico-basal ganglia-thalamo network using ofMRI data from optogenetic stimulation of D1-MSNs and D2-MSNs. A recent study reported consistent results using DCM with ofMRI data from D1-MSN and D2-MSN stimulation (53). As shown in Figure 8, combining DCM or an equivalent modeling scheme with ofMRI data can accurately reveal whole-brain regional interactions. The time series are also accurately reproduced by DCM, matching the experimental ofMRI time series very well ( Figure 8D ). Figures 8E - 8GShows the DCM estimation of interregional effective connectivity (4) using ofMRI data. The DCM results confirmed the activation of the direct pathway during D1-MSN stimulation and the activation of the indirect pathway during D2-MSN stimulation. The defined connections of the direct pathway model were statistically significant (CPu to SNr, GPi to thalamus, SNr to thalamus) or near significant (CPu to GPi) in the case of D1-MSN stimulation. In the D2-MSN stimulation network, important connections included those of the indirect pathway (CPu to GPe, GPe to STN, STN to GPi / SNr, GPi to thalamus). The presence of cortical feedback could also be observed ( Figure 8F and Figure 8G ). On the other hand, the effective connectivity estimates by DCM also indicated several positive projections that were anatomically inhibitory and could not be explained by the classical direct / indirect pathway model, such as the projection from GPi to thalamus during D1-MSN stimulation, which matched several experimental reports of GPi-thalamus paradoxical co-activation (2, 54 - 56). Understanding the mechanisms behind such paradoxical connections requires further microscopic studies of specific synaptic interactions using techniques such as single-cell action potential firing level modeling and single-unit recording, as we will discuss next. Single-cell action potential firing level modeling of cell-type specificity of whole-brain function.

[0411] A key advantage of computational modeling is that it can bridge the spatial scales from whole-brain dynamics to single-cell activity and interpret data from different experimental modalities ranging from fMRI to extracellular recordings (57, 58). DCM and other macroscale and mesoscale brain models typically use neural mass models or mean-field models as the basic units to describe the collective neural activity in brain regions or cortical columns (42 - 44). On the other hand, single-cell action potential firing models computationally depict the microscopic biophysical characteristics of how action potential firing at the single-cell level controls and regulates brain function / dysfunction (59 - 61).

[0412] Despite the ability to capture and model macroscale and mesoscale interactions between separate regional populations of neurons in the brain using fMRI and DCM, there are surely additional advantages in modeling the interactions between individual neurons of different cell types. Neurological disorders may have different effects on specific cell populations within a brain region. Optogenetic stimulation of one type of cell population while inhibiting another type of cell population in the globus pallidus externa (GPe) prolonged the therapeutic effect in a mouse model of Parkinson's disease (52). Optogenetic studies widely hypothesize and support that Parkinson's disease leads to hyperactivity of D2-MSNs and hypoactivity of D1-MSNs, thereby impairing the balance between the direct and indirect pathways (46, 47, 62). DYT1 dystonia, an inherited early-onset dystonia, is associated with dysfunction of cholinergic interneurons in the striatum and altered D2 receptor function (63, 64). Alterations in the firing patterns of one cell type can also lead to large-scale changes. Optogenetic stimulation in striatal cholinergic interneurons, a subpopulation that accounts for less than 2% of the striatum, can generate broadband oscillations in the motor network (29). By modeling at the single-cell action potential firing level with cell type specificity, it is easier to address the heterogeneity and rich microscale interactions within a region with biophysical details. fMRI-based DCM or other regional brain dynamics models can serve as a bridge between whole-brain dynamics and single-cell action potential firing level activity, enabling the construction of large-scale cell type-specific biophysical models that can test hypotheses at the neuronal level.

[0413] Cell type-specific single-cell action potential firing level models that can accurately predict circuit function and dysfunction can become powerful tools for designing or optimizing therapies. So far, many large-scale models without cell type specificity have been constructed to test various hypotheses behind deep brain stimulation (DBS), an existing therapy for Parkinson's disease (65 - 67). However, since each cell type in the basal ganglia has different synaptic and physiological properties, accurate models will require cell type-specific parameters that can conform to experimentally obtained cell type-specific data. We envision that fMRI, DCM, and electrophysiological recordings can be combined to build large-scale biophysical models that can model whole-brain activity at the single-cell action potential firing level. Figure 9A This vision is demonstrated. Through fMRI and electrophysiological experiments that provide multi-scale data, and DCM that provides whole-brain interaction information, single-cell level biophysical models with an accurate cell type-specific large-scale context can be built and validated ( Figure 9B ). As Figure 9C and Figure 9DAs shown, it is possible to design single-cell action potential firing level models that can reproduce experimental neuronal activity and population dynamics with high precision. With such models, we can envision simulating whole-brain action potential firing activity from "virtual neuromodulation" without the need for in vivo experiments. Then, we will be able to use these results to better optimize therapeutic targets and parameters where the simulated action potential firing matches our desired response. For example, recent studies (68) have employed optogenetics and computational modeling at the single-cell level in three different cell types in the motor cortex to show that DBS on cortical somatostatin interneurons can rescue Parkinson's symptoms. We envision that in the future, fMRI-enabled whole-brain models with single-cell action potential firing level precision will ultimately enable the systematic design of neuromodulation therapies with predictable outcomes.

[0414] Model the whole-brain pathological dynamics and their relationship to brain function.

[0415] Brain circuits are associated with neurological disorders in addition to their utility in modeling local and global brain function. This is also important for understanding the underlying pathology of many disorders. In the case of Parkinson's disease and other synucleinopathies, the seeding and progressive accumulation of pathological α-synuclein (α-syn) lead to dopaminergic neurodegeneration, which ultimately results in striatal imbalance and the cardinal Parkinsonian signs (69). Although the specific biochemical transport mechanisms of these proteins remain unknown, several recent studies have shown that whole-brain diffusion patterns are highly dependent on the inoculation site (70), and anatomical connectivity highly predicts susceptible regions after inducing seeded α-syn pathology (71).

[0416] To understand the link between anatomical connectivity, pathology, and function, it is important to reliably measure the whole-brain pathological state. Over the past decade, several significant advances have been made in whole-brain tissue clearing techniques. These techniques range from hydrogel-based techniques such as CLARITY (72) to solvent-based techniques like 3DISCO (73), where the hydrogel-based techniques can enable multiplex brain imaging but may require long incubation times, and the solvent-based techniques are generally faster but may bleach the fluorescence signal. Combining these tissue clearing methods with whole-brain immunolabeling and light-sheet microscopy allows for high-resolution examination of whole-brain pathology using biochemical reporters (74, 75). Since these three-dimensional whole-brain histology datasets now provide micron-level resolution and exceed terabytes in size, it is necessary to employ automated registration and segmentation techniques (76) to capture the rich information provided by the data. Figure 10ADepicts how a computational pipeline can be used to systematically analyze a Parkinson's disease model, where injection of α-synuclein PFFs is used to trigger brain-wide pathology. The brain can be immunolabeled and cleared at each time point using the iDISCO method (74), where imaged aggregates are automatically segmented and registered to a standardized atlas. As Figure 10B depicted, longitudinal analysis of the pathology can reveal a highly dynamic, brain-wide spread process after seeding of α-syn, which is consistent with many histological studies (69, 71). Registration to a standardized atlas can provide a huge advantage, enabling systematic statistical comparison and analysis of disease pathology together with connectome (77), genetic (78), and vascular (79) databases, all residing in the same reference coordinate space. Most notably, the Allen Connectivity Atlas (77) plays a crucial role in the development of models describing the spread of pathology through the connectome (71). For example, network diffusion models represent the whole-brain circuitry as a directed graph, where neuroanatomical regions serve as nodes and axonal pathways serve as edges between these nodes ( Figure 10C ). They have found wide applicability in predicting the spread of α-syn pathology in both rodent and human imaging studies (71, 80) as well as in human dementia (81) and neuroimaging studies of progressive supranuclear palsy (82). In Figure 10C , we present examples of how whole-brain models can accurately reconstruct regional variability in the pathology. Compared to previous models that rely on serial tissue sections (71) or in vivo human imaging data (80), this type of modeling can provide a more comprehensive, higher-resolution description of pathological dynamics, which includes all brain regions, and is important for quantitative model description. Given that transgenic mice (such as those with modified LRRK2 (71, 83) or SNCA (69) expression) exhibit significantly altered diffusion patterns, the models can additionally incorporate brain-intrinsic genetic or cell type-specific differences. As Figure 10D depicted, reweighting the connectivity matrix by regional expression of genes of interest can allow assessment of the relevance of that gene in disease spread. Henderson et al. weighted the network diffusion model with SNCA expression, demonstrating that in silico simulations of circuitry and genes can recapitulate the observed α-synuclein pathology (71).

[0417] An accurate whole-brain model that can predict the pathophysiology of both the future and past states of these progressive disorders would have important clinical implications. For example, a computational model that takes any pathological state and predicts the future state could guide interventions that rely on the predicted pathological localization. Similarly, a model that can iterate backward and calculate previous states, even back to the initial seeding site, could further assist in classifying disease progression for diagnostic purposes. Since neurodegenerative disorders such as Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy have long been hypothesized to form a common neuropathological spectrum (84), modeling the various circuits that drive different pathologies and clinical manifestations will play an important role in precisely defining these disorders.

[0418] Although functional and histological readouts provide different information about the brain and disease, their co-localization may be important in both deepening our understanding of neural circuits and developing new therapeutic approaches. For example, combining ofMRI and whole-brain pathological markers could help develop therapies for underlying pathologies such as α-syn pathology in Parkinson's disease. We present an example of the spatial co-localization between optogenetic stimulation-induced changes and functional changes measured by ofMRI during the delivery of the same stimulation ( Figure 10E and Figure 10F ). This optogenetic stimulation paradigm reduces the α-synuclein aggregate count at the site of stimulation and in various downstream regions. When co-localized with brain activity, their similar spatial positions but opposite polarities are evident, revealing that positive activity co-localizes with pathological reduction, while negative activity highly co-localizes with pathological increase.

[0419] This ability to target pathology with neuromodulation while predicting subsequent whole-brain changes in pathology could provide new insights into neuromodulation therapies for Parkinson's disease and related disorders. For example, parameters such as the duration and site of neuromodulation could be customized for patients based on their current pathological state and the expected impact of the neuromodulation parameters on pathology. Furthermore, therapies could even be designed while taking into account the expected future state of the pathology.

[0420] In summary, the ability to read out and model pathology while measuring whole-brain network function with optogenetic fMRI will allow the development of circuit-based models that bridge the gap between pathology and function.

[0421] Conclusion

[0422] These recent advances in cell type-specific neuromodulation, whole-brain functional imaging, and computational modeling are beginning to pave the way for a major turning point in neuroscience. Our goal is to establish new methods to simulate brain function that can replicate and predict behaviors of interest. This will transform the treatment of a range of neurological disorders, including Parkinson's disease.

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Claims

1. A method for inhibiting beta-band oscillations in a subject's brain by optogenetic neuromodulation of medium spiny neurons according to a method comprising the following steps: (a) Optogenetically inhibiting D1-medium spiny neurons (D1-MSNs) in the globus pallidus internus (GPi) region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are inhibited; (b) Optogenetically inhibiting D2-medium spiny neurons (D2-MSNs) in the globus pallidus externus (Gpe) region of the subject's brain, wherein D2-MSN-mediated beta-band oscillations are inhibited; (c) Periodically optogenetically stimulating the D1-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are inhibited; or (d) Randomly optogenetically stimulating the D1-MSNs and the D2-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations and D2-MSN-mediated beta-band oscillations are inhibited; or (a)-(d) in any combination.

2. The method according to claim 1, wherein the method comprises: (a) Optogenetically inhibiting the D1-MSNs in the GPi region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are inhibited; (b) Optogenetically inhibiting the D2-MSNs in the Gpe region of the subject's brain, wherein D2-MSN-mediated beta-band oscillations are inhibited; (c) Periodically optogenetically stimulating the D1-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations are inhibited; and (d) Randomly optogenetically stimulating the D1-MSNs and the D2-MSNs in the Gpe region of the subject's brain, wherein D1-MSN-mediated beta-band oscillations and D2-MSN-mediated beta-band oscillations are inhibited.

3. The method according to claim 1 or 2, wherein the optogenetic inhibition of the D1-MSNs or the D2-MSNs in the GPi region or the Gpe region comprises: Introducing a recombinant polynucleotide encoding a light-responsive ion channel into the D1-MSNs or the D2-MSNs in the GPi region or the Gpe region, wherein the light-responsive ion channel is expressed in the D1-MSNs or the D2-MSNs; and Irradiating the light-responsive ion channel with light of a wavelength that activates the light-responsive ion channel, wherein ion conduction through the light-responsive ion channel in response to light absorption results in hyperpolarization and inhibition of the D1-MSNs or the D2-MSNs.

4. The method according to claim 3, wherein the light-responsive ion channel is a light-responsive anion-conducting opsin or a light-responsive proton-conducting modulator.

5. The method according to claim 4, wherein the light-responsive anion-conducting opsin conducts chloride ions (Cl - ).

6. The method according to claim 4 or 5, wherein the anion-conducting opsin is anion-conducting channelrhodopsin or halorhodopsin.

7. The method according to claim 6, wherein the halorhodopsin is Natronomonas pharaonis halorhodopsin (NpHR), enhanced NpHR (eNpHR) 1.0, eNpHR 2.0, or eNpHR 3.

0.

8. The method according to claim 6, wherein the anion-conducting channelrhodopsin is iC1C2, SwiChR, SwiChR++, or iC++.

9. The method according to claim 4, wherein the light-responsive proton-conducting regulator is bacteriorhodopsin or archaerhodopsin.

10. The method according to claim 9, wherein the light-responsive proton-conducting regulator is Arch from Halorubrum sodomense, ArchT from Halorubrum sp., TP009 from Leptosphaeria maculans, or Mac from Leptosphaeria maculans.

11. The method according to claim 1 or 2, wherein the optogenetic stimulation of the D1-MSN or the D2-MSN in the Gpi region or the Gpe region comprises: introducing a recombinant polynucleotide encoding a light-responsive ion channel into the D1-MSN or the D2-MSN in the Gpi region or the Gpe region, wherein the light-responsive ion channel is expressed in the D1-MSN or the D2-MSN; and irradiating the light-responsive ion channel with light of a wavelength that activates the light-responsive ion channel, wherein ion conduction through the light-responsive ion channel in response to light absorption results in depolarization and activation of the D1-MSN or the D2-MSN.

12. The method according to claim 11, wherein the light-responsive ion channel is a light-responsive cation-conducting opsin.

13. The method according to claim 12, wherein the light-responsive cation-conducting opsin conducts calcium cations (Ca 2+ ).

14. The method according to claim 12 or 13, wherein the light-responsive cation-conducting opsin is a light-responsive cation-conducting channelrhodopsin.

15. The method according to claim 14, wherein the light-responsive cation-conducting channelrhodopsin is Chlamydomonas reinhardtii channelrhodopsin or Volvox carteri channelrhodopsin.

16. The method according to claim 15, wherein the light-responsive cation-conducting channelrhodopsin is Chlamydomonas reinhardtii channelrhodopsin-1 (ChR1), Chlamydomonas reinhardtii channelrhodopsin-2 (ChR2), Volvox carteri channelrhodopsin-1 (VChR1), or chimeric ChR1-VChR1 channelrhodopsin.

17. The method according to any one of claims 1-16, wherein the polynucleotide encoding the light-responsive ion channel is provided by a viral vector.

18. The method according to claim 17, wherein the viral vector is a lentiviral vector or an adeno-associated virus (AAV) vector.

19. The method according to claim 17 or 18, wherein the viral vector is stereotactically injected into the posterior cingulate cortex.

20. The method according to any one of claims 17-19, wherein the vector further comprises a neuron-specific promoter operably linked to the polynucleotide encoding the light-responsive ion channel.

21. The method according to any one of claims 17-20, wherein the expression of the light-responsive ion channel is inducible.

22. The method according to any one of claims 1-21, wherein irradiating the light-responsive ion channel comprises delivering light from a light source to the light-responsive ion channel using a fiber-optic based optical neural interface.

23. The method according to claim 22, wherein the light source is a solid-state diode laser.

24. The method according to any one of claims 1-23, wherein the subject has Parkinson's disease.

25. The method according to any one of claims 1-24, wherein optogenetically inhibiting the D1-MSNs comprises continuous shunting inhibition of the D1-MSNs in the GPi region of the subject's brain.

26. The method according to any one of claims 1-25, wherein optogenetically inhibiting the D2-MSNs comprises continuous shunting inhibition of the D2-MSNs in the GPe region of the subject's brain.

27. The method according to any one of claims 1-26, wherein periodically optogenetically stimulating the D1-MSNs in the GPe region of the subject's brain comprises periodic stimulation at a frequency of 130 Hz.

28. The method according to any one of claims 1-27, wherein randomly optogenetically stimulating the D1-MSNs and the D2-MSNs in the GPe region of the subject's brain comprises using a plurality of stimulation sequences with pulses having a random interval of 130 Hz, wherein each neuron is stimulated with one of the stimulation sequences in the stimulation sequence such that synchronous neurons are decoupled.

29. The method according to any one of claims 1-28, wherein optogenetically stimulating the D1-MSNs or the D2-MSNs comprises directly activating the D1-MSNs or the D2-MSNs with an intensity of 500 pA, 700 pA or 900 pA.

30. A method of treating Parkinson's disease in a subject, the method comprising: Positioning a first electrode at a first location in the medial globus pallidus (GPi) region of the subject's brain to deliver electrical stimulation to D1-medium spiny neurons in the GPi region; Positioning a second electrode at a second location in the lateral globus pallidus (GPe) region of the subject's brain to deliver electrical stimulation to D1-medium spiny neurons and D2-medium spiny neurons in the GPe region; And Apply electrical stimulation to the Gpi region of the subject's brain using the first electrode and to the Gpe region of the subject's brain using the second electrode in a manner effective to suppress beta band oscillations for treating Parkinson's disease.

31. The method according to claim 30, wherein the electrical stimulation is applied unilaterally or bilaterally using the first electrode or the second electrode.

32. The method according to claim 30 or 31, wherein the first electrode or the second electrode is a depth electrode or a surface electrode.

33. The method according to any one of claims 30-32, wherein the first electrode or the second electrode is a non-brain penetrating surface electrode array or a brain penetrating electrode array.

34. The method according to any one of claims 30-33, wherein the first electrode is placed on the surface of the Gpi region.

35. The method according to any one of claims 30-34, wherein the second electrode is placed on the surface of the Gpe region.

36. The method according to any one of claims 30-35, wherein the method further comprises using a visual analogue scale, a speech rating scale, the Movement Disorder Society-sponsored revised Unified Parkinson's Disease Rating Scale (MDS-UPDRS), the Hoehn and Yahr (HnY) scale, or the Montreal Cognitive Assessment (MoCA) scale to evaluate the effectiveness of the treatment of the subject.

37. A computer-implemented method for modeling the propagation of beta band oscillations and the response to neuromodulation in a subject's brain, the computer performing the following steps, including: a) receiving functional magnetic resonance imaging data of the neural activity of D1-medium spiny neurons (D1-MSNs) and D2-medium spiny neurons (D2-MSNs) in the caudate putamen (CPu) region, the lateral globus pallidus (GPe) region, the medial globus pallidus (GPi) region, the subthalamic nucleus (STN) region, the substantia nigra reticulata (SNr) region, the thalamus (THL) region, and the motor cortex (MCX) region of the subject's brain before and during optogenetic stimulation; b) performing spectral dynamic causal modeling on the effective connectivity strength between the CPu region, the GPe region, the GPi region, the STN region, the SNr region, the THL region, and the MCX region; c) receiving experimental electrophysiological data of the CPu region; d) using the experimental electrophysiological data of the CPu region to estimate the effective connectivity strength of GABAergic connections; e) using the dynamic causal modeling of the functional magnetic resonance imaging data to estimate the effective connectivity strength of glutamatergic connections; f) performing biophysical modeling using the Hodgkin-Huxley model to generate simulated electrophysiological data using the effective connectivity strength estimates; g) iteratively optimizing the GABAergic projections until the simulated electrophysiological data matches the experimental electrophysiological data of the CPu region; h) Calculate the temporal profile of the average power of the β-band frequency of each neuron in the CPU region, the GPe region, the GPi region, the STN region, the SNr region, the THL region, and the MCX region; And i) Compare the total amount of co-occurring β-band oscillation power before optogenetic stimulation with the co-occurring β-band oscillation power during optogenetic stimulation to model the propagation of β-band oscillations in the brain of the subject.

38. The computer-implemented method according to claim 37, wherein the D1-MSN is a Huxley-Hudgkin neuron.

39. The computer-implemented method according to claim 37 or 38, wherein the experimental electrophysiological data includes single neuron recordings.

40. The computer-implemented method according to claim 39, wherein the single neuron recordings are from GABAergic neurons in the CPU region.

41. The computer-implemented method according to any one of claims 37-40, wherein glutamatergic connections are modeled as 1-to-1 connections, and the connection strength is proportional to the effective connectivity estimated by DCM.

42. The computer-implemented method according to any one of claims 39-41, wherein the CPU-GPi / GPe and GPi / SNr-thalamic GABAergic projections are modeled as 1-to-n diffusive projections with a connectivity strength wGABA, where n and wGABA are free parameters, and n and wGABA are searched across the parameter space until the simulated action potential firing rate statistically matches the single neuron recordings.

43. The computer-implemented method according to any one of claims 37-42, wherein the optogenetic stimulation includes optogenetic inhibition of the D1-MSN in the GPi region of the brain of the subject.

44. The computer-implemented method according to claim 43, wherein the optogenetic inhibition of the D1-MSN includes continuous shunting inhibition of the D1-MSN in the GPi region of the brain of the subject.

45. The computer-implemented method according to any one of claims 37-44, wherein the optogenetic stimulation includes optogenetic inhibition of the D2-MSN in the GPe region of the brain of the subject.

46. The computer-implemented method according to claim 45, wherein the optogenetic inhibition of the D2-MSN includes continuous shunting inhibition of the D2-MSN in the GPe region of the brain of the subject.

47. The method according to any one of claims 37-46, wherein the optogenetic stimulation includes directly activating the D1-MSN or the D2-MSN at an intensity of 500 pA, 700 pA, or 900 pA.

48. The computer-implemented method according to any one of claims 37-47, wherein the optogenetic stimulation includes periodically optogenetically stimulating the D1-MSN in the Gpe region of the brain of the subject.

49. A computer-implemented method according to any one of claims 37-48, wherein the optogenetic stimulation comprises using a plurality of stimulation sequences with pulses having random intervals to randomly optogenetically stimulate the D1-MSNs and the D2-MSNs in the Gpe region of the subject's brain, wherein each neuron is stimulated with one of the stimulation sequences in the stimulation sequence such that synchronous neurons are decoupled.

50. A computer-implemented method according to any one of claims 37-49, wherein the optogenetic stimulation is performed by periodic stimulation at a frequency of 130 Hz.

51. A system for modeling the propagation of beta-band oscillations in a subject's brain using a computer-implemented method according to any one of claims 37-50, the system comprising: a) a storage component for storing data, wherein the storage component has instructions for modeling the propagation of beta-band oscillations based on an analysis of functional magnetic resonance imaging data and experimental electrophysiology data stored therein; b) a computer processor for processing the functional magnetic resonance imaging data and the experimental electrophysiology data using one or more algorithms, wherein the computer processor is coupled to the storage component and is configured to execute the instructions stored in the storage component to receive the input functional magnetic resonance imaging data and the experimental electrophysiology data and analyze the data according to a computer-implemented method according to any one of claims 37-50; and c) a display component for displaying information about the propagation of beta-band oscillations in the subject's brain.

52. A non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, cause the processor to perform a method according to any one of claims 37 to 50.

53. A kit comprising the non-transitory computer-readable medium according to claim 52 and instructions for modeling the propagation of beta-band oscillations from functional magnetic resonance imaging data and experimental electrophysiology data.

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