System and method for precision image mapping via functional magnetic resonance imaging

Abstract

A computing device for use in a system to implement Precision Imaging Drug Trials (PIDT) that integrate Precision Functional Mapping (PFM) for mapping brain activity of a subject. The computing device includes a processor programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during various states. Moreover, the processor is programmed to compare data points from measurements and produce maps for the measurements based on the comparison of the data points. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map for detection of which targets in the brain an experimental compound impacts.

Description

CT SYSTEM AND METHOD FOR PRECISION IMAGE MAPPING VIA FUNCTIONAL MAGNETIC RESONANCE IMAGING CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent ApplicationNo.63 / 647,914, filed May 15, 2024, which is hereby incorporated by reference in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under DA007261,MH112473, and MH121276 awarded by the National Institutes of Health. The government has certain rights in the invention. BACKGROUND

[0003] The field of the invention relates generally to brain mapping systemsand, more particularly, to systems and methods for mapping of brain activity using state data collected from a brain of a subject via magnetic resonance imaging (MRI).

[0004] In magnetic resonance imaging (MRI), a subject is placed in a magnetstructure. When the subject is in the magnetic field generated by a magnet of the magnet structure, magnetic moments of nuclei, such as protons, attempt to align with the magnetic field but process about the magnetic field in a random order. In acquiring an MRI image, a magnetic field (referred to as an excitation field), is generated by a radio-frequency (RF) coil and may be used to rotate, or “tip,” the net magnetic moment of the nuclei. A signal, which is referred to as an MR signal, is emitted by the nuclei, after the excitation signal is terminated. To use the MR signals to generate an image of a subject, magnetic field gradientCT pulses (e.g., Gx, Gy, and Gz) are used. The gradient pulses are used to scan through the space of spatial frequencies or inverse of distances. A Fourier relationship exists between the acquired MR signals and an image of the subject, and therefore the image of the subject can be derived by reconstructing the MR signals.

[0005] Magnetic resonance imaging (MRI) has proven useful in diagnosis ofmany diseases. MRI provides detailed images of soft tissues, abnormal tissues such as tumors, and other structures, which cannot be readily imaged by other imaging modalities, such as computed tomography (CT). Further, MRI operates without exposing patients to ionizing radiation experienced in modalities such as CT and x-rays. Various implementations of MRI are known, including functional magnetic resonance imaging (fMRI), useful in brain mapping.

[0006] Brain mapping includes a set of neuroscience techniques that aregenerally predicated on the mapping of biological quantities or properties onto spatial representations of a subject's brain resulting in at least one map. At least some known neuroimaging systems or techniques are used frequently in clinical and research settings for brain mapping such that brain function can be monitored. For example, fMRI may be used to enable researchers and clinicians to see visual images of the brain, wherein the images may be used to identify brain activity within a plurality of networks of the brain. Functional MRI have various implementations such as resting fMRI (also known as R-fMRI) and task MRI (also known as T-fMRI).

[0007] Conventional tools such as R-fMRI, T-fMRI, and EEG techniquesmay be used to help measure and analyze brain activity, cognition, and behavior, and are particularly useful in studying brain function and drug effects. For example, T-fMRI techniques are sometimes used to track changes in blood flow, allowing researchers toCT identify areas of the brain that are most active during different tasks or mental states, or, in the case of R-fMRI, brain activity during a resting or task-negative state where no task is being performed. R-fMRI can be particularly useful in examining effects of neurological or mental conditions, and T-fMRI can be useful in studying cognitive brain tasks such as listening to music and identifying specific regions of the brain involved in such tasks (e.g., language comprehension). EEG’s typically use electrodes attached to the head (e.g., scalp) for measuring electrical activity in the brain, and can be useful in providing valuable insights into brain function and help diagnose brain disorders.

[0008] However, these conventional tools may not provide enough sensitivityin investigating how drugs affect brain activity during tasks or different mental states. For example, while fMRI techniques can provide excellent spatial resolution for brain mapping, at least some known techniques lack precise temporal resolution and are better suited for capturing slow fluctuations rather than fast-speed brain operations. These limitations may also make it difficult to pinpoint specific pathological processes. At least some known EEG systems have poor sensitivity and spatial resolution, limiting the ability to pinpoint specific pathological processes. Additionally, EEG abnormalities can sometimes make it difficult to diagnose specific mental disorders.

[0009] Understanding how drugs alter brain activity is beneficial for drugdelivery method optimization, gaining insight into drug effects and potential side effects, evaluating candidate compounds, predicting drug efficacy, and assessing drug safety panels. It would be beneficial to have an MRI technique that has the desired sensitivity to more exactly measure brain activity than known systems and techniques and a better understanding of drug effects on the brain, in particular for individuals with brain and / or psychiatric disorders.CT

[0010] This background section is intended to introduce the reader to variousaspects of art that may be related to various aspects of the present disclosure, which are described and / or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art. SUMMARY

[0011] In one aspect, a computing device for use in a system for mappingbrain activity of a subject generally comprises a processor. The processor is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during various states (e.g., resting state, task state). Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject (and / or prior data of the same subject). The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.

[0012] In another aspect, a system for mapping brain activity of a subjectgenerally comprises a sensing system and a computing device that is coupled to the sensing system. The sensing system is configured to detect a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during various states. The computing device includes a communication interface that is configured to receive at least one signal representative of the measurements, and a processor that is coupledCT to the communication interface. The processor is programmed to select the measurements of brain activity. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject (and / or prior data from the same subject). The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.

[0013] In yet another aspect, a method for mapping brain activity of a patientgenerally comprises selecting, via a processor, a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during various states. At least one data point from each of the plurality of measurements is compared, via the processor, with a corresponding data point from a previously acquired data set from at least one other subject (and / or prior data from the same subject). At least one map is produced, via the processor, for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The brain activity is categorized, via the processor, in a plurality of networks in the brain based on the map.

[0014] Various refinements exist of the features noted in relation to theabove-mentioned aspects. Further features may also be incorporated in the above-mentioned aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to any of the illustrated embodiments may be incorporated into any of the above-described aspects, alone or in any combination.CT BRIEF DESCRIPTION OF DRAWINGS

[0015] The accompanying drawings, which are incorporated in and form apart of the specification, illustrate the embodiments of the present disclosure and together with the description, serve to explain the principles of the disclosure. Additional details of the above-described systems and methods are included in Appendix A and Appendix B, each attached hereto and incorporated herein by reference in their respective entireties.

[0016] FIG. 1 is a schematic diagram of an example system.

[0017] FIG. 2 is a block diagram of an example computing device of thesystem shown in FIG.1.

[0018] FIG. 3 is a flow diagram of an example method for mapping of brainactivity using the system shown in FIG.1.

[0019] FIG. 4 shows a graphical depiction of a study protocol according tothe disclosed subject matter.

[0020] FIG. 5A shows functional connectivity (FC) change maps ofparticipants in a study according to the disclosed subject matter.

[0021] FIG. 5B shows maps outlining a relationship between whole brainfunctional connectivity and subjective drug experience.

[0022] FIG. 5C shows plots outlining a relationship between functionalconnectivity change and mystical experience questionnaire data.

[0023] FIG. 5D shows plots outlining head motion, heart rate, and respiratoryrate data.CT

[0024] FIG. 6A shows psilocybin and methylphenidate mapping.

[0025] FIG. 6B shows PET-based 5-HT2A and norepinephrine transporterdensity.

[0026] FIG. 6C shows PSIL v. 5-HT2A density and MTP vs. NET densityplots.

[0027] FIG. 7 shows a schedule of assessments.

[0028] FIG. 8 shows mystical experience questionnaire results.

[0029] FIG. 9 shows quality-based analysis of psilocybin PFM dataset.

[0030] FIG. 10 shows a participant flow chart.

[0031] FIG. 11 shows baseline characteristics.

[0032] FIG. 12A shows a comparison of head motion to prior psychedelicfMRI datasets (baseline head motion).

[0033] FIG. 12B shows a comparison of head motion to prior psychedelicfMRI datasets (on-psychedelic head motion).

[0034] FIG. 12C shows a comparison of head motion to prior psychedelicfMRI datasets (on-psychedelic data).

[0035] FIG. 13 shows physiological data collected per participant.

[0036] FIG. 14A shows quality and signal metrics for a psilocybin PFMdataset (cortical infraslow power).CT

[0037] FIG. 14B shows quality and signal metrics for a psilocybin PFMdataset (head motion.

[0038] FIG. 14C shows quality and signal metrics for a psilocybin PFMdataset (Newmann’s modularity).

[0039] FIG. 15 shows study challenges, solutions, and lessons learned.

[0040] FIG. 16A shows a design for a study protocol for quantifyingpsilocybin effects with PFM.

[0041] FIG. 16B shows a timeline for a study protocol for quantifyingpsilocybin effects with PFM.

[0042] FIG. 16C shows a head motion data for a study protocol forquantifying psilocybin effects with PFM.

[0043] FIG. 16D shows a timeline for a study protocol for quantifyingpsilocybin effects with PFM.

[0044] FIG. 16E shows scores for a study protocol for quantifying psilocybineffects with PFM.

[0045] FIG. 17A shows acute psilocybin effects on functional brainorganization (psilocybin mapping).

[0046] FIG. 17B shows acute psilocybin effects on functional brainorganization (MTP mapping).

[0047] FIG. 17C shows acute psilocybin effects on functional brainorganization (day-to-day mapping).CT

[0048] FIG. 17D shows acute psilocybin effects on functional brainorganization (FC change plot).

[0049] FIG. 17E shows acute psilocybin effects on functional brainorganization (whole-brain FC change).

[0050] FIG. 17F shows acute psilocybin effects on functional brainorganization (individual FC change maps).

[0051] FIG. 17G shows acute psilocybin effects on functional brainorganization (whole-brain FC and mystical experience relationship).

[0052] FIG. 17H shows acute psilocybin effects on functional brainorganization (relationship between FC change and MEQ).

[0053] FIG. 18 shows unthresholded vertex-wise FC change maps.

[0054] FIG. 19A shows FC distance and condition matrices for FC measuresderived from fMRI scans.

[0055] FIG. 19B shows FC distance and condition matrices for FC measuresderived from fMRI scans.

[0056] FIG. 19C shows FC distance and condition matrices for FC measuresderived from fMRI scans.

[0057] FIG. 20A shows network changes compared across differentconditions, brains structures, and measures (mode functional network map).

[0058] FIG. 20B shows network changes compared across differentconditions, brains structures, and measures (network selectivity of cortical FC change).CT

[0059] FIG. 20C shows network changes compared across differentconditions, brains structures, and measures (mode functional network map of psilocybin FC change).

[0060] FIG. 21 shows pulse and respiratory rates across conditions.

[0061] FIG. 22A shows alternative methods to computing FC change (asreported).

[0062] FIG. 22B shows alternative methods to computing FC change (afterGSR).

[0063] FIG. 22C shows alternative methods to computing FC change(similarity).

[0064] FIG. 22D shows alternative methods to computing FC change(evoked responses).

[0065] FIG. 23A shows a comparison of analyses with and without PhysIO-based regression (whole-brain FC).

[0066] FIG. 23B shows a comparison of analyses with and without PhysIO-based regression (without regression).

[0067] FIG. 23C shows a comparison of analyses with and without PhysIO-based regression (multi-dimensional scaling).

[0068] FIG. 23D shows a comparison of analyses with and without PhysIO-based regression (without regression).CT

[0069] FIG. 24A shows data-driven clustering of brain network variability(whole-brain FC).

[0070] FIG. 24B shows data-driven clustering of brain network variability(dimension 1).

[0071] FIG. 24C shows data-driven clustering of brain network variability(re-analysis).

[0072] FIG. 24D shows data-driven clustering of brain network variability(average effects).

[0073] FIG. 25A shows multi-dimensional scaling, dimension edge weights(group parcellation).

[0074] FIG. 25B shows multi-dimensional scaling, dimension edge weights(group weights).

[0075] FIG. 26A shows average FC matrices by condition (groupparcellation).

[0076] FIG. 26B shows average FC matrices by condition (average FCmatrices).

[0077] FIG. 27A shows spatial desynchronization of cortical activity duringpsilocybin (NGSC).

[0078] FIG. 27B shows spatial desynchronization of cortical activity duringpsilocybin (whole-brain entropy).CT

[0079] FIG. 27C shows spatial desynchronization of cortical activity duringpsilocybin (parcel entropy).

[0080] FIG. 27D shows spatial desynchronization of cortical activity duringpsilocybin (spatial entropy).

[0081] FIG. 27E shows spatial desynchronization of cortical activity duringpsilocybin (LSD).

[0082] FIG. 27F shows spatial desynchronization of cortical activity duringpsilocybin (5HT2A).

[0083] FIG. 28 shows correlations with mystical experience scores.

[0084] FIG.29A shows effects of perceptual task performance on psilocybin-associated FC change and desynchronization (resting and task).

[0085] FIG. 29B shows effects of perceptual task performance on psilocybin-associated FC change and desynchronization (during resting and task).

[0086] FIG. 30A shows auditory-visual matching fMRI task (schematic).

[0087] FIG. 30B shows auditory-visual matching fMRI task (performance).

[0088] FIG. 30C shows auditory-visual matching fMRI task (reaction time).

[0089] FIG. 30D shows auditory-visual matching fMRI task (activationmaps).

[0090] FIG. 30E shows auditory-visual matching fMRI task (contrasts).

[0091] FIG. 30F shows auditory-visual matching fMRI task (timecourse).CT

[0092] FIG. 31 shows whole-brain FC changes for scans.

[0093] FIG. 32A shows effects of ask on psilocybin-associated FC changeand desynchronization after regressing out evoked responses (grounding - FC).

[0094] FIG. 32B shows effects of ask on psilocybin-associated FC changeand desynchronization after regressing out evoked responses (grounding - desynchronization).

[0095] FIG. 33A shows persistent effects of psilocybin (hippocampus).

[0096] FIG. 33B shows persistent effects of psilocybin (pre- and post-PSIL).

[0097] FIG. 33C shows persistent effects of psilocybin (connectivity).

[0098] FIG. 33D shows persistent effects of psilocybin (timecourse).

[0099] FIG. 33E shows persistent effects of psilocybin (schematic).

[0100] FIG. 34 shows participant demographics and neuropsychologicalassessments.

[0101] FIG. 35 compares aspects of EEG, PET and Precision fMRI.

[0102] FIG. 36 is task fMRI ‘Scene > Face’ contrast maps (t-statistics) fortwo individuals and for group average (N = 10).

[0103] FIG. 37 compares functional networks defined in group averaged data(left) versus in a single individual using repeated sampling and PFM (right).

[0104] FIG. 38A is a comparison across fMRI study methods of power todetect a stimulants biomarker.CT

[0105] FIG. 38B is a conceptual design and power estimate for a singleascending dose biomarker study.

[0106] FIG. 39A shows Psilocybin and methylphenidate associated FCchange between (top) and within (bottom) network.

[0107] FIG. 39B is PET-based maps of 5HT-2A receptor and norepinephrinetransporter radiotracer binding.

[0108] FIG. 39C graphs comparison of PFMT drug effect maps to PET maps.Each dot represents a brain area from the Gordon parcellation (324 cortical areas).

[0109] FIG. 40A IS A GRAPH OF sources of connectome (whole brain FC)measurement variability.

[0110] FIG. 40B is a graph comparing maximum achievable prediction accuracy asa function of study design (total fMRI budget, scan cost per hour and overhead cost per participant) in large existing RS-FC datasets. DETAILED DESCRIPTION

[0111] The embodiments described herein include systems, apparatus, andmethods for mapping brain activity of a subject. This includes a computing device with a processor that is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of a subject (e.g., patient, participant) during various states (e.g., resting, task) that the subject may be in. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject (and / or prior data from the same subject). The processor is also programmed to produce atCT least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.

[0112] More specifically, some embodiments described herein include novelsystems, apparatus, and methods for using such a computing device in conjunction with magnetic resonance imaging (MRI) to conduct Precision Imaging Drug Trials (PIDT) for measuring biomarkers of brain penetration and functional target engagement to assess the patient-specific effects of therapeutics on brain circuits with highest precision, sensitivity and specificity.

[0113] PIDT generally integrates Precision Functional Mapping (PFM) withdense repeated sampling, advanced fMRI sequences, controls for physiological arousal, and individual-specific analyses to improve signal-to-noise ratio (SNR) and detect intervention effects in individuals, providing extremely cost-effective means to decipher the mechanisms of novel and established molecules, assess for brain penetrance and build diagnostic tools for personalized treatment approaches. At least some of the techniques described herein enable the identification of brain functional areas, subnetworks, and networks at the individual level, allowing for more precise and reliable measurement of engagement of specific circuits (e.g., fronto-striatal reward / motivation circuit) by compounds. Additionally, some of the techniques described herein combine task fMRI (T-fMRI) and resting fMRI (R- fMRI) to repeatedly measure acute, subacute, and chronic drug effects in the same individual to precisely assess neurocircuitry changes and link them to clinical and other behavioral effects, at rest and during behavior. The method aspects will be in part apparent and in part explicitly discussed in the following description.CT

[0114] The example systems, apparatus, and methods described hereinovercome at least some known disadvantages associated with at least some known fMRI and brain mapping techniques as described herein. Various benefits and advances result from these techniques. First, a longitudinal (e.g., not cross-sectional) design is realized, permitting assessment of the effects of a drug on a biomarker cross-sectionally versus measuring effects of a drug within an individual longitudinally are fundamentally different. The circuit-level effects of a drug relative to day-to-day variability in circuit measures may be an order of magnitude larger than group differences in circuit-level effects relative to individual variability. Second, each participant typically completes multiple (>3) baseline visits. This makes it possible to have adequate data in single participants to define brain functional areas at the individual level, measure individual (day-to-day) variability, and acclimate participants to the MRI environment. Third, brain functional areas and networks can be defined at the individual level. This makes it possible to dramatically increases effect size of T-fMRI analysis, more accurately align / compare across individuals, and disambiguate specific circuits in which drug-relate changes occur. For example, using PFM, fronto-striatal circuits critical to neuropsychiatric diseases and monoaminergic neurotransmitter systems at the single subject level can be defined. These circuits cannot be resolved in group-averaged data. Fourth, measurement and control for physiological and arousal-related confounds. It is clear that arousal has large effects on resting and task fMRI. This makes it possible to perform monitoring of pulse and respiration enables improvement of signal-to-noise ratio through PhysIO-based confound removal, and control for time of day. Fifth, advanced techniques for echoplanar and noise removal permit: (i) multi-echo (multi-echo sequences enables separate estimation of S0 (spin history artifact) and R2* (BOLD contrast) components of the MRI signal for every voxel. With removal of S0 artifact, 10 minutes of multi-echo data yields better test-retest reliability than 30 minutes of single-echo data; (ii)CT confound removal: gold-standard denoising procedures for T-fMRI and R-fMRI; and (iii) removal of thermal noise (thermal denoising operating on fMRI data and only removes components that cannot be distinguished from zero-mean Gaussian distributed noise and substantially improves SNR). Sixth, combining Task fMRI (T-fMRI) and Resting fMRI (R- fMRI). It is increasingly clear that the neurobiological effect of psychoactive drugs can be highly dependent on context. Combining task fMRI (T-fMRI) and resting fMRI (R-fMRI) as necessary offers complementary and synergistic benefits as tools for localizing and assessing neurocircuitry at rest and during behavior and assessing drug-by-environment interactions. Seventh, these techniques may allow for maximizing drug effect. Assess drug effects over multiple ascending doses / sessions (to improve statistical power) and titrate to maximum tolerated dose (to improve effect size). Various embodiments described herein may achieve more or fewer of the above-described advantages and may achieve different advantages than other embodiments.

[0115] PIDT offers a novel individual-specific method for precise assessmentof functional brain effects, their patterns and downstream behavioral changes of potentially psychoactive molecules. PIDT offers an approach for screening novel psychopharmacological compounds that is substantially more sensitive than existing tools (resting fMRI, task fMRI, EEG). It interrogates the mechanisms of novel and established compounds, as well as testing for inter-individual differences in drug response to assess i) brain penetration, ii) target engagement of specific circuits (e.g., fronto-striatal reward / motivation circuit) and neurotransmitter systems (using PET-based receptor maps), and iii) similarity to existing drug classes (based on drug biomarkers from public fMRI datasets), allowing for the assessment of drug effects with greater reliability in smaller sample sizes.CT

[0116] FIG. 1 illustrates a schematic diagram of an example system 100 formapping brain activity of a subject / patient as disclosed herein. As used herein, the term “brain activity” includes the various activities within a brain of the subject that correspond to various tasks performed by the subject, or activities from other states of the subject that the subject may be in (e.g., resting state). For example, the brain transmits and receives signals in the form of hormones, nerve impulses, and chemical messengers that enable the subject to move, eat, sleep, and think. In the example embodiment, system 100 is used to identify locations within a plurality of networks within the brain that are responsible for such brain activities.

[0117] The system 100 includes a computing device 110 (also referred to asa workstation) having a display 112 and a keyboard 114. The workstation 110 includes a processor 116, such as a commercially available programmable machine running a commercially available operating system and other software, and a memory device 118, such as one or more of onboard commercially available RAM / ROM / HDD. The workstation 110 provides an operator interface that allows scan prescriptions / protocols to be entered into the system 100. Beyond local storage such as memory device 118, the workstation 110 includes may also be operatively coupled to various remote (e.g., networked or cloud) storage devices, such as a pulse sequence server 120, a data acquisition server 122, a data processing server 124, and a data store server 126. The workstation 110 and each server 120, 122, 124, and 126 communicate with each other. Other embodiments may include different components, and / or components may be combined. For example, in other embodiments, the servers 120, 122, 124, and 126 may be part of the workstation 110, or may be combined in one server, or any combination of local / remote location of such devices.CT

[0118] In an example embodiment, the system 100 may be a magneticresonance imaging (MRI) system. For example, and without limitation, the pulse sequence server 120 may be configured to respond to instructions downloaded from the workstation 110 to operate various systems, such as a gradient system 128 and a radiofrequency (“RF”) system 130. The instructions are used to produce gradient and RF waveforms in MR pulse sequences. An RF coil 138 and a gradient coil assembly 132 are used to perform the prescribed MR pulse sequence. The RF coil 138 is shown as a whole-body RF coil. The RF coil 138 may also be a local coil that may be placed in proximity to the anatomy to be imaged, or a coil array that includes a plurality of coils. In this embodiment, gradient waveforms used to perform the prescribed scan are produced and applied to the gradient system 128, which excites gradient coils in the gradient coil assembly 132 to produce the magnetic field gradients Gx, Gy, and Gz used for position-encoding MR signals. The gradient coil assembly 132 forms part of a magnet assembly 34 that also includes a polarizing magnet 136 and the RF coil 138. The RF system 130 includes an RF transmitter for producing RF pulses used in MR pulse sequences. The RF transmitter is responsive to the scan prescription and direction from the pulse sequence server 120 to produce RF pulses of a desired frequency, phase, and pulse amplitude waveform. The generated RF pulses may be applied to the RF coil 138 by the RF system 130. Responsive MR signals detected by the RF coil 38 are received by the RF system 130, amplified, demodulated, filtered, and digitized under direction of commands produced by the pulse sequence server 120. The RF coil 138 is described as a transmitter and receiver coil such that the RF coil 138 transmits RF pulses and detects MR signals. The system 100 may include a transmitter RF coil that transmits RF pulses and a separate receiver coil that detects MR signals. A transmission channel of the RF system 130 may be connected to a RF transmission coil and a receiver channel may be connected to a separate RF receiverCT coil. Often, the transmission channel is connected to the whole-body RF coil 138 and each receiver section is connected to a separate local RF coil.

[0119] The RF system 130 may also include one or more RF receiverchannels. Each RF receiver channel includes an RF amplifier that amplifies the MR signal received by the RF coil 38 to which the channel is connected, and a detector that detects and digitizes quadrature components of the received MR signal. The magnitude of the received MR signal may then be determined as the square root of the sum of the squares of such quadrature components.

[0120] The pulse sequence server 120 may also optionally receive subjectdata from a physiological acquisition controller 140. The controller 140 may receive physiological signals from sensors connected to the subject, such as electrocardiograph (“ECG”) signals from electrodes, or pulse signals from a pulse oximeter, or respiratory signals from a respiratory monitoring device such as a bellows. The physiological signals are typically used by the pulse sequence server 120 to synchronize, or “gate,” the performance of the scan with the subject's physiological parameters, such heartbeat or respiration rate. The pulse sequence server 120 may also connect to a scan room interface circuit 42 that receives signals from sensors associated with the condition of the subject and the magnet system 34. Through the scan room interface circuit 142, a patient positioning system 144 receives commands to move the subject to desired positions before and / or during the scan.

[0121] The digitized MR signal samples produced by the RF system 30 arereceived by the data acquisition server 122. The data acquisition server 122 may operate in response to instructions downloaded from the workstation 110 to receive real-time MR data and provide buffer storage such that no data is lost by data overrun. In some scans, the data acquisition server 122 may do little more than simply pass the acquired MR data to the dataCT processing server 124. In other cases, such as scans that need information derived from acquired MR data to control further performance of the scan, the data acquisition server 122 is programmed to produce the needed information and convey it to the pulse sequence server 120. For example, during prescans, MR data is acquired and used to calibrate the pulse sequence performed by the pulse sequence server 120. Also, navigator signals may be acquired during a scan and used to adjust the operating parameters of the RF system 130 or the gradient system 128, or to control the view order in which k-space is sampled.

[0122] The data processing server 124 receives MR data from the dataacquisition server 122 and processes the data in accordance with instructions downloaded from the workstation 110. Such processing may include, for example, Fourier transformation of raw k-space MR data to produce two or three-dimensional images (e.g., brain mapping images), the application of filters to a reconstructed image, the performance of image reconstruction of acquired MR data, the generation of functional MR images, and the calculation of motion or flow images.

[0123] Images reconstructed by the data processing server 124 are conveyedback to, and stored at, the workstation 110 and / or associated storage devices. In some embodiments, real-time images are stored in a database memory cache (not shown in FIG. 1), from which they may be output to operator display 112 or a display 146 that is located near the magnet assembly 134 for use by attending physicians. Batch mode images or selected real time images may be stored in a host database on disc storage 148 or on a cloud. When such images have been reconstructed and transferred to storage, the data processing server 124 notifies the data store server 126. The workstation 110 may be used by an operator of the workstation to process or otherwise interact with the images, such as archiving theCT images, producing films, or sending the images via a network to other facilities for analysis and / or further processing.

[0124] System 100 and its components operate as a sensing system that isconfigured to detect a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject during various states, such as a resting state. The lines / arrows connecting the various components shown in FIG.1 comprise data conduits for communication and / or data transfer between the components. For example, all of the devices may be connected to and communicate through the same (e.g., local) network. System 100 is further configured to generate at least one spectroscopic signal representative of a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject during various states. More specifically, system 100 may generate an altered magnetic field within the brain to measure various parameters of the brain. In another suitable embodiment, the sensing system may be a specialized MRI, such as a functional magnetic resonance imaging device that is used to measure a variation in blood flow (hemodynamic response) related to neural activity in the brain or spinal cord (not shown) of the subject. The sensing system may also (or alternatively) include an electrocorticography device having at least one electrode (not shown) to measure at least one voltage fluctuation within the brain or other sensors to measure other desired physiological quantities of the subject. It should be noted that the present disclosure is not limited to any one particular type of imaging and electrical technique or device, and one of ordinary skill in the art will appreciate that the current disclosure may be used in connection with any type of technique or device that enables system 10 to function as described herein.

[0125] In the example embodiment, computing device 110 is configured toreceive at least one signal representative of a plurality of measurements of brain activity fromCT the sensing system components. More specifically, computing device 110 is configured to receive at least one signal representative of an altered magnetic field within the brain of the subject from the sensing system components. Alternatively, computing device 110 may be configured to receive at least one signal representative of at least one voltage fluctuation within the brain from at least one electrode, or signals from other sensors.

[0126] The servers (e.g., 120, 122, etc.) and other memory storage devices(e.g., 148, etc.) may collectively, or in other partial combination, be categorized as a data management system 150 (see dashed line grouping elements 120, 122, 124, 126, 148) in FIG. 1. Data management system may also include any device capable of accessing the same network as the components of system 100, without limitation, other desktop computers, laptop computers, mobile devices, or other web-based connectable equipment. The at least one of the storage / memory devices of data management system 150 includes a database (not shown) that includes previously acquired data of other subjects. In the example embodiment, database can be fully or partially implemented in a cloud computing environment such that data from the database is received from one or more computers (not shown) within system 100 or remote from system 100. In the example embodiment, previously acquired data of the other subjects may include, for example, a plurality of measurements of brain activity that is representative of at least one parameter of a brain of each of the subjects during a resting or task state. The database can also include any additional information of each of the subjects that enables system 100 to function as described herein.

[0127] More specifically, in the example embodiment, data managementsystem 150 transmits the data for the subjects to computing device 110. While the data is shown as being stored in database 52 within data management system 150, it should be notedCT that the data of the subjects may be stored in another system and / or device. For example, computing device 110 may store the data therein.

[0128] During operation, while the subject is in a resting or task state, thesensing system (e.g., via 134) uses a magnetic field to align the magnetization of some atoms in the brain of the subject and radio frequency fields to systematically alter the alignment of this magnetization. As such, rotating magnetic fields are produced and are detectable by a scanner within the sensing system. More specifically, in the example embodiment, the sensing system detects a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject during a resting or task state. The sensing system also generates at least one spectroscopic signal representative of the plurality of measurements and transmits the signal(s) to computing device 110 via designated data conduit(s). Moreover, data of other subjects may be transmitted to computing device 110 from the database via designated data conduit(s) (e.g., the network that the system components are connected to). As explained in more detail below, computing device 110 produces at least one map, such as a functional connectivity (FC) map, for each of the measurements based on a comparison of at least one state data point of the subject and a corresponding data point from the previously acquired data set from at least one other subject (and / or prior data from the same subject). Computing device 110 uses the map to categorize or classify the brain activity in a plurality of networks in the brain. Like numbers are used to indicate like elements (e.g., 110 / 210).

[0129] Various embodiments may include more, fewer, and / or differentcomponents, features, techniques, and the like than those described above.

[0130] FIG. 2 is a block diagram of computing device 210. In an exampleembodiment, computing device 210 includes a user interface 252 that receives at least oneCT input from a user, such as an operator of the sensing system of system 100 (shown in FIG. 1). User interface 252 may include a keyboard 214 that enables the user to input pertinent information. User interface 252 may also include, for example, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad, a touch screen), a gyroscope, an accelerometer, a position detector, an audio input interface (e.g., including a microphone), and / or VR headset (none of which are shown).

[0131] Moreover, in the example embodiment, computing device 210includes a presentation interface 254 that presents information, such as input events and / or validation results, to the user. Presentation interface 254 may also include a display adapter 256 that is coupled to at least one display device 212. More specifically, in the example embodiment, display device 212 may be a visual display device, such as a cathode ray tube (CRT), a liquid crystal display (LCD), an organic LED (OLED) display, and / or an “e-ink” display. Alternatively, presentation interface 254 may include an audio output device (e.g., an audio adapter and / or a speaker) and / or a printer.

[0132] Computing device 210 also includes a processor 216 and a memorydevice 218. Processor 216 is coupled to user interface 252, presentation interface 254, and to memory device 218 via a system bus 258. In the example embodiment, processor 216 communicates with the user, such as by prompting the user via presentation interface 254 and / or by receiving user inputs via user interface 252.

[0133] In the example embodiment, memory device 218 includes one or moredevices that enable information, such as executable instructions and / or other data, to be stored and retrieved. In the example embodiment, memory device 218 stores, without limitation, application source code, application object code, configuration data, additional input events, application states, assertion statements, validation results, and / or any other typeCT of data. Computing device 104, in the example embodiment, may also include a communication interface 230 that is coupled to processor 214 via system bus 220. Moreover, communication interface 230 is communicatively coupled to sensing system of system 100 and to data management system 150 (shown in FIG.1).

[0134] In the example embodiment, processor 214 may be programmed byencoding an operation using one or more executable instructions and providing the executable instructions in memory device 218. In the example embodiment, processor 214 is programmed to select a plurality of measurements that are received from sensing system components of system 100 of brain activity that is representative of at least one parameter of the brain of the subject during a resting state. The plurality of measurements may include, for example, a plurality of voxels of at least one image of the subject's brain, wherein the image may be generated by processor 216 within computing device 210. The image may also be generated by an imaging device (not shown) that may be coupled to computing device 210 and sensing system, wherein the imaging device may generate the image based on the data received from sensing system and then the imaging device may transmit the image to computing device 210 for storage within memory device 218. Alternatively, the plurality of measurements may include any other type of measurement of brain activity that enables system 100 to function as described herein.

[0135] Processor 216 may also be programmed to perform a correlationanalysis. More specifically, in the example embodiment, processor 216 may be programmed to compare at least one data point from each of the plurality of measurements with a corresponding data point from a previously acquired data set from at least one other subject (and / or prior data from the same subject). For example, processor 216 may be programmed to compare a resting state data point from each selected voxel from an image of the subjectCT with a corresponding data point that is located within the same voxel of the previously acquired data set of the other subject. Processor 216 may also be programmed to produce at least one map (not shown in FIG. 2) of the brain of the subject, such as a functional connectivity map, for each of the plurality measurements. The map is based on the comparison of the resting state data point and the corresponding previously acquired data point. The map, for example, may illustrate the location within the brain of a measured brain activity. Processor 216 may be programmed to produce the map by using the various compared data points in a known algorithm to calculate a plurality of outputs, such as, for example, at least one output vector. Processor 216 may also be programmed to categorize or classify the measured brain activity in a plurality of networks in the brain based on the map. For example, processor 216 may be programmed to categorize the measured brain activity to a particular neural network of the brain of the subject based on the location of the measured brain activity on the map of the subject's brain.

[0136] During operation, as the subject is in a resting state, sensing systemdetects a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject. Sensing system transmits at least one signal representative of the measurements to computing device 210 via system bus 260 (e.g., a data conduit). More specifically, the signals are transmitted to and received by communication interface 256 within computing device 210. Communication interface 256 then transmits the signals to processor 216 for processing and / or to memory device 218, wherein the data may be stored and transmitted to processor 216 at a later time. Processor 216 may generate an image of the plurality of measurements. Alternatively, sensing system may transmit the signals to an imaging device (not shown), wherein an image of the measurements may beCT generated. The image may then be transmitted to computing device 210, wherein the image is stored within memory device 218 and transmitted to processor 216 for processing.

[0137] Moreover, data of other subjects may be transmitted to computingdevice 210 from database of data management system 150 (shown in FIG.1) via a network. More specifically, the data may be received by communication interface 256 and then transmitted to processor 216 for processing and / or to memory device 218, wherein the data may be stored and transmitted to processor 216 at a later time. Computing device 210 may obtain the data at any time during operation.

[0138] In the example embodiment, computing device 210 produces at leastone map for each of the plurality of measurements received. More specifically, processor 216 first selects each of the plurality of measurements, received from sensing system. For example, in the example embodiment, processor 216 selects each of the voxels from the image. Alternatively, processor 214 may select any other types of measurements for brain activity that enables system 100 to function as described herein. Moreover, a user may see the image on the computing device 210, via presentation interface 254, and select the measurements, such as voxels, via user interface 252.

[0139] When each of the measurements has been selected, processor 214 thenperforms a correlation analysis. More specifically, processor 214 compares at least one data point from each of the selected measurements with a corresponding data point from a previously acquired data set from at least one other subject, wherein computing device 104 obtained the data set from database 110. For example, processor 214 may compare at least one resting state data point from each selected voxel of the image of the subject with a data point that is located within the same voxel of the previously acquired data set of at least one other subject.CT

[0140] When processor 216 has completed the correlation analysis, processor216 then produces at least one map (not shown in FIG.2) of the brain of the subject, such as a functional connectivity map, for each of the measurements. More specifically, processor 216 produces a map of the brain of the subject based on each of the comparisons of each of the resting state data points and the corresponding previously acquired data points. The map, for example, may illustrate the location within the brain of a measured brain activity. Processor 216 then categorizes or classifies the measured brain activity in a plurality of networks in the brain based on the map. For example, based on the location of the measured brain activity in the map, processor 216 categorizes the measured brain activity to a particular neural network of the brain of the subject. The map may be presented to the user via presentation interface 254. Moreover, a textual representation and / or a graphical output for the various categorizations may also be presented to the user via presentation interface 254.

[0141] In other embodiments, the computing device may include more,fewer, and / or different components, features, techniques, and the like than those described above.

[0142] FIG. 3 is flow diagram of an example method 300 for mapping ofbrain activity of a brain of a subject using system 100 (shown in FIG. 1). At step 302, the applicable sensing component(s) of system 100 detect a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject during a certain state (e.g., resting or task). At step 304, the applicable sensing component(s) of system 100 transmit at least one signal representative of the measurements to a computing device 110 / 210 (shown in FIGS. 1 and 2). At step 306, the signals are received by a communication interface 256 (shown in FIG.2). At step 308, the measurements are selected by a processor 116 / 216 (shown in FIGS. 1 and 2). At step 310, at least one data point fromCT each of the measurements is compared with a corresponding data point from a previously acquired data set (e.g., from at least one other subject). At step 312, at least one map for each of the measurements is produced based on the comparison of the resting state data point and the corresponding previously acquired data point. At step 314, the brain activity is categorized in a plurality of networks in the brain based on the map. At step 316, the map and / or an output for the categorization is / are displayed to a user, via a presentation interface 254 (shown in FIG. 2). In other embodiments, the method for mapping may include more, fewer, and / or different steps than those described above.

[0143] The embodiments of the system and method for task-less mapping ofbrain activity using resting / task state data of a brain of a subject, as described herein, were used in the following examples.

[0144] Example 1- Psychiatric Drugs

[0145] In example 1, the techniques described herein are used to test theimpact on the brain of various drugs. One group of drugs of interest include psychiatric drugs, including but not limited to psilocybin, using other drugs such as methylpheidate as a control for comparison purposes. FIG. 4 illustrates an example of a PIDT protocol 400 using a PICOT (Participants, Intervention, Control, Outcomes, Timing) format which can be used for any variety of drugs, but in this case used with respect to psychiatric drugs. This study recruited healthy adults with previous psychedelic exposure. Participants were enrolled in a cross-over study design using a precision functional mapping (PFM) approach. Key inclusion and exclusion are included, see methods section for complete list of criteria. Outcomes included subjective experiences, cognitive flexibility, and extensive MRI (including structural, task-based, resting state, and diffusion). MRI sequences were performed before the first drug, during the first and second drug, between the first and secondCT drug, and after the second drug. A subset of participants also wore pulse oximeter and respiratory belt throughout the entire study for physiological assessments. Four out of seven participants completed a replication protocol.

[0146] FIGS. 5A-5D illustrate individual-specific functional connectivitychange maps for drug sessions. FIG. 5A shows individual participant methylphenidate (MTP) and psilocybin (PSIL) FC change maps 500. The left most column shows individuals’ functional networks. The right three columns show FC change maps, generated by calculating Euclidean distance from baseline seedmaps for each vertex. For each session the total score on the Mystical Experience Questionnaire (MEQ: out of a maximum of 150) is given in the upper right corner. P5 had an episode of emesis 30 minutes after drug ingestion during PSIL2. In FIG.5B, a relationship between whole brain FC change and subjective drug experience (MEQ score) is plotted (P = 3.5 x 10-6) in plot 502. Each dot represents one drug session (psilocybin or methylphenidate). In FIG.5C, a relationship between FC change and MEQ (r2) is mapped across the cortical brain surface and presented in map 504. FIG.5D is a series of plots 506 illustrating a comparison of MEQ score (y-axes) to global desynchronization (top left; NGSC change, drug minus baseline), head motion (bottom left; framewise displacement (FD) in mm), heart rate change (top right; drug minus baseline), and respiratory rate change (bottom right; drug minus baseline), for all drug sessions. Statistics (rho, P) are based on Pearson correlation. In the case of Δ NGSC, statistics are reported before and after the removal of an outlier point (> 3 SD lower than mean).

[0147] FIGS. 6A-6C illustrate a comparison of PIDT drug effect maps toneurotransmitter receptor / transporter maps. PIDT drug effects are measured with functional connectivity, change (Euclidean distance) is calculated across the cortex. FIG. 6A shows map 600 illustrating psilocybin (PSIL) and methylphenidate (MTP) associated FC change.CT FIG. 6B shows map 602 illustrating PET-based maps of 5HT-2A receptor and norepinephrine transporter. FIG.6C shows plots 604 illustrating a comparison of PIDT drug effect maps to Neurotransmitter receptor / transporter maps.

[0148] PIDT offers an approach for screening novel psychopharmacologicalcompounds that is substantially more sensitive than existing tools (resting fMRI, task fMRI, EEG). It interrogates the mechanisms of novel and established compounds, as well as testing for inter-individual differences in drug response to assess 1) brain penetration, 2) target engagement of specific circuits (e.g., fronto-striatal reward / motivation circuit) and neurotransmitter systems (using PET-based receptor maps), and 3) similarity to existing drug classes (based on drug biomarkers from public fMRI datasets), allowing for the assessment of drug effects with greater reliability in smaller sample sizes.

[0149] Psilocybin’s acute and persistent effects on brain networks, aprecision imaging drug trial.

[0150] As described herein, psilocybin is a psychedelic drug and a promisingexperimental therapeutic many psychiatric conditions. Precision functional mapping (PFM) combines densely repeated resting state fMRI sampling and individual-specific network mapping to improve signal-to-noise ratio and effect size of functional connectivity. Data is presented from a randomized cross-over study in which PFM was used to characterize psilocybin’s acute and persistent effect on the brain networks. Seven healthy volunteers (mean age 34.1 years, SD=9.8; n=3 females, n=6 Caucasians) underwent 1) baseline imaging, 2) two scans 60-90 minutes after psilocybin or methylphenidate ingestion, and 3) longitudinal imaging for up to two weeks after drug exposure. Four individuals also participated in an open-label psilocybin replication protocol over 6 months later. This dataset includes extensive resting state (using advanced high-resolution fMRI), task fMRI,CT structural, and diffusion basis spectral imaging as well as assessments of subjective experience and personality. This unique dataset can be released as a resource for neuroscientists to study the acute and persistent effects of psilocybin (or methylphenidate) on brain networks.

[0151] Psilocybin is a serotonin receptor 2A (5-HT2A) agonist that hasshown positive, rapid benefits in clinical trials for numerous psychiatric indications, including depression, end-of-life anxiety, obsessive compulsive disorder, eating disorders, and alcohol use disorder. These trials have found immediate (hours to days) and persistent (weeks to months) benefit from a single dose, making psilocybin a promising treatment with broad applications in psychiatry.

[0152] The neurobiological mechanisms of psilocybin’s immediate andpersistent effects remain an active and important area of investigation. Effects include improvement in positive mood and the psychological trait of openness. The discussion on ‘therapeutic mechanisms’ of psychedelic drugs has focused on the acute subjective psychological experience, including the profound ‘mystical experience’ frequently reported by individuals undergoing psychedelic therapy. However, to understand how these drugs produce a persistent clinical response (i.e., post- acute drug effects), it is necessary to understand circuit adaptations that underlie psychological drug effects.

[0153] Functional magnetic resonance imaging is a tool to explore the effectsof psychedelic compounds on specific brain circuits in humans. Recent studies have reported decrease in cerebral blood flow and functional connectivity in the default mode network. It has also been noted that overall BOLD signal power decreases across the cortex. Yet, psychedelic brain imaging still faces challenges and limitations. Individual variability inCT treatment-related alteration of neurovascular coupling, head motion, and autonomic hyperarousal are potential confounds in prior fMRI studies of acute psychedelic effects.

[0154] Precision functional mapping (PFM) is a method that mitigates manyof the aforementioned limitations. PFM combines dense repeated sampling with individual- specific analysis to overcome many limitations of conventional resting fMRI. PFM has revealed new details of cortical and subcortical brain network organization obscured by conventional group-average techniques and large within-subject changes in network organization following an intervention.

[0155] A PFM dataset with longitudinal brain mapping, psychological, andphysiological measures to characterize the acute and persistent changes in brain activity and functional connectivity in healthy young adults immediately following a 25 mg dose of psilocybin or active placebo (40 mg of methylphenidate or MTP) (as shown in FIG.1). Seven individuals underwent dense repeated sampling with resting state and task-based MRI before, after, and during psilocybin exposure. To assess variability of psilocybin’s effects within and across individuals, four participants underwent a second dose after six months following a replication protocol.

[0156] In addition to employing PFM in this study, Framewise IntegratedReal-time MRI Monitoring [FIRMM], multi-echo EPI imaging, Nordic de-noising, physiological monitoring (pulse-ox, respiratory belt), and regression to provide state-of-the- art data quality and spatial resolution in longitudinal connectomics measurements were implemented. Using this dataset, it was recently reported that 1) 25mg of psilocybin led to acute disruptions in functional connectivity in the cortex and subcortex, and these changes were three-times greater than active-placebo-based functional connectivity changes; and 2) persistent decreases in functional connectivity were observed between the anteriorCT hippocampus and cortex (default mode network), lasting for weeks and normalizing after six months.

[0157] Study Design And Rationale: A randomized controlled cross-overstudy (such as shown in FIG. 4) was designed using PFM to evaluate individual-level brain connectivity pre-, post-, and during psilocybin or MTP exposure. Healthy, younger adults were selected as the study population to reduce confounding by psychiatric diagnoses or medications. To reduce negative effects of drug exposure on participant experience, anticipatory anxiety, and imaging quality, participants were required to have had at least one previous lifetime psychedelic exposure (e.g., psilocybin, mescaline, ayahuasca, LSD) and no use in the six months before participating in the study. For example, Usona Institute, a United States non-profit medical research organization with FDA authorization, produces medical- grade psilocybin, which can used in studies to study the drug.

[0158] Participants underwent two separate imaging sessions during activedrug exposure, one with psilocybin, 25mg and one with methylphenidate, 40mg. Drug sessions can be spaced 1-2 weeks apart. MTP was selected as the active control condition to simulate the cardiovascular effects and physiological arousal (i.e., controlling for dopaminergic effects on blood pressure and heart rate) associated with psilocybin. After 6 months of completing the initial study, participants were invited to participate in a replication protocol and receive another dose of psilocybin 25mg in addition to imaging.

[0159] Drug sessions were facilitated by two clinical research staff whocompleted the certain training (e.g., Usona Institute facilitator training program). The role of the study facilitators was to build a rapport with the participant throughout the study, prepare them for their drug dosing days, and to monitor participant safety during dosing day visits.CT The facilitation pair consisted of an experienced clinician (lead clinical facilitator) and a trainee (co-facilitator).

[0160] A minimum of three separate non-drug imaging sessions werecompleted before, between and after drug sessions. Dosing day imaging sessions were started 60 minutes following drug administration, and typically lasted ~120 minutes, spanning the period of peak blood concentration. The number of non-drug sessions was dependent on availability of the participant, scanner, and scanner support staff. Scans occurred at the same time of day for each participant, regardless of drug vs. non-drug day.

[0161] Inclusion / Exclusion Criteria: Healthy adults between the ages of 18and 45 years with previous psychedelic exposure (> 6 months prior to enrollment) were recruited via campus-wide advertisement and colleague referral. Participants were enrolled for a certain period of time (e.g., ~14 months). Exclusion criteria included: contradictions to MRI scanning (bone hardware, implantable devices) contraindications to psilocybin exposure (e.g., hypertension, metabolic or cardiovascular disease, or pregnancy); diagnosis of psychiatric condition (including substance use disorders); current use of any psychotropic medication (including TCAs, SSRIs, SNRIs, MAOIs, antipsychotics, lithium, valproate, tramadol and others); previous adverse reactions to psychedelics; immediate family history of any psychotic disorder (e.g., schizophrenia spectrum or psychotic mood disorders). Of note, participants who met entry criteria at screening but were taking an antidepressant or antidepressant plus an augmenting agent (e.g., atypical antipsychotic, lithium, a second antidepressant) and did not experience any response to these medications were eligible to enter a medication taper in which psychotropic medications were withdrawn under the supervision of a study psychiatric medical provider. After two weeks of medication taper (4 weeks if on fluoxetine), these participants were then eligible to undergo baseline assessment.CT

[0162] Study Measures: After providing informed consent, participantsunderwent screening tests, including an electrocardiogram, urine drug screen, basic metabolic panel, complete blood count and a urine pregnancy test. A study physician acquired a medical history, performed a physical exam, and reviewed labs to ensure that participants did not have exclusionary health conditions. Once medically cleared, participants were scheduled for imaging sessions and drug dosing days to ensure appropriate timing of pre-, post-, and dosing day scans (see ‘Imaging visits’ and ‘Medication visits’ below). FIG.7 illustrates the study’s schedule of activities 700.

[0163] Scales and Questionnaires: Assessments were conducted before,during, and after treatment sessions. Subjective data and objective measures of the medication experiences were collected, as well as a personality survey and safety parameters. Assessments obtained are described below in items 1-5:

[0164] 1) International Personality Item Pool-Five-Factor Model (Mini-IPIP): The Mini-IPIP is a 20-question staff-administered survey to determine the Big Five factors of an individual’s personality: extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience. The Mini-IPIP was administered at the following time points: baseline, post-drug one, post-drug two.

[0165] 2) Mystical Experience Questionnaire (MEQ): The MEQ is a 30-itemself-reported questionnaire that measures mystical experiences. It measures four factors: a) mystical (freedom from boundaries of one’s personal self and a feeling of unity to what is greater than one’s self), b) positive mood (sense of awesomeness or awe), c) transcendence of time and space (being outside of real of time), d) ineffability (sense that experience cannot be described well in words). The MEQ was administered at the following time points:CT baseline, post-drug one, post-drug two. The MEQ was administered on the same day of drug administration.

[0166] 3) Challenging effects questionnaire: This self-report questionnaireevaluates challenging experience with psychedelics (panic or fear, grief, isolation, feeling as though one is dying, feeling insane, physiological distress, and paranoia). It will be administered as part of the pre-screening phone assessment to determine eligibility, and will be repeated at both 24-hour post-dose phone calls.

[0167] 4) Persisting effects questionnaire: This questionnaire encompassesthe Mystical Experiences Questionnaire (MEQ30), The MEQ30 is a self-report questionnaire that evaluates discrete mystical experiences based on 4 factors: unity, positive mood, transcendence of time and space, ineffability. It is a subset of the status of consciousness questionnaire and has been validated as a means of assessing psilocybin-induced mystical experiences. This measure has been used in many studies of hallucinogenic compounds, and is sensitive to the effects of psilocybin immediately following the treatment session.

[0168] 5) Emotional breakthrough inventory: This is a 6-item survey thatwill be used to assess if the psychedelic experience produced an emotional breakthrough.

[0169] Imaging visits: For each subject, all imaging visits were scheduled atthe same approximate time of day (morning, afternoon, evening) to minimize time-of-day effects. The goal was to have visits occurring on days 1-2, 3-4, 5-7, and ~14 after each drug. For replication protocol visits, the goal was to have at least one MRI visit before and one visit after psilocybin dose.

[0170] MRI Acquisition: MRI scanning was conducted on a Siemens PrismaMRI scanner (Siemens, Erlangen, Germany). Imaging sessions are illustrated in FIG. 4.CT Imaging sessions included a combination of structural MRI (T1- and T2-weighted images), two or more resting state fMRI (513 frames 15 minutes each), and two sessions of task fMRI (233 frames, 6 minutes, and 50 seconds each). Structural scans were obtained at 0.9mm isotropic resolution. BOLD multi-echo 5 (TEs: 14.20 ms, 38.93 ms, 63.66 ms, 88.39 ms, 113.12 ms), TR 1761ms, flip angle = 68 degrees, and in-plane acceleration (IPAT / grappa) = 2. This sequence acquired 72 axial slices (144mm coverage). For resting state and task MRI, three frames at the end were utilized to estimate electronic noise. During all imaging sessions, Framewise Integrated Real-time MRI Monitoring (FIRMM) was used to provide feedback on head motion during scanning and eye tracking was used to ensure that participants remained awake. Participants were provided with feedback about head motion between scans.

[0171] Resting state fMRI: During resting state fMRI scans, participantswere instructed to visually fixate on a white crosshair presented on a black background. An aim was to acquire 45 minutes of usable data to measure cortico-subcortical networks. Given that aggressive data cleaning has been shown to remove 25% of the data, 60 minutes of resting state fMRI was obtained.

[0172] Task based fMRI: During some MRI visits, participants completedtwo event-related fMRI task scans. This was a suprathreshold auditory-visual matching task in which participants are presented with a naturalistic visual image (duration 500 ms) and coincident spoken English phrase and are asked to respond with a button press to indicate if the image and phrase are ‘congruent’ (for example, an image of a beach, and the spoken word beach) or ‘incongruent’. Both accuracy and response time of button presses were recorded. Each trial was followed by a jittered inter-stimulus interval optimized for event-CT related designs. Task fMRI scans employed the same sequence used in resting fMRI, included 48 trials (24 congruent, 24 incongruent), and lasted 410s each.

[0173] Diffusion MRI and Diffusion Basis Spectrum Imaging (DBSI): Giventhat increasing evidence suggests that psychedelics work as potent anti-inflammatory agents, coupled with the key role inflammation plays in psychiatric illnesses, diffusion MRI was acquired using sequences optimized for diffusion basis spectrum imaging (DBSI). This included acquisitions with b-values of 1500 and 3000, 102 directions, each with 99 directions (TR-3500ms, TE=83ms) – identical to sequences being used in ABCD. DBSI models inflammation-associated cellularity (DBSI-restriction fraction, RF) and vasogenic edema (DBSI-hindered fraction, HF) while accounting for partial volume effects resulting from cerebrospinal fluid contamination and crossing fibers. Diffusion scans on psilocybin were omitted during MRI sequence if there were time constraints or to mitigate a participant’s comfort level.

[0174] Physiological assessments: A subset of participants wore a Siemensbuilt-in respiratory belt and a pulse oximeter during all MRI sessions. Prior to MRI scanning, belts were placed around the individuals’ ribs, with the sensor just below the ribcage. A pulse oximeter was placed on the non-dominant index finger. All data from respiratory belt and pulse oximeter were preprocessed using a toolbox (e.g., PhysIO) to extract pulse- and respiration-based metrics, fMRI-aligned timecourses, as well as physiological regressors for nuisance regression of fMRI data.

[0175] Preparatory sessions: In addition to undergoing MRI sessions,preparatory sessions were held one or two days before drug administration. Sessions were held in a dedicated research treatment room where the study drug was also administered. The purpose of preparatory sessions was to build a therapeutic alliance between facilitators andCT participants. The participant’s personal history, developmental stage, current life situation, and intentions for and expectations of drug sessions were reviewed. Preparatory sessions occurred per Usona facilitator training guidelines.

[0176] Medication Visits - Drug administration: Participants received either25mg of psilocybin or 40mg of methylphenidate. Both facilitators and subjects were blinded. Medications were taken with water and lemon ginger tea. Mindfulness occurred for 10 minutes, and then participants were invited to lie on the sofa with eye shades as well as headphones and a curated music play list to induce calm and reduce external stimulation (see plot 800 in FIG.8). One hour after drug administration, participants were transported to the MRI suite wearing headphones and eye shades. Following the MRI, participants were transported back to the dedicated testing room and encouraged to direct their attention internally until subjective drug effects were completely resolved. Study facilitators then completed a modified post-session integration checklist with participants. Regardless of drug received, dosing sessions were 7-8 hours in length.

[0177] Medication Visits - Patient safety and monitoring: As both psilocybinand methylphenidate impact cardiac and vascular functioning, heart rate and blood pressure were measured at regular intervals during dosing days (e.g., 30, 60, 90, 120, 180, 240, 300, 360, 420, and 480 minutes after drug ingestion). Subjects were also briefly queried about adverse effects during vital signs monitoring using an adverse events checklist employed in prior studies; time of onset, time of resolution, event description, and whether intervention was required. Rescue medications (risperidone for agitation, lorazepam for anxiety and niacin for chest pain) were available as needed. The Columbia Suicide Severity Rating Scale (C-SSRS) was used to assess for suicidal ideation and behavior during drug exposure. Participants had access to a physician who was physically present throughout dosing day.CT

[0178] Treatment guess: After each medication dosing, participants wereasked to guess whether they received psilocybin or MTP.

[0179] Data management: De-identified assessment scores, raw data fromstructural MRI and fMRI scans was uploaded into the Central Neuroimaging Data Archive (CNDA).

[0180] rsfMRI processing and surface projection: Preprocessing of fMRIdata included: 1) removal of thermal noise using NORDIC (a local PCA approach in which temporal components of an fMRI signal that are indistinguishable from Gaussian noise are eliminated); 2) sync interpolation to correct asynchronous slice acquisition; 3) compute affine spatial registration of all volumes within a run; 4) exclusion of odd / even slice intensity differences from interleaved acquisition (debanding); 5) compute affine spatial registration across fMRI runs; 6) compute an run volume mean (of all low-noise volumes); 7) computation of field distortion on the basis of a spin echo field maps using FSL top-up; and 8) gain field correction using FSL fast (computed on the run volume mean). Resampling in MNI1522mm3 atlas space was accomplished for all echoes in one step combining (i) motion correction; (ii) distortion correction; (iii) gain field correction; (iv) linear registration of average volumes across visits; and (v) non-linear MNI152 atlas registration via the fsl fnirt. The multi-echo data in MNI152 space then were combined using the weighted summation approach. After cross-modal registration, the data underwent several pre-processing steps. These included: FreeSurfer segmentation for tissue-based regression, elimination of signals with false variance, temporal filtering to include 0.009-0.08Hz bands, and frame censoring. False signals included: six parameters obtained by rigid body correction of head motion, extra-axial noise, white matter, and ventricles. Frame censoring was performed on all rsfMRI data at FD>0.30. After preprocessing, blood-oxygen level dependent (BOLD) dataCT was analyzed using each participant’s cortical surface and nuclei. Voxels with high coefficients of variation were excluded from volume to surface mapping. Resting data was then sampled into a set of about 385 brain regions using a previously validated parcellation of cortical (324) and subcortical (61) brain areas. These regions can be used for functional connectivity analysis.

[0181] Data analysis: The primary goal of this study was to test significantdifferences between baseline and psilocybin scans using a within-subject design, followed by testing if differences replicate across participants. For the present data resource, descriptive analytics were conducted on the participant population using behavioral assessments and physiological data. Imaging quality was also assessed via assessing whole brain network similarity matrices and brain modularity. Physiological measurements were recorded at 400 Hz with clocked timestaps and extracted using a toolbox (e.g., PhysIO Toolbox) as raw plethysmography signals. All signals were visually inspected and entire sessions were rejected if significant clipping occurred or if signals were noisy, without regular oscillations expected in pulse or respiratory plethysmography. Pulse rate and respiratory rate were calculated by first determining the peaks and troughs of the wave using Matlab’s findpeaks function, then calculating instantaneous pulse rate or respiratory rate between each peak. This custom rate calculation agreed with PhysIO toolbox for respiratory rate but offered higher temporal resolution with respect to pulse rate. Physiological regressors for fMRI analysis were created using PhysIO Toolbox.

[0182] FIG. 9 shows a similarity matrix 900. For the similarity matrix of FIG.9, correlation calculations were done at the parcel level. The effects of condition (baseline, psilocybin, between, MTP, after), and participant were directly examined by calculating the similarity between each functional network matrix (i.e., Pearson correlation between theCT linearized upper triangles of the parcellated FC matrix between a pair of 15 minute fMRI scans), creating a second-order ‘‘similarity” matrix. Spectral power in the infra-slow band (0.009 to 0.06 Hz) was evaluated in each parcel infra-slow as the cosine Fourier transform cortical power was taken by first computing spectral power in each parcel and for each rsfMRI session using the BOLD signal autocovariance function method computed in a manner that allows for motion censoring. To allow for missing frames (due to motion censoring), infraslow spectral power (<0.06 & F>0.009) was averaged within each parcel and then average a crossover cortical parcels to generate a single value for each session.

[0183] Modularity was calculated using the equation:2 ^^ = − ^^^^^^where the network (including into a set of nonoverlapping modules M, and euv is the proportion of all links that connect nodes in module μ with nodes in module ν. Matlab code can be utilized to calculate modularity and other graph measures as shown herein. Linear mixed effects models with random effects for subject were used to test the effect of drug condition (psilocybin, MTP, baseline, and post- psilocybin) on modularity and BOLD infraslow power.

[0184] Results - Participant demographics and characteristics: Seven adultsconsented and completed the study (mean age of 34.1 years, SD=9.8). FIG. 10 shows participant disposition diagram 1000. 24 individuals were screened for the study, 7 consented, and 7 completed the replication protocol. Three individuals were female, and six were Caucasian. Additional baseline characteristics of the study population are included in table 1100 of FIG.11.CT

[0185] Results - Subjective experiences and adverse events: Overall, self-reports of mystical experiences on psilocybin vs. methylphenidate (active placebo) were significantly higher (p<0.05) across all four mystical factors. Average mystical, positive mood, transcendence, and ineffability factor scores (out of 5 points) were: 3.4 vs. 0.61, 3.8 vs. 1.2, 3.8 vs. 0.33, and 3.7 vs. 0.86 for psilocybin vs. placebo, respectively (see FIG. 8). FIG. 8 illustrates Mystical Experiences Questionnaire (MEQ) results. Data presented are mean and standard deviations of measurement in all seven participants. Participants were asked to look back on their drug experience on the same day of receiving PSIL or MTP and rate the degree to which they experienced criteria across five factors (transcendence, positive mood, ineffability, mystical) from a scale of 0 to 5. A score of 0 indicates did not experience at all, and a score of 5 is given to extremely experienced (more than any other time in their life). Asterisks: *p<0.05, two-tailed t-test.

[0186] One participant (P3) reported a higher MEQ score on MTP vs.psilocybin. Specifically, this participant reported higher ineffability (4.2 vs. 0), transcendence (1.5 vs 1), and mystical experience (2.8 vs. 1.1) on MTP vs. psilocybin. Positive mood was scored the same on both MTP and psilocybin. When this participant came back for the replication protocol, she obtained higher mystical experience scores: 4.1 on ineffability, score 5.0; transcendence, 2.1; mystical experience, 4.4, and positive mood, 4.1.

[0187] All participants but P3 correctly guessed when they receivedpsilocybin and MTP on participant blinding questionnaires (e.g., stated “positive I received [active or placebo] drug”). Individuals stated that the multiple baseline scans prior to drug exposure improved the ease of the psychedelic experience in the MRI scanner.

[0188] There were no adverse effects that occurred while participants wereon psilocybin or MTP and undergoing MRI. Four to six hours after psilocybin ingestion, sideCT effects reported included: 14% headache [n=1], 28% nausea [n=2], 28% anxiety [n=2]. No participant required rescue medications for severe side effects.

[0189] MRI sequences and head motion: Because psychedelics increase headmovement, training participants beforehand to remain still and comfortable in the scanner was critical for the collection of behaviorally relevant brain-wide signals. Thus, participants underwent 4-9 non-drug MRI visits prior to their first drug imaging session and FIRMM was used to provide participants feedback about head motion at each session. Consequently, compared with prior psychedelics imaging datasets, participants were able to provide a greater quality (see FIGS.12A, 12B) and quantity (see FIG.12C) of data while still achieving a mystical experience on psilocybin (see FIG.5). See baseline head motion plot 1200 in FIG. 12A, on-psychedelic head motion plot 1202 in FIG.12B, and on-psychedelic data plot 1204 in FIG. 12C. Combining the protocol and replication visits, an average of 39.4(SD = 12.2) “off drug” 15-minute resting state scans were obtained from 7 participant (see FIG. 8). An average of 4.7 (SD 2.0) 15-minute resting state scans on psilocybin per participant and 2.7 (SD = 0.8) 15-minute resting state scans on MTP were also obtained. In this study, 86% (n=6 out of 7) had at least one usable 15-minute resting scan on psilocybin (i.e., framewise head motion was greater than 0.2mm). Only participant P2 did not. By comparison to prior psilocybin dataset (see FIGS.12A-12C), a study of psilocybin (ref), 46% (n=7 out of 15) had any usable resting state data (any scan with average framewise head motion less than or equal to 0.2mm). In known studies of LSD, 75% (n=15 out of 20) had one resting state scan meeting framewise head motion requirements.

[0190] FIGS. 12A and 12B illustrate average head motion (framedisplacement, FD in millimeters) for each scan off (FIG.12A) and on (FIG.12B) psychedelic drug was compared between this dataset and prior psychedelic fMRI studies. In FIGS.CT 12A / 12B, the dotted line at 0.2mm represents a reasonable cutoff for exclusion of a session. Sessions with average FD of 0.2mm or lower are considered “usable”. Lower and upper quartiles of box-plots were calculated as follows: Q1−1.5×IQR, Q3+1.5xIQR, respectively. Asterisks indicate significant (* p<0.05, **p<0.005, unpaired t-test, two tailed) difference in head motion compared to two other highly cited datasets (Psil-2012 and LSD-2016). FIG. 12C show the totality of resting state data (usable and unusable) on psychedelic in minutes.

[0191] Physiological data: Pulse and respiratory rates obtained during fMRIscans were analyzed within and across subjects and conditions (see plot 1300 in FIG. 13). Within subject, baseline sessions (before drug 1) were used as the control condition to assess for both effect of drug and after-drug on pulse and respiratory rate. Changes were observed in drug conditions (MTP, PSIL) but no consistent changes were observed in off-drug conditions (between, after). Thus, for group-level analysis, non-drug sessions were combined and a mixed linear effects model was used to assess effects of MTP and PSIL on the physiological parameters. The average baseline pulse rate was 72 beats per minute (bpm, 95% CI, 68-77) and the respiratory rate was 11 respirations per minute (rpm, 95% CI, 9-13). A mixed linear effects model was used to determine the relative effects of MTP and PSIL on the session means of physiological parameters. On average, MTP was associated with a 16.7 bpm increase in pulse rate (95% CI:11.0-20.3, PLME = 3.12 x 10-10). PSIL was associated with a 21.1 bpm increase in pulse rate (95% CI 16.6-25.6, PLME = 4.04 x 10-17). No significant difference in HR was observed between MTP and PSIL (P = 0.399).

[0192] FIG. 13 illustrates in plot 1300 heart rate (HR, beats per minute) orrespiratory rate (RR, breaths per minute) for each participant based on drug condition for each participant (participants who came back from replication are also included in illustration). Red dots are representative of physiological factors on psilocybin, blue dots areCT physiological values on active-placebo (MTP, MTP), and baseline data is represented by an empty-filled circle. Asterisks adjacent to each circle denote significance value of p<0.05 compared to baseline.

[0193] Similarity matrix and modularity: To explore macro trends in the data,whole-brain connectome similarity within and across participants was visualized (see FIG. 9). The 5x5 red boxes observed along the diagonal demonstrate the overall similarly within- participant across sessions (as indicated by ‘Within Participant’). And the 3x5 red rectangles on the top right and lower left corners represent within participant similarity when participants returned for replication protocol 6-12 months later. The psilocybin condition stands out as substantially less like other conditions (including MTP) in every subject. Interestingly, there is PSIL-PSIL similarity within and across participants (suggested a shared effect of psilocybin.

[0194] As described herein, FIG. 9 illustrates whole brain network similaritymatrix and intra-subject data reliability. Each row and column represents brain networks from one participant in one study condition (baseline, Drug 1, between, Drug 2, after) and each edge represents similarity of functional networks between a pair of conditions. Networks from the replication protocol are shown at the end of the matrix. To the right, two sources of similarity – ‘within participant’ and ‘within class’ (e.g., brain networks from two participants’ PSIL condition) are demonstrated. For example, Participant 1’s whole brain functional networks without and on drug sessions are similar when compared to themselves vs. Participant 4 (see solid black squares in chart). When Participant 1 came back for replication, their whole brain functional networks on psilocybin were highly correlated to their replication session on psilocybin six months later (see dotted box in chart). ParticipantCT 2 was excluded, given a large degree of unusable data (framewise head displacement>0.2mm).

[0195] Network modularity, a measurement of brain-network segregation,was recently shown to significantly decrease in individuals with treatment-resistant depression following treatment with psilocybin. In this dataset, network modularity decreased from baseline during psilocybin (P = 1.8754*10-9) but did not remain significantly changed in the weeks following psilocybin ((FIG.6; P = 0.68). MTP also had no significant effect on modularity (P = 0.85).

[0196] FIGS. 14A-14C illustrate quality and signal metrics for psilocybinPFM dataset. Each dot (represents a resting state scans) is / are shown on the x-axis and red vertical bars depict participants’ scans on psilocybin. FIG.14A shows plots 1400 for cortical infraslow power (0.009-0.06hz). Drug exposure had no correlation with cortical infraslow power. Plot 1402 in FIG.14B shows head motion (also see FIG.6A). Plot 1404 in FIG.14C shows Newmann’s modularity. Overall, modularity was lower on psilocybin (LME model P = 1.8754*10-9) and returned to baseline level after. (LME model P = 0.68).

[0197] Intraparticipant reliability: To assess variability in effects ofpsilocybin within-participant across doses, four participants underwent a second psilocybin dose 6-12 months after their initial dose (including at least one MRI visit before and after dosing). Baseline, during psilocybin, and post-psilocybin rsfMRI, diffusion, structural, and task-based MRI were conducted. When individuals came back for the replication protocol, the second psilocybin dose produced similar whole brain network changes (similarity to replication r= 0.78, similarity across participants r = 0.45, P < 0.001).CT

[0198] As described herein, state-of-art imaging protocols and a novel PFMapproach designed to evaluate the acute and persistent brain effects of psilocybin in healthy younger adults was employed. The resulting dataset is ideally suited to explore a range of questions about the individual differences and shared effects of psilocybin on brain networks.

[0199] This dataset substantially expands the existing body of availablepsychedelics fMRI data. Over 50% of the published literature on psychedelics effects on rsfMRI are based on two data sets. This may be due to the barriers involved in researching psychedelic compounds. To aid other scientists interested in creating similar datasets, study challenges, lessons learned, and solutions to such research are summarized table 1500 in FIG.15.

[0200] This high-quality datasets described herein can be used as a resourcefor neuroscientists to study the effects of psilocybin on neural networks at an individual level.

[0201] Various software packages can be incorporated into the abovepipelines for data analysis, including but not limited to Matlab (e.g., Matlab R2020b), Cifti matlab utilities (including spin test), Connectome Workbench 1.5, Freesurfer v6.2, FSL 6.0, and 4dfp tools.

[0202] Additional Aspects of Example 1 - Psilocybin desynchronizes humanbrain networks

[0203] Additional aspects of Example 1 are described below. A single doseof psilocybin, a psychedelic which acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus. Prior group- averaged resting-state functional MRI (rs-fMRI) studies of acute psychedelic effectsCT suggested global decreases in signal power and functional connectivity. Yet, it remains unclear how brain network changes relate to subjective psychedelic experiences and whether connectivity is altered long-term. Here, individual-specific brain changes with longitudinal precision functional mapping (~18 MRI visits per participant) were tracked. Healthy adults were tracked before, during, and for 3 weeks after high-dose psilocybin (25mg) and methylphenidate (40mg) and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted connectivity in cortex and subcortex, acutely causing more than 3-fold greater FC changes than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolves network distinctions by reducing correlations within and anti-correlations between networks. Psilocybin-driven FC changes were strongest in the default mode network (P < 0.001), which is connected to the anterior hippocampus and is thought to create our sense of space, time, and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience (r2 = 0.81). Performing a perceptual task reduced psilocybin-driven FC changes and desynchronization, suggesting a neurobiological basis for grounding – connecting with physical reality during psychedelic therapy. Psilocybin induced persistent decreases in FC between the anterior hippocampus and default mode network, lasted for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a candidate neuroanatomical and mechanistic correlate of pro-plasticity and therapeutic effects of psychedelics.

[0204] Psychedelic drugs can reliably induce powerful acute changes in theperception of self, time and space via agonism of the serotonin 2A receptor (5HT2A receptor). In clinical trials, a single high dose of psilocybin (25 mg) has demonstrated rapid and sustained symptom relief in depression, addiction and end-of-life anxiety. TakenCT together, these observations indicate that psychedelics should induce potent acute (lasting ~6 hrs) and persistent (24 hr to 21 days after ingestion) neurobiological changes.

[0205] In rodent models, transient activation of the 5HT2A receptors by apsychedelic can alter neuronal communication in 5HT2A-rich regions (e.g. the medial frontal lobe) and induce persistent plasticity-related phenomena. Persistent effects of psychedelics observed days to weeks later include increases in the expression of genes that contribute to synaptic plasticity (c-Fos, BDNF, Arc), and neurite and synapse growth, in vitro, and in vivo. Synaptogenesis in the medial frontal lobe and anterior hippocampus are thought to be key to psilocybin’s neurotrophic antidepressant effects. Yet, inherent limitations of rodent models, and imperfect homology to the human 5HT2A receptor, limit the strength of these assertions.

[0206] Understanding the effects of psychedelics on human brain networksis critical to unlocking their therapeutic mechanisms. In humans, during the ~6 hour duration of action, psilocybin increases glutamate signaling and glucose metabolism, broadly decreases the power of electrophysiological signals reduces hemodynamic fluctuations, and decreases segregation between functional networks. The drivers of these acute changes are poorly understood, particularly in the subcortex.

[0207] Preliminary efforts to identify network changes in the weeks afterpsilocybin have yielded mixed results. Moreover, persistent effects of psilocybin on hippocampal-cortical circuits have yet to be characterized in humans. The ventromedial prefrontal cortex and anterior / middle hippocampus are functionally connected to the default mode network. Increased FC between the hippocampus and default mode network has been associated with depression symptoms and decreased FC is associated with treatment. TheseCT 5HT2A receptor rich and depression associated default mode regions are candidates for mediating the neurotrophic antidepressant effects of psychedelics.

[0208] Precision functional mapping utilizes dense repeated fMRI samplingto reveal the timecourse of individual-specific intervention-driven brain changes. This approach accounts for inter-individual variability in brain networks and capitalizes on the high stability of networks within individuals from day-to-day. Using precision functional mapping, individual-specific acute and persistent brain changes following a single high dose of psilocybin were observed.

[0209] Healthy young adults received psilocybin (PSIL) 25 mg andmethylphenidate (MTP, generic Ritalin, dose-matched for arousal effects) 40 mg 1-2 weeks apart and underwent regular MRI sessions (~18 per participant) before, during, between, and after the two drug doses (see FIGS.16A-16E, FIG.7). FIG.16A illustrates a protocol 1600 similar to that as shown in FIG.4. FIG.16B shows plot 1602 for participant visits data. FIG. 16C shows plot 1604 for head motion data. FIG. 16D shows plot 1606 for study timeline (example participant) data. FIG.16E shows plot 1608 for mystical experience questionnaire data (similar to FIG.8). Dense pre-drug sampling familiarized participants with the scanner and established baseline variability. FIG. 16A: Schematic illustrating the study protocol of the individual-specific precision functional mapping study of acute and persistent effects of psilocybin (single dose: 25 mg). Repeated longitudinal study visits enabled high-fidelity individual brain mapping, measurement of day-to-day variance, and acclimation to the scanner. The open label replication protocol 6-12 months later included one or two scans each of baseline, psilocybin, and after drug. FIG. 16B: timeline of imaging visits for 7 participants. FIG.16C: head motion comparisons across psychedelics studies. Average head motion (FD, framewise displacement, in mm) off and on drug is compared between ourCT dataset and prior psychedelic fMRI studies. Dotted line at FD of 0.2 mm. Asterisk: P < 0.05, two tailed t-test, uncorrected. FIG. 16D: timeline for an example participant (P1). e) Participants reported significantly higher scores on all dimensions of the mystical experience questionnaire during psilocybin (red) than placebo (40 mg methylphenidate; blue).

[0210] FIGS. 17A-17E illustrate acute psilocybin effects on functional brainorganization. Functional connectivity change (Euclidean distance) was calculated across the cortex and subcortical structures. Effects of drug condition were tested with a linear mixed effect (LME) model in N = 6 longitudinally sampled participants (FIGS. 17A and 17B are thresholded at P < 0.05 based on permutation testing with threshold-free cluster enhancement; see unthresholded statistical maps 1800 in FIG.18 and individual FC change maps in FIGS.5A-5D. FIG.17A illustrates maps 1700 showing Psilocybin (PSIL) associated FC change, including in subcortex. FIG. 17B illustrates map 1702 for Methylphenidate (MTP) associated FC change. FIG. 17C illustrates map 1704 for typical day-to- day variability as a control to the drug conditions (unthresholded: not included in LME model). FIG. 17D illustrates plot 1706 for average within-network FC Change in cortex, based on individual-specific networks. Open circles represent individual participants, bars represent average magnitude. Asterisks indicates that FC change is larger in default mode network than other networks (one-sided Pspin < 0.001), as determined by a rotation-based null model (spin test) in which a participant’s networks were iteratively rotated a random amount around the spherical expansion of the cortical surface. FIG.17E illustrates plot 1708 for whole-brain FC change (Euclidean distance from baseline) across conditions. The full FC distance matrix and session labels are shown in FIGS. 19A-19C. FC change for MTP, PSIL, and day-to-day are in comparison to same-participant baseline. White dots indicate median. Asterisks indicate P < 0.001 for two-sided t-tests (see Methods). FIGS.17F and 17GCT compare differences in FC change to differences in psychedelic experiences within individuals. FIG. 17F shows map 1710 for Individual FC change maps and Mystical Experience Questionnaire (MEQ) scores for two examples (see FIGS. 5A-5D for all drug sessions). FIG.17G shows plot 1712 for a relationship between whole brain FC change and mystical experience rating is plotted (LME model was used to test the relation; P = 3.5 x 10- 6). Each dot represents one drug session (psilocybin or methylphenidate). FIG. 17H shows map 1714 for a relationship between FC change and MEQ (r2) is mapped across the cortical brain surface.

[0211] FIG.18 shows T-statistic maps, resulting from the linear mixed effects(LME) model based on vertex-wise FC change (Euclidean distance from baseline scans) across the cortex and subcortical structures for every scan. Higher t values indicate a larger change from baseline (pre-drug) scans. Effects of drug condition (Baseline, Psilocybin, Methylphenidate, Post-Psilocybin, Post-Methylphenidate), were modeled as fixed effects. For example, if drug 1 was Psilocybin and drug 2 was Methylphenidate, then scans between drug visits were labeled Post-Psilocybin and scans after drug 2 were labeled Post- Methylphenidate.

[0212] For FIGS. 19A-19C, FC matrices between rs-fMRI sessions werecompared to quantify contributors to variability in whole-brain FC. Under this approach, the effects of group, individual, session, and drug (as well as their interactions) are examined by first calculating the Euclidean distance among every pair of FC matrices (i.e., distance among the linearized upper triangles). In FIG. 19A, in the resulting second-order distance matrix, each row and column show whole-brain FC from a single study visit. The colors in the matrix indicate distance between functional networks for a pair of visits (i.e., Euclidean distance between the linearized upper triangles of two FC matrices). FIGS.19B and 19C demonstrateCT how the distance matrix was subdivided to compare different conditions. FIG. 19B shows black triangles represent distinct individuals. Replication protocol visits are listed at the end. FIG. 19C shows task and rest scans are shown in white and orange, respectively. Note that psilocybin scans (black arrows pointing to P1 Psilocybin scans in FIG. 19A are very dissimilar to no-drug scans from the same individual (left arrow; in FIG. 19A) but have heightened similarity to psilocybin scans from other individuals (right arrow in FIG.19A).

[0213] Psilocybin acutely caused profound and widespread brain FC changes(see FIG.14A) across most of the cerebral cortex (P < 0.05 based on two-sided linear mixed effects model and permutation testing), but most prominent in association networks (FC change mean (SD): association cortex = 0.44 (0.03), primary cortex = 0.36 (0.05)). In the subcortex the largest psilocybin-associated FC changes were seen in default mode network connected parts of the thalamus, basal ganglia, cerebellum, and hippocampus (see FIG.17A and FIG.18). In the hippocampus, foci of strong FC disruption were located in the anterior hippocampus (MNI coordinates: -24, -22, -16 and 24, -18, -16). In the basal ganglia, the largest FC disruptions were seen in mediodorsal and paraventricular thalamus and anteromedial caudate. In the cerebellum the largest FC changes were seen in the default mode network connected areas.

[0214] By comparison, methylphenidate-associated FC changes localized tosensorimotor systems (see FIG. 17B and FIG. 18) and paralleled the map of day-to-day variability (FIG. 17C) likely due to arousal effects. Psilocybin-associated FC change was largest in the default mode network (FIG.17D and FIGS.20A-20C including map 2000, plot 2002, and plot 2004; average across all psilocybin sessions; one-sided Pspin < 0.001; Pspin > 0.05 for all other networks). Whereas methylphenidate-associated FC change was largestCT in motor and action networks (Pspin = 0.002; Pspin > 0.05 for all other networks; see FIG. 17B).

[0215] FIG. 20A shows a mode functional network map (N = 6 participants;Infomap-based). FIG.20B shows a network selectivity of cortical FC change is assessed for different conditions (and separately for psilocybin initial and replication doses). Left column of bar plots shows FC change based on Euclidean distance, right column is based on (decrease in) bivariate correlation (Similarity). Drug effects measured by Similarity (z(r)) values (right) are inverted to be consistent with distance (left). Colored bars (colored by network, as defined in FIG.20A indicate that the network showed change values greater than would be expected by chance, based on permutation of network labels (Pspin-test < 0.05, 10,000 null rotation). FIG. 20C shows network selectivity of psilocybin-associated FC change (as shown in FIG.20A) is assessed for different subcortical structures (here, network colors do not indicate any statistical test).

[0216] FIG. 21 shows Mean heart rate (HR) and respiratory rate (RR) fromcontinuous measurement during fMRI scans is given for each participant and all participants (average). A linear mixed effects model, as described previously, was used to test of a given condition was significantly different from baseline. Asterisks indicates p < 0.001. No significant difference between psilocybin (PSIL) and methylphenidate (MTP) was found for HR or RR.

[0217] Despite methylphenidate and psilocybin causing similar increases inheart rate (FIG. 18 showing plots 1800), psilocybin’s effects on FC were more than 3-fold larger than methylphenidate’s (FIG. 14E; post-hoc two-sided t-test; P = 3.6 x 10-6). The psilocybin effects also dwarfed those of other control conditions (FIG. 14E; day-to- day change (normalized) = 1; task = 1.22, methylphenidate = 1.10, high head motion = 1.29,CT psilocybin = 3.52, between person = 3.53; these effects were robust to pre-processing choices: FIG.19 and FIG.20). To put psilocybin’s effects in perspective, it helps to consider that the mean differences in an individuals’ brain organization on / off drug were as large as those between different persons (FIG.17E).

[0218] The Psychedelic Experience: The large amount of data collected perparticipant, under the individual-specific imaging paradigm, allowed us to move beyond group-analyses and compare the subjective psychedelic experience (Mystical Experience Questionnaire: MEQ) to brain function data session-by-session (FIG. 17F). The MEQ is a self-assessment instrument used to measure the intensity of mystical experiences, including feelings of connectedness, transcendence of time and space, and a sense of awe, with a maximum score of 150. Across psilocybin sessions and participants, FC change tracked with the intensity of the subjective experience (FIG. 17F; FIGS. 5A-5D). Correlating the whole brain FC change (x-axis) against the MEQ scores (y-axis) for all drug sessions (FIG. 14G) revealed an r2 = 0.81 (LME model predicting MEQ score: effect of FC change, t = 7.68; P = 3.5 x 10-6). Head motion was not significantly correlated with MEQ scores (effect of FD, t = -1.26, P = 0.23). Projecting the relationship between someone’s mystical experience and the corresponding FC change onto the brain (FIG. 17H; vertex-wise) showed it to be driven by association cortex, relatively sparing primary motor and sensory regions.

[0219] Acquiring multiple scans at multiple psilocybin sessions, enabled usto determine that the variability in psilocybin’s brain effects was more likely due to differences in subjective experience than noise (likelihood ratio test for a participant- specific response to psilocybin, P = 0.00245).

[0220] The Psychedelic Dimension: FIGS. 24A-24D illustrate data-drivenclustering of brain network variability. Multi-dimensional scaling blind to session labels wasCT used to assess brain changes across conditions. FIG. 24A shows plots 2400. In the scatter plots, each point represents whole-brain functional connectivity from a single 15-minute scan, plotted in a multidimensional space based on the similarity between scans. Dimensions 1 and 4 showed strong effects of psilocybin (Top: Points are colored based on drug condition. Dark red denotes that the participant had an episode of emesis shortly after taking psilocybin. Bottom: Points are colored based on participant identity). Dimension 1 (aka Dim1) separates psilocybin (PSIL) from non-drug and methylphenidate (MTP) scans in most cases. A participant’s psilocybin scans are often more similar to other psilocybin scans than to other scans from the same participant not on psilocybin. See FIGS. 25A-25B for full Dimension 1-4 matrices (FIG. 25A showing maps 2500 and FIG. 25B showing matrices 2502). FIG. 24B shows maps 2402 for Visualization of Dimension 1 weights revealed that psychedelic administration also caused a reliable increase in Dimension 1 score. Top 1% of edges (connections) are projected onto the brain to show the connections most affected by psilocybin. FIG.24C shows plots 2404 for re-analysis of Dimension 1 in extant datasets with intravenous psilocybin (left) and LSD (right). Asterisks indicate Puncorrected< 0.001 in a two- tailed, paired t-test of change in Dimension 1 score on psychedelics versus placebo. FIG. 24D shows maps 2406 for average effects of psilocybin on network FC, shown separately for within network integration (left) and between network segregation (right). For network integration (left), blue indicates a loss of FC (correlations) from a region to all other regions within the same network (loss of integration) (see FIGS.26A-26B for full correlation matrix, including maps 2600 of FIG.26A and matrices 2602 of FIG.26B). For network segregation (right), blue indicates a loss of FC (anti-correlations) from a region to all other regions in different networks (loss of segregation); also see FIGS.26A-26B). Dissolution of functional brain organization corresponds to decreased within- network integration and decreased between network segregation.CT

[0221] FIG. 25A shows group parcellation (324 cortical and 61 subcorticalparcels). FIG. 25B shows weights from the first 4 dimensions generated by multi- dimensional scaling of the full dataset. The color of each pixel in the plot represents the weight of a given edge. Dimension 1 captures the loss of network integration (on diagonal boxes) and segregation (off diagonal boxes) of psilocybin. Dimensions 2 and 3 primarily explain individual differences (MTP) on sensorimotor systems (suspected arousal and do not show network patterns as clearly. Dimension 4 captures shared effects of psilocybin (PSIL) and methylphenidate (MTP) on sensorimotor systems (suspected arousal effects).

[0222] FIG. 26A shows group parcellation (324 cortical and 61 subcorticalparcels). FIG.26B shows average FC matrices and condition differences (Top left shows the group average FC adjacency matrix. Bottom left shows the effect of psilocybin, e.g., increased correlation between Dorsal attention, Fronto-parietal, and Default Mode network to each other and to other cortical, limbic, and cerebellar systems. Top right shows effect of methylphenidate). For comparison and validation, we compared methylphenidate to the main effect of stimulant use within the last 24 hours (bottom right, N = 487 yes, N = 7992 no) in ABCD rs-fMRI data (bottom right).

[0223] To examine the latent dimensions of brain network changes multi-dimensional scaling (MDS) on the parcellated FC matrices from every fMRI scan were performed. MDS is blind to session labels (e.g., drug, participant). Yet, Dimension 1 (Dim1) – which explained the largest amount of variability – separated psilocybin from other scans (FIG.24A), apart from one session where the participant (P5R) had emesis 30 minutes after taking psilocybin (dark red dots on the left of FIG. 24A). The higher score on Dim1 associated with psilocybin, corresponded to reduced segregation between the default mode network, and other networks (fronto-parietal, dorsal attention, salience, and action-CT mode) that are typically anticorrelated with it (FIG.24B, FIGS. 25A-25B). To determine if this reflects a common effect of psilocybin that generalizes across datasets and psychedelics, Dim1 scores for extant datasets from participants receiving I.V. psilocybin and LSD were calculated. Psychedelic treatment increased Dim1 in nearly every participant in the psilocybin and LSD datasets (FIG. 27C), suggesting that this is a common effect across psychedelic drugs and individuals.

[0224] Subtraction of average FC (psilocybin minus baseline) revealed apattern of FC change similar to Dim1 (FIG. 24D, FIGS. 25A-25B, FIGS. 26A-26B). Consistent with previous psychedelics studies, psilocybin increased FC between networks (particularly fronto-parietal, default mode, and dorsal attention), whereas FC within networks was relatively less impacted. A similar pattern of loss of segregation between brain networks is produced by nitrous oxide and ketamine, suggesting that the psychedelic dimension observed here may generalize to psychedelic-like dissociative drugs.

[0225] By comparison, methylphenidate decreased within network FC in thesensory, motor, and auditory regions (FIGS.26A-26B), consistent with previous reports and similar to the effects of caffeine. To verify that observations in the sample (N = 6) were generalizable, stimulant effects in the study to those in the Adolescent Brain Cognitive Development (ABCD) Study (N = 487 taking stimulants) were compared. The effect of stimulant use in ABCD was consistent with methylphenidate-associated FC changes in the dataset (FIGS.26A-26B).

[0226] Desynchronization Explains FC Change: FIGS. 27A-27F. Spatialdesynchronization of cortical activity during psilocybin. FIG. 27A shows maps 2700 for Normalized global spatial complexity (NGSC) captures the complexity of brain activity patterns. It is derived from the number of spatial principal components (PCs) needed toCT explain the underlying structure. Higher entropy = desynchronized activity. Psilocybin (PSIL) in red, methylphenidate (MTP) in blue, no drug in grey. FIG.27B shows plots 2702 for whole-brain entropy (NGSC) is shown for every fMRI scan for a single participant (P6). At right, increases during psilocybin were present in all participants (box and whiskers indicate quartiles and 99.7th percentiles for non-drug scans, red circles indicate psilocybin) FIG. 27C shows plots 2704 for parcel entropy (computed on individual-specific parcels) within functional brain areas shows similar psilocybin-driven increases as whole-brain entropy. FIG. 27D shows maps 2706 for Psilocybin-associated spatial entropy (individual specific parcels, averaged across participants) is visualized on the cortical surface. Psilocybin-associated increases in entropy were largest in association cortex. FIG. 27E shows maps 2708 for LSD-associated increases in spatial entropy were similar to those induced by psilocybin and FIG.27F shows maps 2710 for corresponded spatially to 5HT2A receptor density.

[0227] Multi-unit recording studies suggest that agonism of 5HT2A receptorsby psychedelics desynchronizes populations of neurons which typically co-activate. It was hypothesized that this phenomenon, observed at a larger spatial scale, might account for psilocybin-associated FC change (FIGS.17A-17H). It was observed that the typically stable spatial structure of resting fMRI fluctuations was disrupted and desynchronized by psilocybin (Supplementary Videos 2-7: brain activity timecourses during drug sessions for each participant). Therefore, brain signal synchrony using Normalized Global Spatial Complexity (NGSC) – a measure of spatial entropy that is independent of the number of signals was quantified. NGSC calculates cumulative variance explained by subsequent spatiotemporal patterns (FIG. 27A). The lowest value of NGSC (= 0) means that the timecourse for every vertex / voxel is identical. The highest value of NGSC (= 1) means thatCT the timecourse for every vertex / voxel is independent – indicating maximal desynchronization (or spatial entropy).

[0228] Psilocybin significantly increased NGSC acutely with levelsreturning to pre-drug baseline by the following session (FIGS. 27B-27C). The increase in NGSC was observed at the whole-brain level (FIG.27B; LME model: t = 4.8, P = 2.0 x 10-6), and correlated with the subjective experience, while nuisance variables did not (MEQ: FIG.28 showing plots 2500; r = 0.80, P = 3.5 x 10-4, after single outlier removal).

[0229] Increased NGSC was also observed for individual-defined brain areas(FIG. 27C; LME model: t = 2.2, P= 6.6 x 10-4), with the largest increases in association cortex and minimal changes in primary cortex (FIG. 27D). Global and local desynchronization replicated in an LSD dataset (FIG. 27E) and the distribution of these effects appear to match a map of 5HT-2A receptor density (serotonin 2A) (FIG.27F).

[0230] FIG. 28 shows a comparison of MEQ score (y-axes) to globaldesynchronization (top left; NGSC change, drug minus baseline), head motion (bottom left; framewise displacement (FD) in mm), heart rate change (top right; drug minus baseline), and respiratory rate change (bottom right; drug minus baseline), for all drug sessions. Statistics (rho, P) are based on Pearson correlation. In the case of Δ NGSC, statistics are reported before and after the removal of an outlier point (> 3 SD lower than mean).

[0231] Task Engagement Reduces Desynchronization: FIGS. 29A-29Bshowing maps / plots 2900 in FIG. 29A and maps / plots 2902 in FIG. 29B. Effects of perceptual task performance on psilocybin-associated FC change and desynchronization. FIG. 29A shows maps / plots 2900 for Psilocybin-associated FC change from resting scans (left) and from task scans (right). FIG. 29N shows maps / plots 2902 for regional NGSCCT change (psilocybin minus baseline) from rest scans (left) and from task scans (right). Bar graphs on bottom indicate corresponding whole-brain FC change (FIG. 29A) and whole- brain NGSC values (FIG.29B) during rest / task for baseline and drug conditions (Psilocybin, MTP (methylphenidate)). Error bars indicate standard error. Asterisks indicate P < 0.001 for an interaction of psilocybin x task in linear mixed effects models (see Methods section herein).

[0232] To investigate how psilocybin-driven brain changes are influenced bytask states, participants were asked to complete a simple auditory-visual matching task in the scanner (see Methods: Perceptual fMRI task described herein). Participants performed this task with high accuracy during drug sessions (FIGS. 30A-30C, where FIG. 30A shows sample test items 3000, FIG. 30B shows plot 3002, and FIG. 30C shows plot 3004. Also, FIG.30D shows map 3006, FIG.30E shows map 3008, FIG.30F shows map 3010, and FIG. 30G shows plots 3012). Engagement in the task significantly decreased the magnitude of psilocybin-associated network disruption and desynchronization (FIGS. 29A-29B; FC Change LME: effect of task t = -4.0, P= 8.7 x 10-5, interaction of task*psilocybin t = 5.0, P = 9.5 x 10-9; NGSC LME: effect of task t = 7.9, P =7.4 x 10-14, interaction of task*psilocybin t = -3.9, P = 1.0 x 10-4). These results were robust to scan order effects (FIG. 31, showing plot 3100) and regression of evoked responses (FIGS. 32A-32B, where FIG. 32A shows plots 3200, and FIG.32B shows plots 3202).

[0233] FIG. 30A shows a schematic of auditory / visual matching task design.FIG. 30B shows a comparison of performance (‘No Drug’ and psilocybin conditions are at ceiling). FIG. 30C shows a comparison of reaction time (RT). FIG. 30D shows Task fMRI activation maps (beta weights). FIG. 30E shows contrasts (simple subtraction) using the canonical hemodynamic response function (HRF). FIG. 30F shows eight a priori regions ofCT interest for timecourse analyses. FIG. 30 G shows average timecourses from the regions of interest shown in FIG. 30F, calculated using finite impulse response model over 11 TR x 1.761 s / TR = 19.37 seconds, for the main effect of time. * P < 0.05, ANOVA of Condition x HRF Beta (Main effect of all trials).

[0234] FIG. 31 shows Y-axes show FC change (Euclidean distance betweenvectorized FC matrices) between each scan and baseline. Labels underneath each scan indicate session, scan, and rest / task. For example, ‘Base3-2R’ indicates baseline visit 3, scan 2, rest; ‘MTP-3T’ indicates methylphenidate visit, scan 3, task.

[0235] FIGS. 32A-32B, paralleling FIGS. 29A-29B, shows bar graphsindicating corresponding FC change and NGSC values from different rest / task and drug conditions. Error bars indicate standard error. Asterisks indicate P < 0.001 for an interaction of psilocybin x task in linear mixed effects models (see Methods).

[0236] The reduction of psilocybin-driven brain changes during taskperformance seems to parallel the psychological principle of ‘grounding’ – directing one’s attention externally as a means of alleviating intense or distressing thoughts or emotions. Grounding techniques are commonly employed in psychedelic-associated psychotherapy to lessen overwhelming or distressing effects of psilocybin. Task-related reductions in network desynchronization provide strong evidence for context-dependent effects of psilocybin on brain activity and functional connectivity and fill an important gap between preclinical studies of context dependence and clinical observations.

[0237] Classical animal studies documented that psychedelics reduce optictract responses to photic stimulation of the retina, indirectly reducing visual cortex activation. These effects were replicated by documenting reduced task-evoked responses in primaryCT visual cortex (FIGS. 30D-30F). To assess if psilocybin affects the hemodynamic response function (HRF) elsewhere, evoked responses during the perceptual task (FIG. 30G) was analyzed. The evoked responses were not significantly changed by psilocybin, indicating that alterations in the HRF are unlikely to account for the large observed FC changes.

[0238] Persistent Decreases In Hippocampal-Cortical Connectivity: Toassess whether persistent neurotrophic and psychological effects of psychedelics might be associated with persistent FC changes after psilocybin, compared FC changes 1-21 days post-psilocybin to pre-psilocybin. FIGS.33A-33E. Persistent effects of psilocybin. FIG.33A shows map 3300 for Hippocampus FC change maps (left hippocampus; unthresholded t- maps, as in FIG.18. Acute psilocybin FC change is shown on top and persistent FC change (3 weeks after psilocybin) on the bottom. FIG. 33B shows plots 3302, where each dot represents the FC change score for the anterior hippocampus for a single scan pre-PSIL (left) and post-PSIL (right) for every participant (colored as in FIGS. 24A-24D). Participants showed a post-psilocybin increase in FC change in the anterior hippocampus (P = 0.003; LME model, pre- vs post-psilocybin). FIG.33C shows maps 3304, for connectivity from an anterior hippocampus seed (MNI coordinates: -24, - 22, -16 and 24, -18, -16) pre-psilocybin (left), post-psilocybin (middle), and persistent change (post- minus pre-) for an example participant (P3). The red border on the right-most brain outlines the individual-specific default mode network. A decrease in hippocampal FC with parietal and frontal components of the default mode network is seen. FIG. 33D shows plot 3306 for timecourse of anterior hippocampus - default mode network for all participants / scans (participant colors as in FIG. 33B. A moving average is shown in black. FIG 33E shows map 3308 for a schematic ofhippocampal-cortical circuits. Whole brain FC change scores were small, indicating that thebrain’s network structure had mostly returned to baseline (FIG.18).CT

[0239] Atypical cortico-hippocampal connectivity has beenassociated with affective symptoms and hippocampus neurogenesis is observed after psilocybin. Further, acute decreases in hippocampal glutamate after psilocybin correlate with decreased default mode network connectivity and ego dissolution. Thus, it was investigated if the same region of the anterior hippocampus which showed strong acute FC change also showed persistent FC change. Significant FC change in the three-week post-drug period (FIGS. 33A, 33B; LME mean change = 0.095, Ppre:post-PSIL = 0.003) was observed. No persistent FC differences were observed post-methylphenidate (see Methods – Persistent effect analysis; LME ‘FC change’ 90% CI = -0.056 – 0.080; Equivalence δ = + / - 0.086, Ppre:post-MTP = 0.77).

[0240] Functional connectivity between the anterior hippocampusand default mode network was decreased post-psilocybin (FIGS. 33C, 33D). Timecourse visualization, after aligning them so that psilocybin dose = day 0, suggests that connectivity is reduced for 3 weeks post-psilocybin (FIG. 33D; AntHip-DMN FC mean (95% CI): pre- PSIL = 0.180 (0.169-0.192); post-PSIL = 0.163 (0.150-0.176). This observation is compelling, as it localized to the anterior hippocampus, a brain region showing substantial synaptogenesis following psilocybin. Reduced hippocampal-cortical FC may reflect increased plasticity of self-oriented hippocampal circuits (FIG.33E).

[0241] Linking Micro- And Macro-Scale Psychedelic Effects: Thesynchronized patterns of co-fluctuations during the resting state are believed to reflect the brain’s perpetual task of modeling reality. It follows that the stability of functional network organization across day, task, methylphenidate, and arousal levels (but not between individuals), reflects the subjective stability of waking consciousness. By contrast, the much larger changes induced by psilocybin fit with participants’ subjectiveCT reports of a radical change in consciousness. The large magnitude of psilocybin’s effects, in comparison to the effects of methylphenidate, suggests that observed changes are not merely due to increased arousal or nonspecific effects of monoaminergic stimulation. The observation that psychedelics desynchronize brain activity at regional and global scales provides a bridge between prior findings at the micro- and macro-scale. Multi-unit recording studies suggest that agonism of 5HT2A receptors by psychedelics does not uniformly increase or decrease firing of pyramidal neurons, but rather serves to desynchronize pairs or populations of neurons which co-activate under typical conditions. Meanwhile, previous resting fMRI studies have reported a range of acute changes following ingestion of psilocybin, ayahuasca, and LSD which broadly converge on a loss of network connectivity and an increase in global integration. Disruption of synchronized activity at multiple scales may explain the paradoxical observation that psychedelics produce an increase in metabolic activity, a decrease in the power of local fluctuations, and a loss of the brain’s segregated network structure. This desynchronization of neural activity has been described as an increase in entropy or randomness of brain activity in the psychedelic state. The results support the hypothesis that these changes underpin the cognitive and perceptual changes associated with psychedelics.

[0242] Acute Desynchronization May Drive Persistent Changes: Thedramatic departure from typical synchronized patterns of co-activity may be key not only to understanding psilocybin’s acute effects, but also its persistent neurotrophic effects. Changes in resting activity are linked to shifts in glutamate-dependent signaling during psilocybin exposure. This phenomenon, shared by ketamine and psychedelics, engages homeostatic plasticity mechanisms - a neurobiological response to large deviations in typical network activity patterns. This response to novelty includes rapid upregulation in expression ofCT BDNF, mTOR, EF2 and other plasticity-related immediate early genes – which are thought to play a key role in antidepressant response. Consistent with this notion, psilocybin produced the largest changes in the default mode network, frequently associated with neuropsychiatric disorders, and in a region of the anterior / middle hippocampus associated with the self and the present moment in time. Psychedelics rapidly induce synaptogenesis in the hippocampus and cortex, effects that appear to be necessary for rapid antidepressant-like effects in animal models. However, to understand the underpinnings of psychedelics’ unique effects, human studies are needed. Advances in precision functional mapping and individual- level characterization enabled us to identify desynchronization of resting state fMRI signals, connect these changes with subjective psychedelic effects, and localize these changes to depression-relevant circuits (default mode network, hippocampus). These analyses rely on precise characterization of an individuals’ baseline brain organization (e.g., individual definition of brain areas, networks, and day-to-day variability) to understand how that organization is altered by an intervention. This precision drug mechanism study was conducted in non-depressed volunteers. Verification Of Psilocybin’s Proposed Antidepressant Mechanism Will Require Precision Patient Studies: Novel methods to measure neurotrophic markers in the human brain102 will provide a critical link between mechanistic observations at the cellular, brain-networks and psychological levels.

[0243] Methods - Study design: Healthy young adults (N = 7, 18-45 years)were enrolled between a ~14 month period in a randomized cross-over precision functional brain mapping study. The purpose of the study was to evaluate differences in individual- level connectomics pre-, during, and post- psilocybin exposure. Participants underwent imaging during drug sessions with psilocybin (PSIL) 25mg, or methylphenidate (MTP) 40mg as well as non-drug imaging sessions. Drug condition categories were 1) Baseline, 2)CT Drug 1 (methylphenidate or psilocybin), 3) Between, 4) Drug 2 and 5) After. Randomization allocation was conducted via REDCap and generated by team members who prepare study materials including drug or placebo but otherwise had no contact with participants. A minimum of 3 non-drug imaging sessions were completed during each non-drug window: baseline, between and after drug sessions. The number of non-drug MRI sessions was dependent on availability of the participant, scanner and scanner support staff. Dosing day imaging sessions were conducted 60-180 minutes following drug administration during peak blood concentration. One participant (P2) was not able tolerate fMRI while on psilocybin and had trouble staying awake on numerous fMRI visits after psilocybin and was excluded from analysis (except for data quality metrics in FIGS. 16A-16E). MTP was selected as the active control condition to simulate the cardiovascular effects and physiological arousal (i.e., controlling for dopaminergic effects) associated with psilocybin. Good manufacturing practices (GMP) psilocybin was used. Drug sessions were facilitated by two clinical research staff who completed an approved in-person or online facilitator training). The role of the study facilitators was to build a therapeutic alliance with the participant throughout the study, prepare them for their drug dosing days, and to observe and maintain participant safety during dosing day visits. The pair consisted of an experienced clinician (lead clinical facilitator) and a trainee (co-facilitator). The predefined primary outcome measure was precision functional mapping (numerous visits, very long scans to produce individual connectomes) examining the effects of psilocybin on cortical and cortico- subcortical brain networks that could explain its rapid and sustained behavioral effects. Predefined secondary outcome measures included 1) assessment of hemodynamic response to evaluate how 5HT2A receptors agonism by psychedelics may alter neurovascular coupling, 2) assessment of acute psychological effects of psilocybin using the mystical experience questions, and 3) assessment of personality change using the InternationalCT Personality Item Pool-Five-Factor Model. Changes in pulse rate and respiratory rate during psilocybin and placebo were later added as secondary outcome measures.

[0244] Replication protocol: Participants were invited to return 6-12 monthsafter completing the initial cross-over study for a replication protocol. This included 1-2 baseline fMRIs, a psilocybin session (identical to initial session, except for lack of blinding), and 1-2 ‘after’ sessions within 4 days of the dose.

[0245] Participants: Healthy adults ages 18 to 45 years were recruited viacampus-wide advertisement and colleague referral. Participants were required to have had at least one previous lifetime psychedelic exposure (e.g., psilocybin, mescaline, ayahuasca, LSD), but no psychedelics exposure within the past 6 months. Individuals with psychiatric illness (depression, psychosis, addiction) based on DSM-5 were excluded. Demographics and data summary details are provided in FIG.34, which shows table 3400.

[0246] MRI: Participants were scanned roughly every other day over thecourse of the experiment (FIGS. 16A-16E). Imaging was performed at a consistent time of day to minimize diurnal effects in functional connectivity. Neuroimaging was performed on a scanner (e.g., Siemens Prisma scanner) in neuroimaging labs (NIL). Structural scans (T1w and T2w) were acquired for each participant at 0.9 mm isotropic resolution, with real-time motion correction. Structural scans from different sessions were averaged together for the purposes of Freesurfer segmentation and nonlinear atlas registrations. To capture high resolution images of blood oxygenation level-dependent (BOLD) signal, an echo-planar imaging sequence with 2 mm isotropic voxels, multi-band 6, multi-echo 5 (TEs: 14.20ms, 38.93ms, 63.66ms, 88.39ms, 113.12 ms), TR 1761 ms, flip angle = 68 degrees, and in-plane acceleration (IPAT / grappa) = 2 were used. This sequence acquired 72 axial slices (144 mm coverage). Each resting scan included 510 frames (lasting 15:49 minutes) as well as 3 framesCT at the end used to provide estimate electronic noise. Every session included two 15-minute resting-state fMRI scans during which participants were instructed to hold still and look at a white fixation crosshair presented on a black background. Head motion was tracked in real time using Framewise Integrated Real-time MRI Monitoring software (FIRMM). An eye- tracking camera (e.g., EyeLink) was used to monitor participants for drowsiness.

[0247] Perceptual fMRI Task: Participants also completed a previouslyvalidated event-related fMRI task. This was a suprathreshold auditory-visual matching task in which participants are presented with a naturalistic visual image (duration 500 ms) and coincident spoken English phrase and are asked to respond with a button press to indicate if the image and phrase are ‘congruent’ (for example, an image of a beach, and the spoken word beach) or ‘incongruent’. Both accuracy and response time of button presses were recorded. Each trial was followed by a jittered inter-stimulus interval optimized for event- related designs. In a subset of imaging sessions, two task fMRI scans were completed following the two resting scans. Task fMRI scans employed the same sequence used in resting fMRI, included 48 trials (24 congruent, 24 incongruent), and lasted a total of 410s. In analyses, the two task scans were concatenated to better match the length of the resting- state fMRI scans. Note the stimulus order in the two trials did not vary across session. The order of rest and task scans was not counterbalanced across sessions to avoid concern that task scans may influence subsequent rest scans.

[0248] Resting fMRI processing and resting state network definition: RestingfMRI data were preprocessed using an in-house processing pipeline. Briefly, this included removal of thermal noise using NORDIC denoising, correction for slice timing and field distortions, alignment, optimal combination of multiple echoes by weighted summation, normalization, non-linear registration, bandpass filtering, and scrubbing at a movementCT threshold of 0.3mm to remove reduce the influence of confounds. Tissue-based regressors were computed in volume (white matter, ventricles, extra-axial CSF) and applied following projection to surface. Task-based regressors were only applied when indicated. Details on rsfMRI preprocessing are provided in supplementary methods. Visualizations of motion, physiological traces, and signal across the brain (‘grayplots’) before and after processing were provided on video.

[0249] Surface Generation And Brain Areal Parcellation: Surface generationand processing of functional data followed procedures similar to known techniques. To compare FC and resting state networks across participants, a group-based surface parcellation and community assignments was used. For subcortical regions, a set of regions of interest generated to achieve full coverage and optimal region homogeneity was used. A subcortical limbic network was defined based on neuroanatomy: amygdala, antero-medial thalamus, nucleus accumbens, anterior hippocampus, posterior hippocampus. These regions were expanded to cover anatomical structures (e.g., anterior hippocampus). To generate region-wise connectivity matrices, time courses of all surface vertices or subcortical voxels within a region were averaged. Functional connectivity was then computed between each region timeseries using bivariate correlation and Fisher z- transformed for group comparison.

[0250] Individualized Network And Brain Area Mapping: Canonical large-scale networks using the individual-specific network matching approach described previously were identified. Briefly, cortical surface and subcortical volume assignments were derived using the graph-theory-based Infomap algorithm. In this approach, the correlation matrix from all cortical vertices and subcortical voxels, concatenated across all a participant’s scans was calculated. Correlations between vertices within 30 mm of each otherCT were set to zero. The Infomap algorithm was applied to each subject’s correlation matrix thresholded at a range of edge densities spanning from 0.01% to 2%. At each threshold, the algorithm returned community identities for each vertex and voxel. Communities were labeled by matching them at each threshold to a set of independent group average networks described in prior techniques. In each individual and in the average, a ‘consensus’ network assignment was derived by collapsing assignments across thresholds, giving each node the assignment it had at the sparsest possible threshold at which it was successfully assigned to one of the known group networks. See FIG.5A and FIGS.20A-20C for individual and group mode assignments, respectively. The following networks (and associated acronyms) were included. Association networks: default mode network (DMN), fronto-parietal network (FPN), dorsal attention network (DAN), parietal memory (PMem), ventral attention network (VAN), action-mode network (AMN), salience network, context network. Primary networks: visual (VIS), somato-motor (SM or motor), somato-motor face (SMF, or motor face), auditory (AUD). In order to compute local (areal) desynchronization, brain areas at the individual level using a previously described areal parcellation approach were also defined. Briefly, for each participant, vertex-wise FC was averaged across all sessions to generate a dense connectome. Then, abrupt transitions in FC values across neighboring vertices was used to identify boundaries between distinct functional areas.

[0251] Linear Mixed Effects (LME) Model: To take advantage of the multi-level precision functional mapping study design, a linear mixed effects model was used. Every scan was labeled on the following dimensions: Subject ID, MRI visit, task (task / rest), drug condition (pre-psilocybin, PSIL, MTP, post-psilocybin), and head motion (average framewise displacement, FD). rs-fMRI metrics (described below) were set as the dependent variable, drug (drug condition), task, FD (motion), and drug*task were defined as fixedCT effects, and Subject ID and MRI session were random effects. Let yijbe the rs-fMRI metric (e.g. FC change score at a given vertex) for the j-th observation (15-minute fMRI scan) within the i-th participant. The linear mixed-effects model can be written as: yij=β0+βdrug*drugij+βFD*FDij+βtask*taskij+βtask*drug*taskij*drugij+u0i+v0j+εij(2) where: β0 is the intercept term; βdrug, βFD, βtask and βtask*drug are the coefficients for the fixed effects predictors; drugij; FDij; and taskijare the values of the fixed effects predictors for the j-th observation within the i-th group; u0irepresents the random intercept for the i-th participant, accounting for individual-specific variability; v0jrepresents the random intercept for thej-th observation within thei-th participant, capturing scan-specific variability; and εijis the error term representing unobserved random variation.

[0252] In Matlab (Wilkinsonian notation), this model is expressed forevery vertex Y(vertex) = fitlme(groupd, FC_Change(vertex) ~ drug + FD + task + task*drug + (1 |SubID) + (1 |session)). To compensate for the implementations of this LME model on multiple rs-fMRI-related dependent variables, differences were highlighted when P < 0.005. All P values reported are not corrected for multiple comparisons.

[0253] Vertex-Wise FC Change: FC change (‘distance’) was calculated atthe vertex level to generate FC change maps and a linear mixed effects model (Eq. 1) was employed in combination with wild bootstrapping and threshold-free cluster enhancement (TFCE) to estimate P values for t-statistic maps resulting from the model (FIGS.17A-17H, FIGS.29A-29B). Wild bootstrapping is an approach to permutation testing that was designed for models that are not independent and identically distributed and which are heteroscedastic. First, a FC change map was generated for every scan by computing, for each vertex, the average distance between its FC seedmap and the FC seedmap for each of that subject’sCT baseline scans. Since each participant had multiple baseline visits, FC change was computed for baseline scans by computing distance from all other baseline scans (excluding scans within the same visit). This provided a measure of day-to-day variability. Second, the distance value was used as the dependent variable Yij in the LME model to generate a t- statistic. Third, a wild bootstrapping procedure was implemented as follows. A large number of bootstrap samples (B = 1,000) were generated using the Rademacher procedure 124, where the residuals were randomly inverted. Specifically, a Rademacher vector was generated by randomly assigning -1 or 1 values with equal probability to each observation's residual. By element-wise multiplication of the original residuals with the Rademacher vector, bootstrap samples were created to capture the variability in the data. For the observed t-statistic-map, and each bootstrap sample, the TFCE algorithm was applied to enhance the sensitivity to clusters of significant voxels or regions while controlling for multiple comparisons. The value of the enhanced cluster statistic derived from the bootstrap samples, was used to create a null distribution under the null hypothesis. By comparing the original observed cluster statistic with the null distribution, P values were derived to quantify the statistical significance of the observed effect. The P values were obtained based on the proportion of bootstrap samples that produced a maximum cluster statistic exceeding the observed cluster statistic. The combined approach of wild bootstrapping with the Rademacher procedure and TFCE provided method to estimate p-values for the multi-level (drug condition, subject, session, task) design. This methodology accounted for the complex correlation structure, effectively controlled for multiple comparisons, and accommodated potential autocorrelation in the residuals through the Rademacher procedure. By incorporating these techniques, association with psilocybin and other conditions were reliably identified amidst noise and spatial dependencies.CT

[0254] Whole Brain FC Change: For analyses in FIG. 17E, FIG. 17G, FIGS.24A-24D, FIGS. 19A-19C, FIGS. 25A-25B, FIGS. 26A-26B, and FIGS. 22A-22D, FIGS. 23A-23D, FIG.31, distance calculations were computed on the FC matrix (computed using z-transformed Pearson correlation of timecourses from parcellated brain areas). The effects of day-to-day, drug condition, task, and FD and drug*task were directly examined by calculating the distance between each functional network matrices generated from each scan. Root-mean-squared Euclidean distance was computed between the linearized upper triangles of the parcellated FC matrix between each pair of 15-minute fMRI scans, creating a second- order distance matrix (FIGS. 19A-19C). Subsequently, the average distance (reported as ‘whole-brain FC change’) was examined for FC matrices that were from the same individual within a single session, from the same individual across days (’day-to- day’), from the same subject between drug and baseline (e.g., Psilocybin), from the same individual but different tasks (’task:rest’), from the same individual between highest motion scans and baseline (‘hi:lo motion’), from different individuals (‘between person’). In the ‘high head motion’ comparison (‘hi:lo motion’ in FIG. 22A-22D), the two non-drug scans with the highest average FD were labeled and compared against all other baseline scans. A LME model (Eq. 1) and post-hoc t-tests were used to assess statistical differences between drug conditions. A related approach using z-transformed Pearson correlation (‘similarity’ rather than distance) was also taken and results were unchanged (FIG.22C).

[0255] FIGS. 22A-22D show FC change was defined as the averageEuclidean distance between vectorized FC matrices, for a variety of conditions including: from the same individual within a single session, from the same individual across days (’day:day’), from the same individual but during different drug states (e.g. ’psil:no-drug’), from the same individual but during different tasks (‘task:rest’), from the same individualCT comparing highest motion scans and baseline (‘hi:lo motion’), and from different individuals (‘person:person’). FIG. 22A shows FC change, as reported in the manuscript. FIG. 22B shows FC change after global signal regression (GSR). FIG. 22C shows FC change calculated using ‘similarity’ (bivariate correlation) rather than difference, yielded similar results (note that change in similarity is inverted in panel b for comparison). FIG.22D shows FC change computed after the evoked responses were regressed out from task fMRI scans. The grounding effect of task performance remained.

[0256] FIGS. 23A-23D: Data shown are for the subset of participants withthe highest quality pulse and respiratory traces. FIG.23A shows whole-brain FC change scores, generated as in FIG. 17D by comparing every scan to baseline, but with PhysIO- based nuisance regression of respiratory rate (RR) and heart rate (HR). Normalized FC change values for each condition are listed above the violin plots. FIG.23B shows the same as FIG. 23A, but without PhysIO-based nuisance regression, so exact same as in FIG. 17D, but for a subset of the date with usable physiological recordings data. FIG. 23C Left shows scores on Dim 1 - generated using multi-dimensional scaling on the entire cohort and then multiplying Dim1 weights by FC weights for each scan. (Dim1 score LME (linear mixed effects) model, PSIL (psilocybin) vs Baseline, t = 4.5, P = 2.0 x 10-5). FIG.23C Right shows desynchronization (Global NGSC (normalized global spatial complexity) LME, PSIL vs Baseline, t = 3.0, P = 0.0034). All analyses with PhysIO-based nuisance regression. FIG. 23D: Same analyses as in FIG. 23C, but without PhysIO-based nuisance regression (Dim1 score LME, PSIL vs Baseline, t = 4.1, P = 9.0 x 10-5; Global NGSC LME, PSIL vs Baseline, t = 4.4, P = 3.1 x 10-5).

[0257] Participant-specific response and relationship to subjective report: Totest if variability in participant-specific response to psilocybin was larger than would beCT expected by chance, a likelihood ratio test for variance of random slopes for a participant- specific response to psilocybin was used. The difference in log likelihood ratios was compared to a null distribution of one million draws from a mixture of chi-squared distributions with degrees of freedom 1 and 2. It is noted that the likelihood ratio test of variance components is a non-standard problem as the covariance matrix of the random effects is positive definite and the variances of random effects are non-negative. And the test statistic for the likelihood ratio in this linear mixed effects model was compared against a 50:50 mixture of two independent chi-squared distributions, each with one and two degrees of freedom, respectively. Subjective experience was assessed for drug sessions using the mystical experience questionnaire (MEQ; see Supplementary methods). A LME model across all drug sessions, similar to the one described above, but with MEQ total score as the dependent variable was applied. Whole-brain FC change and framewise displacement (FD) were modeled as fixed effects, and participant was modeled as a random effect. The same model was solved using FC change from every vertex to generate a vertexwise map of the FC change versus MEQ.

[0258] Normalized FC change: The conditions above were compared bycalculating normalized FC change scores using the following procedure: It was (i) determined FC change for each condition compared to baseline as described above, (ii) subtracted within-session distance for all conditions (such that within-session FC change = 0), (iii) divided all conditions by day-to-day distance (such that day-to-day FC change = 1). Thus, normalized whole brain FC change values (e.g., PSIL v Base = 4.58) could be thought of as proportional to day-to-day variability.

[0259] Data-driven multidimensional scaling: A classical multidimensionalscaling (MDS) approach to cluster parcellated connectomes across fMRI scans was used.CT This data driven approach was used to identify how different parameters (e.g., task, drug, individual) affect similarity / distance between networks. MDS places data in multidimensional space based on the dissimilarity (Euclidean distance) among data points – where in this case a data point represents the linearized upper triangle of a FC matrix. Every matrix was entered into the classical MDS algorithm (implemented, without limitation, using MATLAB 2019, cmdscale.m). Multiple dimensions of the data were explored. The eigenvectors were multiplied by the original FC matrices to generate a matrix of eigen- weights that corresponded to each dimension. These eigenweights were also applied to other rs-fMRI psychedelics datasets to generate dimensions scores (see Other Datasets).

[0260] Network specificity - rotation-based null model (spin test): To assessnetwork specificity of FC change values, average FC change of matched null networks consisting of randomly rotated networks with preserved size, shape, and relative position to each other was calculated. To create matched random networks, each hemisphere of the original networks a random amount around the x, y, and z axes on the spherical expansion of the cortical surface was rotated. This procedure randomly relocated each network while maintaining networks’ size, shape, and relative positions to each other. Random rotation followed by computation of network-average FC change score was repeated 1000 times to generate null distributions of FC change scores. Vertices rotated into the medial wall were not included in the calculation. Actual psilocybin FC change was then compared to null rotation permutations to generate a p-value for the 12 networks that were consistently present across every subject’s infomap parcellation. For bar graph visualization (FIGS. 17A-17H, FIG.20B), networks with greater change (P < 0.05 based on null rotation permutations) are shown their respective color and other networks are shown in grey.CT

[0261] Normalized Global Spatial Complexity: An approach previouslyvalidated to assess spatial complexity (termed entropy) or neural signals was used. Temporal principal component analysis (PCA) was conducted on the full BOLD dense timeseries, which yielded m principal components (m = ~80K surface vertices and subcortical voxels) and associated eigenvalues. The normalized eigenvalue of the i-th principal component was calculated aswhere m was the number of principal components, λi and λ′i represented the eigenvalue and the normalized eigenvalue of the i-th principal component respectively. Lastly, the NGSC, defined as the normalized entropy of normalized eigenvalues, was computed using the equation:

[0262] The NGSC computed to 1.The lowest value NGSC = 0 would mean the brain-wide BOLD signal consisted of exactly one principal component or spatial mode, and there is maximum global functional connectivity between all vertices. The highest value NGSC = 1 would mean the total data variance is uniformly distributed across all m principal components, and a maximum spatial complexity or a lowest functional connectivity is found. NGSC was additionally calculated at the ‘parcel level’. To respect areal boundaries, this was done by first generatingCT a set of individual-specific parcels in every subject (on all available resting fMRI sessions concatenated) using prior known procedures.

[0263] Persistent Effects Analysis: To assess the persistent effects ofpsychedelics, FC changes 1-21 days post-psilocybin to pre-drug baseline were compared. The FC change analysis (described above) indicated that connectivity at the ‘whole-brain’ level did not change following psilocybin (FIGS.20A-20C). A screen was conducted with P < 0.05 threshold to identify brain networks or areas showing persistent effects. This analysis identified the anterior hippocampus as a candidate region of interest (ROI) for persistent FC change (see ‘Baseline / After Psilocybin FC Change Analysis’ in Supplementary Methods). Change in anterior hippocampus ‘FC change’ pre- versus post-psilocybin using the LME model described previously was assessed. In this model, all sessions prior to psilocybin (irrespective or cross-over order) were labeled as ‘pre-psilocybin, and all sessions within 21 days after psilocybin were labeled as post-psilocybin. As a control, anterior hippocampus ‘FC change’ per- versus post-methylphenidate using both the LME model, and an equivalence test was tested. To control for potential persistent psilocybin effects, only the block of scans immediately before and after MTP were used (for example, if a participant took MTP as drug 1, then all baseline scans were labeled as ‘pre-MTP” and all between (drugs 1 & 2) scans were labeled as ‘post-MTP). Equivalence testing (to conclude no change in anterior hippocampus after MTP) was accomplished by setting δ = 1 / 2 SD of FC change across pre-MTP sessions. The 90% confidence interval of change in ‘FC change’ between pre- and post-MTP sessions was computed. If the bounds of the 90% confidence interval are within + / - δ then equivalence is determined.

[0264] Other Datasets: Raw fMRI and structural data from prior techniqueswere run through in-house registration and processing pipeline described above. TheseCT datasets were used for replication, external validation, and generalization to another classic psychedelic (i.e., LSD) for the measures described above (e.g., normalized global spatial complexity, and the MDS-derived psilocybin FC dimension (Dimension 1). Prior known data (2012): N = 15 healthy adults completed two scanning sessions (psilocybin and saline) which included eyes-closed resting state BOLD scan for 6 minutes prior to and following I.V. infusion of drug. fMRI data were acquired using a gradient- echo EPI sequence, TR / TE 3000 / 35 ms, field-of-view = 192 mm, 64 × 64 acquisition matrix, parallel acceleration factor = 2, 90° flip angle. Prior known data (2016): N = 22 healthy adults completed two scanning sessions (LSD and saline), which included eyes-closed resting state BOLD scan acquired for 22 minutes following I.V. drug infusion lasting 12 min. fMRI data were acquired using a gradient echo planar imaging sequence, TR / TE = 2000 / 35ms, field-of-view = 220 mm, 64 × 64 acquisition matrix, parallel acceleration factor = 2, 90° flip angle, 3.4 mm isotropic voxels.

[0265] The ABCD (Adolescent Brain Cognitive Development) databaseresting state functional MRI (Annual Release 2.0, DOI 10.15154 / 1503209) was used to replicate the effects of stimulant use on functional connectivity. Preprocessing included framewise censoring with a criterion of frame displacement (FD) less than or equal to 0.2 mm in addition the standard predefined preprocessing procedures. Participants with fewer than 600 frames (equivalent to 8 minutes of data after censoring) were excluded from the analysis. Parcel-wise group-averaged functional connectivity matrices were constructed for each participant as described above for 385 regions on intertest in the brain. Use of a stimulant (e.g., methylphenidate, amphetamine salts, lisdexamfetamine) in the last 24 hours was assessed by parental report. Subjects with missing data were excluded. Regression analysis was used to assess the relationship between functional connectivity (edges) andCT stimulant use in the last 24 hours. Framewise displacement (averaged over frames remaining after censoring) was used as a covariate to account for motion-related effects. T-values that reflect the relationship between stimulant use and functional connectivity were visualized on a color scale from -5 to +5 to provide a qualitative information about effect of stimulant use on FC.

[0266] Supplementary Methods and Results - Exclusion criteria: Exclusioncriteria included contraindications to MRI scanning (bone hardware, IUD, implantable devices) contraindications to psilocybin exposure (e.g., hypertension, cardiovascular disease, pregnancy); diagnosis of psychiatric condition (including substance use disorders); current use of certain psychotropic medication; previous adverse reactions to psychedelics (assessed with the Challenging Experience Questionnaire; immediate family history of any schizophrenia spectrum disorder.

[0267] Supplementary Methods and Results – Study screening: Afterprescreening and providing informed consent, participants underwent screening tests, including an electrocardiogram, urine drug screen, complete metabolic panel, and a urine pregnancy test. A study physician performed a physical exam and reviewed labs to ensure that participants did not have health conditions that would compromise their safety during the study. Once medically cleared, participants were scheduled for all planned imaging sessions and drug dosing days to ensure appropriate timing of pre-, post- and dosing day scans.

[0268] Supplementary Methods and Results – Subjective and cognitiveassessments: Assessments were conducted before, during, and after treatment sessions. This included subjective ratings, objective measures, personality survey and safety assessments. Assessments obtained are described below: International Personality Item Pool-Five-FactorCT Model (Mini-IPIP): The Mini-IPIP is a 20-question survey administered to determine the Big Five factors of an individual’s personality: extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience. The Mini-IPIP was administered at the following time points: baseline, post-drug one, post-drug two. Mystical Experience Questionnaire (MEQ): The MEQ is a 30-item self-report questionnaire that measures mystical experiences. It measures four factors: a) mystical (freedom from boundaries of one’s personal self and a feeling of unity to what is greater than one’s self), b) positive mood (sense of awesomeness or awe), c) transcendence of time and space (being outside of real of time), d) ineffability (sense that experience cannot be described well in words). The MEQ was administered at the following time points: baseline, post-drug one, post-drug two. We administered the MEQ on the same day of drug administration and the day after dosing day and determined that MEQ scores were similar on both days. Therefore, MEQ scores on dosing days were reported.

[0269] Supplementary Methods and Results – Set and setting protocol:Preparation and integration sessions were held in a dedicated research treatment room where the study drug was administered. Preparatory sessions were held one or two days before drug administration. Integration sessions were held one day after drug administration. The purpose of preparatory sessions was to build a therapeutic alliance between facilitators and participants. The participant’s personal history, developmental stage, current life situation, and intentions for and expectations of drug sessions were reviewed. Preparation and integration sessions occurred per Usona facilitator training guidelines.

[0270] Supplementary Methods and Results – Drug administration: Ondosing visits, following checking vitals, urine drug screen, and urine pregnancy test, participants received either 25 mg of psilocybin or 40 mg of methylphenidate. BothCT facilitators and subjects were blinded. Medications were taken with lemon ginger tea. Following a 10-minute guided mindfulness meditation, participants were invited to lie on the sofa with eye shades as well as headphones and a curated music play list. One hour after drug administration, participants were transported to the MRI suite. Following the MRI, participants were transported back to the dedicated testing room and encouraged to direct their attention internally until subjective drug effects were resolved. When drug effects were resolved, study facilitators and participants completed post-dose questionnaires and a release checklist. Regardless of drug received, dosing sessions were 6-8 hours in length. Heart rate and blood pressure were measured at regular intervals during dosing days (e.g., 30, 60, 90, 120, 240, 360, 420, and 480 minutes after drug ingestion). Subjects were also briefly queried about adverse effects during vital signs monitoring using an adverse events checklist. Rescue medications (risperidone for agitation, lorazepam for anxiety and niacin for chest pain) were available as needed. The Columbia Suicide Severity Rating Scale (C-SSRS) was used to assess for suicidal ideation and behavior during drug exposure. Participants had access to a physician who was physically present throughout the dosing day.

[0271] Supplementary Methods and Results – Treatment guess: After eachdrug session (in the initial blinded cross-over portion of the study), participants were asked to guess if they had received psilocybin or methylphenidate. 6 / 7 participants correctly guessed which dose was psilocybin. Curiously, P3, the only participant who guessed incorrectly, showed a smaller FC change during psilocybin than any other subject (with the exception of P5 replication dose, in which the participant vomited 30 minutes after swallowing the capsule).CT

[0272] Supplementary Methods and Results – Data management: De-identified assessment scores, raw data from structural MRI and fMRI scans were uploaded into the Central Neuroimaging Data Archive (CNDA).

[0273] Supplementary Methods and Results – Resting-state functional MRIprocessing, and surface projection: Preprocessing of fMRI data included: 1) removal of thermal noise using NORDIC (a local PCA approach in which temporal components of an fMRI signal that are indistinguishable from Gaussian noise are eliminated); 2) compensation for asynchronous slice acquisition using sinc interpolation; 3) compute affine spatial registration of all volumes within a run; 4) elimination of odd / even slice intensity differences resulting from interleaved acquisition (debanding); 5) compute affine spatial registration across fMRI runs; 6) compute an run volume mean (of all low-noise volumes); 7) computation of field distortion on the basis of a spin echo field maps using FSL top-up; and 8) gain field correction using FSL fast (computed on the run volume mean). Resampling in MNI1522 mm3atlas space was accomplished for all echoes in one step combining (i) motion correction; (ii) distortion correction; (iii) gain field correction; (iv) linear registration of average volumes across visits; and (v) non-linear MNI152 atlas registration via the fsl fnirt. Optimal combination of echoes was then computed in MNI152 space using the weighted summation approach: (5)where TEnis time of the nthecho and time constant τ matches expected relaxation time. Finally, the voxel-wise intensities were adjusted (one scalar per run) to obtain a mode value of 1000 in the distribution of intensities summed over all voxels and volumes. FollowingCT cross-modal registration, data were passed through several additional preprocessing steps: (i) tissue-based regressors were computed based on FreeSurfer segmentation; (ii) temporal filtering to retain frequencies in the 0.009–0.08Hz band; and (iii) frame censoring (iv) removal by regression of the following signals that contain spurious variance: (a) six parameters obtained by rigid body correction of head motion, (b) signal from white matter, ventricles and extra-axial sources of noise (nuisance regressors were also bandpass filtered to match timecourse frequencies). Where indicated, respiratory and pulse-oximetry traces were used to generate additional physiological regressors using the PhysIO software package. The first four frames of each BOLD run were excluded. As has been reported previously, some participants exhibited high-frequency peaks in the power spectrum of head motion time courses, primarily in the phase-encoding (y) dimension. Thus, we low-pass filtered the y-translation estimate time courses at 0.1Hz in all participants prior to computing FD to prevent superfluous data loss. It was observed that this had minimal effect on the computation of FD (see FD computation with and without y-translation filtering in Supplementary Video 1). Frame censoring was implemented using framewise displacement with a threshold of 0.3 mm. This frame-censoring criterion was uniformly applied to all rs- fMRI data before functional connectivity computations. BOLD runs were excluded completely if they retained less than 50% usable frames after motion scrubbing. Individualized cortical surfaces and subcortical volumes were generated for each participant’s T1 MRI using FreeSurfer automated segmentation. Segmentation errors were manually corrected. Following preprocessing, BOLD data were sampled to each participant’s individual cortical surface and subcortical volume using Connectome Workbench. Brain surface visualizations were generated using Connectome Workbench.CT

[0274] Supplementary Methods and Results – Task fMRI analyses: TaskfMRI data were analyzed using a two-level approach. First solving a GLM for each session, second an ANOVA was used to test if evoked responses differed significantly between drug conditions (no drug, MTP, PSIL). fMRI data were preprocessed similar to resting data, with the exception of no nuisance regression and an FD threshold of 0.7 mm. A generalized linear model was computed in two different ways: 1) vertexwise GLM, using an assumed hemodynamic response function to visualize the magnitude of task-evoked responses. 2) parcel-wise GLM, using a finite impulse response model to model evoked response for 11 TRs (19.37 seconds) after each trial. A set of a priori regions of interest (ROIs) relevant to the task were selected from the prior parcellation. These included: left / right calcarine sulcus (V1), left / right auditory cortex (A1), left language (Wernicke’s area), left hand knob, left angular gyrus, and right angular gyrus (default mode). Trial conditions (congruent, incongruent; button press, no button press) were collapsed to model a main effect of task. Additional demean and detrend terms and 6 movement parameters were added to generate a general linear model (GLM). This GLM was solved to estimate beta weights separately for each task visit. In level 2 analyses, a two-way ANOVA was conducted using the anovan function in MATLAB. This analysis allowed us to account for the effects of the drug (as a primary factor) as well as individual participants (as a secondary factor). A P value associated with the 'drug' factor of P < 0.05, would indicate that the drug has a significant effect on evoked response. Of the 8 a priori regions on interest, only left and right V1 showed significant effect of drug (left V1 P = 0.03, right V1 P = 0.02, all other P > 0.1, uncorrected). Post-hoc comparison (matlab: multcompare) for these regions indicated that left and right V1 showed differences in peak activation between non-drug and psilocybin conditions of P < 0.05.CT

[0275] Supplementary Methods and Results – Physiological monitoringduring MRI: Recordings of pulse and respirations were added to the protocol prior to enrolling P4. Pulse and respiratory signals were recorded at 400 Hz with clocked timestamps. Physiological measurements were extracted using the PhysIO Toolbox as raw plethysmography signals. All signals were visually inspected and entire sessions were rejected if significant clipping occurred or if signals were noisy, without the regular oscillations expected in pulse or respiratory plethysmography. Instantaneous pulse rate (PR) and respiratory rate (RR) were determined by first labeling the peaks and troughs of the wave using Matlab’s findpeaks function, then calculating pulse rate or respiratory rate between each peak. This custom rate calculation agreed with PhysIO toolbox for respiratory rate but offered higher temporal resolution with respect to pulse rate. Physiological regressors for fMRI analyses were created using the 1343 automated PhysIO Toolbox. Pulse and respiratory rates were analyzed across all participants and conditions (FIG. 21). There was substantial variability in pulse rates across sessions. A mixed linear effects model was used to determine the relative effects of MTP and PSIL on the session means of physiological parameters. Because preliminary analyses suggested no change across no-drug conditions (baseline, between and after), all no-drug conditions were labeled the same in the LME. The average no-drug pulse rate was 72 beats per minute (95% CI: 68-77 bpm). On average, MTP was associated with a 16.7 bpm increase in pulse rate (95% CI: 11.0-20.3, PLME = 3.12 x 10-10). PSIL was associated with a 21.1 bpm increase in pulse rate (95% CI: 16.6-25.6, PLME = 4.04 x 10-17). No significant difference in HR was 1352 observed between MTP and PSIL (P = 0.399). The no-drug respiratory rate was 11 respirations per minute (95% CI: 9-13 rpm) and there were no significant differences in respiratory rate across any conditions. To assess if physiological confounds could explain observed psilocybin-associated FC changes, the two participants with the highest quality pulse and respiratory data (P4, P5) were selectedCT and added physiological regressors during the nuisance regression step. This included 19 regressors generated from pulse-oximetry, respiratory belt, and their combination, generated using the PhysIO toolbox. The study results were not altered by the inclusion of PhysIO- generate regressors (FIGS.23A-23D).

[0276] Supplementary Methods and Results – Regression of evoked responsein preprocessing: In our analyses of the effects of task on FC change and NGSC, and analyses of interactions between drug and task (FIGS.29A-29B), evoked responses were not removed from timecourses prior to computing FC. To test if task evoked responses were affecting the observed results, we repeated the computation of whole-brain FC change and NGSC on timecourses following regression of evoked responses. Specifically, SPM’s 3-parameter hemodynamic response function was convolved with the task design matrix plus a parameter coding response / non-response trials. These four timecourses were added to the list of regressors (along with other tissue- and movement-based nuisance regressors) prior to nuisance regression and smoothing. Whole-brain FC change and NGSC were then re- computed on residual timecourses for all task scans. We observed that the interaction of task with psilocybin was unaffected by regressing out evoked responses (FIGS. 32A-32B).

[0277] Example 2 - Precision Functional Mapping Trials to accelerateneuropsychiatric drug development

[0278] Reliable human biomarkers for brain circuit target engagement arecritical for advancing drug development in central nervous system (CNS) disorders. Positron Emission Tomography (PET), Electroencephalogram (EEG), and functional magnetic resonance imaging have been used for this purpose. FMRI offers superior spatial resolution for tracking brain function. However, previous fMRI research has demonstrated low signal- to-noise ratio, and lack of replicable targets, preventing its adoption in drug development.CT Precision Functional Mapping (PFM)—is a novel approach to fMRI that identifies functional brain areas at the individual level rather than through atlases and group averaging. Individual-specific definition of brain areas and within-patient (n-of-1) analyses dramatically enhance statistical power while group averaging degrades the biological relevance and utility of fMRI. Combined with advances in MRI hardware, software, and image processing, PFM studies can detect drug biomarkers with high sensitivity at low costs.

[0279] Example embodiments in this example describe a PrecisionFunctional Mapping Trial (PFM-T), which combines PFM methods with imaging on and off drug, to rapidly and cost-effectively quantify brain penetration, and pharmacodynamics. Example embodiments of PFM-Ts are described, including assessing biomarkers for psilocybin and methylphenidate. Although explained with respect to assessing biomarkers for certain drugs, the methods and techniques described herein may be applied to any appropriate biomarker assessment.

[0280] The inventors of the present application have determined that PFM isparticularly well suited for emerging CNS compounds with novel mechanisms of action. It enables early detection of brain penetration and functional engagement, even in the absence of PET ligands or behavioral readouts.

[0281] In central nervous system (CNS) drug clinical development, humanbrain biomarkers play a crucial early role by providing insight into drug action and target engagement. Once initial clinical testing confirms a compound’s safety and tolerability, key questions emerge to guide clinical development: is the drug modulating a specific region of the brain with sufficient ED50 (median effective dose); what receptor or neural circuit mechanism are involved; how rapidly is the drug metabolized in the brain; which clinical indication(s) hold the most promise for further evaluation. Investment in early clinicalCT development can enhance efficiency by mitigating the risk of costly late-stage failures. Historically, Positron Emission Tomography (PET), EEG, and fMRI have been used to probe these questions. While each method offers utility, they also present significant limitations. For many novel therapeutics, it remains unclear whether these modalities can reliably detect target engagement signals.

[0282] FMRI measures how specific functional brain areas respond (withtask fMRI) or communicate (with resting-state fMRI, usually). Task fMRI has been used to probe cognitive processes involved in attention, memory, emotion regulation, decision- making, and other domains. Resting-state fMRI typically measures functional connectivity between collections of areas which underly the above cognitive processes, such as the default mode (DMN), salience, task control, sensorimotor network and others. In neuropsychopharmacology research, these techniques can evaluate changes induced by drug exposure. Precision Functional Mapping (PFM) represents a novel fMRI approach that maps functional brain areas at the individual level, bypassing reliance on standardized atlases or group averages. Unlike traditional neuroimaging methods, PFM first precisely defines functional brain areas in each participant using resting-state fMRI data. Brain areas defined in this way, while variable across individuals, are highly reliable across imaging sessions and align to functional activation from task fMRI. This crucial step enables the detection of subtle drug effects on brain activity or connectivity, even when behavior is unaffected. Recent PFM studies have identified robust drug responses using small cohorts, underscoring its sensitivity.

[0283] The inventors of the present application applied dense repeatedsampling and precision functional mapping to investigate the effects of psilocybin and methylphenidate. The goal was to use this new approach to fMRI to tackle unansweredCT questions in psychedelic drug research. The study included just 7 participant, and cost $90,000, yet the results demonstrated robust and reproducible biomarkers. High-dose psilocybin (25 mg) produced an FC decrease in the default mode network that was highly consistent across participants. By acquiring baseline, on-drug, and post-drug data for each participant, acute changes in FC were able to be charted that were massive. Psilocybin driven changes in hippocampal-cortical FC were also observed that persisted for weeks after.

[0284] Supporting the validity of PFM drug biomarkers, FC changes afterpsilocybin matched the PET-based map of 5HT2A receptor density and FC changes after methylphenidate 40mg (stimulant, the active placebo) matched the PET-based map of NE Transporter density, as seen in Figs. 39A-C. Figs. 39A-C demonstrate that PFM-T drug effect maps match known receptor / transporter drug targets. PFM-T drug effects are measured with functional connectivity (FC) change for each brain areas. FIG. 39A shows Psilocybin and methylphenidate associated FC change between (top) and within (bottom) network. FIG.39B is PET-based maps of 5HT-2A receptor and norepinephrine transporter radiotracer binding. FIG. 39C graphs comparison of PFMT drug effect maps to PET maps. Each dot represents a brain area from the Gordon parcellation (324 cortical areas).

[0285] PFM is particularly well suited for emerging CNS compounds withnovel mechanisms, including rapid-acting antidepressants, cognition-enhancing agents, and neuroplasticity-modulating compounds. Table 1 (below) is an overview of indications and drug classes which have shown promising evidence of fMRI-based biomarkers that could be used in a PFM study. In the table, SSRI is selective serotonin, reuptake inhibitor; BOLD is blood-oxygen-level-dependent; DMN is default mode network; FC is functional connectivity; ADHD is attention-deficit / hyperactivity disorder; mPFC is medial prefrontal cortex. PFM can capture early evidence of brain penetration and target engagement in scenarios where other modalities do not.CTTable 1

[0286] EEG is typically acquired using a cap with electrodes that can be fittedto the scalp to measure electrical activity in the cortex. EEG has the distinct advantage of being portable, and relatively inexpensive. FIG. 35 compares aspects of EEG, PET and Precision fMRI, including cost. The cost is typically in the range of $100s for standard research EEG. For some drug classes, it can establish brain penetration and provide insight into CNS pharmacodynamics. Frequency-specific changes suggest engagement of certain neurotransmitter targets. For example, GABA agonists (and other sedative hypnotics) typically produce increased beta (13–30 Hz) and decreased alpha (8–12 Hz) power. However, for other common CNS drug classes, such as SSRIs, NMDA antagonists, dopamine agonists and anticholinergics, EEG has not provided reliable readouts of target engagement.

[0287] EEG from scalp electrodes detects cortical surface activity with hightemporal resolution but not signals from deeper brain structures. The hippocampus, basal ganglia and amygdala, often at the core of neuropsychiatric pathology, are not directlyCT accessed with EEG. Drugs targeting these deeper regions may not produce clear EEG signals. Since EEG relies on transmission of small bioelectrical currents through the scalp, it lacks resolution to detect neuroanatomical patterns. This also makes EEG more sensitive to non- drug-related confounds such as movement, eye blinks, or background electronic noise.

[0288] PET was the original tool for functional brain imaging, and thepredecessor to fMRI. Landmark PET studies demonstrated the ability to measure brain blood flow and brain metabolism. However, participants are exposed to radiation during PET imaging. Guidelines limited such studies to one or two PET scans per participant. This constraint drove PET imagers to develop new methods to co-register brains and average results across a group to achieve adequate statistical power.

[0289] Today, in CNS drug development, PET is primarily used to assessdrugs that target specific receptor for which a reliable radiotracer exists. The success of PET in this context has helped expand and optimize established drug classes. For example, if a new D2 antagonist antipsychotic can achieve 65-75% receptor D2 occupancy in the striatum, it is likely that it will achieve efficacy for psychosis while minimizing unwanted extrapyramidal side effects (such as dystonic reaction, parkinsonism). For new D2 antipsychotics, PET imaging with 11C-raclopride is a pivotal step in establishing CNS target engagement and dose selection for subsequent clinical development. With this tool in hand, pharmaceutical companies successfully developed roughly 60 first- and second-generation antipsychotics.

[0290] However, for most novel drug targets, appropriate ligands and optimalreceptor occupancy are unknown, either because ligands do not exist for the drug target (e.g. cannabinoid CB1 receptor), or receptor occupancy is a poor indicator of downstream effects (e.g. clozapine and NMDA antagonists like ketamine). While the number of radioligands available will continue to expand, PET remains limited by radiation and thus dependent onCT group averaging Finally, the cost of synthesis, radiolabeling, infusion, and measurement of PET radioligands is not trivial. Depending on a variety of factors, a single PET scan with a custom radiotracer may cost $10,000 or more.

[0291] Functional MRI measures brain activity by detecting changes in bloodoxygenation levels (BOLD) signal. Historically, the same study design principles and image analysis software developed for group averaging of PET scans were adapted to begin studying brain function with lower radiation, lower cost, and better resolution using fMRI.

[0292] Task fMRI measures brain activity in response to specific stimuli orcognitive demands. It has been useful for studying cognitive processes involved in attention, memory, emotion regulation, and decision-making. Resting state fMRI measures fluctuations in the BOLD signals in the absence of a task. Regions with related functions exhibit correlated fluctuations in their BOLD signals. Thus, functional connectivity analysis measures the correlation of resting BOLD signal between different regions of the brain. This approach has identified networks of functionally connected areas, such as the default-mode (DMN), salience, action-mode (AMN), sensorimotor network and many others.

[0293] During the transition from PET to fMRI in the 1980s and 1990sestablished group-averaging approaches were carried forward, with rare but noteworthy exceptions. While the broad organization of brain networks is conserved across humans, the exact anatomical location of functional areas varies significantly between individuals. For instance, a specific region in retrosplenial cortex involved in processing of visual scenes may vary in shape and location between individuals by as much as 1 cm, as see in FIG.36. FIG. 36 is task fMRI ‘Scene > Face’ contrast maps (t-statistics) for two individuals and for group average (N = 10) and demonstrates group-averaging across inter-individual differences decreases fMRI effect sizes. Boundaries of each individual’s ‘context association network’, defined from independently acquired resting fMRI, are overlaid in white on the sameCT individual’s task contrast (left two rectangles), and then mismatched across individuals (third rectangle). Group average (fourth rectangle) shows lower activation peaks. Data are from a cohort of N=10 who underwent dense sampling of task and resting fMRI. Even modern nonlinear and surface-based registration approaches may fail to adequately align functional brain areas across individuals. This anatomical variability means group averaging reduces both statistical power and spatial resolution in fMRI analyses. Group-level results may therefore appear more statistically significant with greater spatial smoothing (up to 10 mm). While group-averaged data might suggest large measurement errors in fMRI, careful dissection of sources of variance reveals that technical (measurement) variability and day- to-day fluctuations account for only a small portion of observed variance compared to true individual variability of functional areas.

[0294] Still today, fMRI studies, particularly pharmaco-fMRI, often use aclassical two-arm placebo-controlled design, acquiring 5-8 minutes of fMRI data per participant, and comparing group-averaged FC or evoked response between a pair of atlas- defined regions. At least some such studies have shown small effect sizes, poor reproducibility, and failed to validate targets. Comparisons of test-retest reliability in existing datasets have been leveraged to conclude that previously standard group-averaged fMRI is inferior to EEG as a tool for phase 1 drug research. Thus, like early electric cars and solar panels, the high cost and flawed design of conventional fMRI research led some to prematurely conclude that fMRI lacks translational utility in drug development.

[0295] It may be questioned whether neuroanatomical precision is ofrelevance to drug development. Critics highlight the limited sensitivity of group-level fMRI biomarkers. Comparing the spatial resolution of PET, EEG, and fMRI (FIG. 35) with the historical utility of each in early phase development may lead to underestimating the valueCT of anatomical specificity. However, emerging evidence challenges this perspective. The ability to resolve brain function at millimeter scale, has provided critical new translational targets in mood, addiction, neurodegeneration and seizure disorders.

[0296] Recent analyses of clinical outcomes in brain stimulation trials havemade it clear that efficacy often depends on precise targeting of brain areas / nuclei, at millimeter resolution. Small nuclei of the limbic system (i.e. in the basal ganglia and hippocampus) are equally critical in drug development. Consider D2 antipsychotics. Higher resolution PET scanning allows separate measurement of on-target D2 binding in the mesolimbic pathway (e.g. ventral striatum, nucleus accumbens) and off-target binding in the nigrostriatal pathway (dorsal striatum) millimeters away. Advances in MRI hardware, software, and image processing now makes it possible to resolve brain activity in the cortex and subcortex at 1mm3resolution.

[0297] Precision functional mapping is an approach to fMRI which takescanonical brain areas and brain networks, shared across people, and precisely localizes them in a single individual’s brain. The paradigm shift behind PFM is the realization that the precise topography of brain areas and networks varies across individuals and no single individual looks like the group average, as seen in FIG. 37. FIG. 37 compares functional networks defined in group averaged data (left) versus in a single individual using repeated sampling and PFM (right). When network detection is done at an individual level, fine- grained details of brain network organization emerge that are not visible in group averaged data - for example, the ‘intrusion’ of small regions in the lateral prefrontal cortex that are associated with action-mode, salience, and other systems. Thus, analyses based on individual-defined functional networks and repeated sampling within-participant (N=1) can be orders of magnitude more powerful than studies that rely on group-averaging of fMRI data. By avoiding averaging of fMRI data across participants, PFM turns individualCT differences and measurement variability from pitfalls into strengths and overcomes limitations of conventional fMRI. By combining this philosophy with imaging advances (e.g. multi-band, multi-echo, thermal denoising), PFM has yielded many neuroanatomical discoveries.

[0298] The maturing of the PFM approach has included development andvalidation of several tools to define individual-specific brain areas and networks. In addition to precise parcellation of cortical areas, these approaches have improved our understanding of functional anatomy of the subcortex, hippocampus, amygdala, and brainstem nuclei. Individualized network parcellation has led to the discovery of a somato-cognitive action network (SCAN) embedded in the central sulcus implicated in Parkinson’s disease.

[0299] PFM may measure objective biomarkers of psychopathology. At leastone advance over prior attempts to identify FC biomarkers of depression was analyzing the spatial patterns of individual-specific functional networks. An enlarged salience network was discovered as a trait biomarker of individuals with recurrent depression. The biomarker showed adequate effect size (Cohen’s D > 1) to be used for prospective cohort enrichment. This could help overcome the challenges associated with the use of subjective scales.

[0300] PFM has also proven to be well suited as a translational tool. Oneknown approach showed that dominant arm casting disconnects inter-hemispheric motor circuits – reporting large effect sizes (Cohen’s D > 1.5) in a small sample (N=3). An N=1 study mapping brain networks in an individual with bilateral perinatal strokes found dramatic evidence of network plasticity in the perinatal period. The PFM approach was applied to study the effects of psilocybin and methylphenidate (N=7). With this approach, acute changes in FC were charted that were massive. Some were strongly linked to the subjective psychedelic ratings (r2 = 0.81) while others were more closely linked to 5HT-2A receptorCT agonism across drugs, suggesting utility as biomarkers for non-hallucinogenic psychedelic analogs.

[0301] Precision Functional Mapping Trials (PFM-T) - pharmaco-fMRI re-imagined.

[0302] PFM-T is a drug biomarker approach built on the principles of PFM.Included in the definition of PFM-T is a set of prescriptions that work in synergy to enhance sensitivity of early-phase studies. The approach is optimized to establish brain penetration and target engagement with high sensitivity in small cohorts.

[0303] These principles includes: 1) Individual-specific functional mapping,2) Within-individual [NOT between-cohort] study design, 3) High fidelity measurement and repeated sampling, 4) Physiological markers and arousal control, and 5) Advanced imaging techniques and noise reduction.

[0304] 1) Individual-specific functional mapping. Area parcellationapproaches typically identify brain areas based on sharp transitions in connectivity across the cortical sheet. Individualized network parcellation tools typically use community detection or clustering algorithms to segment the cortex and subcortex into a set of communities or ‘resting state networks’. Some define networks empirically while others begin with atlas-based a priori brain networks and iteratively refine network boundaries within the individual.

[0305] 2) Within-individual [NOT between-cohort] study design. Trialdesigns in which a participant can serve as their own control should be used whenever possible. By assessing drug placebo differences within the same individuals, cross-over designs control for individual variability and increase statistical power. A mixed effects model can be used to assess the effect of drug on brain biomarker while controlling forCT individual, session, motion, and other covariate or confounds. Repeated on / off sampling or a range of doses further boosts statistical power.

[0306] 3) High fidelity measurement and repeated sampling. Many restingfMRI studies collect 5-8 minutes of data per participant. This may be inadequate to capture reliable FC measures or detect clinically meaningful differences. A recent systematic analysis of cost tradeoffs in brain-wide association studies found 40 minutes or more of resting fMRI data per participant is optimal. Multiple visits per condition allow measurement of day-to-day and session-to- session variability, helping to distinguish estimands from natural fluctuations and measurement error. Between-individual differences and FC measurement reliability are quantified in Figs.40A and 40B. FIG. 40A graphically presents sources of connectome (whole brain FC) measurement variability. FIG. 40B is a graph comparing maximum achievable prediction accuracy as a function of study design (total fMRI budget, scan cost per hour and overhead cost per participant) in large existing RS- FC datasets. When overhead cost per participant exceeds $1,000 it is optimal to collect much longer resting scans than has typically been acquired.

[0307] 4) Physiological markers and arousal control. Physiological changesinfluence the fMRI and electrophysiological signal and obscure neurobiological drug effects. By monitoring and accounting for factors such as heart rate, respiration, and arousal levels, such confounds can be mitigated. PhysIO is an open source toolbox that makes it easy to regress physiological confounds out of fMRI data. Vital sign monitoring: Recording pulse and respiration to adjust for physiological noise. Time-of-day: Scheduling imaging sessions at consistent times to control for circadian influences. Arousal level: Incorporating measures of arousal to account for fluctuations that could impact FC

[0308] 5) Advanced imaging techniques and noise reduction. Multi-echosequences: enables separate estimation of S0 (spin history artifact) and R2* (blood oxygenation contrast) components of the MRI signal for every voxel. With removal of S0 artifact, 10 min of multi-echo data yields test-retest reliability similar to 30 min of single-CT echo data, with the largest gains in areas critical to neuropsychiatric illness; mPFC, striatum. Confound removal: Our imaging group has developed gold-standard denoising procedures for T-fMRI and R-fMRI. Removal of thermal noise: NOise Reduction with Distribution Corrected PCA (NORDIC), operates on fMRI data, removing components that cannot be distinguished from zero-mean Gaussian distributed noise, and substantially improves SNR. The NORDIC denoising technique uses phase data to remove thermal noise, further improving the SNR of fMRI signal.

[0309] Within-individual analyses

[0310] Like PFM, the core of PFM-T is within-individual (N-of-1) imaginganalyses. Each individual serves as their own control. Within-patient analyses, using individual-specific functional areas, allows for detection of both resting-state and task-based brain connectivity changes which are obscured by traditional group-averaging methods. Group effects are assessed only as a second level analysis, after comparisons of changes on / off drug has been measured within individuals. Combining this with multiple visits per estimand, PFM-Ts can model and control for person-to-person and day-to-day variability – which may be as large or larger than drug effects.

[0311] In traditional biomarker studies, each study participant may completea single visit. In such a design, it is impossible to say how much signal variance is caused by data fidelity, measurement error, day-to-day variability, or individual differences. Within- individual repeated measurement designs unlock a hidden power – once these different sources of variance can be independently modeled, then measuring variability can provide meaningful insights into how an estimand varies across individuals. This makes it easier to determine what covariates affect drug response.

[0312] Together, defining brain areas in individuals and testing drug effectslongitudinally, greatly enhance the ability to detect functional connectivity or task relatedCT endpoints. Note that hypotheses must be posed in a way that respects brain circuits and networks as a basic functional unit – e.g. ‘we hypothesize that methylphenidate will reduce functional connectivity within the dorsal somatomotor system’. FIG. 38A is a comparison across fMRI study methods of power to detect a stimulants biomarker. To demonstrate the importance of study design and data quality, this example takes a known FC biomarker of stimulant drugs (decreased FC in the somatomotor network) and computes effect size of methylphenidate (generic ritalin) across two different datasets and five analysis methods. Analyses 1 and 2 use data from the Adolescent Brain Cognitive Development (ABCD) Study, cross-sectional data from N=4,320 (390 on stimulant); Analyses 3-5 use data from a longitudinal PFM study including multiple scans on methylphenidate 40mg (Siegel et al., 2024a). Analysis 1 uses all available ABCD data in a mixed effects model controlling for covariates; Analysis 2 is ABCD data, exclusion of high head motion subjects (N=285); Analysis 3 is PFM data, repeated measures but parallel arm design; Analysis 4 is PFM data, paired design with group somatomotor parcels: Analysis 5 is PFM data, paired design with individual-defined somatomotor parcels. Based on effect sizes, the number of study participants needed to achieve 95% power to the detect drug effect is computed. FIG.38B is a conceptual design and power estimate for a single ascending dose biomarker study. For each participant, PFM-style brain area definition is done, and a predefined FC biomarker is estimated at each dose / timepoint. This means that each participant serves as their own control, reducing noise and making it easier to detect systematic changes with dose. Assuming a Pearson correlation between drug and biomarker of ~0.5 and an ICC of 0.9, the power to detect a correlation of 0.5 is 94.4%.

[0313] PFM-T - Reducing measurement variabilityCT

[0314] Measurement variability has been consistently under-recognized infMRI studies. Reliable measurement of functional connectivity and definition of individual networks requires 20+ minutes of high-quality resting fMRI data. However, this is improving with fMRI methods advances.

[0315] Another advance in fMRI acquisition is the ability to carefullymonitor and control for physiological confounds. Accounting for factors such as pulse, respiration, and arousal levels, mitigates confounding influences that obscure the true effects of the drug or other invention. Furthermore, time-of-day consistency in imaging sessions helps control for circadian fluctuations that might otherwise introduce noise into the data.

[0316] The overall design of PFM-T, with its individualized mapping andadvanced fMRI techniques, delivers high sensitivity and specificity for detecting drug effects with much smaller sample sizes than conventional fMRI-based methods.

[0317] Task versus rest and practice effects

[0318] Resting and task fMRI, offer complementary insights intoneurocircuitry, capturing how drugs modulate brain activity and connectivity at rest and response during cognitive tasks, respectively. However, the example approaches described herein approach to longitudinal imaging does present a challenge for task fMRI. In studies where individuals are asked to complete that same task on multiple occasions, there will typically be a practice effect; a decrement in evoked response over time. FC biomarkers avoid practice effects. Still, recent drug task fMRI studies have shown that evoked activity can provide valuable evidence of target engagement despite practice effects (for example, reward anticipation in the nucleus accumbens during a monetary incentive delay task). When feasible, it may be worthwhile to include both task and rest fMRI. In the context of drug studies, this approach also allows for observation of drug-by-environment interactions.CT Further research may explore the impact of repeating task fMRI paradigms numerous times in such a design.

[0319] Adoption of PFM-T for drug development

[0320] MRI hardware and software technology continue to advance ataccelerating rates, offering greater potential and further insights. While many known pre- 2010 fMRI studies were accomplished on 1.5 tesla scanners, 7 tesla scanners are now becoming an industry standard. Recent advances in MRI hardware, software, and image processing now make it possible to resolve brain activity at <1mm3resolution. The limit of spatial specificity of neurovascular coupling is around 0.1mm, leaving ample room for further advances.

[0321] Conventional fMRI has provided important insights into brainfunction, but technological and methodological limitations - such as group-averaging, and insufficient data quantity and quality - have limited effect sizes, reproducibility, and translational utility. These constraints have led drug developers to favor modalities like EEG (for its portability) or PET (for its proven target-engagement assays) in early-phase clinical development. Yet each alternative has trade-offs: EEG cannot resolve small circuits and deep structures implicated in neuropsychiatric disorders, while PET faces barriers like radiation exposure, cost, and similar group-level analysis limitations.

[0322] Precision Functional Mapping Trials (PFM-T) represent atransformative advance over these modalities, offering millimeter-scale neuroanatomical precision, within-individual repeated sampling, and the ability to model and control for variability across individuals and sessions. By leveraging these strengths, PFM-T provides a highly sensitive and specific tool for assessing target engagement, pharmacodynamics, and neurocircuitry effects in early-phase drug development. PFM techniques make it possible to detect replicable drug effects in small cohorts (N<10, cost < $1M). This approach not onlyCT addresses the limitations of conventional fMRI but also surpasses EEG and PET in its ability to deliver actionable insights with smaller sample sizes and lower costs.

[0323] As the field of CNS drug development continues to grapple with highfailure rates and escalating costs, PFM-T offers a promising pathway to improve R&D productivity by enabling faster, more reliable proof-of-concept studies and greatly reducing the risk of costly late-stage failures.

[0324] As compared to known systems that are used for brain mapping, theembodiments described herein enable a substantially efficient system for brain mapping. More specifically, the embodiments described herein include a computing device for use in a system for mapping brain activity of a subject that generally comprises a processor programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during various states. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject (and / or prior data from the same subject). The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the (resting or task) state data point(s) and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map. By using previously acquired data points to categorize the brain activity in a plurality if networks in the brain of the subject, the drawbacks of prior techniques may be avoided. Moreover, by having the processor select the plurality of measurements, a user may no longer need to spend a considerable amount of time determining which measurements, such as voxels, to select. The techniques disclosed herein are not well- understood, routine, or conventional, and represent improvements in the computing andCT analytical technology of the technical field, and also represent a practical application withing the technical field.

[0325] For all of the above-described embodiments and usages, any codeand / or data or other information may be stored in a memory of the above-described system, and / or in a remote (e.g., cloud) storage system (e.g., in a dedicated database or other centralized storage mechanism). Embodiments of the invention may be described in the general context of computer-executable instructions, such as program modules, executed by one or more computers or other devices. The computer-executable instructions may be organized into one or more computer-executable components or modules. Generally, program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types. Aspects of the invention may be implemented with any number and organization of such components or modules. For example, aspects of the invention are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other embodiments of the invention may include different computer-executable instructions or components having more or less functionality than illustrated and described herein. Aspects of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

[0326] In operation, a computer executes computer-executablecode / instructions embodied in one or more computer-executable components stored on one or more computer-readable media to implement aspects of the invention described and / orCT illustrated herein. Code can include application source code, application object code, configuration data, additional input events, application states, assertion statements, validation results, and / or any other type of data. The order of execution or performance of the operations in embodiments of the invention illustrated and described herein is not essential, unless otherwise specified. That is, the operations may be performed in any order, unless otherwise specified, and embodiments of the invention may include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the invention.

[0327] The raw and / or processed data and / or any related graphical or otherrepresentations of the data may be processed by the above-described computer system or the like and output for display on a display device such as a TV, monitor, mobile device (e.g., mobile phone or tablet) and the like such that a technician / practitioner / evaluator / therapist / user can view and / or manipulate the data (e.g., the data may be presented in a visual format for presenting certain aspects of the test results, for example as shown in the applicable above-noted figures). For example, a display monitor may be connected (e.g., wired or wirelessly) to the above- described computer system to provide a visual output on the computer system. The computer system may have an operating system with a graphical user interface capable of being used by a user to (i) input, view, execute and / or manipulate the above- described computer code and / or (ii) process the obtained sensor data and any related graphical representations of such data in the manners described above. The operating system may be capable of running software applications such as those described above (e.g., MatLab and the like) for carrying out the above-described techniques and also any necessary post-processing and / or outputting of the obtained sensorCT data for viewing, such as for viewing by a therapist that is treating / diagnosing a patient / test subject. Additional software for other code / data manipulations and / or for generating other visuals relating to the data may also be present on the computer system.

[0328] In the present disclosure, all or part of the units or devices of anysystem and / or apparatus, and / or all or part of functional blocks in any block diagrams and flow charts may be executed by one or more electronic circuitries including a semiconductor device, a semiconductor integrated circuit (IC) (e.g., such as a processor), or a large-scale integration (LSI). The LSI or IC may be integrated into one chip and may be constituted through combination of two or more chips. For example, "processor" as used herein refers generally to any programmable system including systems and microcontrollers, reduced instruction set circuits (RISC), application specific integrated circuits (ASIC), programmable logic circuits (PLC), and any other circuit or processor capable of executing the functions described herein. The functional blocks other than a storage element may be integrated into one chip. The integrated circuitry that is called LSI or IC in the present disclosure is also called differently depending on the degree of integrations, and may be called a system LSI, VLSI (very large-scale integration), or ULSI (ultra large-scale integration). For an identical purpose, it is possible to use an FPGA (field programmable gate array) that is programmed after manufacture of the LSI, or a reconfigurable logic device that allows for reconfiguration of connections inside the LSI or setup of circuitry blocks inside the LSI. Furthermore, part or all of the functions or operations of units, devices or parts or all of devices can be executed by software processing (e.g., coding, algorithms, etc.). In this case, the software is recorded one or more non-transitory computer-readable recording media, such as one or more ROMs, RAMs (e.g., DRAM, SRAM), optical disks, hard disk drives, solid-state memory, servers, cloud storage, and so on and so forth, having stored thereon executable instructions whichCT can be executed to carry out the desired processing functions and / or circuit operations. For example, when the software is executed by a processor, the software causes the processor and / or a peripheral device to execute a specific function within the software. The system / method / device of the present disclosure may include (i) one or more non-transitory computer-readable recording mediums that store the software, (ii) one or more processors (e.g., for executing the software or for providing other functionality), and (iii) a necessary hardware device (e.g., a hardware interface). Artificial intelligence in any and all types and formats may be utilized in any of the steps, techniques, protocols, analyses, and / or any other manipulation, generation, or other creation of data, results and / or any information described herein. This includes but is not limited to computer visions, machine learning, deep learning, neural networks, algorithms, and any data, models, and training needed for such. The above examples are example only, and thus are not intended to limit in any way the definitions and / or meanings of the terms.

[0329] Data conduits and any other communication or data transfer asdescribed herein may comprise wired or wireless connections. For example, a wired network connection (e.g., Ethernet or an optical fiber), a wireless communication means, such as radio frequency (RF), e.g., FM radio and / or digital audio broadcasting, WiFi (e.g., IEEE 802.11 standards), WIMAX, a short-range wireless communication channel such as BLUETOOTH, a cellular phone technology (e.g., GSM), a satellite communication link, and / or any other suitable communication means. Such data conduits, in particular wired versions, can also be referred to as a system bus.

[0330] The embodiments were chosen and described in order to best explainthe principles of the disclosure and its practical application to thereby enable others skilled in the art to best utilize the disclosure in various embodiments and with various modificationsCT as are suited to the particular use contemplated. Aspects of the disclosed embodiments may be mixed to arrive at further embodiments within the scope of the invention.

[0331] As various modifications could be made in the constructions andmethods herein described and illustrated without departing from the scope of the disclosure, it is intended that all matter contained in the foregoing description or shown in the accompanying drawings shall be interpreted as illustrative rather than limiting. Thus, the breadth and scope of the present disclosure should not be limited by any of the above- described example embodiments, but should be defined only in accordance with the following claims appended hereto and their equivalents.

[0332] Various aspects of the embodiments described above may be usedalone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and is therefore not limited in its application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects describe in other embodiments.

[0333] Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.

[0334] Also, the phraseology and terminology used herein is for the purposeof description and should not be regarded as limiting. The use of “including,” “comprising,”CT “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

[0335] The word “example” is used herein to mean serving as an example,instance, or illustration. Any embodiment, implementation, process, feature, etc. described herein as example should therefore be understood to be an illustrative example and should not be understood to be a preferred or advantageous example unless otherwise indicated.

[0336] It should be noted that, as used herein, the term “couple” is not limitedto a direct mechanical, electrical, and / or communication connection between components, but may also include an indirect mechanical, electrical, and / or communication connection between multiple components.

[0337] Having thus described several aspects of at least one embodiment, itis to be appreciated that various alternations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure and are intended to be within the spirit and scope of the principles described herein. Accordingly, the foregoing description and drawings are by way of example only.

Claims

CT WHAT IS CLAIMED IS:

1. A brain activity mapping system for deployment in a precision imaging clinical trial environment for measuring biomarkers of brain penetration, the system comprising a computing device, the computing device including: a memory device, the memory device configured to store an anatomic imaging map of a subject's brain and a state network map of the subject's brain generated using both resting functional magnetic resonance imaging (fMRI) data and task fMRI data collected from a brain of the subject with an fMRI technique, the state network map comprising, for each state network of a plurality of state networks, data structures assigned to the state network defining a topography of the state network of the subject's brain, the plurality of state networks comprising at least one of a default mode network (DMN), fronto-parietal network (FPN), dorsal attention network (DAN), parietal memory (PMem), ventral attention network (VAN), action-mode network (AMN), salience network, context network, visual network (VIS), somato-motor network (SM), somato-motor face network (SMF), and auditory network (AUD); and a processor operatively coupled to the memory device, the processor configured to: control an magnetic resonance imaging (MRI) machine to repeatedly measure acute, subacute, and chronic physiological parameters of the subject; receive the state network map and the anatomic imaging map from the memory device; coregister the state network map with the anatomic imaging map to form a combined anatomic and functional map; andCT output the combined anatomic and functional map of the subject's brain.

2. The system of claim 1, wherein the precision imaging clinical trial environment includes a clinical trial for testing psychiatric drugs on a subject.

3. The system of claim 2, wherein the psychiatric drugs include psychedelics.

4. The system of claim 3, wherein the psychedelics include psilocybin.

5. The system of claim 1, wherein the subject comprises a plurality of subjects, the data structures comprise voxels, and each state network map is derived from a plurality of correlation maps each comprising a plurality of elements, each element comprising a correlation between a one time-series measurement and an additional time-series measurement selected from a plurality of time-series measurements, the one time-series measurement obtained from one location within the brain of an individual subject from the plurality of subjects during a resting or task state, and the additional time-series measurement obtained from one of a plurality of additional locations within the brain of the individual subject during a resting or task state.

6. The system of claim 1, wherein the computing device is further configured to utilize a linear mixed effects model.

7. The system of claim 1, further comprising the MRI machine operatively coupled to the processor of the computing device.CT 8. A computer implemented method of brain activity mapping in a precision imaging clinical trial environment for measuring biomarkers of brain penetration, the method comprising: controlling an MRI machine to repeatedly measure acute, subacute, and chronic physiological parameters of a subject; receiving a state network map of the subject’s brain and an anatomic imaging map from a memory device, the state network map of the subject’s brain generated using both resting functional magnetic resonance imaging (fMRI) data and task fMRI data collected from a brain of the subject with an fMRI technique, the state network map comprising, for each state network of a plurality of state networks, data structures assigned to the state network defining a topography of the state network of the subject's brain, the plurality of state networks comprising at least one of a default mode network (DMN), fronto-parietal network (FPN), dorsal attention network (DAN), parietal memory (PMem), ventral attention network (VAN), action-mode network (AMN), salience network, context network, visual network (VIS), somato-motor network (SM), somato-motor face network (SMF), and auditory network (AUD); coregistering the state network map with an anatomic imaging map to form a combined anatomic and functional map; and outputting the combined anatomic and functional map of the subject's brain.

9. The method of claim 8, wherein the precision imaging clinical trial environment includes a clinical trial for testing psychiatric drugs on a subject.

10. The method of claim 9, wherein the psychiatric drugs include psychedelics.CT 11. The method of claim 10, wherein the psychedelics include psilocybin.

12. The method of claim 8, wherein the subject comprises a plurality of subjects, the data structures comprise voxels, and each state network map is derived from a plurality of correlation maps each comprising a plurality of elements, each element comprising a correlation between a one time-series measurement and an additional time-series measurement selected from a plurality of time-series measurements, the one time-series measurement obtained from one location within the brain of an individual subject from the plurality of subjects during a resting or task state, and the additional time-series measurement obtained from one of a plurality of additional locations within the brain of the individual subject during a resting or task state.

13. The method of claim 9, further comprising testing effects of psychiatric drug condition using a linear mixed effects model.

14. The method of claim 8, further comprising generating the state network map of the subject’s brain generated from resting state fMRI data and task fMRI data acquired from the subject’s brain.

15. A non-transitory computer readable medium storing instructions thereon that when executed by at least one processor, cause the at least one processor map brain activity in a precision imaging clinical trial environment for measuring biomarkers of brain penetration by: control an MRI machine to repeatedly measure acute, subacute, and chronic physiological parameters of a subject;CT receive a state network map of the subject’s brain and an anatomic imaging map from a memory device, the state network map of the subject’s brain generated using both resting functional magnetic resonance imaging (fMRI) data and task fMRI data collected from a brain of the subject with an fMRI technique, the state network map comprising, for each state network of a plurality of state networks, data structures assigned to the state network defining a topography of the state network of the subject's brain, the plurality of state networks comprising at least one of a default mode network (DMN), fronto-parietal network (FPN), dorsal attention network (DAN), parietal memory (PMem), ventral attention network (VAN), action-mode network (AMN), salience network, context network, visual network (VIS), somato-motor network (SM), somato-motor face network (SMF), and auditory network (AUD); coregister the state network map with an anatomic imaging map to form a combined anatomic and functional map; and output the combined anatomic and functional map of the subject's brain.

16. The non-transitory computer readable medium of claim 15, wherein the precision imaging clinical trial environment includes a clinical trial for testing psychiatric drugs on a subject.

17. The non-transitory computer readable medium of claim 16, wherein the psychiatric drugs include psychedelics.

18. The non-transitory computer readable medium of claim 17, wherein the psychedelics include psilocybin.CT 19. The non-transitory computer readable medium of claim 15, wherein the subject comprises a plurality of subjects, the data structures comprise voxels, and each state network map is derived from a plurality of correlation maps each comprising a plurality of elements, each element comprising a correlation between a one time-series measurement and an additional time-series measurement selected from a plurality of time-series measurements, the one time-series measurement obtained from one location within the brain of an individual subject from the plurality of subjects during a resting or task state, and the additional time- series measurement obtained from one of a plurality of additional locations within the brain of the individual subject during a resting or task state.

20. The non-transitory computer readable medium of claim 16, wherein the instructions further cause the at least one processor to test effects of psychiatric drug condition using a linear mixed effects model. .

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