Individualized cognitive function modulation device based on cross-frequency coupling

Abstract

The application discloses an individualized cognitive function adjusting device based on cross-frequency coupling, which comprises a peak frequency automatic extraction module, a spectrum analysis and automatic frequency extraction are used to obtain the peak frequency of the frontal lobe theta oscillation and the parietal lobe gamma oscillation of an individual baseline as the stimulation frequency of transcranial alternating current stimulation; a target site determination module is used to automatically output the stimulation site of tACS by determining the positive and negative power values corresponding to the peak frequency between the left and right brain regions, and the stimulation equipment outputs current to the corresponding brain region according to the output stimulation site; a signal-to-noise ratio calculation module is used to adaptively output the stimulation current intensity of tACS according to the signal-to-noise ratio value at the peak frequency; and a multi-mode regulation model is used to set the peak nesting and valley nesting modes according to the cross-frequency tACS, so that the targeted regulation of cognitive function disorders such as memory, attention and emotion processing is realized.

Description

Technical Field

[0001] This invention relates to the field of neuromodulation, and more particularly to a personalized cognitive function modulation device based on cross-frequency coupling. Background Technology

[0002] High- and low-frequency co-coding can generate complex modulation structures through the interaction between different frequency bands, regulating communication at different spatiotemporal scales. This plays a crucial role in neuronal computation that coordinates cognitive processes such as human perception, memory, and attention. This temporal interaction of high- and low-frequency neural oscillations at different scales is called cross-frequency coupling (CFC), a mechanism by which low-frequency oscillations in the prefrontal cortex control the posterior cortex from top to bottom. In particular, theta-gamma coupling (TGC) has been shown to promote cognitive activities such as perception, learning, and memory. [1-3] Existing research has found that TGC (transient genomic cascade) is related to the maintenance of working memory, recall of long-term memory, attention, learning, and the exchange and changes of information between neurons in these tasks. [2,4,5] Heusser et al. found that in the formation of human episodic memory, events in different sequences are represented by higher gamma energies nested in different phases of theta oscillations. This encoding pattern is related to temporal sequential memory, and they pointed out that TGC may be the encoding mechanism of sequential memory. [6] Friese et al.'s research also showed that successful memory encoding is associated with an increase in the interaction strength between frontal theta oscillations and posterior cortical gamma oscillations. [7] Furthermore, research has shown that cortical oscillation mechanisms control human socio-emotional behavior through the coupling of slow theta oscillations and fast gamma oscillations. The dorsolateral prefrontal cortex is involved in TGC patterns and controls these socio-emotional behaviors by upregulating areas such as the parietal cortex. [8] .

[0003] Transcranial alternating current stimulation (tACS) is an emerging frequency-specific modulation technique that improves specific cognitive functions by modulating endogenous neural oscillations at specific frequencies. In recent years, cross-frequency tACS stimulation patterns have attracted considerable attention, particularly theta-gamma tACS, which is mainly divided into peak-coupled tACS and trough-coupled tACS. Both nesting patterns use a low-frequency signal as the baseband signal, synchronously superimposing a high-frequency signal onto the peaks or troughs of the baseband signal. Alekseichuk et al. found that applying theta-gamma tACS to the prefrontal cortex, especially when high gamma peaks were nested within theta peaks, significantly improved working memory and global functional connectivity in healthy subjects, outperforming single-frequency theta-tACS. [2] Furthermore, Riddle et al., by applying theta-gamma tACS, found that theta-gamma coupling increased in the quantitative dimension of cognitive control task rules (size of the memory set). [9] Abelmann et al., by applying theta-gamma tACS to the prefrontal cortex, found that when the theta oscillations in the prefrontal cortex were coupled with an increase in the power of the gamma oscillations in the motor cortex, social emotional control was significantly improved. [8] Based on the current research status, cross-frequency tACS has been proposed as a novel tACS approach to demonstrate the causal role of coupling between oscillations of different frequencies in cognitive function, and it is expected to become a new therapeutic strategy to enhance or improve cognitive impairment.

[0004] However, due to individual differences, the frequency of neural oscillation targets varies among individuals, as do the intensity of neural oscillation responses and the abnormal brain regions. Different stimulation frequencies, intensities, stimulation sites, and nesting patterns are key factors influencing the effectiveness of tACS regulation. Summary of the Invention

[0005] This invention provides an individualized cognitive function modulation device based on theta-gamma cross-frequency coupling. The invention acquires an individual's baseline resting EEG signal, extracts the peak frequencies of frontal lobe theta oscillations and parietal lobe gamma oscillations as stimulation frequencies, and automatically outputs the stimulation site and current intensity by calculating the power difference and signal-to-noise ratio at the peak frequencies. The invention offers two theta-gamma tACS nested modes for selection, achieving individualized targeted and precise modulation through high- and low-frequency oscillation collaborative decoding, thereby improving or enhancing cognitive functions such as attention, memory, and emotion processing. See the description below for details.

[0006] A personalized cognitive function modulation device based on cross-frequency coupling, the device comprising:

[0007] The peak frequency automated extraction module uses spectral analysis and automated frequency extraction to obtain the peak frequencies of the frontal lobe theta oscillation and parietal lobe gamma oscillation at the individual baseline, which are then used as the stimulation frequencies for transcranial alternating current stimulation.

[0008] The target site determination module is used to automatically output the stimulation site of tACS by determining whether the power value corresponding to the peak frequency in the left and right brain regions is positive or negative. Based on the output stimulation site, the stimulation device outputs current to the corresponding brain region.

[0009] The signal-to-noise ratio (SNR) calculation module is used to calculate the SNR value at the peak frequency and adaptively output the stimulation current intensity of tACS based on the magnitude of the SNR value.

[0010] A multimodal regulation model is used to set peak nesting and trough nesting modes based on the cross-frequency tACS to achieve targeted regulation of cognitive dysfunctions such as memory, attention, and emotion processing.

[0011] Specifically, the peak frequencies of the frontal theta oscillation and parietal gamma oscillation of the individual baseline are obtained by utilizing spectral analysis and automated frequency extraction as follows:

[0012] Welch spectrum analysis was used to calculate the power spectrum curves of the theta band range for each epoch under leads F3 and F4, and the average was superimposed at the epoch level.

[0013] The power spectrum difference curve of the theta band between lead F3 and lead F4 is obtained by spectral subtraction.

[0014] Using the built-in peak and maximum value functions in MATLAB, the peak frequency of the theta band is obtained. By calculating the relative power curves of the gamma band under leads P3 and P4, the individual gamma peak frequency is obtained.

[0015] Furthermore, the stimulation site used to automatically output tACS by determining the positive or negative power values ​​corresponding to the peak frequencies in the left and right brain regions is specifically:

[0016] When the relative power values ​​at the peak frequencies of theta and gamma are both greater than 0, the stimulation site is F4-P4;

[0017] When the relative power value at the peak frequency of theta is greater than 0 but the relative power value at the peak frequency of thegamma is less than 0, the stimulation site is F4-P3.

[0018] When the relative power values ​​at the peak frequencies of theta and gamma are both less than 0, the stimulation site is F3-P3;

[0019] When the relative power value at the peak frequency of theta is less than 0 but the relative power value at the peak frequency of gamma is greater than 0, the stimulation site is F3-P4.

[0020] The method includes:

[0021] Based on the calculated peak frequencies of theta and gamma and the stimulation site, the signal-to-noise ratio of theta and gamma oscillations at the target site is calculated as follows:

[0022]

[0023] Where i refers to the theta or gamma frequency band, This refers to the power value at the peak frequency of that frequency band. This refers to the total power value within that frequency band.

[0024] The beneficial effects of the technical solution provided by this invention are:

[0025] 1. This invention addresses the problems of large individual differences in theta and gamma oscillations, lack of stimulation parameter optimization methods, and insufficient development of closed-loop control strategies. It proposes a new individualized closed-loop precise theta-gamma tACS control device. Through an automated parameter optimization algorithm, it solves the existing problems of individual differences and insufficient targeted control information, which helps to achieve precise closed-loop targeted control.

[0026] 2. This invention extracts the baseline frontal lobe theta oscillation and parietal lobe gamma oscillation, calculates the peak frequency of individual theta and gamma activities, and achieves targeted output of stimulation frequency; it automatically outputs stimulation sites based on the positive or negative power difference at the peak frequency.

[0027] 3. This invention achieves automatic setting of stimulation current intensity by calculating the signal-to-noise ratio at the peak frequencies of theta and gamma. In addition, it configures two nested modes of theta-gamma coupling to improve cognitive functions such as attention, memory, and socio-emotional behavior control through closed-loop precise regulation, thereby assisting in the clinical treatment of diseases accompanied by cognitive impairment. Attached Figure Description

[0028] Figure 1 A schematic diagram for determining the peak frequencies of individual theta and gamma oscillations;

[0029] Figure 2 This is a schematic diagram of the stimulation sites;

[0030] Figure 3 Schematic diagrams of waveforms in theta-gamma modes of nested peaks and nested troughs;

[0031] Figure 4 This is a schematic diagram of the operation of an individualized cognitive function modulation device based on theta-gamma cross-frequency coupling. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0033] Example 1

[0034] A personalized cognitive function modulation device based on theta-gamma cross-frequency coupling, see [link to relevant documentation]. Figure 1 The device includes:

[0035] The peak frequency automated extraction module uses spectral analysis and automated frequency extraction algorithms to obtain the peak frequencies of the frontal lobe theta oscillation and parietal lobe gamma oscillation at the individual baseline, which are used as the stimulation frequencies for transcranial alternating current stimulation (tACS).

[0036] The target site determination module is used to automatically output the stimulation site of tACS by determining whether the power value corresponding to the peak frequency in the left and right brain regions is positive or negative. Based on the output stimulation site, the stimulation device outputs current to the corresponding brain region.

[0037] The stimulation sites are the stimulation electrodes, which are directly connected to the stimulation channels of the stimulation device. The stimulation electrodes are fixed to the corresponding brain regions on the subject's head using electrode caps. Based on the stimulation sites output at these locations, such as F3 and P4, the stimulation device will output current at channels F3 and P4. Its main function is to locate the brain regions with abnormal responses based on the power response, and then electrically stimulate the abnormal brain regions.

[0038] The signal-to-noise ratio (SNR) calculation module is used to calculate the SNR value at the peak frequency and then adaptively output the stimulation current intensity of tACS based on the SNR value.

[0039] The multimodal regulation model is used to set two nested modes of theta-gamma tACS based on the cross-frequency tACS, namely peak nesting and trough nesting, to achieve targeted regulation of cognitive dysfunctions such as memory, attention, and emotion processing.

[0040] In summary, the embodiments of the present invention achieve personalized, targeted, and precise regulation through the above modules, thereby improving or enhancing cognitive functions such as attention, memory, and emotion processing.

[0041] Example 2

[0042] The following are specific examples. Figures 2-3 The calculation formula for the scheme in Example 1 is further described below:

[0043] I. Preprocessing Module:

[0044] Using the 10-20 international standard lead system, 2-minute resting EEG data with eyes open were collected from the frontal lobe F3, F4, parietal lobe P3, P4 and right mastoid M2 leads. The reference electrode was the left mastoid M1. The collected resting EEG data were preprocessed.

[0045] The specific steps include: binaural average rereference, bandpass filtering of 1-100Hz; downsampling to 500Hz; using independent component analysis (ICA) to remove artifacts such as electrooculography and electromyography; and then dividing the continuous 2-minute resting data into 5-second epochs.

[0046] II. Automated Peak Frequency Extraction Module:

[0047] The Welch spectrum analysis algorithm was used to calculate the power spectrum curves of the theta (4-8Hz) frequency band for each epoch under leads F3 and F4, and the curves were superimposed and averaged at the epoch level. The power spectrum difference curve of the theta band between lead F3 and lead F4 was obtained by spectral subtraction, i.e., the relative power curve (e.g., ...). Figure 1(A)). Subsequently, using the built-in peak finding function findpeaks() and maximum value finding function max() in MATLAB, the peak frequency of the theta band is obtained, such as... Figure 1 As shown in Figure (A), the peak theta frequency of the individual in the example is 6.8 Hz. Similarly, repeating the above steps, the peak gamma frequency of the individual is obtained by calculating the relative power curves of the gamma band (60-90 Hz) under leads P3 and P4, as shown in Figure (A). Figure 1 As shown in Figure (B), the peak gamma frequency of the individual in the example is 74 Hz.

[0048] III. Target Site Determination Module:

[0049] By determining the sign of the relative power corresponding to the peak frequency, the stimulation site is automatically adjusted; the embodiments of this invention involve a total of 4 site selection schemes (such as...). Figure 2 As shown in the figure, the automatic selection of stimulation sites is as follows: when the relative power values ​​at both the peak frequencies of theta and gamma are greater than 0, the stimulation site is F4-P4; when the relative power value at the peak frequency of theta is greater than 0 but the relative power value at the peak frequency of gamma is less than 0, the stimulation site is F4-P3; when the relative power values ​​at both the peak frequencies of theta and gamma are less than 0, the stimulation site is F3-P3; when the relative power value at the peak frequency of theta is less than 0 but the relative power value at the peak frequency of gamma is greater than 0, the stimulation site is F3-P4.

[0050] The stimulation sites are primarily four locations: the left and right frontal lobes and the left and right parietal lobes. These four sites were chosen because the frontal and parietal lobes are closely related to cognitive function. These four sites correspond to the electrode placement positions of the international 10-20 system: F3 and F4 in the frontal lobes, and P3 and P4 in the parietal lobes.

[0051] IV. Signal-to-noise ratio calculation module:

[0052] Calculate the signal-to-noise ratio (SNR) at the peak frequency. Based on the calculated peak frequencies of theta and gamma and the stimulation site, calculate the SNR of theta and gamma oscillations at the target site, as shown in the following formula:

[0053]

[0054] Where i refers to the theta or gamma frequency band, This refers to the power value at the peak frequency of that frequency band. This refers to the total power value within that frequency band. Figure 1Taking the extracted peak frequency as an example, if i is theta and the stimulation sites determined by the target site determination module in step three are F4 and P4, then, This refers to the power value at 6.8Hz with lead F4. This refers to the total power value in the 4-8Hz range under lead F4.

[0055] The output rules for the Theta-gamma tACS regulation parameters are as follows:

[0056] 1) Stimulation Frequency: Based on the peak frequency extraction operation in the automatic peak frequency extraction module, the peak frequency of each individual is obtained. Here, the peak frequency of theta and gamma oscillations is defined as... and Since the relationship between frequency and angular velocity is ω=2πf, taking a sine wave as an example, we can obtain:

[0057]

[0058] in, Let i be the peak frequency of band i. Since i∈{θ, γ}, i.e. This represents the peak frequency of theta or gamma oscillations.

[0059] 2) Stimulation sites: Based on the sign of the relative power corresponding to the peak frequency, the individual target brain region is obtained, mainly including four site schemes, namely (1) F4-P4; (2) F4-P3; (3) F3-P3; (4) F3-P4: selected as follows:

[0060]

[0061] Wherein, ΔP θ ΔP represents the relative power of the peak frequency of theta under lead F3 minus the peak frequency of theta under lead F4. γ This represents the relative power of the peak gamma frequency in lead P3 minus the peak gamma frequency in lead P4.

[0062] 3) Stimulation Intensity: The intensity of the stimulation current is selected based on the calculated SNR, mainly including two types: 1mA and 2mA. The intensity output discrimination formula is as follows:

[0063]

[0064] Where A represents the magnitude of the current intensity, and the specific settings are as follows:

[0065]

[0066] Where T represents the threshold value, which can be set according to usage requirements, and SNR... θ The signal-to-noise ratio (SNR) in the theta band is... γ This indicates the signal-to-noise ratio in the gamma band.

[0067] 4) Nested Modes: This embodiment of the invention mainly configures two theta-gamma tACS nested modes: peak nesting and trough nesting, such as... Figure 3 As shown.

[0068] Among them, the baseband signal s is obtained by calculating the peak frequencies of theta and gamma based on the frequency calibration and the current magnitude. θ (t) and carrier signal s γ (t). The waveform selection for the nested peak mode is as follows:

[0069]

[0070] Correspondingly, the output trough nesting pattern corresponds to the following waveform:

[0071]

[0072] Where β1, β2∈[0, A] represent amplitude modulation exponents, which can be adjusted accordingly as needed.

[0073] by and Taking the stimulation sites as F4-P4, the current magnitude as 1mA, and β1=0.5 and β2=0.6 as examples, the output waveforms of the two peak frequency signals and the nested mode are as follows: Figure 3 As shown.

[0074] Example 3

[0075] The following is combined Figure 4 The feasibility of the schemes in Examples 1 and 2 is verified, as detailed below:

[0076] Figure 4 This is a schematic diagram of the overall process of an embodiment of the present invention. Power curves are obtained by analyzing the power spectra of four leads (F3, F4, P3, and P4) under a 2-minute open-eye state. Based on the left-right hemisphere oscillation lateralization phenomenon proposed in existing research, and combined with spectral subtraction, the relative power spectrum curves of frontal lobe theta and parietal lobe gamma are plotted, and the peak frequencies of theta and gamma oscillations are determined. Further, through comparative analysis and signal-to-noise ratio extraction, the targeted stimulation sites and current magnitudes are obtained. According to the regulatory purpose, a nested peak and trough pattern is selectively chosen to achieve precise regulation of cognitive functions such as attention, memory, and emotion processing.

[0077] The main objective of this invention is to propose an individualized cognitive function modulation device based on theta-gamma coupling. By extracting the peak frequencies of theta in the frontal lobe and gamma in the parietal lobe, and combining this with signal-to-noise ratio determination, adaptive output can be achieved for stimulus frequency, stimulus location, and stimulus intensity. This enables more precise and targeted modulation of cognitive abilities such as attention, memory, and emotional and behavioral control.

[0078] This invention can effectively and precisely regulate cognitive functions such as attention, memory, and emotional and behavioral control, and is personalized and targeted, yielding considerable social and economic benefits. The optimal implementation plan is proposed to be through patent transfer, technological cooperation, or product development.

[0079] References

[0080] [1] Canolty, Ryan T., and Robert T. Knight. "The functional role of cross-frequency coupling." Trends in cognitive sciences 14.11 (2010): 506-515.

[0081] [2]Alekseichuk, Ivan, et al. "Spatial working memory in humans depends on theta and high gamma synchronization in the prefrontal cortex." CurrentBiology 26.12(2016):1513-1521.

[0082] [3]Abubaker,Mohammed,Wiam Al Qasem,and Eugen ."Working Memoryand Cross-Frequency Coupling of Neuronal Oscillations."Frontiers inpsychology(2021):4506.

[0083] [4]Davoudi,Saeideh,Amirmasoud Ahmadi,and Mohammad Reza Daliri."Frequency–amplitude coupling:a new approach for decoding of attended featuresin covert visual attention task."Neural Computing and Applications 33.8(2021):3487-3502.

[0084] [5]Nakazono,Tomoaki,Susumu Takahashi,and Yoshio Sakurai."Enhancedtheta and high-gamma coupling during late stage of rule switching task in rathippocampus."Neuroscience 412(2019):216-232.

[0085] [6]Heusser,Andrew C.,et al."Episodic sequence memory is supported bya theta–gamma phase code."Nature neuroscience 19.10(2016):1374-1380.

[0086] [7]Friese,Uwe,et al."Successful memory encoding is associated withincreased cross-frequency coupling between frontal theta and posterior gammaoscillations in human scalp-recorded EEG."Neuroimage 66(2013):642-647.

[0087] [8]Abelmann A.Influencing Social-Emotional Actions: The Effect ofTheta-Gamma Coupled tACS on Social-Emotional Actions and aPFC-M1 FunctionalConnectivity[J].2019.

[0088] [9]Riddle J,Mcferren A,Frohlich F.Causal role of cross-frequency coupling in distinct components of cognitive control[J].Progress inNeurobiology,2021,202:102033.

[0089] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0090] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A personalized cognitive function modulation device based on cross-frequency coupling, characterized in that, The device includes: The peak frequency automated extraction module uses spectral analysis and automated frequency extraction to obtain the peak frequencies of the frontal lobe theta oscillation and parietal lobe gamma oscillation at the individual baseline, which are then used as the stimulation frequencies for transcranial alternating current stimulation. The target site determination module is used to automatically output the stimulation site of tACS by determining whether the power value corresponding to the peak frequency in the left and right brain regions is positive or negative. Based on the output stimulation site, the stimulation device outputs current to the corresponding brain region. The signal-to-noise ratio (SNR) calculation module is used to calculate the SNR value at the peak frequency and adaptively output the stimulation current intensity of tACS based on the magnitude of the SNR value. A multimodal regulation model is used to set peak nesting and trough nesting modes based on the cross-frequency tACS to achieve targeted regulation of cognitive dysfunctions in memory, attention, and emotion processing.

2. The personalized cognitive function modulation device based on cross-frequency coupling according to claim 1, characterized in that, The specific method for obtaining the peak frequencies of the frontal lobe theta oscillation and parietal lobe gamma oscillation at the individual baseline using spectral analysis and automated frequency extraction is as follows: Welch spectrum analysis was used to calculate the power spectrum curves of the theta band range for each epoch under leads F3 and F4, and the average was superimposed at the epoch level. The power spectrum difference curve of the theta band between lead F3 and lead F4 is obtained by spectral subtraction. Using the built-in peak and maximum value functions in MATLAB, the peak frequency of the theta band is obtained. By calculating the relative power curves of the gamma band under leads P3 and P4, the individual gamma peak frequency is obtained.

3. The personalized cognitive function modulation device based on cross-frequency coupling according to claim 1, characterized in that, The specific stimulation sites used to automatically output tACS by determining the positive or negative power values ​​corresponding to the peak frequencies in the left and right brain regions are as follows: When the relative power values ​​at the peak frequencies of theta and gamma are both greater than 0, the stimulation site is F4-P4; When the relative power value at the peak frequency of theta is greater than 0 but the relative power value at the peak frequency of thegamma is less than 0, the stimulation site is F4-P3. When the relative power values ​​at the peak frequencies of theta and gamma are both less than 0, the stimulation site is F3-P3; When the relative power value at the peak frequency of theta is less than 0 but the relative power value at the peak frequency of gamma is greater than 0, the stimulation site is F3-P4.

4. The personalized cognitive function modulation device based on cross-frequency coupling according to claim 1, characterized in that, The device includes: Based on the calculated peak frequencies of theta and gamma and the stimulation site, the signal-to-noise ratio of theta and gamma oscillations at the target site is calculated as follows: Where i refers to the theta or gamma frequency band, This refers to the power value at the peak frequency of that frequency band. This refers to the total power value within that frequency band.

Images