Systems and methods for monitoring functional neuroplasticity

1 . A computer-implemented method of monitoring neuroplasticity within at least one resting state functional network of a brain of a subject, the method comprising:

a. obtaining a plurality of time sequences of signals from the brain of a subject in the absence of stimuli, each of the plurality of time sequences of signals comprising a plurality of pulses, each pulse of the plurality of pulses comprising a peak amplitude, wherein the at least one time sequence of signals is indicative of resting state neuroactivity within at least one resting state functional network within the brain of the subject;

b. receiving, using a computing device, the plurality of time sequences of signals;

c. projecting, using the computing device, the plurality of time sequences of signals to a 2D cortex model comprising a plurality of vertices mapped to cortical positions of the subject;

d. identifying, using the computing device, functionally connected regions corresponding to resting state functional networks by pair-wise evaluating Pearson correlations between the plurality of time sequences of signals;

e. identifying, using the computing device, a plurality of plasticity pulses, each plasticity pulse comprising a large amplitude signal pulse and a unilateral signal pulse, wherein each plasticity pulse of the plurality of plasticity pulses is identified by:

i. comparing, using the computing device, the peak amplitude of one pulse of the plurality of pulses to a constant threshold amplitude and classifying, using the computing device, the one pulse as large amplitude if the peak amplitude is above the constant threshold amplitude; and

ii. obtaining, using the computing device, a peak difference comprising a difference between the peak amplitude of the one pulse and a corresponding contralateral peak amplitude of a contralateral pulse, wherein the peak amplitude and the contralateral peak amplitude comprise matched signal acquisition times and correspond to contralateral positions within a single functionally connected region;

iii. comparing, using the computing device, the peak difference to a constant peak difference threshold and classifying, using the computing device, the one pulse as a unilateral if the peak difference is above the constant peak difference threshold; and

iv. adding, using the computing device, the one pulse to the plurality of plasticity pulses if the one pulse is classified as large amplitude and unilateral;

f. transforming, using the computing device, the plurality of plasticity pulses into a summary parameter indicative of the presence of plasticity pulses, the summary parameter comprising at least one of: a production rate of plasticity pulses, a mean amplitude of plasticity pulses, an amplitude of low frequency fluctuations, and any combination thereof;

g. generating, using the computing device, a determination of neuroplasticity based on the summary parameter; and

h. displaying, using the computing device, the determination of neuroplasticity to a clinical practitioner, wherein the determination of neuroplasticity comprises at least one of a presence of neuroplasticity, a magnitude of neuroplasticity, and a spatial extent of neuroplasticity within the brain of the subject.

2 . The method of claim 1 , wherein each of the at least one resting state functional networks is selected from a dorsal attention network (DAN), a ventral attention network (VAN), a cingulo-opercular network (CO), a somato-motor network (SMN), an auditory network (AN), a visual network (VIS), a frontoparietal control network (FPC), a language network (LAN), and a default mode network (DMN).

3 . The method of claim 1 , wherein the at least one plurality of time sequences of signals comprises one of resting-state functional MRI (rs-fMRI) signals, electroencephalography (EEG) recordings, and microelectrode recordings.

4 . The method of claim 1 , wherein the constant threshold amplitude comprises a first constant value selected from a range between about a 0.2% signal change and about a 1.2% signal change from a baseline signal level.

5 . The method of claim 4 , wherein the constant threshold peak difference comprises a second constant value selected from a second range between about a 0.1% signal change to about a 0.4% signal change from a baseline signal level.

6 . A computer-implemented method of evaluating an efficacy of a neuroactive therapy, the method comprising:

a. obtaining, using a computing device, a pre-treatment determination of neuroplasticity prior to administration of a neuroactive therapy;

b. obtaining, using the computing device, at least one post-treatment determination of neuroplasticity at least once after administration of the neuroactive therapy; and

c. determining, using the computing device, the efficacy of the neuroactive therapy based on the pretreatment determination of neuroplasticity and the at least one post-treatment determination of neuroplasticity, wherein the efficacy of the neuroactive therapy is proportional to an increase in post-treatment neuroplasticity relative to pre-treatment neuroplasticity;

wherein obtaining the pre-treatment and post-treatment determinations of neuroplasticity comprise:

i obtaining a plurality of time sequences of signals from the brain of a subject in the absence of stimuli, each of the plurality of time sequences of signals comprising a plurality of pulses, each pulse of the plurality of pulses comprising a peak amplitude, wherein the at least one time sequence of signals is indicative of resting state neuroactivity within at least one resting state functional network within the brain of the subject;

ii. receiving, using the computing device, the plurality of time sequences of signals;

iii. projecting, using the computing device, the plurality of time sequences of signals to a 2D cortex model comprising a plurality of vertices mapped to cortical positions of the subject;

iv. identifying, using the computing device, functionally connected regions corresponding to resting state functional networks by pair-wise evaluating Pearson correlations between the plurality of time sequences of signals;

v. identifying, using the computing device, a plurality of plasticity pulses, each plasticity pulse comprising a large amplitude signal pulse and a unilateral signal pulse, wherein each plasticity pulse of the plurality of plasticity pulses is identified by:

1) Comparing, using the computing device, the peak amplitude of one pulse of the plurality of pulses to a constant threshold amplitude and classifying, using the computing device, the one pulse as large amplitude if the peak amplitude is above the constant threshold amplitude;

2) obtaining, using the computing device, a peak difference comprising a difference between the peak amplitude of the one pulse and a corresponding contralateral peak amplitude of a contralateral pulse, wherein the peak amplitude and the contralateral peak amplitude comprise matched signal acquisition times and, correspond to contralateral positions within a single functionally connected region;

3) comparing, using the computing device, the peak difference to a constant peak difference threshold and classifying, using the computing device, the one pulse as a unilateral if the peak difference is above the constant peak difference threshold; and

4) Adding, using the computing device, the one pulse to the plurality of plasticity pulses if the one pulse is classified as large amplitude and unilateral; and

vi. transforming, using the computing device, the plurality of plasticity pulses into a summary parameter indicative of the presence of plasticity pulses, the summary parameter comprising at least one of: a production rate of plasticity pulses, a mean amplitude of plasticity pulses, an amplitude of low frequency fluctuations, and any combination thereof; and

vii. generating, using the computing device, the determination of neuroplasticity based on the summary parameter.

7 . The method of claim 6 , wherein the neuroactive therapy is selected from a neuroactive medication, a physical therapy, an occupational therapy and a speech therapy.

8 . A computer-implemented method of screening a neuroactive medication for use in a therapy, the method comprising:

a. obtaining, using a computing device, a pre-treatment determination of neuroplasticity prior to administration of the neuroactive medication to a subject;

b. obtaining, using the computing device, at least one post-treatment determination of neuroplasticity at least once after administration of the neuroactive medication to the subject; and

c. selecting, using the computing device, the neuroactive medication for a therapy if the at least one post-treatment determination of neuroplasticity indicates higher neuroplasticity relative to the pre-treatment determination of neuroplasticity;

wherein obtaining the pre-treatment and post-treatment determinations of neuroplasticity comprise:

i. obtaining a plurality of time sequences of signals from the brain of a subject in the absence of stimuli, each of the plurality of time sequences of signals comprising a plurality of pulses, each pulse of the plurality of pulses comprising a peak amplitude, wherein the at least one time sequence of signals is indicative of resting state neuroactivity within at least one resting state functional network within the brain of the subject;

ii. receiving, using the computing device, the plurality of time sequences of signals;

iii. receiving, using the computing device, the plurality of at least one time sequences of signals;

iv. identifying, using the computing device, functionally connected regions corresponding to resting state functional networks by pair-wise evaluating Pearson correlations between the plurality of time sequences of signals;

v. identifying, using the computing device, a plurality of plasticity pulses, each plasticity pulse comprising a large amplitude signal pulse and a unilateral signal pulse, wherein each plasticity pulse of the plurality of plasticity pulses is identified by:

1) Comparing, using the computing device, the peak amplitude of one pulse of the plurality of pulses to a constant threshold amplitude and classifying, using the computing device, the one pulse as large amplitude if the peak amplitude is above the constant threshold amplitude;

2) obtaining, using the computing device, a peak difference comprising a difference between the peak amplitude of the one pulse and a corresponding contralateral peak amplitude of a contralateral pulse, wherein the peak amplitude and the contralateral peak amplitude comprise matched signal acquisition times and, correspond to contralateral positions within a single functionally connected region;

3) Comparing, using the computing device, the peak difference to a constant peak difference threshold and classifying, using the computing device, the one pulse as a unilateral if the peak difference is above the constant peak difference threshold;

4) Adding, using the computing device, the one pulse to the plurality of plasticity pulses if the one pulse is classified as large amplitude and unilateral; and

vi. transforming, using the computing device, the plurality of plasticity pulses into a summary parameter indicative of the presence of plasticity pulses, the summary parameter comprising at least one of: a production rate of plasticity pulses, a mean amplitude of plasticity pulses, an amplitude of low frequency fluctuations, and any combination thereof; and

vii. generating, using the computing device, the determination of neuroplasticity based on the summary parameter.

9 . A computer-implemented method of providing a biofeedback to a subject undergoing a neuroactive therapy, the method comprising:

a. monitoring, using a computing device, a determination of neuroplasticity during administration of the neuroactive therapy to the subject, wherein the determination of neuroplasticity is obtained by:

i. obtaining a plurality of time sequences of signals from the brain of a subject in the absence of stimuli, each of the plurality of time sequences of signals comprising a plurality of pulses, each pulse of the plurality of pulses comprising a peak amplitude, wherein the at least one time sequence of signals is indicative of resting state neuroactivity within at least one resting state functional network within the brain of the subject;

ii. receiving, using the computing device, the plurality of sequences of signals;

iii. projecting, using the computing device, the plurality of time sequences of signals to a 2D cortex model comprising a plurality of vertices mapped to cortical positions of the subject;

iv. identifying, using the computing device, functionally connected regions corresponding to resting state functional networks by pair-wise evaluating Pearson correlations between the plurality of time sequences of signals;

v. identifying, using the computing device, a plurality of plasticity pulses, each plasticity pulse comprising a large amplitude signal pulse and a unilateral signal pulse, wherein each plasticity pulse of the plurality of plasticity pulses is identified by:

1) Comparing, using the computing device, the peak amplitude of one pulse of the plurality of pulses to a constant threshold amplitude and classifying, using the computing device, the one pulse as large amplitude if the peak amplitude is above the constant threshold amplitude;

2) obtaining, using the computing device, a difference between the peak amplitude of the one pulse and a corresponding contralateral peak amplitude of a contralateral pulse, wherein the peak amplitude and the contralateral peak amplitude comprise matched signal acquisition times and, correspond to contralateral positions within a single functionally connected region;

3) Comparing, using the computing device, the peak difference to a constant peak difference threshold and classifying, using the computing device, the one pulse as a unilateral if the peak difference is above the constant peak difference threshold; and

4) Adding, using the computing device, the one pulse to the plurality of plasticity pulses if the one pulse is classified as large amplitude and unilateral; and

vi. transforming, using the computing device, the plurality of plasticity pulses into a summary parameter indicative of the presence of plasticity pulses, the summary parameter comprising at least one of: a production rate of plasticity pulses, a mean amplitude of plasticity pulses, an amplitude of low frequency fluctuations, and any combination thereof; and

vii. generating, using the computing device, the determination of neuroplasticity based on the summary parameter; and

b. displaying, using the computing device, the determination of neuroplasticity to the subject as the biofeedback.