Method for Bayesian super-resolution of electroencephalographic source analysis and transcranial electrical stimulation
A method for achieving super-resolution in localizing electrical fields measured at the head surface with electroencephalography through a generative model of the cerebral cortex that has a very high resolution of cortical surface dipoles constructed from the known properties of human cerebral cortex and adapted to optimize the Bayesian explanation the individual's cortical surface electrical fields. The iterative optimization of the prior (generative) with the posterior (observed) fields with extensive data from extended recordings provides a probabilistic estimation of the individual's functional brain activity that can be used to train artificial neural network approximations of the individual's mental activity.
1 . A method for Bayesian super-resolution of a source localization of a subject's cortical activity measured from EEG data, comprising:
performing a first Bayesian inversion of the EEG data applied on a high-resolution physical model of the subject's head tissue conductivity to obtain a first posterior estimate of the source localization at a high resolution; and
employing the first posterior estimate as an extended prior distribution in a second Bayesian inversion of the EEG data applied on a high-resolution computational model of the subject's brain connectivity and function to obtain a second posterior estimate of the source localization at a super-resolution.
2 . The method of claim 1 , wherein the high-resolution computational model replaces each 100 macrocolumn cortical patch with 100 patches of 1 macrocolumn each.
3 . The method of claim 2 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
4 . The method of claim 1 , wherein the high-resolution computational model parameters are constrained by structural MRI and diffusion-weighted MRI tractography data.
5 . The method of claim 4 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
6 . The method of claim 1 , further comprising modeling subcortical systems as latent variables inferred from cortical activity variance.
7 . The method of claim 6 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
8 . The method of claim 1 , wherein the EEG data are collected at 500-1000 samples/second over weeks or years.
9 . The method of claim 8 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
10 . The method of claim 1 , wherein the high-resolution computational model integrates low-resolution brain connectivity, high-resolution local columnar network neurophysiology, and subcortical regulatory models reflecting evolved hierarchic vertical integration of human neural systems.
11 . The method of claim 10 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
12 . The method of claim 1 , wherein the high-resolution computational model comprises a PNE.
13 . The method of claim 12 , wherein the PNE model replaces each 100 macrocolumn cortical patch with 100 patches of 1 macrocolumn each.
14 . The method of claim 13 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
15 . The method of claim 12 , wherein the PNE model parameters are constrained by structural MRI and diffusion-weighted MRI tractography data.
16 . The method of claim 15 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
17 . The method of claim 12 , further comprising modeling subcortical systems as latent variables inferred from cortical activity variance.
18 . The method of claim 17 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
19 . The method of claim 12 , wherein the EEG data are collected at 500-1000 samples/second over weeks or years.
20 . The method of claim 19 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
21 . The method of claim 12 , wherein the PNE integrates low-resolution brain connectivity, high-resolution local columnar network neurophysiology, and subcortical regulatory models reflecting evolved hierarchic vertical integration of human neural systems.
22 . The method of claim 21 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
23 . The method of claim 12 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.
24 . The method of claim 1 , further comprising applying transcranial electrical stimulation to the subject's head based on the second posterior estimate of the source localization.