[Faculty] Fwd: [CSRC.COLLOQUIUM] "Individually Calibrated Models for EEG/MEG Inverse Mapping"

[Jose Castillo]

[image: SDSU_CSRC Logo.jpg]




DATE:  *Friday, May 1, 2020*



TITLE:

*Individually Calibrated Models for EEG/MEG Inverse Mapping*


TIME:  *3:00-4:00PM*




LOCATION:
Join Zoom Meeting -  https://SDSU.zoom.us/j/99859040967
<https://sdsu.zoom.us/j/99859040967>

Note: Recent videos from previous colloquium talks for

Spring 2020 can be seen on the CSRC website in the colloquium archive
section or on the CSRC YouTube page here
<https://www.youtube.com/channel/UCN0ZEztlmyDqG2pm-Rle_Eg/feed>.




SPEAKER/BIO:


*Dr. **Mark Pflieger, Cortech Solutions, Inc., Computational Science
Research Center & Center for Clinical and Cognitive Neuroscience, San Diego
State University*





ABSTRACT:

The inverse problem of mapping an individual’s extracranial EEG potentials
and MEG fields back to their current generators in the cortex and other
brain structures has no unique solution (Helmholz). Consequently, many
electromagnetic inverse solution methods are available. Mosher and
colleagues showed that a broad spectrum of linear inverse methods, ranging
from model-driven minimum norms to data-driven beamformers, are equivalent
in theory. That is, they all share a general formula that combines three
basic mappings: (i) sensor mappings that capture observed correlation
patterns of extracranial signals; (ii) forward mappings from intracranial
source currents to extracranial sensor fields; and (iii) source mappings
that capture modeled correlation patterns of intracranial signals. In
practice, however, inverse mappings differ due to various ways of obtaining
the sensor, forward, and source mappings. Noting that each mapping varies
from person to person, I will propose ways to calibrate each of the basic
mappings for an individual person: (i) using the statistical framework of
switching linear dynamical systems to model latent states underlying
EEG/MEG sensor mappings; (ii) using co-resonant modes of simultaneous
EEG-MEG plus brain/head geometry derived from structural MRI to tune tissue
conductivities (for skin, skull, CSF, skull, CSF, gray matter, and white
matter) of the forward mapping; and (iii) using simultaneous EEG-fMRI to
fit a multi-level source mapping model of cortical functional connectivity
to observed sensor mapping data. The inverse mapping that results from the
general formula is a hybrid method that that harmonizes model-driven and
data-driven inverse methods.



Host: Jose Castillo

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