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Proceedings Paper

Neural connectivity analysis by using 3D TMS-EEG with source localization and sliding window coherence techniques
Author(s): Deepa Gupta; Xiaoming Du; Elliot Hong; Fow-Sen Choa
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Paper Abstract

Studying electroencephalography (EEG) in response to transcranial magnetic stimulation (TMS) is gaining popularity for investigating the dynamics of complex neural architecture in the brain. For example, the primary motor cortex (M1) executes voluntary movements by complex connections with other associated subnetworks. To understand these connections better, we analyzed EEG signal response to TMS at left M1 from schizophrenia patients and healthy controls and in contrast with resting state EEG recording. After removing artifacts from EEG, we conducted 2D to 3D sLORETA conversion, a well-established source localization method, for estimating signal strength of 68 source dipoles or cortical regions inside the brain. Next, we studied dynamic connectivity by computing time-evolving spatial coherence of 2278 (=68*(68-1)/2) pairs of cortical regions, with sliding window technique of 200ms window size and 20ms shift over 1sec long data. Pairs with consistent coherence (coherence>0.8 during 200+ sliding windows of patients and controls combined) were chosen for identifying stable networks. For example, we found that during the resting state, precuneus was steadily coherent with middle and superior temporal gyrus in the left hemisphere in both patient and controls. Their connectivity pattern over the sliding windows significantly differed between patients and controls (pvalue<0.05). Whereas for M1, the same was true for two other coherent pairs namely, superamarginal gyrus with lateral occipital gyrus in right hemisphere and medial orbitofrontal gyrus with fusiform in left hemisphere. The TMS-EEG dynamic connectivity results can help to differentiate patient and normal subjects and also help to better understand the brain architecture and mechanisms.

Paper Details

Date Published: 7 May 2019
PDF: 6 pages
Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 110181H (7 May 2019); doi: 10.1117/12.2519065
Show Author Affiliations
Deepa Gupta, Univ. of Maryland, Baltimore County (United States)
Xiaoming Du, Univ. of Maryland School of Medicine (United States)
Elliot Hong, Univ. of Maryland School of Medicine (United States)
Fow-Sen Choa, Univ. of Maryland, Baltimore County (United States)

Published in SPIE Proceedings Vol. 11018:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII
Ivan Kadar; Erik P. Blasch; Lynne L. Grewe, Editor(s)

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