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Abstract #1432

Can Tractography Predict Electrophysiological Connectivity?

S. Shailja1, Dian Lyu2, Gustavo Chau Loo Kung3, Leili Mortazavi4, Erpeng Dai1, Vivek P. Buch5, Karl Deisseroth3, Josef Parvizi2, and Jennifer A McNab1
1Radiology, Stanford University, Palo Alto, CA, United States, 2Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States, 3Bioengineering, Stanford University, Palo Alto, CA, United States, 4Psychology, Stanford University, Palo Alto, CA, United States, 5Neurosurgery, Stanford University, Palo Alto, CA, United States

Synopsis

Keywords: Tractography, Structural Connectivity, Neurotransmission, Functional Connectivity

Motivation: To infer electrophysiological functional connectivity from tractography-based structural connectivity.

Goal(s): Determine the accuracy of electrophysiological connectivity prediction using tractography and vice versa.

Approach: Five patients with refractory focal epilepsy were recruited. After 3T diffusion tensor imaging, the causal electrophysiological connectivity was measured by applying single-pulse electrical stimulation to ~200 implanted depth electrodes and recording the pulse-evoked potentials. Structural connectivity was computed using probabilistic streamline counts and white matter tracts defined by a tractography atlas. We study the correspondence between these structural and electrophysiological connectivities.

Results: Electrode pairs that are structurally connected are electrophysiologically connected with probabilities 0.75-0.94 (mean:0.86, std:0.07).

Impact: If we can predict the probability of electrophysiological signals via tractography then we have a non-invasive tool for predicting electrophysiological signaling in the brain with broad scientific and clinical applications including guidance for placement of electrodes for functional neurosurgeries.

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