| Paper: | PS-1B.39 |
| Session: | Poster Session 1B |
| Location: | Symphony/Overture |
| Session Time: | Thursday, September 6, 18:45 - 20:45 |
| Presentation Time: | Thursday, September 6, 18:45 - 20:45 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1028-0 |
| Authors: |
Jennifer Stiso, Ankit Khambhati, University of Pennsylvania, United States; Tommaso Menara, University of California Riverside, United States; Ari Kahn, Joel Stein, Sandihitsu Das, University of Pennsylvania, United States; Richard Gorniak, Joseph Tracy, Jefferson University, United States; Brian Litt, Kathryn Davis, University of Pennsylvania, United States; Fabio Pasqualetti, University of California Riverside, United States; Timothy Lucas, Danielle Bassett, University of Pennsylvania, United States |
| Abstract: |
Direct electrical stimulation of the brain has the potential to serve as a powerful tool for investigating circuit function, treating disease, and enhancing cognitive abilities. Such exogenous approaches are currently being evaluated as effective methods to modulate human memory. However, the lack of a validated, quantitative model of how stimulation affects the whole brain makes selection and optimization of stimulation paradigms difficult. A mechanistic model of the effects of stimulation would significantly contribute to basic neuroscience and facilitate the development of novel stimulation therapies. Mechanistic biophysical models can successfully predict the local spread of stimulation. However, they are computationally expensive and often focus on modeling the spread of electrical discharge from the electrode rather than its true effects on brain activity and cognitive processes. Network control theory (NCT) presents a promising solution to this problem by stipulating a dynamical model of how white matter tracts (structural connections in the human brain) constrain the effects of stimulation. Here we test the efficacy of NCT as a quantitative model of stimulation by taking advantage of intracranial data collected in humans, where stimulation is systematically applied to the cortex and is paired with a measure of memory encoding (from a previously tested classifier). |
