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Correcting reward‐related neural dysfunction in substance use disorders using a robot-guided TMS by Dr. Travis Baker

Correcting reward‐related neural dysfunction in substance use disorders using a robot-guided TMS by Dr. Travis Baker

Event Date: 
Wednesday, July 8, 2020
Event Location: 
Webinar

Reward-related processes appears to be one of the most consistently and severely affected functions in substance use disorders (SUD). By combining human electrophysiology and robot-assisted image-guided TMS (Ri-TMS), the proposed research aims to examine and restore reward functioning in SUD. In this talk, Travis Baker will first discuss the application of Ri-TMS for addiction medicine, and then discuss a novel application of cortical thickness maps to select individualized Ri-TMS targets. The lab's efforts point to a decisive role of integrating Ri-TMS methods to modulate reward functioning in health and disease, which could accelerate the pace of scientific progress and have a positive impact on mental health care. Research reported in this talk was supported by the National Institute On Drug Abuse of the National Institutes of Health.

The presenter is Dr. Travis Baker who earned his PhD in 2012 from the University of Victoria in the Brain and Cognitive Science program under the supervision of Dr. Clay Holroyd, and supported by a Frederick Banting and Charles Best Canada Graduate Scholarships Doctoral Award, Canadian Institute of Health Research. He also holds a Masters of Science degree in Experimental Neuropsychology (University of Victoria) and received a B.A. (with distinction: Psychology) from Vancouver Island University. Prior to joining Rutgers University faculty, he worked as a post-doctoral fellow at the Montreal Neurological Institute in the Department of Neurology and Neurosurgery, and at CHE Sainte-Justine Children’s Hospital Research Center.

This session will be followed by a live demonstration of a robotized TMS setup by Dr. Baker. The registration is open until 24:00 (CET) on 06.07.2020.

Register here

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