Structural brain connectivity in predicting treatment outcomes in major depression

(1) CAMH is a research hospital that aims to improve the provision of clinical care to clients with psychiatric conditions through research. Through analyses of large-scale multi-modal data and clinical trials with biological assessments, the Kimel TIGR lab seeks to identify biological bases of psychiatric conditions that can be targeted using existing and novel treatments, such as SSRIs, SNRIs, repeated TMS, and iTBS, among others.
(2) This project aims to a) develop novel machine learning tools that link structural brain connectivity to cognitive function and clinical symptoms in several datasets, starting with the large-scale UK Biobank data (63k participants with MRI). Additionally, b) the project will test the performance of graph neural nets alongside other machine learning tools in predicting treatment outcomes in clinical trials for depression treatment.
(3) If successful, this work may lead to wide-spread adoption of graph neural networks in neuroscience. In the longer term, if confirmed by prospective biomarker-guided clinical trials such as those that Dr. Zhukovsky is currently involved in (SMART Trial, McLean hospital with Prof Pizzagalli, Wellcome Leap funded; co-leading biomarker search as part of the BAARD trial across CAMH, Pittsburgh and Washington University St Louis, NIH funded with Dr. Felsky), biomarker-guided algorithms could help target medications for depression treatment.

Faculty Supervisor:

Hans-Arno Jacobsen

Student:

Partner:

Centre for Addiction and Mental Health

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services

University:

University of Toronto

Program: