Uncovering Brain Biomarkers Using SVD in Neuroimaging Data
Explore the methodology of hypothesis-free searching for biomarkers in large imaging datasets using Singular Value Decomposition (SVD). Dr. J. Bruce Morton and Daamoon Ghahari delve into the application of SVD and General Linear Modeling to identify potential biomarkers for ADHD and other neuropsychiatric disorders. The discussion includes insights on the ABCD study and large neuroimaging consortiums as well as a demonstration of SVD with simulated data.
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Presentation Transcript
Hypothesis-free search for biomarkers in a large imaging data set using SVD Dr. J Bruce Morton & Daamoon Ghahari Cognitive Development & Neuroimaging Lab Methods Lunch June 24th 2019
Outline - Current methods of diagnosing ADHD & other neuropsychiatric disorders - The search for relevant brain biomarkers - ABCD & large neuroimaging consortiums - What is SVD and how can it be used in the search for biomarkers - Combining SVD with General Linear Modeling - Demonstration of SVD with simulated data
ABCD & Large Neuroimaging Consortiums > 11,000 children
ABCD & Large Neuroimaging Consortiums White Matter Metrics FA MD Tract Volumes Features Participants
