Pattern Analysis of fMRI via machine learning/sparse models: application to brain development

University Of Pennsylvania, Philadelphia PA

Investigators

Linked publications & trials

Abstract

Abstract Resting state fMRI (rsfMRI) provides reproducible, task-independent biomarkers of coherent functional activity linking different brain regions. The main goal of the proposed project is to leverage advances in signal processing and machine learning methods to derive clinically useful biomarkers based on patterns of functional connectivity, and to test these biomarkers in a large study of brain development. Central to our methodology are 1) computing a subject-specific functional parcellation of the brain, which defines nodes for characterizing individualized functional brain networks; 2) extracting sparse connectivity patterns for robustly representing brain networks; 3) capturing heterogeneity in brain networks across individuals in a given population; and 4) deriving individualized predictive indices of psychosis risk from brain connectivity in a large study of brain development. This novel suite of functional connectivity analysis tools will be developed and validated based on data from the Human Connectome Project and the Philadelphia Neurodevelopmental Cohort (PNC). Finally, these techniques will be applied to PNC data in order to delineate heterogeneity in network development in youth with psychosis-spectrum symptoms. Our hypothesis is that patterns of functional connectivity in adolescents with psychosis-spectrum symptoms will be different from those in typically developing adolescents, and this difference will display a high degree of heterogeneity that is linked to underlying heterogeneity in pathologic neurodevelopmental trajectories. Moreover, we expect that machine learning techniques will allow us to predict on an individual basis which adolescents with psychosis-spectrum symptoms will remain stable, which will revert to normal, and which will progress to psychosis, based on their baseline functional connectivity signatures. Our methods are generally applicable to rsfMRI studies for detecting and quantifying spatio-temporal functional connectivity patterns in diverse fields, including diagnosing brain abnormalities in neuropsychiatric diseases, and finding associations of functional connectivity with different cognitive functions. All methods will be made publicly available and form an important new resource for the broader neuroscience community.