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.
This proposal develops a suite of advanced functional imaging pattern analysis methods, aiming to delineate heterogeneity in brain network development in youth with psychosis-spectrum symptoms, ultimately leading to early biomarkers of neuropsychiatric disorders. Methodologically, the proposed work leverages upon the strengths of sparse and non-negative decompositions of imaging data, which offer several advantages over conventional mass-univariate and linear multivariate methods. One of the largest and most comprehensive cohorts of 1,600 individuals ages 8 through 21 provides unique imaging and clinical data to support the application of these methods to brain development.
|Zhu, Hancan; Cheng, Hewei; Yang, Xuesong et al. (2016) Metric Learning for Multi-atlas based Segmentation of Hippocampus. Neuroinformatics :|