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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB022573-02
Application #
9307803
Study Section
Biomedical Imaging Technology B Study Section (BMIT-B)
Program Officer
Pai, Vinay Manjunath
Project Start
2016-07-01
Project End
2020-03-31
Budget Start
2017-04-01
Budget End
2018-03-31
Support Year
2
Fiscal Year
2017
Total Cost
$549,838
Indirect Cost
$201,856
Name
University of Pennsylvania
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Pehlivanova, Marieta; Wolf, Daniel H; Sotiras, Aristeidis et al. (2018) Diminished Cortical Thickness is Associated with Impulsive Choice in Adolescence. J Neurosci :
Li, Hongming; Satterthwaite, Theodore D; Fan, Yong (2018) BRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging 2018:101-104
Xia, Cedric Huchuan; Ma, Zongming; Ciric, Rastko et al. (2018) Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun 9:3003
Sun, Yan; Zhang, Yang; Zhang, Dai et al. (2018) GABRA2 rs279858-linked variants are associated with disrupted structural connectome of reward circuits in heroin abusers. Transl Psychiatry 8:138
Zhu, Xiaofeng; Zhang, Weihong; Fan, Yong et al. (2018) A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis. Neuroinformatics 16:351-361
Zheng, Qiang; Wu, Yihong; Fan, Yong (2018) Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation. Front Neuroinform 12:69
Zhu, Xiaofeng; Li, Hongming; Fan, Yong (2018) Parameter-Free Centralized Multi-Task Learning for Characterizing Developmental Sex Differences in Resting State Functional Connectivity. Proc Conf AAAI Artif Intell 2018:2660-2667
Ma, Qiongmin; Zhang, Tianhao; Zanetti, Marcus V et al. (2018) Classification of multi-site MR images in the presence of heterogeneity using multi-task learning. Neuroimage Clin 19:476-486
Zheng, Qiang; Tasian, Gregory; Fan, Yong (2018) TRANSFER LEARNING FOR DIAGNOSIS OF CONGENITAL ABNORMALITIES OF THE KIDNEY AND URINARY TRACT IN CHILDREN BASED ON ULTRASOUND IMAGING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1487-1490
Zhao, Xiaomei; Wu, Yihong; Song, Guidong et al. (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98-111

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