Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises
Year: 2020
Type: review
Source: Biological Psychiatry
Institutions: Georgia Institute of Technology, Emory University, Georgia State University, Chinese Academy of Sciences, Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Center for Translational Research in Neuroimaging and Data Science, University of Chinese Academy of Sciences, University of New Mexico
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
Awards: G1635530505, Unified multivariate data-driven solutions for static and dynamic brain connectivity, Data-driven approaches to identify biomarkers from multimodal imaging big data, Mining the Genomewide Scan: Genetic Profiles of Structural Loss in Schizophrenia, G327412290, Informed Data-Driven Fusion of Behavior, Brain Function, and Genes, RII Track-2 FEC: Developmental Chronnecto-Genomics (Dev-CoG): A Next Generation Framework for Quantifying Brain Dynamics and Related Genetic Factors in Childhood, Multimodal Imaging of Neuropsychiatric Disorders (MIND): Mechanisms &, G5244786451, Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders, G6521376908, G772009288, G8423037688, Phase III COBRE: Multimodal Imaging of Neuropsychiatric Disorders (MIND), Generalizing and Standardizing fMRI Tools for Differentiating Mental Illnesses and Predicting Medication-Class Response in Patients, Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity, Multivariate methods for identifying multitask/multimodal brain imaging biomarkers
OpenAlex ID: W3008923041