TY - JOUR
AU - 
AB - REVIEW published: 06 August 2018 doi: 10.3389/fnins.2018.00525 Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging 1,2 † 1† 1,3 Yuhui Du * , Zening Fu and Vince D. Calhoun 1 2 The Mind Research Network, Albuquerque, NM, United States, School of Computer & Information Technology, Shanxi University, Taiyuan, China, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) Edited by: data, have been employed to reflect functional integration of the brain. Alteration in brain Russell A. Poldrack, functional connectivity (FC) is expected to provide potential biomarkers for classifying or Stanford University, United States predicting brain disorders. In this paper, we present a comprehensive review in order Reviewed by: Emily Finn, to provide guidance about the available brain FC measures and typical classification National Institute of Mental Health strategies. We survey the state-of-the-art FC analysis methods including widely used (NIMH), United States Dante R. Chialvo, static functional connectivity (SFC) and more recently proposed dynamic functional Center for Complex Systems & Brain connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven Sciences (CEMSC3), Argentina spatial network and functional network connectivity (FNC) 
TI - Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging
JF - Frontiers in Neuroscience
DO - 10.3389/fnins.2018.00525
DA - 2018-08-06
UR - https://www.deepdyve.com/lp/unpaywall/classification-and-prediction-of-brain-disorders-using-functional-FXkxHPt8EG
DP - DeepDyve
ER -