Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder.
In this study, a resting-state fMRI based classifier, for the first time, was proposed and applied to discriminate children with attention-deficit/hyperactivity disorder (ADHD) from normal controls. On the basis of regional homogeneity (ReHo), a mapping of brain function at resting state, PCA-based Fisher discriminative analysis (PC-FDA) was trained to build a linear classifier. Permutation test was then conducted to identify the brain areas with the most significant contribution to the final discrimination. Experimental results showed a correct classification rate of 85% using a leave-one-out cross-validation. Moreover, some highly discriminative brain regions, like the prefrontal cortex and anterior cingulate cortex, well confirmed the previous findings on ADHD. Interestingly, some important but less reported regions such as the thalamus were also identified. We conclude that the classifier, using resting-state brain function as classification feature, has potential ability to improve current diagnosis and treatment evaluation of ADHD.
Zhu
Chao-Zhe
CZ
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, PR China. czzhu@bnu.edu.cn
Zang
Yu-Feng
YF
Cao
Qing-Jiu
QJ
Yan
Chao-Gan
CG
He
Yong
Y
Jiang
Tian-Zi
TZ
Sui
Man-Qiu
MQ
Wang
Yu-Feng
YF
D000293:Adolescent; D000465:Algorithms; D001185:Artificial Intelligence; D001289:Attention Deficit Disorder with Hyperactivity / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D002648:Child; D003627:Data Interpretation, Statistical; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging; D008297:Male; D025341:Principal Component Analysis; D015203:Reproducibility of Results