Identifying neural patterns of functional dyspepsia using multivariate pattern analysis: a resting-state FMRI study

• Published in: PloS one 2013-07, Vol.8 (7), p.e68205-e68205
• Main Authors: Liu, Peng, Qin, Wei, Wang, Jingjing, Zeng, Fang, Zhou, Guangyu, Wen, Haixia, von Deneen, Karen M, Liang, Fanrong, Gong, Qiyong, Tian, Jie
• Format: Article
• Language: English
• Subjects: Abnormalities; Adult; Behavior; Biology; Brain; Brain Mapping; Case-Control Studies; Cerebellum; Chinese medicine; Classifiers; Correlation; Correlation analysis; Cortex (cingulate); Cortex (motor); Cortex (temporal); Diagnostic systems; Dyspepsia; Dyspepsia - physiopathology; Feasibility studies; Female; Functional magnetic resonance imaging; Homogeneity; Humans; Magnetic resonance; Magnetic Resonance Imaging; Male; Mathematics; Medical imaging; Medical research; Medicine; Multivariate Analysis; Neuroimaging; NMR; Nuclear magnetic resonance; Patients; Pattern analysis; Pattern Recognition, Physiological; Permutations; Prefrontal cortex; Principal components analysis; Rest; Somatotropin; Statistics as Topic; Studies; Supplementary motor area; Support vector machines; Temporal lobe; Thalamus; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0068205

Previous imaging studies on functional dyspepsia (FD) have focused on abnormal brain functions during special tasks, while few studies concentrated on the resting-state abnormalities of FD patients, which might be potentially valuable to provide us with direct information about the neural basis of FD. The main purpose of the current study was thereby to characterize the distinct patterns of resting-state function between FD patients and healthy controls (HCs). Thirty FD patients and thirty HCs were enrolled and experienced 5-mintue resting-state scanning. Based on the support vector machine (SVM), we applied multivariate pattern analysis (MVPA) to investigate the differences of resting-state function mapped by regional homogeneity (ReHo). A classifier was designed by using the principal component analysis and the linear SVM. Permutation test was then employed to identify the significant contribution to the final discrimination. The results displayed that the mean classifier accuracy was 86.67%, and highly discriminative brain regions mainly included the prefrontal cortex (PFC), orbitofrontal cortex (OFC), supplementary motor area (SMA), temporal pole (TP), insula, anterior/middle cingulate cortex (ACC/MCC), thalamus, hippocampus (HIPP)/parahippocamus (ParaHIPP) and cerebellum. Correlation analysis revealed significant correlations between ReHo values in certain regions of interest (ROI) and the FD symptom severity and/or duration, including the positive correlations between the dmPFC, pACC and the symptom severity; whereas, the positive correlations between the MCC, OFC, insula, TP and FD duration. These findings indicated that significantly distinct patterns existed between FD patients and HCs during the resting-state, which could expand our understanding of the neural basis of FD. Meanwhile, our results possibly showed potential feasibility of functional magnetic resonance imaging diagnostic assay for FD.