title: Anticorrelated networks in resting-state fMRI-BOLD data.
abstract: In this work, abundant anticorrelated networks were successfully detected in rest-stating fMRI-BOLD data from 20 subjects. Spatial independent component analysis (sICA) method was applied at both individual and group levels. At the individual level, for each subject, 30 independent components (IC) were estimated, and each IC was transformed using Z-score mapping. The voxels with >5 and < -5 Z-score were denoted as positive signals (PS) and negative signals (NS) respectively. The correlation coefficients between the mean time series of the PS and NS voxels were computed; if the calculated coefficients were <-0.3, the PS and NS voxels were considered to form an anticorrelated PS-NS network. It was found that 36.5% of the ICs contained an anticorrelated PS-NS network. The spatiotemporal patterns of most PS-NS networks varied from subject to subject, but three networks displaying spatial patterns were comparably consistent among different subjects. For group-level analysis, no anticorrelated PS-NS networks were detected. Our results suggest that future investigations adopt a broader approach for negative BOLD signal characterization. Combined consideration of PS and NS systems help to better elucidate hemodynamic and neuronal brain behavior and further develop understanding of neural mechanisms of brain information processing.
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.|State key laboratory of high performance computing, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.
authors.forename: Yadong
authors.initials: Y
authors.lastname: Liu
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.
authors.forename: Liangming
authors.initials: L
authors.lastname: Huang
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.
authors.forename: Ming
authors.initials: M
authors.lastname: Li
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.
authors.forename: Zongtan
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.
authors.forename: Dewen
authors.initials: D
authors.lastname: Hu
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis; D013223:Statistics as Topic; D055815:Young Adult
keywords: Negative BOLD signal; anticorrelated network; low frequency fluctuation; resting-state network