title: Neurophysiological architecture of functional magnetic resonance images of human brain.
abstract: We investigated large-scale systems organization of the whole human brain using functional magnetic resonance imaging (fMRI) data acquired from healthy volunteers in a no-task or 'resting' state. Images were parcellated using a prior anatomical template, yielding regional mean time series for each of 90 regions (major cortical gyri and subcortical nuclei) in each subject. Significant pairwise functional connections, defined by the group mean inter-regional partial correlation matrix, were mostly either local and intrahemispheric or symmetrically interhemispheric. Low-frequency components in the time series subtended stronger inter-regional correlations than high-frequency components. Intrahemispheric connectivity was generally related to anatomical distance by an inverse square law; many symmetrical interhemispheric connections were stronger than predicted by the anatomical distance between bilaterally homologous regions. Strong interhemispheric connectivity was notably absent in data acquired from a single patient, minimally conscious following a brainstem lesion. Multivariate analysis by hierarchical clustering and multidimensional scaling consistently defined six major systems in healthy volunteers-- corresponding approximately to four neocortical lobes, medial temporal lobe and subcortical nuclei- - that could be further decomposed into anatomically and functionally plausible subsystems, e.g. dorsal and ventral divisions of occipital cortex. An undirected graph derived by thresholding the healthy group mean partial correlation matrix demonstrated local clustering or cliquishness of connectivity and short mean path length compatible with prior data on small world characteristics of non-human cortical anatomy. Functional MRI demonstrates a neurophysiological architecture of the normal human brain that is anatomically sensible, strongly symmetrical, disrupted by acute brain injury, subtended predominantly by low frequencies and consistent with a small world network topology.
authors.affiliation: Brain Mapping Unit and Wolfson Brain Imaging Centre, university of Cambridge, Addenbrooke's Hospital, Cambridge CB2 2QQ, UK.
authors.forename: Raymond
authors.initials: R
authors.lastname: Salvador
authors.forename: John
authors.initials: J
authors.lastname: Suckling
authors.forename: Martin R
authors.initials: MR
authors.lastname: Coleman
authors.forename: John D
authors.initials: JD
authors.lastname: Pickard
authors.forename: David
authors.initials: D
authors.lastname: Menon
authors.forename: Ed
authors.initials: E
authors.lastname: Bullmore
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001933:Brain Stem / Q000098:blood supply; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000502:physiology; D002560:Cerebrovascular Circulation / Q000502:physiology; D016000:Cluster Analysis; D017352:Echo-Planar Imaging; D005260:Female; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D015999:Multivariate Analysis; D009415:Nerve Net / Q000502:physiology