title: Functional clustering: identifying strongly interactive brain regions in neuroimaging data.
abstract: Brain imaging data are generally used to determine which brain regions are most active in an experimental paradigm or in a group of subjects. Theoretical considerations suggest that it would also be of interest to know which set of brain regions are most interactive in a given task or group of subjects. A subset of regions that are much more strongly interactive among themselves than with the rest of the brain is called here a functional cluster. Functional clustering can be assessed by calculating for each subset of brain regions a measure, the cluster index, obtained by dividing the statistical dependence within the subset by that between the subset and rest of the brain. A cluster index value near 1 indicates a homogeneous system, while a high cluster index indicates that a subset of brain regions forms a distinct functional cluster. Within a functional cluster, individual brain regions are ranked at the center or at the periphery according to their statistical dependence with the rest of that cluster. The applicability of this approach has been tested on PET data obtained from normal and schizophrenic subjects performing a set of cognitive tasks. Analysis of the data reveals evidence of functional clustering. A comparative evaluation of which regions are more peripheral or more central suggests distinct differences between the two groups of subjects. We consider the applicability of this analysis to data obtained with imaging modalities offering higher temporal resolution than PET.
authors.affiliation: Neurosciences Institute, San Diego, California 92121, USA.
authors.forename: G
authors.initials: G
authors.lastname: Tononi
authors.forename: A R
authors.initials: AR
authors.lastname: McIntosh
authors.forename: D P
authors.initials: DP
authors.lastname: Russell
authors.forename: G M
authors.initials: GM
authors.lastname: Edelman
mesh_terms: D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D003071:Cognition / Q000502:physiology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D011597:Psychomotor Performance / Q000502:physiology; D012559:Schizophrenia / Q000000981:diagnostic imaging / Q000503:physiopathology; D014055:Tomography, Emission-Computed