Metabolic connectivity by interregional correlation analysis using statistical parametric mapping (SPM) and FDG brain PET; methodological development and patterns of metabolic connectivity in adults
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Regionally connected areas of the resting brain can be detected by fluorodeoxyglucose-positron emission tomography (FDG-PET). Voxel-wise metabolic connectivity was examined, and normative data were established by performing interregional correlation analysis on statistical parametric mapping of FDG-PET data.
Materials and methods
Characteristics of seed volumes of interest (VOIs) as functional brain units were represented by their locations, sizes, and the independent methods of their determination. Seed brain areas were identified as population-based gyral VOIs (n = 70) or as population-based cytoarchitectonic Brodmann areas (BA; n = 28). FDG uptakes in these areas were used as independent variables in a general linear model to search for voxels correlated with average seed VOI counts. Positive correlations were searched in entire brain areas.
In normal adults, one third of gyral VOIs yielded correlations that were confined to themselves, but in the others, correlated voxels extended to adjacent areas and/or contralateral homologous regions. In tens of these latter areas with extensive connectivity, correlated voxels were found across midline, and asymmetry was observed in the patterns of connectivity of left and right homologous seed VOIs. Most of the available BAs yielded correlations reaching contralateral homologous regions and/or neighboring areas. Extents of metabolic connectivity were not found to be related to seed VOI size or to the methods used to define seed VOIs.
These findings indicate that patterns of metabolic connectivity of functional brain units depend on their regional locations. We propose that interregional correlation analysis of FDG-PET data offers a means of examining voxel-wise regional metabolic connectivity of the resting human brain.
KeywordsInterregional correlation analysis Metabolic connectivity Probabilistic map FDG-PET
This study was supported by the Brain Research Center of the 21st Century Brain Frontier Project of the Korean Ministry of Science (M103KV010017-07K2201-01710), by the Korean Science and Engineering Foundation (KOSEF), and by the Korean Ministry of Science and Technology (MOST) through its National Nuclear Technology Program (#M20504070004-05A0707-00410). We deeply appreciate the efforts of Professors Zilles and Amunts for transferring their Brodmann Areas to Korean templates. This study utilized KREONET (the Korean Research Network) a Giga-bps high speed network.
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