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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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Abstract

Purpose

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.

Results

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.

Conclusions

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.

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Acknowledgments

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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Authors and Affiliations

  1. Department of Nuclear Medicine, Seoul National University, College of Medicine, 28 Yeongeon-dong, Jongno-gu, Seoul, 110–744, Korea

    Dong Soo Lee, Hyejin Kang, Heejung Kim, Hyojin Park, Jungsu S. Oh, Jae Sung Lee & Myung Chul Lee

  2. Programs in Brain and Neuroscience, Seoul National University, Seoul, Korea

    Hyejin Kang

  3. Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Korea

    Heejung Kim & Hyojin Park

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  1. Dong Soo Lee
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Correspondence to Dong Soo Lee.

Electronic supplementary material

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Supplementary Fig. 1

Number of gyral VOIs with correlation ranging from (1) autocorrelation only to (2) correlation with contralateral homologous and adjacent/remote areas (GIF 13 KB)

Supplementary Fig. 2

Left and right Heschl’s gyri showing areas of autocorrelation only (GIF 184 KB)

Supplementary Fig. 3

Left and right Putamen showing areas correlated with themselves and contralateral homologues (GIF 192 KB)

Supplementary Fig. 4

Left and right precentral gyri showing areas correlated more distant regions even beyond midline (GIF 204 KB)

Supplementary Fig. 5

Left and right BA44 showing area with widespread correlations. Note the hemispheric asymmetry of correlated regions for left and right BA44 (GIF 195 KB)

Supplementary Fig. 6

a Areas showing interregional correlations with seed VOIs of left and right frontal or parietal lobes. b Areas showing interregional correlations with seed VOIs of left and right temporal and occipital lobes (GIF 195 KB; GIF 183 KB)

Supplementary Fig. 7

Correlation between sizes of correlated areas and seed VOI sizes. Sizes of correlated areas were measured sizes minus seed VOI sizes (GIF 7 KB)

Supplementary Table 1

Sizes of seed VOIs belonging to each lobe represented by numbers of voxels in areas with more than a 10% probability (10–100%) (DOC 65 KB)

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Lee, D.S., Kang, H., Kim, H. et al. Metabolic connectivity by interregional correlation analysis using statistical parametric mapping (SPM) and FDG brain PET; methodological development and patterns of metabolic connectivity in adults. Eur J Nucl Med Mol Imaging 35, 1681–1691 (2008). https://doi.org/10.1007/s00259-008-0808-z

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  • DOI: https://doi.org/10.1007/s00259-008-0808-z

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