Literature DB >> 25271073

Parcellation of the human orbitofrontal cortex based on gray matter volume covariance.

Huaigui Liu1, Wen Qin, Haotian Qi, Tianzi Jiang, Chunshui Yu.   

Abstract

The human orbitofrontal cortex (OFC) is an enigmatic brain region that cannot be parcellated reliably using diffusional and functional magnetic resonance imaging (fMRI) because there is signal dropout that results from an inherent defect in imaging techniques. We hypothesise that the OFC can be reliably parcellated into subregions based on gray matter volume (GMV) covariance patterns that are derived from artefact-free structural images. A total of 321 healthy young subjects were examined by high-resolution structural MRI. The OFC was parcellated into subregions-based GMV covariance patterns; and then sex and laterality differences in GMV covariance pattern of each OFC subregion were compared. The human OFC was parcellated into the anterior (OFCa), medial (OFCm), posterior (OFCp), intermediate (OFCi), and lateral (OFCl) subregions. This parcellation scheme was validated by the same analyses of the left OFC and the bilateral OFCs in male and female subjects. Both visual observation and quantitative comparisons indicated a unique GMV covariance pattern for each OFC subregion. These OFC subregions mainly covaried with the prefrontal and temporal cortices, cingulate cortex and amygdala. In addition, GMV correlations of most OFC subregions were similar across sex and laterality except for significant laterality difference in the OFCl. The right OFCl had stronger GMV correlation with the right inferior frontal cortex. Using high-resolution structural images, we established a reliable parcellation scheme for the human OFC, which may provide an in vivo guide for subregion-level studies of this region and improve our understanding of the human OFC at subregional levels.
© 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  gray matter volume covariance; human brain; magnetic resonance imaging; orbitofrontal cortex; parcellation

Mesh:

Year:  2014        PMID: 25271073      PMCID: PMC6869245          DOI: 10.1002/hbm.22645

Source DB:  PubMed          Journal:  Hum Brain Mapp        ISSN: 1065-9471            Impact factor:   5.038


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