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

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.

Parcellation of the human hippocampus based on gray matter volume covariance: Replicable results on healthy young adults

“…However, those commonly used fMRI and probabilistic tractography connectivity‐based parcellation approaches, may not be optimal for small‐sized structures like the hippocampus, due to their low spatial resolution. Inspired by the parcellation of insula (Kelly et al, ) and orbitofrontal cortex (Liu et al, ) with GMV covariance, we parcellated the human hippocampus based on this technique which emerged from high‐resolution structural images (Mechelli et al, ). GMV covariance examines covariation of gray matter morphology between brain regions across populations.…”

“…It has been suggested that GMV covariance recapitulates functional brain networks (Seeley, Crawford, Zhou, Miller, & Greicius, ) and the pattern of interregional structural covariance is akin to the pattern of functional connectivity (Alexander‐Bloch, Giedd, & Bullmore, ), thereby driving the brain functioning in an efficient way. GMV covariance has been used to reliably parcellate brain regions such as the insula (Kelly et al, ) or the orbitofrontal cortex (Liu, Qin, Qi, Jiang, & Yu, ). These two studies (Kelly et al, ; Liu et al, ) employed GMV correlation (GMVCorr), which is a bivariate voxel‐to‐voxel correlation method that utilizes second‐order statistics to depict the association of the GMV between two voxels within the region of interest (ROI).…”

“…GMV covariance has been used to reliably parcellate brain regions such as the insula (Kelly et al, ) or the orbitofrontal cortex (Liu, Qin, Qi, Jiang, & Yu, ). These two studies (Kelly et al, ; Liu et al, ) employed GMV correlation (GMVCorr), which is a bivariate voxel‐to‐voxel correlation method that utilizes second‐order statistics to depict the association of the GMV between two voxels within the region of interest (ROI). In contrast, independent component analysis (ICA) (McKeown & Sejnowski, ), is a multivariate blind source separation method, which utilizes higher order statistics to model common associations across multiple voxels.…”

“…We utilized GMVCorr (Kelly et al, ; Liu et al, ) as well as MICA (Blessing et al, ; Kim et al, ; Moher Alsady et al, ), which are two different approaches to quantify GMV covariance (Mechelli et al, ; Xu, Groth, Pearlson, Schretlen, & Calhoun, ), to parcellate the human hippocampus based on T1‐weighted MRI scans from two independent data sets. We hypothesized that the combination of bivariate GMVCorr and multivariate MICA would provide converging and complementary information and improve the structural covariance parcellation.…”

Cytoarchitectonic Characterization and Functional Decoding of Four New Areas in the Human Lateral Orbitofrontal Cortex

“…Functional analyses of the lateral OFC revealed that this region is involved in the processing of gustatory, olfactory or somatosensory rewarding stimuli but also the processing of emotional punishments, evaluating and updating the emotional status, maintaining social behavior, active retrieval of information, semantic processing, verb generation, processing of stimuli that have a coherent temporal structure, music listening, assigning value to certain things and events and decision-making (Papathanassiou et al, 2000;Levitin and Menon, 2003;Kringelbach and Rolls, 2004;Petrides, 2005;Campbell-Meiklejohn et al, 2012;Alluri et al, 2013;Liu et al, 2015;Neubert et al, 2015;Rolls et al, 2015;Hirose et al, 2016). Tasks that require quick responses while retrieving information from the long-term and working memory also showed activations in the lateral OFC.…”