Reduced gray matter volume in the default-mode network associated with insulin resistance
Data files
Dec 18, 2023 version files 11.39 MB
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Fig2_Tmap.nii
8.49 MB
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mean_zmapARC.dscalar.nii
578.53 KB
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mean_zmapDMH.dscalar.nii
578.53 KB
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mean_zmapLHA.dscalar.nii
578.53 KB
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mean_zmapPVH.dscalar.nii
578.53 KB
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mean_zmapVMH.dscalar.nii
578.53 KB
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README.md
2.25 KB
Abstract
Insulin resistance may lead to structural and functional abnormalities of the human brain. However, the mechanism by which insulin resistance impairs the brain remains elusive. In this study, we used two large neuroimaging databases to investigate the brain regions where insulin resistance was associated with the gray matter volume and to examine the resting-state functional connectivity between these brain regions and each hypothalamic nucleus. Insulin resistance was associated with reduced gray matter volume in the regions of the default-mode and limbic networks in the cerebral cortex in older adults. Resting-state functional connectivity was prominent between these networks and the paraventricular nucleus of the hypothalamus, a hypothalamic interface connecting functionally with the cerebral cortex. Furthermore, we found a significant correlation in these networks between insulin resistance-related gray matter volume reduction and network paraventricular nucleus of the hypothalamus resting-state functional connectivity. These results suggest that insulin resistance-related gray matter volume reduction in the default-mode and limbic networks emerged through metabolic homeostasis mechanisms in the hypothalamus.
Saki Asano et al. (2023) Cerebral Cortex 33:11225–11234, 2023.
This study investigated the association of insulin resistance with the gray matter volume (GMV) and resting-state functional connectivity (RSFC) in the human brain using a cohort dataset including structural images and blood samples from >1000 older adults in Japan and the Human Connectome Project (HCP) dataset of RSFC.
The Nifti file showing that the regional GMV reduction was negatively associated with homeostasis model assessment for insulin resistance (HOMA-IR) is provided.
The Cifti files showing that the activity of hypothalamic nuclei was correlated with those of cerebrocortical parcels are provided.
The regional GMV reduction was negatively associated with HOMA-IR
We observed that the GMV reduction in the precuneus, superior frontal gyrus, medial frontal gyrus, parietal operculum, and ventromedial prefrontal cortex was negatively associated with HOMA-IR (Fig. 2).
The file of t map: Fig2_Tmap.nii
The activity of hypothalamic nuclei was correlated with those of cerebrocortical parcels
The functional connectivity was calculated between the hypothalamic nuclei and cerebrocortical parcels, into which the entire cerebral cortex was parcellated as functional units (Glasser et al. 2016). The functional connectivity between the cerebrocortical parcels (n=360) and the PVH are shown in Fig. 4B, and that between the cerebrocortical parcels and each of the other four hypothalamic nuclei are shown in Fig. S7.
The file of the correlation map between the PVH and the cerebrocortical parcels: mean_zmapPVH.dscalar.nii
The file of the correlation map between the ARC and the cerebrocortical parcels: mean_zmapARC.dscalar.nii
The file of the correlation map between the DMH and the cerebrocortical parcels: mean_zmapDMH.dscalar.nii
The file of the correlation map between the VMH and the cerebrocortical parcels: mean_zmapVMH.dscalar.nii
The file of the correlation map between the LHA and the cerebrocortical parcels: mean_zmapLHA.dscalar.nii
Softwares
The Nifti files can be opened using SPM, MRIcron, and FSL. The Cifti files can be opened using wb_view.
Data from the Bunkyo Health Study, which aimed to prevent diseases requiring long-term care in older people, was used in the present study. Of 1629 participants in the database, insulin-dependent T2DM patients were excluded, and then the data of 1609 without any missing data were analyzed. We analyzed the gray matter volumes using a VBM software implemented in SPM12 and insulin resistance (HOMA-IR) was calculated using fasting blood sampling data.
We also analyzed the functional connectivity between the HOMA-IR-related cerebral region and the hypothalamic nuclei. For this analysis, we used the data of resting-state functional images of HCP (N = 418).