Default mode network perturbations in Alzheimer's disease: an fMRI study in Klang Valley, Malaysia
DOI:
https://doi.org/10.31117/neuroscirn.v7i1.284Keywords:
Alzheimer's disease, Voxel-based morphometry, Seed-based analysis, Grey matter volume, Fault mode networkAbstract
The default mode network (DMN) is a large neural network that has a significant correlation with Alzheimer's disease (AD). Grey matter volume (GMV) and functional connectivity (FC) involving the regions of the DMN have been noted to differ significantly between AD and healthy older adults. Nevertheless, there is a paucity of data on the structural and functional changes in the DMN of AD patients in Malaysia. We conducted a cross-sectional study in Klang Valley, Malaysia, to evaluate AD subjects compared to healthy controls (HC) using a resting-state functional MRI (rs-fMRI) experiment. We recruited 22 subjects (AD=11, HC=11) and conducted neuropsychological tests such as the Montreal Cognitive Assessment (MoCA), Mini Mental State Examination (MMSE), and Clinical Dementia Rating (CDR). The subjects then underwent rs-fMRI scans, and subsequently, we quantitatively analysed the GMV by Voxel based Morphometry (VBM) using the structural data. We also utilised the CONN toolbox on Statistical Parametric Mapping (SPM) software to evaluate the FC and activation of the nodes of the DMN. In comparison with the HC group, the AD group demonstrated a reduction in GMV in the right and left inferior temporal gyrus, left superior frontal gyrus, right superior frontal gyrus medial segment, right gyrus rectus, right temporal lobe, left putamen, and right precuneus. Moreover, there was a significant decrease in the FC of the nodes of the DMN noted on rs-fMRI (cluster-size corrected p<0.05). In particular, the precuneus and anterior cingulate cortex had decreased FC in AD compared to HC. Hence, structural and resting-state fMRI can detect distinct imaging biomarkers of AD based on GMV and DMN functional connectivity profiles. This tool can be used as a non-invasive tool for improving the feature detection and diagnosis of AD in the Malaysian population.
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