Voxel-based dysconnectomic brain morphometry with computed tomography in Down syndrome

Area of Science:

• Neuroimaging
• Gerontology
• Genetics

Background:

• Alzheimer's disease (AD) poses a significant health challenge for aging adults with Down syndrome (DS).
• Conventional diagnostic tools and MRI are often less reliable or feasible for this population due to severe disability or intolerance to prolonged scans.
• Computed tomography (CT) offers a faster alternative but has limitations in morphometric analysis due to poor contrast resolution.

Purpose of the Study:

• To implement an automated analysis of CT scans for characterizing brain differences across dementia stages in adults with DS.
• To explore the utility of CT-based neuroimaging in a population where MRI is challenging.
• To investigate correlations between brain morphometry, dementia severity, and AD biomarkers using CT.

Main Methods:

• An automated algorithm was used to analyze CT scans from 98 individuals with DS.
• Voxel-based correlations were performed to link clinical dementia stages and AD plasma biomarkers (phosphorylated tau-181, neurofilament light chain) with brain volumes.
• Dysconnectomic patterns, analyzing white matter (WM) and gray matter (GM) integrity, were delineated.

Main Results:

• Dementia severity showed a negative correlation with GM and WM volumes in temporal lobe regions, including the parahippocampal gyri.
• Dysconnectome analysis indicated an association between WM loss and temporal lobe GM volume reduction.
• AD biomarkers were negatively associated with GM volume in the hippocampal and cingulate gyri.

Conclusions:

• Automated CT analysis and dysconnectomic approaches can successfully identify AD-related brain morphometric differences in adults with DS.
• This method provides a valuable neuroimaging avenue for populations with DS for whom MRI is difficult to obtain.
• CT-based analysis offers a promising tool for understanding AD progression in DS.