Structure–function coupling using fixel-based analysis and functional magnetic resonance imaging in Alzheimer’s disease and mild cognitive impairment
Year: 2025
Type: article
Abstract: Functional MRI (fMRI) and diffusion-weighted imaging (DWI) help explore correlations between structural connectivity (SC) and functional connectivity (FC; SC–FC coupling). Studies on mild cognitive impairment (MCI) and Alzheimer’s disease (AD) observed coupling disruptions, co-occurring with cognitive decline. Advanced “fixel-based” analyses improved DWI’s accuracy in assessing microstructural and macrostructural features of white matter (WM), but previous aging coupling studies commonly defined SC via tensor-based tractography and streamline counts, thereby missing fiber-specific information. We investigated different types of fixel-FC coupling and their relation to cognition in 392 participants (Agemean = 73; 207 females) from the ADNI. Two hundred twenty-five controls, 142 MCI, and 25 AD with diffusion-weighted and resting-state fMRI scans were analyzed. Structural connectomes were constructed using average fixel metrics (fiber density (FD), fiber-bundle cross-section log, and combined [FDC]) as edges. SC–FC coupling for each SC metric was calculated at overall network, edge, and node levels. Overall DMN, node- and edge-specific coupling differences were found across SC measures and groups. DMN nodal coupling significantly predicted Mini-Mental Status Examination score and verbal memory. In conclusion, different types of fixel-based coupling alterations can be observed across the neurocognitive aging spectrum, in particular, FD–FC and FDC–FC coupling between DMN regions are associated with cognitive functioning.
Source: Network Neuroscience
DOI: 10.1162/netn_a_00461
Institution: Nanyang Technological University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,750
Funder: Nanyang Technological University
Award: G1042584184
OpenAlex ID: W4409310084
