Alterations in topological and dynamical parameters correlate with disease biomarkers and neuropsychological scores in prodromic stages of dementia.
Brain network analysis from routine MRI scans correlates strongly with memory decline and Alzheimer's markers, potentially enabling personalized early intervention.
Combining graph theory network analysis with virtual brain modeling of MRI data provides a multiparametric, subject-specific characterization of mild cognitive impairment that correlates strongly (R²~70%) with neuropsychological scores and AD biomarkers. This approach could inform personalized stratification for future therapeutic intervention in prodromic dementia.
What the study was
- Study design
- Cross-sectional cohort study; graph theory + virtual brain modeling of MRI data
- Population
- MCI patients, healthy controls, and Alzheimer's disease patients; IRCCS Mondino Foundation cohort
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- Scientific Reports
Why it surfaced
Novel combination of graph theory and virtual brain modeling achieves R²~70% correlation with neuropsychological scores in MCI — subject-specific, not group-level. Relevant to aging/dementia diagnostics topic. Score limited by small cohort (size not specified), single-center, and exploratory design.
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