New brain connectivity model predicts dementia years before diagnosis

By Vijay Kumar MalesuJun 10 2024Reviewed by Lily Ramsey, LLM In a recent study published in Nature Mental Health , a group of researchers evaluated if a neurobiological model of the default-mode network effective connectivity can predict future dementia diagnosis at the individual level.

Early DMN dysconnectivity patterns are linked to genetic risk factors for AD and social isolation, suggesting their potential as preclinical biomarkers. Further research is needed to validate effective connectivity analysis for early dementia diagnosis and refine prevention strategies. Effective connectivity was estimated using spectral dynamic causal modeling , fitting a fully connected model for each participant and using parametric empirical Bayes modeling for group-level differences.

The incident cases had a median time to diagnosis of 3.7 years. The total sample had a mean age of 70.4 at the time of MRI data acquisition, and cases and controls were matched on age, sex, ethnicity, handedness, and geographical location of the testing center. Fifteen connectivity parameters significantly differed, with increased inhibition from the Ventromedial Prefrontal Cortex to Left Parahippocampal Formation and Left Intraparietal Cortex to lPHF, and attenuated inhibition from Right Parahippocampal Formation to Dorsomedial Prefrontal Cortex .

Volumetric models yielded moderate diagnostic value and chance-level prognostication. Functional connectivity models were performed at the chance level for both diagnosis and prognostication. Cognitive data alone had moderate diagnostic performance and chance-level prognostication.

Dementia Alzheimer's Disease Cortex Diagnostic Genetic Imaging Magnetic Resonance Imaging Mental Health Preclinical Research

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