Individualized Atrophy-Based Prediction of Dementia Progression in Familial Frontotemporal Lobar Degeneration With...

Area of Science:

• Neuroimaging
• Neurodegenerative diseases
• Biostatistics

Background:

• Familial frontotemporal lobar degeneration (f-FTLD) onset is variable, complicating clinical management and trial enrollment.
• Individualized brain atrophy maps show potential for predicting dementia conversion in f-FTLD.
• Predicting dementia onset is crucial for timely intervention and efficient clinical trial design.

Purpose of the Study:

• To evaluate a Bayesian linear mixed-effect (BLME) prediction method for identifying accelerated brain volume loss.
• To assess the utility of BLME-identified atrophy clusters in predicting dementia conversion in f-FTLD.
• To compare the predictive performance of BLME with other neuroimaging approaches.

Main Methods:

• Utilized longitudinal MRI scans from 234 asymptomatic or prodromal f-FTLD mutation carriers (C9orf72, GRN, MAPT), including 21 dementia converters.
• Developed BLME models to establish individual gray matter trajectories and identify clusters of accelerated volume loss.
• Employed time-varying Cox proportional hazard models and ROC curves to test prediction accuracy and discriminate converters within 24 months.

Main Results:

• BLME cluster volume significantly predicted dementia conversion in f-FTLD mutation carriers across different genetic subtypes (C9orf72, GRN, MAPT).
• The predictive performance of BLME atrophy maps was comparable to regional volumes.
• BLME demonstrated superior discrimination of dementia converters versus non-converters within a 24-month timeframe, indicated by larger AUCs.

Conclusions:

• Individualized atrophy scores derived from Bayesian modeling accurately predict dementia progression in asymptomatic f-FTLD mutation carriers.
• This approach may offer enhanced utility over other structural imaging methods for shorter timeframes relevant to clinical trial designs.
• BLME-based prediction holds promise for improving the management and research of f-FTLD.