# Individualized Atrophy‐Based Prediction of Dementia Progression in Familial Frontotemporal Lobar Degeneration With Bayesian Linear Mixed‐Effects Modeling

## TL;DR

This study uses brain imaging and a Bayesian model to predict dementia progression in people with a genetic risk for frontotemporal lobar degeneration.

## Contribution

A Bayesian linear mixed-effects model is introduced to predict dementia conversion using individualized brain atrophy patterns.

## Key findings

- BLME cluster volume predicted dementia conversion in f-FTLD mutation carriers overall and in specific gene groups.
- BLME outperformed other methods in predicting dementia within 24 months with higher AUCs.
- Accelerated gray matter loss identified by BLME was a strong predictor of dementia progression.

## Abstract

Age of symptom onset is highly variable in familial frontotemporal lobar degeneration (f‐FTLD). Accurate prediction of onset would inform clinical management and trial enrollment. Prior studies indicate that individualized maps of brain atrophy can predict conversion to dementia in f‐FTLD. We used a Bayesian linear mixed‐effect (BLME) prediction method for identifying accelerated brain volume loss to predict conversion to dementia.

Participants included 234 asymptomatic or prodromal carriers of C9orf72, GRN, or MAPT mutations (including 21 dementia converters) with ≥3 longitudinal magnetic resonance imaging (MRI) T1‐weighted scans. The BLME models established individual voxel‐wise gray matter trajectories using the first 2 scans. Person‐specific clusters of accelerated volume loss were estimated in subsequent scans and tested as predictors of dementia conversion compared with other approaches in time‐varying Cox proportional hazard models covarying for age. Receiver‐operating characteristic (ROC) curves estimated utility of cluster volume in discriminating which participants converted to dementia within 24 months.

The BLME cluster volume predicted conversion to dementia in f‐FTLD mutation carriers overall and separately in C9orf72, GRN, and MAPT, with comparable hazard ratios observed for atrophy W‐maps and regional volumes. Within a 24‐month timeframe, BLME cluster volume discriminated dementia converters from non‐converters with larger areas under the curve (AUCs) than other approaches.

Bayesian‐modeled individualized atrophy scores predict dementia progression among asymptomatic f‐FTLD mutation carriers and may have increased utility compared with other structural imaging methods when studying individuals over shorter timeframes that align with clinical trial design. ANN NEUROL 20269999:n/a–n/a