Genetic frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disease primarily caused by pathogenic mutuations in one of three genes: \textit{C9orf72}, \textit{MAPT}, and \textit{GRN}. Accurately predicting time-to-symptom-onset could improve clinical care and patient stratification in clinical trials. This study aimed to develop a machine learning framework for individualized prediction of symptom onset in genetic FTD using multimodal MRI data under limited sample size conditions. We explored two strategies: (i) a support vector machine (SVM) classifier distinguishing symptomatic FTD from non-FTD scans, using the distance to the decision boundary (DDB) as a proxy for time-to-symptom-onset. While DDB values increased as conversion approached, they lacked precision for individual-level prediction. (ii) Five binary classifiers, each trained to predict conversion within a different time window (1–5 years) before symptom onset. Combining outputs from these classifiers yielded personalized onset predictions with a mean absolute error (MAE) of 0.71 years, outperforming a linear elastic-net regression baseline (MAE = 2.97 years). This approach successfully predicted symptoms five years prior to conversion, which represents an important step toward personalized medicine in genetic FTD.

Dementia, characterized by a significant decline in cognitive abilities, encompasses various neurodegenerative disorders. This includes Alzheimer's disease (AD), frontotemporal dementia (FTD) and their clinical manifestation named primary progressive aphasia (PPA), mainly involving language difficulties. In general these diseases exhibit some disease specific clinical characteristics. However, considerable heterogeneity exists in both atrophy patterns and symptom expression within each disease. Additionally, the overlap in disease patterns between AD and FTD complicates diagnosis. In this study, an unsupervised clustering algorithm is used to identify distinct atrophy patterns within AD and FTD, aiming to explore heterogeneity as well as disease overlap. Through the clustering of grey matter volumes, we identified two primary clusters potentially reflective of disease stage. These clusters were externally validated. Additionally, the two primary clusters were subdivided into four distinct clusters, each representing an unique atrophy pattern. These four clusters exhibited differences in cognitive performance in the five cognitive domains: visuospatial functioning, language, memory/learning, processing speed, and attention/executive functioning. The identified clusters provide inside in the atrophy patterns in AD and FTD, as well as their relation to clinical diagnosis and cognitive performance.