The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10−4) or temporal stage (p = 3.96 × 10−5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.

Young et al. reformulate an event-based model for the progression of Alzheimer's disease to make it applicable to a heterogeneous sporadic disease population. The enhanced model predicts the ordering of biomarker abnormality in sporadic Alzheimer's disease independently of clinical diagnoses or biomarker cut-points, and shows state-of-the-art diagnostic classification performance.

See Stankoff and Louapre (doi:) for a scientific commentary on this article.Grey matter atrophy in multiple sclerosis affects certain areas preferentially. Eshaghi et al. use a data-driven computational model to predict the order in which regions atrophy, and use this sequence to stage patients. Atrophy begins in deep grey matter nuclei and posterior cortical regions, before spreading to other cortical areas.

SummaryThe heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we present a new machine learning technique – Subtype and Stage Inference (SuStaIn) – able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available crosssectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal new subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes, and characterises within-group heterogeneity for the first time. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely revealing their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p=7.18×10--4) or temporal stage (p=3.96×10−5). SuStaIn thus offers new promise for enabling disease subtype discovery and precision medicine.

See Li and Donohue (doi:) for a scientific commentary on this article.The key to developing interventions for Alzheimer’s disease may lie within the less common familial form, which has a predictable presymptomatic phase. Oxtoby et al. use modern computational techniques to characterize familial Alzheimer’s disease progression. The resulting data-driven models allow fine-grained patient staging, with potential utility in clinical trials.