Introduction: While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterized based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely on complex computational models and large training datasets to categorize individual patients into diagnostic subgroups based on distinguishing imaging features. Reliable individual subject data interpretation is hugely important in the clinical setting to expedite the diagnosis and classify individuals into relevant prognostic categories.
Areas covered: This article reviews (1) single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) practical clinical implications, and (3) the limitations of current single-subject data interpretation models.
Expert opinion: Classification studies in FTLD have demonstrated the feasibility of categorizing individual subjects into diagnostic groups based on multiparametric imaging data. Preliminary data indicate that pre-symptomatic FTLD mutation carriers may also be reliably distinguished from controls. Despite momentous advances in the field, significant further improvements are needed before these models can be developed into viable clinical applications.
Keywords: Dementia; Frontotemporal dementia; biomarkers; classification models; diagnosis; machine-learning; magnetic resonance imaging; neuroimaging; precision medicine.