Behavioral variant frontotemporal dementia in a 23-year-old man.

A 23-year-old man presented with behavioral disinhibition, stereotypies, motor apathy, flattened affect, and inappropriate laughter. CT demonstrated generalized cerebral atrophy. He was admitted with a diagnosis of unspecified psychosis and discharged on antipsychotic medication. He was readmitted 3 months later, was diagnosed with schizophrenia, and antipsychotic medication was continued. Owing to symptom progression and aggressive behavior, he was readmitted 2 months later. CT again demonstrated moderate central and cortical cerebral atrophy. MRI showed severe, stable atrophy with frontotemporal predominance, and he was diagnosed with probable behavioral variant frontotemporal dementia (bvFTD).  Over the next year he rapidly deteriorated, with loss of cognitive abilities. Genetic testing revealed several variants, none of which are clearly disease-causing.

Temporal order of clinical and biomarker changes in familial frontotemporal dementia.

Unlike familial Alzheimer's disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.