Project 454834
Longitudinal brain networks integrity in genetic frontotemporal dementia: A GENFI study.
Longitudinal brain networks integrity in genetic frontotemporal dementia: A GENFI study.
Project Information
| Study Type: | Unclear |
| Research Theme: | Clinical |
Institution & Funding
| Principal Investigator(s): | Sanchez-Gonzalez, Erlan |
| Supervisor(s): | Masellis, Mario |
| Institution: | Sunnybrook Research Institute (Toronto, Ontario) |
| CIHR Institute: | Aging |
| Program: | |
| Peer Review Committee: | Fellowships - Post-PhD |
| Competition Year: | 2021 |
| Term: | 3 yrs 0 mth |
Abstract Summary
Frontotemporal dementia is among the most common causes of young onset dementia, with a prevalence approaching that of Alzheimer's disease in those under age 65. Severe behavioral and/or language disruption significantly impairs individuals of working age, often with young families, which represents a major health and economic burden in their lives and for society. The main risk factors are genetic, with three specific genetic variants accounting for a majority of cases. For a long time, clinical trials have failed in their attempts to prevent or slow this disease, due to its immense heterogeneity not only in terms of causative gene mutations, but also in terms of age-of-onset and clinical presentation. Recently, promising early clinical trials were largely informed by the GENetic Frontotemporal Dementia Initiative (GENFI). GENFI is an international consortium to study those with or at-risk of developing genetic frontotemporal dementia, thus allowing characterizing the evolution of the disease from its earliest stages, years before the apparition of symptoms. It is critical to identify pre-symptomatic biomarkers of frontotemporal dementia for effective trials, and functional neuroimaging has already shown great promise in detecting the earliest possible changes, up to 12.5 years before the expected clinical onset. The overall goal of this project is to characterize and follow the integrity of the brain's functional networks in the pre-symptomatic to symptomatic phase to differentiate between causative genetic variants and predict the clinical progression of the disease. This is essential to establish these functional neuroimaging biomarkers' utility in predicting when disease-modifying therapy should be initiated and how responses are best monitored.
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