Behavioral variant frontotemporal degeneration (bvFTD) is a common cause of young-onset neurodegenerative disease and life expectancy is approximately 7 years, but this is highly variable. Recent work associates change in cognitive and MRI imaging with pathologic phases of accumulating FTLD-TDP and FTLD-Tau pathology. However, major gaps in knowledge concern the tremendous variability in the rate of clinical progression in bvFTD, and the factors contributing to this variability. Studies have demonstrated that lifestyle factors moderate the rate of clinical progression in neurodegenerative disease. This is attributed to cognitive reserve, a form of resilience where cognitive strategies help support brain functioning in the face of relentlessly accumulating pathology and thus modulate the rate of longitudinal decline. For example, neuroanatomic factors may also play a role in neural implementation of compensatory function, such as supporting alternate brain networks for optimal performance. Genetic factors associated with single nucleotide polymorphisms (SNPs) also may impact the variable rate of decline in bvFTD by selectively increasing anatomic network vulnerability to disease burden. The overall aim of this proposal is to better understand how lifestyle and genetic factors impact neural networks to influence the rate of longitudinal decline.
In Aim 1, we will examine education, occupation and leisure activities that moderate the rate of longitudinal decline on cognitive and functional measures. We will relate this to MRI of gray matter (GM) and white matter (WM) using powerful graph theoretic analyses that examine key metrics of network connectivity and elucidate the neuroanatomic basis of resilience.
Aim 2 will use arterial spin labeling to enhance models of network connectivity that predict the rate of longitudinal clinical decline, and expect to find key connectomic properties of frontal networks related to cerebral blood flow that contribute to resilience in bvFTD.
In Aim 3, we will examine hypothesis-driven SNPs that may moderate the rate of clinical decline and relate SNP variation to anatomic regions in connectomic networks. This unique combination of lifestyle, genetic and imaging factors will lead to novel predictive models of clinical disease progression that are critical for clinical management and clinical trial endpoints, while providing important prognostic data for patients and families. !
