Network level analysis of progressive brain degeneration in autosomal dominant Alzheimer disease

行政补充项目摘要/摘要

ADMINISTRATIVE SUPPLEMENT PROJECT SUMMARY/ABSTRACT Alzheimer’s disease (AD) is characterized by changes including the accrual of amyloid-b (Ab) plaques and neurofibrillary tau tangles, cortical thinning, hypometabolism, and disruptions in brain connectivity. However, the presence of this pathology does not occur simultaneously, but propagates throughout the cortex decades before symptoms of dementia are apparent. Researchers have noted that Ab, hypometabolism, and tau show consistent focal disruption beginning in lateral parietal, temporal, and the posterior cingulate gyrus. We hypothesize that this regional spread of pathology results in disrupted communication among brain networks resulting in symptoms of cognitive decline. This proposal seeks to 1) characterize the spatiotemporal progression of brain network degeneration and 2) determine the relationship between neuronal atrophy, brain network dysfunction, and cognitive decline. Brain networks can be measured using resting state functional magnetic resonance imaging to index temporal correlations in blood oxygen level dependent signal between brain regions. We will organize brain regions into canonical functional connectivity brain networks and apply the Network Level Analysis (NLA) analysis software, developed as part of K99 EB029343, to determine brain network associations with neuronal atrophy (as indexed with serum neurofilament light; NfL) and symptoms of dementia (as indexed with a global cognition composite score). NLA is an innovative approach to the analysis of connectome-wide associations that leverages cross disciplinary biostatistical approaches and an ontological framework, allowing for derivation of network-based brain-behavior relationships and control of false positive rate at the network level. This administrative supplement will extend the aims of the original award, which proposed validation of NLA using Human Connectome Project data, to include applications in AD. Specifically, this administrative supplement will leverage a fully de-identified pre-existing dataset containing functional connectomes, NfL, and cognitive measures in participants with autosomal dominant AD (ADAD) recruited from the Dominantly Inherited Alzheimer Network (DIAN) study. The analysis of data from individuals with ADAD is particularly significant due to the known timeframe and early onset of cognitive symptoms which allows for modeling of preclinical brain network degeneration while reducing the contribution of age-related confounds. The proposed analyses of DIAN data using NLA fulfills the National Institute of Aging Goal A to “Better understand the biology of aging and its impact on the prevention, progression, and prognosis of disease and disability.” The research team has expertise in Network Level Analysis (Dr. Wheelock), algorithm development (Dr. Eggebrecht), Alzheimer disease pathophysiology (Dr. Gordon) and the resources to generate functional connectomes in the DIAN cohort for secondary data analysis (Dr. Ances). This supplement will foster collaboration between computational scientists and clinicians and afford opportunities for future collaboration to investigate biomarkers in AD.