Muriah Wheelock - US grants

PROJECT SUMMARY/ABSTRACT Determining the mechanisms by which the human brain generates cognition, perception, and emotion hinges upon quantifying the relationships between coordinated brain activity and behavior. NIH-funded brain mapping initiatives such as the Human Connectome Project (HCP) and the Adolescent Cognitive and Behavioral Development (ABCD) study, have accelerated the production of large brain connectivity (i.e. connectome) and behavioral datasets. Contemporary connectome research views the brain as a large-scale, complex network composed of nonadjacent, yet connected brain regions. We propose to leverage the inherent network architecture of the connectome in order to probe fundamental biological mechanisms underlying the development of executive function and internalizing symptoms. In pursuit of this research question, this application proposes to formalize and validate in house analysis pipelines into a Network Level Analysis (NLA) toolbox as a comprehensive, versatile tool for use in connectome-wide association studies. The proposed NLA toolbox fulfills BRAIN Initiative goal #5 to ?Produce conceptual foundations for understanding the biological basis of mental processes through development of new theoretical and data analysis tools?. While the research focus of this career transition award is on the application of NLA to developmental mechanisms of executive function and emotion regulation, this versatile analytic tool will be transformative to connectome data analysis across species, across the lifespan, and in health and disease. As part of tool development, the applicant will validate multiple NLA approaches using in silico connectome-behavior relationships and establish sensitivity and specificity of network level findings as compared to the connectome-wide control of familywise error rate (K99 Aim 1). The applicant will then establish test-retest reliability of NLA approaches using in vivo human connectome and behavioral data available from the HCP-Young Adult cohort (N=1105), and establish brain networks underlying healthy adult executive and emotional function (K99 Aim 2). During the independent R00 phase, she will then investigate changes in connectome architecture supporting the development of executive and emotional function using the ABCD longitudinal connectome and behavioral data (N=~11,000 age 9-14) (R00 Aim 3). During the K99 phase she will extend her training in behavioral neuroscience to include training in machine learning, longitudinal models, and computer science. Building on her strong foundation in human brain connectivity analysis, the applicant will gain advanced skills in biostatistics and best practices in software development to ensure her success as an independent researcher. The advisory committee, including Drs. Smyser (functional connectivity), Marcus (software engineering), Fair (developmental neuroscience), Todorov (biostatistics), Zhang (machine learning), Bassett (connectome analysis), Eggebrecht (toolbox development), and Barch (HCP/ABCD consultant) provide expertise in all core areas spanning experimental disciplines and possess an excellent record of obtaining independent funding and mentoring young scientists.

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.