Innovative biostatistical approaches to network level analyses of connectome-behavior relationships - 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.
