Richard Betzel, Ph.D. - US grants

The field of network neuroscience has developed powerful analysis tools for studying brain networks and holds promise for deepening our understanding of the role played by brain networks in health, disease, development, and cognition. Despite widespread interest, barriers exist that prevent these tools from having broader impact. These include (1) unstandardized practices for sharing and documenting software, (2) long delays from when a method is first introduced to when it becomes publicly available, and (3) gaps in theoretic knowledge and understanding leading to incorrect, delays due to mistakes, and errors in reported results. These barriers ultimately slow the rate of neuroscientific discovery and stall progress in applied domains. To overcome these challenges, we will use open science methods and cloud-computing, to increase the availability of network neuroscience tools. We will use the platform brainlife.io for sharing these tools, which will be packaged into self-contained, standardized, reproducible Apps, shared with and modified by a community of users, and integrated into existing brainlife.io analysis pipelines. Apps will also be accompanied by links to primary sources, in-depth tutorials, and documentation, and worked-through examples, highlighting their correct usage and offering solutions for mitigating possible pitfalls. In standardizing and packaging network neuroscience tools as Apps, this proposed research will engage a new generation of neuroscientists, providing them powerful new and leading to new discoveries. Second, the proposed research will contribute growing suite of modeling analysis that can be modified to suit specialized purposes. Finally, the Brainlife.io platform will serve as part of the infrastructure supporting neuroscience research. Altogether, these advances will lead to new opportunities in network neuroscience research and further stimulate its growth while increasing synergies with other domains in neuroscience.

The human brain is made up of functionally and structurally connected neural elements that form a brain-wide complex network. A principal goal of network neuroscience is to understand how the organization of this network helps support cognition, evolves over the course of the human lifespan, and becomes compromised in disease and neuropsychiatric disorders. However, virtually all progress made towards addressing these questions has relied upon one particular network model for mathematically representing patterns of brain connectivity, at the expense of other models that could provide complementary or unique insight. This project aims to extend and validate an alternative edge-centric framework for representing and analyzing patterns of brain connectivity. The project will deliver new insights into the relationship of brain network organization with cognitive/behavioral phenotypes and shed light on brain network dynamics at ultra-fast timescales are paralleled by changes in subjects' cognitive states. This research will support cross-disciplinary collaboration among the brain sciences, informatics, and statistics, and will support a diverse set of trainees at all levels, from high school to postdoctoral.

This principal innovation of the edge-centric framework is a spatiotemporal decomposition of functional connections into their framewise contributions. This decomposition yields a time series of co-fluctuations for every pair of brain regions (edges in the network). The first aim investigates the novel construct of edge functional connectivity -- the correlation pattern estimated among all pairs of co-fluctuation time series. Edge connectivity will be generated for a large cohort of subjects (N > 1000) using imaging data acquired both at rest and while subjects were performing cognitively demanding tasks. Multivariate statistical methods will be used to discover robust associations between edge connectivity and subjects' behavioral, demographic, and clinical measures. The second aim analyzes co-fluctuation time series directly, taking advantage of the ultra-fast timescale at which they are estimated to investigate potential drivers of brain network reconfiguration during naturalistic viewing (movie-watching). This project advances the edge-centric framework as a viable tool for general neuroscientific discovery and will open the door for future studies to investigate brain-behavior relationships and network dynamics in applied contexts and not restricted to large-scale imaging data.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.