BRAIN Initiative: Cutting-Edge Tools and Resources for the Community

Abstract

The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM-and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources.

Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks

“…In our approach, unrecorded units and associated undetected correlation patterns may produce a reduction in the number of computed correlation triangles, which in turn may result in reduced filtering capabilities. As other technologies from the Brain Initiative (Litvina et al, 2019) and related efforts, such as the Human Brain Project in the EU, become available online and are validated, the methods we develop here will be in a position to take immediate advantage of them. For example, high-density MEAs (HDMEAs) that include tens of thousands of electrodes at cellular and subcellular resolution (Ullo et al, 2014;Muller et al, 2015;Yada et al, 2016;Bullmann et al, 2019) offer optimized acquisition settings and will greatly improve the resolution and accuracy of our approach.…”

The 2021 Epilepsy Research Benchmarks—Respecting Core Principles, Reflecting Evolving Community Priorities

“… The epilepsy community would benefit from learning from successful models for research and clinical care that have been implemented for other diseases. More specifically, there is a need to understand the mechanisms that underlie the epilepsies in order to develop targeted and more precise therapies; develop and validate an array of biomarkers for the epilepsies to identify those most at risk for developing epilepsy and SUDEP, measure progression of disease, and response to treatment; improve preclinical models of epilepsies so that they better recapitulate the human condition; expand epilepsy research and clinical care to underserved communities and diversify the workforce in both areas; and create and utilize innovative new tools and measures, such as those being developed in the BRAIN Initiative 14 that expand the capabilities of basic and clinical research on the epilepsies. …”

“…create and utilize innovative new tools and measures, such as those being developed in the BRAIN Initiative 14 that expand the capabilities of basic and clinical research on the epilepsies.…”

The Influence of Autoregressive Relation Strength and Search Strategy on Directionality Recovery in GIMME

“…The promise of network models for characterizing individual-level processes in intensive longitudinal and functional neuroimaging time series data has precipitated a surge of interest across the behavioral and neural sciences. Many of these models were initially developed to characterize temporal and network dynamics of interactions between brain regions in functional magnetic resonance imaging (fMRI) data, leading to recent advances in the field's understanding of the neural correlates of cognition and psychiatric syndromes (Beltz et al, 2018;Elbich et al, 2019;Gates et al, 2014;Litvina et al, 2019;Mumford & Ramsey, 2014;Nichols et al, 2014;Price, Lane, et al, 2017;Weigard et al, 2019;Wu et al, 2019). Applications of similar network models to data from daily diary and ambulatory assessment studies have also begun to provide key insights into the within-person dynamics of basic psychological phenomena (Bar-Kalifa & Sened, 2019;Hofmans et al, 2019;Lydon-Staley & Bassett, 2018;Yang et al, 2019) and to elucidate person-specific structures, and determinants, of psychopathology Dotterer et al, 2019;Ellison et al, 2019;Jongeneel et al, 2019;Stroe-Kunold et al, 2016;Wright et al, 2015;Yang et al, 2018).…”