This project will bring together expertise in computational and experimental neuroscience, signal processing and network science, statistics, modeling and simulation, to establish innovative methods to model and analyze temporally dynamic brain networks, and to apply these tools to develop predictive models of brain-computer interface (BCI) skill acquisition that can be used to improve performance. Leveraging experimental data and interdisciplinary theoretical techniques, this project will characterize brain networks at multiple temporal and spatial scales, and will develop models to predict the ability to control the BCI as well as methods to engineer BCI frameworks for adapting to neural plasticity. This project will enable a comprehensive understanding of the neural mechanisms of BCI learning, and will foster the design of viable BCI frameworks that improve usability and performance. Intellectual Merit: As a critical innovation, this project proposes to develop a systematic and rigorous approach based on neuroimaging techniques, signal processing, and network science for the modeling and analysis of temporally dynamic neural processes that characterize BCI skill learning. To achieve these goals, we will organize our research around the following objectives: (i) characterizing multiple spatio-temporal scales of dynamic functional brain networks, (ii) modeling BCI skill acquisition and predicting performance from brain network properties, (iii) simulating coadaptive BCI frameworks using dynamic network-based neural features. Results will first be characterized from pure graph-theoretic and neuroscience perspectives, so as to highlight fundamental research challenges, and then validated to clarify the importance and the applicability of our findings to translational efforts in practical BCI scenarios. Our results wil (i) unveil multi-resolution properties of dynamic brain networks, (ii) identify predictive neuromarkers for BCI learning, and ultimately (iii) inform the development of coadaptive BCI frameworks sensitive to subject-specific neural plasticity. The two young PIs - one from the Department of Bioengineering at the University of Pennsylvania and one from the ARAMIS team of the Institut National de Recherche en Informatique et en Automatique (INRIA) located at the Institut du Cerveau et de la Moelle epiniere (ICM) in Paris - bring complementary and interdisciplinary backgrounds to this research project, with a strong track record in network analysis, network neuroscience, multimodal neuroimaging and BCI applications. Their experience and resources will enable the success of this new approach to analyze dynamic networks in BCI learning, design co-adaptive BCI frameworks, and facilitate the use of non-invasive BCI technology for both control of external devices (e.g. neuroprosthetics) as well as neurofeedback applications (e.g. MI-based neurorehabilitation after stroke). Broader Impacts: This interdisciplinary project proposes a transformative approach to analyze large-scale neural systems, and to model and predict BCI skill acquisition. This research provides novel insights into the temporal interconnection structure of the human brain, and proposes entirely new methods to construct dynamic network-based models of neural plasticity from multimodal neuroimaging data. Results will foster the development of innovative predictive neuromarkers for the diagnosis and treatment of neurological disorders and psychiatric disease. The PIs will bring their findings and innovative techniques to the undergrad and graduate programs at their institutions, disseminate findings via dedicated courses, workshops, and publications, and to the community and local middle/highschools via lectures and STEAM outreach events.
| Reddy, Pranav G; Mattar, Marcelo G; Murphy, Andrew C et al. (2018) Brain state flexibility accompanies motor-skill acquisition. Neuroimage 171:135-147 |
| Medaglia, John D; Satterthwaite, Theodore D; Kelkar, Apoorva et al. (2018) Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment. Neuroimage 166:293-306 |
| Lefort-Besnard, Jérémy; Bassett, Danielle S; Smallwood, Jonathan et al. (2018) Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function. Hum Brain Mapp 39:644-661 |
| Braun, Urs; Schaefer, Axel; Betzel, Richard F et al. (2018) From Maps to Multi-dimensional Network Mechanisms of Mental Disorders. Neuron 97:14-31 |
| Baum, Graham L; Roalf, David R; Cook, Philip A et al. (2018) The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage 173:275-286 |
| Muldoon, Sarah F; Costantini, Julia; Webber, W R S et al. (2018) Locally stable brain states predict suppression of epileptic activity by enhanced cognitive effort. Neuroimage Clin 18:599-607 |
| Xia, Cedric Huchuan; Ma, Zongming; Ciric, Rastko et al. (2018) Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun 9:3003 |
| Gerraty, Raphael T; Davidow, Juliet Y; Foerde, Karin et al. (2018) Dynamic flexibility in striatal-cortical circuits supports reinforcement learning. J Neurosci : |
| Mattar, Marcelo G; Wymbs, Nicholas F; Bock, Andrew S et al. (2018) Predicting future learning from baseline network architecture. Neuroimage 172:107-117 |
| Sizemore, Ann E; Giusti, Chad; Kahn, Ari et al. (2018) Cliques and cavities in the human connectome. J Comput Neurosci 44:115-145 |
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