CREST Center for Dynamic Multiscale and Multimodal Brain Mapping Over The Lifespan [D-MAP]

Center for dynamic multiscale and multimodal brain mapping over the lifespan

The Centers of Research Excellence in Science and Technology (CREST) program supports the enhancement of research capabilities of minority-serving institutions through the establishment of centers that effectively integrate education and research. CREST promotes the development of new knowledge, enhancements of the research productivity of individual faculty, and an expanded presence of students historically underrepresented in science, technology, engineering, and mathematics disciplines.

With National Science Foundation support, Georgia State University establishes the Center for dynamic multiscale and multimodal brain mapping over the lifespan [D-MAP] to study the links between brain development across the lifespan. The Center aims to understand brain structure and connectivity across multiple scales with three synergistic research studies.

The proposed work will promote undergraduate development in preparation for STEM education and careers; create opportunities for graduate students to work in multidisciplinary environments; and develop education and training modules that can be integrated into existing graduate and undergraduate curricula.

The study of links between early brain development, adulthood, and senescence throughout the lifespan is an important and understudied area. Subproject 1 (Unimodal Brain Dynamics) develops methods to advance understanding of time-varying brain connectivity and the evolution of whole brain connectivity patterns over time. New methods are needed that can incorporate explicitly spatial information into dynamics, estimate potential nonlinear relationships, and integrate dynamic information across scales.

These methods will be applied to study the short and long-term dynamics of reading acquisition. Subproject 2 (Multimodal Data Fusion) develops novel methods to lead the field in multivariate approaches to model linked changes in multi-modal measures and their trajectories over the lifespan. Key contributions include the incorporation of network subspaces, flexible approaches to identify links between data with mismatched dimensionality, and the development of multimodal models that leverage deep learning to capture more complex relationships.

Initial emphasis will be on multimodal MRI and EEG/MEG data. The focused application is to study the multimodal signatures of cognition and mood. Subproject 3 (Predictive Neuroimaging) focuses specifically on approaches to leverage lifespan data for individualized prediction.

The subproject exploits large open data repositories to develop predictive fingerprints of development and aging along multiple dimensions. Anticipated contributions include novel predictive multimodal models that evolve both within and among individuals, advanced visualization approaches to enhance interpretability, and development and use of neuroinformatics infrastructure for reproducible large N brain imaging data analysis of various populations. The focused application will be to use neuroimaging to predict aspects of linguistic processing.

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.