NSF CREST D-MAP Center

We envision a Georgia State University (GSU) center for dynamic multiscale and multimodal brain mapping across the lifespan [D-MAP]. The proposed center will integrate investigators from 12 departments/programs and 5 colleges/schools across the university, focusing on highly interdisciplinary topics in areas from neuroscience to computer science to biomedical enterprise.

Together, we will address one of the greatest challenges we face today, uncovering the link between brain and behavior across the lifespan with the following subprojects.

Unimodal Brain Dynamics

We will develop methods to advance our understanding of time-varying brain connectivity (i.e. chronnectomics1) and the evolution of whole brain connectivity patterns over time (i.e., from milliseconds to seconds to minutes to years).

Multimodal Data Fusion

We will develop 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 we propose include 1) the incorporation of network subspaces (e.g. different subsets of modalities and networks show different behavior), 2) flexible approaches to identify links between data with mismatched dimensionality, e.g. brain function and structure, and 3) the development of multimodal models that leverage deep learning to capture more complex (potentially nonlinear) relationships. Initial emphasis will be on multimodal MRI and EEG/MEG data.

Predictive Neuroimaging

We will exploit large open data repositories to develop predictive fingerprints of development and aging along multiple dimensions (e.g., age, individuality) including individuals from ages 2-80 (cross-sectional with embedded longitudinal cohorts within each age bracket). Anticipated contributions include 1) novel predictive multimodal models that evolve both within and among individuals, 2) advanced visualization approaches to enhance interpretability, and 3) development and use of neuroinformatics infrastructure for reproducible large N brain imaging data analysis of various populations.

The D-MAP CREST center represents a cutting-edge project that will build on synergistic efforts to provide important contributions to development and aging with applicability to many different aspects of neuroscience. D-MAP also represents a unique educational opportunity by combining a highly important and engaging research topic with a broad range of critical STEM skills including neuroscience, neuroimaging, data mining, analysis, machine learning, data visualization, neuroinformatics, and related technologies. The D-MAP CREST center represents a cutting-edge project that will build on synergistic efforts to provide important contributions to development and aging with applicability to many different aspects of neuroscience. D-MAP also represents a unique educational opportunity by combining a highly important and engaging research topic with a broad range of critical STEM skills including neuroscience, neuroimaging, data mining, analysis, machine learning, data visualization, neuroinformatics, and related technologies.

CREST D-MAP TEAM

The CREST D-MAP leadership team is composed of faculty from a variety of disciplines that specialize in neuroimaging & mentoring.

Leadership Team

  • Vince Calhoun (GSU/GT/Emory, brain imaging analytics, data science, dynamic connectivity, data fusion, deep learning)
  • Vonetta Dotson (GSU psychology & gerontology, neuroscience of aging, imaging, diversity in neuroscience)
  • Robin Morris (GSU psychology, language, education)
  • Sergey Plis (GSU computer science, dynamics, deep learning)
  • Jessica Turner (GSU psychology, imaging genomics)

Affiliated Faculty

Internal Steering Committee
Tim Denning (GSU, VP of research & economic development, Institute for Biomedical Sciences)
Jennifer Sherer (GSU, Director, Entrepreneurship & Innovation)
Geert DeVries (GSU, Director of Biology)
Kimberly Bennekin (GSU, Professor of Mathematics)

External Advisory Committee
Peter Bandettini (NIH, Director Multimodality Center)
Lucina Uddin (U Miami, Brain Imaging, Cognition, Diversity)
Susan Shows (COO, Georgia Research Alliance)
Tony Wilson (U Nebraska, MEG, dynamics, development)
Sandra Chapman (University of Texas as Dallas, brain health)

External Evaluator
Lynn Nordstrom, Cornelius Management

GSU participating faculty
Alireza Aghasi (GSU institute for insight, data mining)
Igor Belykh (GSU math, synchronization, complex networks)
Sarah Brosnan (GSU psychology, sociality, creativity)
Bruce Crosson (GSU psychology, healthy aging, neurobiology)
Jiayu Chen (GSU TReNDS, data fusion, imaging genomics)
Mukesh Dhamala (GSU physics, brain imaging, networks)
Kyle Frantz (GSU biology and neuroscience, science education)
Zening Fu (GSU TReNDS, brain connectivity dynamics)
Armin Iraji (GSU TReNDS), spatial dynamics of fMRI)
Tricia King (GSU psychology, lifespan)
Jean Liu (GSU CS, brain/methylation age, imaging genomics
Robyn Miller (GSU CS, neuroimaging dynamics, deep learning)
Anne Murphy (GSU develop. neuroscience, sex differences)
Rogers Silva (GSU TReNDS, data fusion, brain imaging)
Jing Sui (GSU CS, multiscale/multimodal fusion, deep learning)
Erin Tone (GSU psychology, social affect behavior)
Victor Vergara (GSU CS, brain dynamics, signal processing)
Jing Zhang (GSU math, data analysis, Bayesian brain dynamics)

GSU mentoring faculty
Eyal Aharoni (GSU psychology, emotion, decision making)
Rafal Angryk (GSU CS, big data analytics, machine learning)
Gennady Cymbalyuk (GSU neuroscience, physics, modeling)
Tim Denning (GSU VPR, Institute of Biomedical Sciences)
Lisa Krishnamurthy (GSU physics, MRI physics, analytics)
Jeff Malins (GSU psychology, reading, EEG, individual variation)
Slava Molkov (GSU math, computational neuroscience)
Eddie Nahmias (GSU philosophy, neuroscience of free will)
Andrey Shilnikov (GSU neuroscience/math, dynamics)

Non-GSU REU mentors
Constantine Dovrolis (GT Computer Science, network science)
Valerie Haftel (Morehouse College, neurobiology, training)
Shella Keilholz (GT BME, nonlinear dynamics, fMRI)
Yewande Olubummo (Spelman College, dynamical systems)
Walter Royal (Morehouse Medical Neurosciences, neurobiology)

National lab affiliates
Georgia Tourassi (Oak Ridge, data mining, biomedical data)
Garret Kenyan (Los Alamos, deep learning, neuromorphic comp)

US affiliates
David Danks (Carnegie Mellon, causal modeling)
Michael Cole (Rutgers, causal modeling, dynamics)
Michael Milham (Child Mind Institute, data aggregation/sharing)
Olaf Sporns (University of Indiana, Network Neuroscience)
Kyunghyun Cho (NYU/Facebook, deep learning)
Julia Stephen (The Mind Research Network, MEG)

International affiliates
Gustavo Deco (Pompeu Fabra University, brain dynamics)
Gunter Schumann (Kings College London, multisite imaging,
large-scale brain image consortia building)
Martijn van den Heuvel (Utrecht University, connectomics)
Tonya White (Erasmus University, population neuroscience)

Industry partners
Phillips, Siemens, GE (MRI vendors)
Brainproducts (EEG vendor)
Google/Microsoft/Facebook (educational research support)
Suntrust (local industry, data science)
Nvidia (GPU cards for educational support)
Mathworks (licenses, competitions, deep learning)
Amazon (cloud credits, hackathon support)

GRADUATE PROGRAM

D-MAP will support at least 6 graduate students/year (2 anchored in each subproject) with an additional 6/year collaboratively participating in D-MAP research. Students will develop research projects in one or more subproject and develop collaborative research skills across all subprojects. Professional development, including interactions with academic, government, and industry partners, will complement research development. Topics will include writing, survival skills, self-care, time management, imposter syndrome, important for all of us, but especially URGs. We expect 50+ additional non-D-MAP graduate students to engage in workshops, hackathons, and colloquia. In addition, graduate student involvement in high school, undergraduate, and broader outreach goals will help them develop a well-rounded profile and provide experiences to help guide their own future career choices

 

We are actively recruiting graduate students for the 2022 application cycle. Please contact vcalhoun@gsu.edu, jturner63@gsu.edu, or kfrantz@gsu.edu for more information.

Interdisciplinary educational Program

The Consortium will develop a PhD program in human neuroimaging and data modeling. This program will be affiliated and cross-listed with multiple degree granting departments at GSU. At present, GSU has no PhD programs focused on neuroimaging, thus this new program will fill a critical void. Students will be enrolled and complete the hosting department’s graduate-level common core, plus a series of specialized courses on various neuroimaging topics

Research Training

Beyond coursework, graduate students will receive extensive hands-on training in the laboratories of D-MAP investigators and other affiliated scientists. This training will incorporate all critical aspects of a cutting-edge neuroimaging program, including data acquisition (e.g. MRI, EEG, MEG), brain image reconstruction and analysis, big data analytics and machine learning, and approaches for combining multi-modal imaging data. Students will work in interdisciplinary groups on projects oriented around the proposed D-MAP subproject (i.e., unimodal dynamics, multimodal fusion, and predictive neuroimaging).

Recruitment, retention, & mentoring

Our retention strategy involves consistent mentoring and professional development. D-MAP-funded graduate applicants will be selected as Fellows by the D-MAP research faculty to ensure a good match with overall program objectives. The cohort community building activities will begin the week before the Fall semester starts. D-MAP faculty, in coordination with the research and education codirectors, will implement a proactive retention plan to enhance the success of the project. The plan includes 1) an annual trainee survey to understand issues and expectations; 2) development of an individualized professional development plan with each trainee; and 3) an annual trainee progress review to facilitate the understanding of potential problems, continuous improvement of mentoring, and course corrections as needed.

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