Functional Connectivity

The human brain is a complex network of functionally interconnected regions whose coordinated effort gives rise to different functions. Understanding what these regions are, how they interact, and how this interaction forms a wide range of behavior has long been an essential question for human neuroscience. Neuroimaging techniques have provided a unique opportunity to tackle this question in a data-driven way. Advances in neuroimaging techniques, such as functional Magnetic Resonance Imaging (fMRI), have allowed us to approximately measure the neural activity in the brain. However, fMRI data are not only massive in size but also spatially and temporally complex. One of the research directions in our group is to develop advanced machine-learning techniques to study brain function and its link to behavior.

Relevant Papers

arXiv 2023 Enhancement attacks in biomedical machine learning Matthew Rosenblatt, Javid Dadashkarimi, Dustin Scheinost
Biological Psychiatry 2023 Altered brain dynamics across bipolar disorder and schizophrenia revealed by overlapping brain states Jean Ye, Huili Sun, Siyuan Gao, Javid Dadashkarimi, Matthew Rosenblatt, Raimundo X Rodriguez, Saloni Mehta, Rongtao Jiang, Stephanie Noble, Margaret L Westwater, Dustin Scheinost
IEEE Conference on Information Sciences and Systems 2023 Stacking multiple optimal transport policies to map functional connectomes Javid Dadashkarimi, Matthew Rosenblatt, Amin Karbasi, Dustin Scheinost
arxiv 2022 Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for any atlas when raw data is not available Javid Dadashkarimi, Amin Karbasi, Qinghao Liang, Matthew Rosenblatt, Stephanie Noble, Maya Foster, Raimundo Rodriguez, Brendan Adkinson, Jean Ye, Huili Sun, Chris Camp, Michael Farruggia, Link Tejavibulya, Wei Dai, Rongtao Jiang, Angeliki Pollatou, and Dustin Scheinost
Graphs in Biomedical Image AnaLysis 2022 Transforming connectomes to any parcellation via graph matching  | Best Paper Award  Qinghao Liang, Javid Dadashkarimi, Wei Dai, Amin Karbasi, Joseph Chang, Harrison H. Zhou, and Dustin Scheinost
Nature Molecular Psychiatry 2022 Predicting the future of neuroimaging predictive models in mental health Link Tejavibulya, Max Rolison, Siyuan Gao, Qinghao Liang, Hannah Peterson, Javid Dadashkarimi, Michael C. Farruggia, C. Alice Hahn, Stephanie Noble, Sarah D. Lichenstein, Angeliki Pollatou, Alexander J. Dufford and Dustin Scheinost
Cerebral Cortex 2021 Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders Daniel S Barron, Siyuan Gao, Javid Dadashkarimi, Abigail S Greene, Marisa N Spann, Stephanie Noble, Evelyn M R Lake, John H Krystal, R Todd Constable, Dustin Scheinost
MICCAI 2021 Data-driven mapping between functional connectomes using optimal transport Javid Dadashkarimi, Amin Karbasi, and Dustin Scheinost
Nature Human Behavior 2021 A hitchhiker’s guide to working with large, open-source neuroimaging datasets Corey Horien, Stephanie Noble, Abigail S Greene, Kangjoo Lee, Daniel S Barron, Siyuan Gao, David O’Connor, Mehraveh Salehi, Javid Dadashkarimi, Xilin Shen, Evelyn MR Lake, R Todd Constable, Dustin Scheinost
Nature NP 2021 Functional Connectivity During Frustration: Predictive Modeling of Irritability in Youth Dustin Scheinost, Javid Dadashkarimi, Emily S. Finn, Caroline G. Wambach, Caroline MacGillivray, Alexandra L. Roule, Tara A. Niendam, Daniel S. Pine, Melissa A. Brotman, Ellen Leibenluft Wan-Ling Tseng,
Biological Psychiatry 2020 Predicting BMI From Whole-Brain Functional Connectivity Erin Yeagle, Javid Dadashkarimi, Vivian Duan, Abigail Greene, Daniel Barron,Siyuan Gao, Dustin Scheinost
MICCAI 2019 A mass multivariate edge-wise approach for combining multiple connectomes to improve the detection of group differences | Best Poster in Connectomics in NeuroImage Javid Dadashkarimi, Siyuan Gao, Erin Yeagle, Stephanie Noble, Dustin Scheinost