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
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arXiv | 2023 | Enhancement attacks in biomedical machine learning | Matthew Rosenblatt, Javid Dadashkarimi, Dustin Scheinost | |
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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 | |
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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 | |
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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 | |
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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 | |
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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 | |
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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 | |
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MICCAI | 2021 | Data-driven mapping between functional connectomes using optimal transport | Javid Dadashkarimi, Amin Karbasi, and Dustin Scheinost | |
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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 | |
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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, | |
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Biological Psychiatry | 2020 | Predicting BMI From Whole-Brain Functional Connectivity | Erin Yeagle, Javid Dadashkarimi, Vivian Duan, Abigail Greene, Daniel Barron,Siyuan Gao, Dustin Scheinost | |
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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 |