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Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease

Daniel S. Barron, Siyuan Gao, Javid Dadashkarimi, Abigail S. Greene, Marisa N. Spann, Stephanie Noble, Evelyn Lake, John Krystal, R. Todd Constable, Dustin Scheinost
doi: https://doi.org/10.1101/638825
Daniel S. Barron
1Department of Psychiatry, Yale University, New Haven, CT, USA
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  • For correspondence: daniel.s.barron@yale.edu
Siyuan Gao
2Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, 06520 CT, USA
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Javid Dadashkarimi
2Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, 06520 CT, USA
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Abigail S. Greene
3Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
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Marisa N. Spann
4Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
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Stephanie Noble
5Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
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Evelyn Lake
5Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
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John Krystal
1Department of Psychiatry, Yale University, New Haven, CT, USA
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R. Todd Constable
5Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
6Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
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Dustin Scheinost
5Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
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Abstract

Importance We show that three common approaches to clinical deficits (cognitive phenotype, disease group, disease severity) each offer useful and perhaps complimentary explanations for the brain’s underlying functional architecture as affected by psychiatric disease.

Objective To understand how different clinical frameworks are represented in the brain’s functional connectome.

Design We use an openly available dataset to create predictive models based on multiple connectomes built from task-based functional MRI data. We use these models to predict individual traits corresponding to multiple cognitive constructs across disease category. We also show that these same connectomes statistically differ depending on disease category and symptom burden.

Setting This was a population-based study with data collected in UCLA.

Participants Healthy adults were recruited by community advertisements from the Los Angeles area. Participants with adult ADHD, bipolar disorder, and schizophrenia were recruited using a patient-oriented strategy involving outreach to local clinics and online portals (separate from the methods used to recruit healthy volunteers)

Footnotes

  • http://www.github.com/YaleMRRC/CPM

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 16, 2019.
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Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease
Daniel S. Barron, Siyuan Gao, Javid Dadashkarimi, Abigail S. Greene, Marisa N. Spann, Stephanie Noble, Evelyn Lake, John Krystal, R. Todd Constable, Dustin Scheinost
bioRxiv 638825; doi: https://doi.org/10.1101/638825
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Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease
Daniel S. Barron, Siyuan Gao, Javid Dadashkarimi, Abigail S. Greene, Marisa N. Spann, Stephanie Noble, Evelyn Lake, John Krystal, R. Todd Constable, Dustin Scheinost
bioRxiv 638825; doi: https://doi.org/10.1101/638825

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