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Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation

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Seminar given at the University of Illinois at Chicago Behavioral Neuroscience Seminar Series. The Default Network (DN) is a set of brain regions that are deactivated during the performance of externally triggered goal-drive tasks and active during spontaneous cognition. Activation of the DN during times when it should be off, has been hypothesized to be a symptom of several mental health disorders such as ADHD, depression, and anxiety. We describe the use of real-time fMRI to probe DN function in patient populations and children.

Published in: Science
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Using RealTime fMRI Based Neurofeedback to Probe Default Network Regulation

  1. 1. Using RealTime fMRI Based Neurofeedback To Probe Default Network Regulation R. Cameron Craddock, PhD Director of Imaging, Child Mind Institute Research Scientist, Nathan Kline Institute February 25, 2016
  2. 2. Default Network Task based deactivation Buckner et al. Ann. N.Y. Acad. Sci. 1124: 1-38 (2008).
  3. 3. Default Network Connectivity Greicius et. al. 2007 Biol. Psychiatry
  4. 4. DN Dysregulation Sheline et. al. 2009 PNAS
  5. 5. ICN Competition Fox MD PNAS 2005
  6. 6. RT Neurofeedback of DMN • Test hypothesis of DMN dysregulation in depression, ADHD, aging, etc …
  7. 7. Exp. Design Class Training Labels Training run Time-Labeled Scans Image Recon and SVM Classification Image DataData Acquisition Stimulus Presentation Stimulus Conventional FMRI Test Data Classifier Output Testing Run Real-Time Tracking RSNs LaConte, et al. (2007) Hum Brain Mapp. 28: 1033-1044 Stephen LaConte August 19, 2009
  8. 8. Stimulus seen by volunteer Updated fMRI results Motion tracking and correction Intensity (brightness) of a single voxel, changing during stimulus conditions Controller interface for display parameters
  9. 9. Decoding DN Activity
  10. 10. DMN Modulation Task
  11. 11. Modulating the DMN−2−1012 0 100 200 300 400 Best Subject Worst Subject TR Z−scoreDMNActivity −20246 0 100 200 300 400 TR Z−scoreDMNActivity
  12. 12. Results 0.00.10.20.30.40.50.6 3 1 7 13 6 9 5 10 11 8 4 2 12 Subject Accuracy Feedback No feedback FB NOFB 0.10.20.30.40.50.6 1 2 1 2 Scan Number Accuracy p = 0.055p = 0.68 Accuracy was measured from Pearson’s correlation between task paradigm and DMN activity extracted after post-processing.
  13. 13. Behavioral Correlates Measures that were significantly associated with DN regulation include (p<0.05, FDR corrected): the affect intensity measure (AIM), ruminative responses scale (RRS), and the imaginal processes inventory.
  14. 14. RT fMRI Neurofeedback for Children
  15. 15. All data is being prospectively shared
  16. 16. Currently sharing > 10,000 datasets • 6,422 individuals – 4 – 90 years old – 539 ASD – 383 ADHD – 72 Schizophrenia – 29 Cocaine Dependent – 6 Epilepsy http://fcon_1000.projects.nitrc.org/
  17. 17. Sharing preprocessed data • Make data available to a wider audience of researchers • Evaluate reproducibility of analysis results http://preprocessed-connectomes-project.github.io/
  18. 18. Software to enable a new scale of data analysis • Very large datasets – Need to harness high- performance computing to expedite processing • RS fMRI preprocessing is a moving target – Many new methods are proposed all the time – Need to compare outputs from different processing strategies • Many different toolsets have different strengths – Need to be able to combine tools from different packages http://fcp-indi.github.io/
  19. 19. Principles of Open Neuroscience Data, tools and ideas should be openly shared -The Neuro Bureau Manifesto http://www.neurobureau.org
  20. 20. Acknowledgments Child Mind Institute Michael Milham, MD, PhD Zarrar Shehzad Nathan Kline Institute Amalia McDonald Stan Colcombe, PhD Bennett Leventhal, MD NYU – Child Study Center Adriana DiMartino, MD F. Xavier Castellanos, MD VTCRI Stephen LaConte, PhD Pearl Chiu, PhD Jonathan Lisinski, MS Emory University Helen Mayberg, MD This work is funded by: A NARSAD Young Investigator Award and NIMH R01MH101555

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