The document discusses the use of real-time fMRI-based neurofeedback to study the regulation of the default network (DN), particularly in relation to disorders like depression and ADHD. It details experimental designs, data acquisition methods, and training protocols, as well as behavioral correlates associated with DN regulation. Additionally, it emphasizes the importance of sharing neuroimaging data and tools to enhance reproducibility and collaboration within the research community.

2. 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

3. Default Network
Task based deactivation
Buckner et al. Ann. N.Y. Acad. Sci. 1124: 1-38 (2008).

4. Default Network Connectivity
Greicius et. al. 2007 Biol. Psychiatry

5. DN Dysregulation
Sheline et. al. 2009 PNAS

6. ICN Competition
Fox MD PNAS 2005

7. RT Neurofeedback of DMN
• Test hypothesis of DMN dysregulation in
depression, ADHD, aging, etc …

8. 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

9. 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

10. Decoding DN Activity

11. DMN Modulation Task

12. 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

13. 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.

14. 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.

15. RT fMRI Neurofeedback for Children

16. All data is being prospectively shared

17. 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/

18. Sharing preprocessed data
• Make data available
to a wider audience
of researchers
• Evaluate
reproducibility of
analysis results
http://preprocessed-connectomes-project.github.io/

19. 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/

20. Principles of Open Neuroscience
Data, tools and ideas should be openly shared
-The Neuro Bureau Manifesto
http://www.neurobureau.org

22. 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