RT-Cloud: A Cloud-based Software Framework to Simplify and Standardize Real-Time fMRI

Wallace, Grant; Polcyn, Stephen; Brooks, Paula P.; Mennen, Anne C.; Zhao, Kaixuan; Scotti, Paul S.; Michelmann, Sebastian; Li, Kai; Turk‐Browne, Nicholas B.; Cohen, Jonathan D.; Norman, Kenneth A.

doi:10.31219/osf.io/sbrg7

Cited by 2 publications

(4 citation statements)

References 32 publications

(38 reference statements)

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“…The data were returned to the local Linux as a .txt file containing the final neurofeedback score to be displayed. Real-time processing was handled using the RT-Cloud software package (Wallace et al, 2022; Kumar et al, 2020); see Appendix B for full details on the cloud setup.…”

Section: Methodsmentioning

confidence: 99%

“…We designed our real-time pipeline to use a cloud server for processing, and thus minimize dependency on local computing resources (for a similar approach, see Mennen et al, 2021). The cloud-based real-time pipeline was implemented using the RT-Cloud software package (Wallace et al, 2022; Kumar et al, 2020). Once DICOM files arrived at the local Linux machine, they were kept in memory as bytes and immediately sent to the cloud computer.…”

Section: Classifier Trainingmentioning

confidence: 99%

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Real-time neurofeedback to alter interpretations of a naturalistic narrative

Mennen

Nastase

Yeshurun

et al. 2022

Preprint

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We explored the potential of using real-time fMRI (rt-fMRI) neurofeedback training to bias interpretations of naturalistic narrative stimuli. Participants were randomly assigned to one of two possible conditions, each corresponding to a different interpretation of an ambiguous spoken story. While participants listened to the story in the scanner, neurofeedback was used to reward neural activity corresponding to the assigned interpretation. After scanning, final interpretations were assessed. While neurofeedback did not change story interpretations on average, participants with higher levels of decoding accuracy during the neurofeedback procedure were more likely to adopt the assigned interpretation. Thus, we believe that individualized neurofeedback shaped interpretations successfully when the signal was accurate, although more work is needed to improve this method and validate the result. While naturalistic stimuli introduce a unique set of challenges in providing effective and individualized neurofeedback, we believe that this technique holds promise for individualized cognitive therapy.

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Section: Methodsmentioning

confidence: 99%

Section: Classifier Trainingmentioning

confidence: 99%

Real-time neurofeedback to alter interpretations of a naturalistic narrative

Mennen

Nastase

Yeshurun

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The data were returned to the local Linux machine as a .txt file containing the final neurofeedback score to be displayed. Real-time processing was handled using the RT-Cloud software package ( Kumar et al, 2021 ; Wallace et al, 2022 ); see Appendix B for full details on the cloud setup.…”

Section: Methodsmentioning

confidence: 99%

“…We designed our real-time pipeline to use a cloud server for processing, and thus minimize dependency on local computing resources (for a similar approach, see Mennen et al, 2021 ). The cloud-based real-time pipeline was implemented using the RT-Cloud software package ( Kumar et al, 2021 ; Wallace et al, 2022 ). Once DICOM files arrived at the local Linux machine, they were kept in memory as bytes and immediately sent to the cloud computer.…”

Section: Data Acquisitionmentioning

confidence: 99%