fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

Rotem Leibovitz; Jhonathan Osin; Lior Wolf; Guy Gurevitch; Talma Hendler

doi:https://doi.org/10.1007/978-3-031-16431-6_27

fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

Rotem Leibovitz, Jhonathan Osin, Lior Wolf, Guy Gurevitch, Talma Hendler

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We obtain a personal signature of a person’s learning progress in a self-neuromodulation task, guided by functional MRI (fMRI). The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session. The prediction is made by a deep neural network, which is trained on the entire training cohort of patients. This signal, which is indicative of a person’s progress in performing the task of Amygdala modulation, is aggregated across multiple prototypical brain states and then classified by a linear classifier to various personal and clinical indications. The predictive power of the obtained signature is stronger than previous approaches for obtaining a personal signature from fMRI neurofeedback and provides an indication that a person’s learning pattern may be used as a diagnostic tool. Our code has been made available, (Our code is available via https://github.com/MICCAI22/fmri_nf.) and data would be shared, subject to ethical approvals.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
Editors	Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	282-294
Number of pages	13
ISBN (Print)	9783031164309
DOIs	https://doi.org/10.1007/978-3-031-16431-6_27
State	Published - 2022
Event	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 Sep 2022 → 22 Sep 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13431 LNCS

Conference

Conference	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	18/09/22 → 22/09/22

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

https://doi.org/10.1007/978-3-031-16431-6_27

Cite this

Leibovitz, R., Osin, J., Wolf, L., Gurevitch, G., & Hendler, T. (2022). fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings (pp. 282-294). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13431 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16431-6_27

Leibovitz, Rotem ; Osin, Jhonathan ; Wolf, Lior et al. / fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. editor / Linwei Wang ; Qi Dou ; P. Thomas Fletcher ; Stefanie Speidel ; Shuo Li. Springer Science and Business Media Deutschland GmbH, 2022. pp. 282-294 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits",

abstract = "We obtain a personal signature of a person{\textquoteright}s learning progress in a self-neuromodulation task, guided by functional MRI (fMRI). The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session. The prediction is made by a deep neural network, which is trained on the entire training cohort of patients. This signal, which is indicative of a person{\textquoteright}s progress in performing the task of Amygdala modulation, is aggregated across multiple prototypical brain states and then classified by a linear classifier to various personal and clinical indications. The predictive power of the obtained signature is stronger than previous approaches for obtaining a personal signature from fMRI neurofeedback and provides an indication that a person{\textquoteright}s learning pattern may be used as a diagnostic tool. Our code has been made available, (Our code is available via https://github.com/MICCAI22/fmri_nf.) and data would be shared, subject to ethical approvals.",

author = "Rotem Leibovitz and Jhonathan Osin and Lior Wolf and Guy Gurevitch and Talma Hendler",

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Leibovitz, R, Osin, J, Wolf, L, Gurevitch, G & Hendler, T 2022, fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits. in L Wang, Q Dou, PT Fletcher, S Speidel & S Li (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13431 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 282-294, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore, 18/09/22. https://doi.org/10.1007/978-3-031-16431-6_27

fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits. / Leibovitz, Rotem; Osin, Jhonathan; Wolf, Lior et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. ed. / Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li. Springer Science and Business Media Deutschland GmbH, 2022. p. 282-294 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13431 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

AU - Leibovitz, Rotem

AU - Osin, Jhonathan

AU - Wolf, Lior

AU - Gurevitch, Guy

AU - Hendler, Talma

PY - 2022

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N2 - We obtain a personal signature of a person’s learning progress in a self-neuromodulation task, guided by functional MRI (fMRI). The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session. The prediction is made by a deep neural network, which is trained on the entire training cohort of patients. This signal, which is indicative of a person’s progress in performing the task of Amygdala modulation, is aggregated across multiple prototypical brain states and then classified by a linear classifier to various personal and clinical indications. The predictive power of the obtained signature is stronger than previous approaches for obtaining a personal signature from fMRI neurofeedback and provides an indication that a person’s learning pattern may be used as a diagnostic tool. Our code has been made available, (Our code is available via https://github.com/MICCAI22/fmri_nf.) and data would be shared, subject to ethical approvals.

AB - We obtain a personal signature of a person’s learning progress in a self-neuromodulation task, guided by functional MRI (fMRI). The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session. The prediction is made by a deep neural network, which is trained on the entire training cohort of patients. This signal, which is indicative of a person’s progress in performing the task of Amygdala modulation, is aggregated across multiple prototypical brain states and then classified by a linear classifier to various personal and clinical indications. The predictive power of the obtained signature is stronger than previous approaches for obtaining a personal signature from fMRI neurofeedback and provides an indication that a person’s learning pattern may be used as a diagnostic tool. Our code has been made available, (Our code is available via https://github.com/MICCAI22/fmri_nf.) and data would be shared, subject to ethical approvals.

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DO - https://doi.org/10.1007/978-3-031-16431-6_27

M3 - منشور من مؤتمر

SN - 9783031164309

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings

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A2 - Fletcher, P. Thomas

A2 - Speidel, Stefanie

A2 - Li, Shuo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

Y2 - 18 September 2022 through 22 September 2022

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Leibovitz R, Osin J, Wolf L, Gurevitch G, Hendler T. fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits. In Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 282-294. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-031-16431-6_27

fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

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