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Distributed patterns of brain activity underlying real-time fMRI neurofeedback training


Kopel, Rotem; Emmert, Kirsten; Scharnowski, Frank; Haller, Sven; Van De Ville, Dimitri (2017). Distributed patterns of brain activity underlying real-time fMRI neurofeedback training. IEEE Transactions on Bio-Medical Engineering, 64(6):1228-1237.

Abstract

Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) is an exciting neuroimaging application. In most rt-fMRI NF studies, the activity level of a single region of interest (ROI) is provided as a feedback signal and the participants are trained to up or down regulate the feedback signal. NF training effects are typically analyzed using a confirmatory univariate approach, i.e., changes in the target ROI are explained by a univariate linear modulation. However, learning to self-regulate the ROI activity through NF is mediated by distributed changes across the brain. Here, we deploy a multivariate decoding model for assessing NF training effects across the whole brain. Specifically, we first explain the NF training effect by a posthoc multivariate model that leads to a pattern of coactivation based on 90 functional atlas regions. We then use cross validation to reveal the set of brain regions with the best fit. This novel approach was applied to the data from a rt-fMRI NF study where the participants learned to down regulate the auditory cortex. We found that the optimal model consisted of 16 brain regions whose coactivation patterns best described the training effect over the NF training days. Cross validation of the multivariate model showed that it generalized across the participants. Interestingly, the participants could be clustered into two groups with distinct patterns of coactivation, potentially reflecting different NF learning strategies. Overall, our findings revealed that multiple brain regions are involved in learning to regulate an activity in a single ROI, and thus leading to a better understanding of the mechanisms underlying NF training.

Abstract

Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) is an exciting neuroimaging application. In most rt-fMRI NF studies, the activity level of a single region of interest (ROI) is provided as a feedback signal and the participants are trained to up or down regulate the feedback signal. NF training effects are typically analyzed using a confirmatory univariate approach, i.e., changes in the target ROI are explained by a univariate linear modulation. However, learning to self-regulate the ROI activity through NF is mediated by distributed changes across the brain. Here, we deploy a multivariate decoding model for assessing NF training effects across the whole brain. Specifically, we first explain the NF training effect by a posthoc multivariate model that leads to a pattern of coactivation based on 90 functional atlas regions. We then use cross validation to reveal the set of brain regions with the best fit. This novel approach was applied to the data from a rt-fMRI NF study where the participants learned to down regulate the auditory cortex. We found that the optimal model consisted of 16 brain regions whose coactivation patterns best described the training effect over the NF training days. Cross validation of the multivariate model showed that it generalized across the participants. Interestingly, the participants could be clustered into two groups with distinct patterns of coactivation, potentially reflecting different NF learning strategies. Overall, our findings revealed that multiple brain regions are involved in learning to regulate an activity in a single ROI, and thus leading to a better understanding of the mechanisms underlying NF training.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Psychiatry and Psychotherapy
04 Faculty of Medicine > Neuroscience Center Zurich
04 Faculty of Medicine > Center for Integrative Human Physiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2017
Deposited On:15 Mar 2017 11:38
Last Modified:26 Sep 2017 13:53
Publisher:Institute of Electrical and Electronics Engineers
ISSN:0018-9294
Additional Information:© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher DOI:https://doi.org/10.1109/TBME.2016.2598818
PubMed ID:28541186

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