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Distributed Patterns of Brain Activity Underlying Real-Time fMRI Neurofeedback Training
Univ Geneva, Fac Med, CH-1211 Geneva, Switzerland.;Ecole Polytech Fed Lausanne, Inst Bioengn, CH-1015 Lausanne, Switzerland..
Univ Geneva, Fac Med, CH-1211 Geneva, Switzerland..
Univ Zurich, Neurosci Ctr Zurich, Psychiat Univ Hosp, Zurich, Switzerland.;Univ Zurich, Zurich Ctr Integrat Human Physiol, Zurich, Switzerland..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Univ Hosp Freiburg, Dept Neuroradiol, Freiburg, Germany..
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2017 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 64, no 6, p. 1228-1237Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2017. Vol. 64, no 6, p. 1228-1237
Keywords [en]
Basal ganglia, coactivation, functional networks, insula, neurofeedback (NF), real-time functional magnetic resonance imaging (rt-fMRI), thalamus
National Category
Medical Biotechnology
Identifiers
URN: urn:nbn:se:uu:diva-326205DOI: 10.1109/TBME.2016.2598818ISI: 000402050800004PubMedID: 28541186OAI: oai:DiVA.org:uu-326205DiVA, id: diva2:1129902
Available from: 2017-08-07 Created: 2017-08-07 Last updated: 2017-08-07Bibliographically approved

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