| 1. |
- Haugg, Amelie, et al.
(författare)
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Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?
- 2020
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Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 41:14, s. 3839-3854
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Tidskriftsartikel (refereegranskat)abstract
- Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
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| 2. |
- Haugg, Amelie, et al.
(författare)
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Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.
- 2021
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 237
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Tidskriftsartikel (refereegranskat)abstract
- Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
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| 3. |
- Kopel, Rotem, et al.
(författare)
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Distributed Patterns of Brain Activity Underlying Real-Time fMRI Neurofeedback Training
- 2017
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Ingår i: IEEE Transactions on Biomedical Engineering. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9294 .- 1558-2531. ; 64:6, s. 1228-1237
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Tidskriftsartikel (refereegranskat)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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| 4. |
- Sitaram, Ranganatha, et al.
(författare)
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Closed-loop brain training : the science of neurofeedback
- 2016
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Ingår i: Nature Reviews Neuroscience. - : Springer Science and Business Media LLC. - 1471-003X .- 1471-0048. ; 18, s. 86-100
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Tidskriftsartikel (refereegranskat)abstract
- Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain-machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.
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