Advances in fMRI Real-Time Neurofeedback

Watanabe, Takeo; Sasaki, Yuka; Shibata, Kazuhisa; Kawato, Mitsuo

doi:10.1016/j.tics.2017.09.010

Cited by 220 publications

(227 citation statements)

References 69 publications

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“…Despite increasing interest in the application of rt- fMRI, only a few studies to date have directly evaluated effects of NFT on functional connectivity between brain regions [54, 56, 71, 76-79]. In line with the present findings, a previous study with a relatively small sample of healthy subjects revealed initial evidence for the feasibility of connectivity-informed NF which was associated with increased perception of positive valence stimuli.…”

Section: Discussionsupporting

confidence: 88%

“…Findings may reflect that subjects explored different regulation strategies during the initial 2 runs and maintained the successful strategies during the subsequent training runs. This difference to BOLD activity NFT studies may be explained by the fact that the functional connectivity feedback signal inherently comes at a longer delay and involves higher dimensionality [71, 72], which may lead to a higher difficulty for the subjects to discover successful regulation strategies. Future studies may increase the number of training runs and sessions to determine whether regulatory control on the neural level can be further increased.…”

Section: Discussionmentioning

confidence: 99%

“…For clinical practice it will be important to compare both training and functional outcome success using activity- and connectivity-based feedback approaches. Connectivity-based NF comes at the cost of longer delay times and higher dimensionality [71] so that learning with this signal may be more demanding, possibly limiting efficacy in patients with cognitive impairments. In line with initial studies successfully evaluating the clinical potential of amygdala activity NFTs in patient populations [82], repeated training sessions may be necessary to alleviate symptoms in patient populations [30] or to maintain effects on the behavioral level.…”

Section: Discussionmentioning

confidence: 99%

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Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety

Zhao

Yao

et al. 2019

Psychother Psychosom

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Background: Deficient emotion regulation and exaggerated anxiety represent a major transdiagnostic psychopathological marker. On the neural level these deficits have been closely linked to impaired, yet treatment-sensitive, prefrontal regulatory control over the amygdala. Gaining direct control over these pathways could therefore provide an innovative and promising intervention to regulate exaggerated anxiety. To this end the current proof-of-concept study evaluated the feasibility, functional relevance and maintenance of a novel connectivity-informed real-time fMRI neurofeedback training. Methods: In a randomized crossover sham-controlled design, 26 healthy subjects with high anxiety underwent real-time fMRI-guided neurofeedback training to enhance connectivity between the ventrolateral prefrontal cortex (vlPFC) and the amygdala (target pathway) during threat exposure. Maintenance of regulatory control was assessed after 3 days and in the absence of feedback. Training-induced changes in functional connectivity of the target pathway and anxiety ratings served as primary outcomes. Results: Training of the target, yet not the sham control, pathway significantly increased amygdala-vlPFC connectivity and decreased levels of anxiety. Stronger connectivity increases were significantly associated with higher anxiety reduction on the group level. At the follow-up, volitional control over the target pathway was maintained in the absence of feedback. Conclusions: The present results demonstrate for the first time that successful self-regulation of amygdala-prefrontal top-down regulatory circuits may represent a novel intervention to control anxiety. As such, the present findings underscore both the critical contribution of amygdala-prefrontal circuits to emotion regulation and the therapeutic potential of connectivity-informed real-time neurofeedback.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety

Zhao

Yao

et al. 2019

Psychother Psychosom

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“…As knowledge of brain structure and dynamics dramatically progresses, neuroscientists approach a stage wherein the brain cannot only be described but also be acted upon in increasingly controlled, and even constructive and enhancing fashions (Bassett & Sporns, ; Deca & Koene, ; Medaglia, Zurn, Sinnott‐Armstrong, & Bassett, ; Sahakian et al., ; Sitaram et al., ; Watanabe, Sasaki, Shibata, & Kawato, ).…”

Section: Introductionmentioning

confidence: 99%

Neurofeedback: Principles, appraisal, and outstanding issues

Papo

2019

Eur J of Neuroscience

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Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far‐reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain‐behaviour relationships.

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“…One way to tackle this is to use machine learning and deep learning methods, and a number of studies have shown how this can be used to successfully build biomarkers (i.e. classifiers) in a range of psychiatric disease ( Takagi et al , 2017; Watanabe et al , 2017; Yahata et al , 2016; Yamada et al , 2017). However, these methods need to be validated on genuinely independent data sets to be convincing, and current evidence of generalisable classifiers for chronic pain is lacking.…”

Section: Introductionmentioning

confidence: 99%

Classification and characterisation of brain network changes in chronic back pain: A multicenter study

Seymour¹,

Matsubara²,

Kotecha³

et al. 2018

Wellcome Open Res

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Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

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Advances in fMRI Real-Time Neurofeedback

Cited by 220 publications

References 69 publications

Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety

Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety

Neurofeedback: Principles, appraisal, and outstanding issues

Classification and characterisation of brain network changes in chronic back pain: A multicenter study

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