The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments

Cortese, Aurelio; Tanaka, Saori; Amano, Kaoru; Koizumi, Akira; Lau, Hakwan; Sasaki, Yuka; Shibata, Kazuhisa; Taschereau-Dumouchel, Vincent; Watanabe, Takeo; Kawato, Mitsuo

doi:10.1038/s41597-021-00845-7

Cited by 11 publications

(6 citation statements)

References 30 publications

(18 reference statements)

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“…Multivoxel Pattern Analysis (MVPA) is considered a promising technique that introduces machine learning into the field of psychology, currently used to study information contained in distributed neural activity patterns to infer the functional roles of brain territories and networks ( Mahmoudi et al, 2012 ). In application, based on MVPA, neural feedback decoding (DecNef) can be performed for identified brain activity patterns, guiding clinical interventions for mental illnesses ( Cortese et al, 2021 ). Currently, neuroregulation techniques based on brain activity pattern decoding mainly include invasive and non-invasive techniques ( Temel et al, 2012 ).…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis

Chen,

Yuan,

Dong

et al. 2024

Front. Psychol.

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ObjectiveCollege students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health.MethodsPublications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends.ResultsSpanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications.ConclusionThis study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis

Chen,

Yuan,

Dong

et al. 2024

Front. Psychol.

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“…NFT researchers may wish to examine the value of these approaches for heightening internal and external validity of EEG-based NFT. In a similar vein, fMRI-based NFT has increasingly relied on multivariate decoding methods (Taschereau-Dumouchel et al, 2021), and the potential of these advanced methods has shown promise for basic science and applied research alike (Cortese et al, 2021; Z. Wang et al, 2021a).…”

Section: Discussionmentioning

confidence: 99%

Effects of Neurofeedback training on performance in laboratory tasks: A systematic review

Chiasson

Boylan

Elhamiasl

et al. 2022

Preprint

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Neurofeedback procedures are attracting increasing attention in the neuroscience community. Based on the principle that participants, through suitable feedback, may learn to affect specific aspects of their brain activity, neurofeedback has been applied to basic research, translational, and clinical science alike. A large segment of the extant empirical research as well as review articles have focused on the extent to which neurofeedback interventions affect mental health outcomes, cognitive capacity, aging, and other complex behaviors. Another segment has aimed to characterize the extent to which neurofeedback affects the targeted neural processes. At this time, there is no current systematic review of the effects of neurofeedback on healthy participants performance in experimental tasks. Such a review is relevant in this rapidly evolving field because changes in experimental task performance are traditionally considered a hallmark of changing neurocognitive processes, often established in neurotypical individuals. This systematic review addresses this gap in the literature using the PRISMA method, building on earlier reviews on the same topic. Empirical studies using EEG or fMRI to alter brain processes linked to established, well-defined cognitive and affective laboratory tasks were reviewed. Substantial variability was found regarding the nature of the control for placebo effects, the implementation of the feedback, and the neural targets of feedback. Importantly, only a minority of the studies reported statistically meaningful effects of neurofeedback on performance in cognitive and affective tasks. Examining effect sizes and p-values in a subset of studies found no evidence for reporting bias, while also not finding systematic relations between study characteristics such as sample size or experimental control on the one hand and efficacy on the other. Implications for future work are discussed.

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“…The DecNef Collection: A database regrouping the data of multiple previous decoded neurofeedback experiments (https://bicr-resource.atr.jp/drmd/) (Cortese et al, 2021).…”

Section: Related Resourcesmentioning

confidence: 99%

Real-Time fMRI in the Treatment of Mental Health Disorders

Taschereau-Dumouchel¹,

Cushing²,

Lau³

2022

Preprint

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Multiple mental disorders have been associated with dysregulations of precise brain processes. However, few therapeutic approaches are currently available in order to correct such specific patterns of brain activity. Since the late 60s and early 70s, many have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim at modulating brain activity directly. However, neurofeedback never acquired mainstream acceptance in mental health, in part due to methodological considerations. Here, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging (fMRI) neurofeedback and discuss current and future applications to mental health.

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The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments

Cited by 11 publications

References 30 publications

Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis

Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis

Effects of Neurofeedback training on performance in laboratory tasks: A systematic review

Real-Time fMRI in the Treatment of Mental Health Disorders

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