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Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience.

التفاصيل البيبلوغرافية
العنوان: Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience.
المؤلفون: Andreu-Perez J; Centre for Computational Intelligence, University of Essex, Colchester, UK. javier.andreu@essex.ac.uk., Emberson LL; Department of Psychology, Princeton University, Princeton, NJ, USA., Kiani M; Centre for Computational Intelligence, University of Essex, Colchester, UK., Filippetti ML; Centre for Brain Science, Department of Psychology, University of Essex, Colchester, UK., Hagras H; Centre for Computational Intelligence, University of Essex, Colchester, UK., Rigato S; Centre for Brain Science, Department of Psychology, University of Essex, Colchester, UK.
المصدر: Communications biology [Commun Biol] 2021 Sep 15; Vol. 4 (1), pp. 1077. Date of Electronic Publication: 2021 Sep 15.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group UK Country of Publication: England NLM ID: 101719179 Publication Model: Electronic Cited Medium: Internet ISSN: 2399-3642 (Electronic) Linking ISSN: 23993642 NLM ISO Abbreviation: Commun Biol Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London, United Kingdom : Nature Publishing Group UK, [2018]-
مواضيع طبية MeSH: Artificial Intelligence* , Growth*, Cognitive Neuroscience/*methods , Neuroimaging/*instrumentation , Spectroscopy, Near-Infrared/*statistics & numerical data, Cognitive Neuroscience/instrumentation ; Humans ; Infant
مستخلص: In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI). The proposed approach is exemplified in a study that investigates visual and auditory processing in six-month-old infants. xMVPA not only identified patterns of cortical interactions, which confirmed the existent literature; in the form of conceptual linguistic representations, it also provided evidence for brain networks engaged in the processing of visual and auditory stimuli that were previously overlooked by other methods, while demonstrating similar statistical performance.
(© 2021. The Author(s).)
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تواريخ الأحداث: Date Created: 20210916 Date Completed: 20211203 Latest Revision: 20230206
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC8443619
DOI: 10.1038/s42003-021-02534-y
PMID: 34526648
قاعدة البيانات: MEDLINE
الوصف
تدمد:2399-3642
DOI:10.1038/s42003-021-02534-y