Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience.
| Autor: | 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. |
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| Jazyk: | angličtina |
| Zdroj: | Communications biology [Commun Biol] 2021 Sep 15; Vol. 4 (1), pp. 1077. Date of Electronic Publication: 2021 Sep 15. |
| DOI: | 10.1038/s42003-021-02534-y |
| Abstrakt: | 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).) |
| Databáze: | MEDLINE |
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