ArticleCommunications biology2021
Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Functional near-infrared spectroscopy: Systematic mapping of abnormal brain function features in neurological disorders.Neural regeneration research · 2026Article
- Hand movements prediction via fNIRS and AI algorithms.BMC medical informatics and decision making · 2026Article
- The Marriage of Neurotechnologies and Artificial Intelligence: Ethical, regulatory, and technological aspects.IEEE signal processing magazine · 2025Article
- Task-based functional connectivity in infants after exposure to regular white noise during natural sleep measured by fNIRS.Scientific reports · 2025Article
- Current methods in explainable artificial intelligence and future prospects for integrative physiology.Pflugers Archiv : European journal of physiology · 2025Review
- CT-Net: an interpretable CNN-Transformer fusion network for fNIRS classification.Medical & biological engineering & computing · 2024Article
- The application of eXplainable artificial intelligence in studying cognition: A scoping review.Ibrain · 2024Article
- Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease.Frontiers in aging neuroscience · 2024Article
- Using functional near-infrared spectroscopy to study the early developing brain: future directions and new challenges.Neurophotonics · 2023Article
- Artificial intelligence evaluation of COVID-19 restrictions and speech therapy effects on the autistic children's behavior.Scientific reports · 2023Article
- Optical imaging and spectroscopy for the study of the human brain: status report.Neurophotonics · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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