Neuropsychology Meets Cognitive Neuroscience and AI: Towards Explainable and Actionable Models in Academic Ecosystems

The convergence of neuropsychology, cognitive neuroscience and artificial intelligence (AI) is changing the way we understand and intervene in human cognition across the lifespan. Recent advances in machine learning, deep learning and generative AI have enabled increasingly sophisticated analysis of complex, multimodal data - from neuroimaging and behavioural metrics to psychophysiological signals and digital health data. These technologies support the identification of subtle biomarkers, improve the prediction of cognitive decline or recovery and enable personalised intervention strategies.

Conversely, cognitive neuroscience and neuropsychology provide valuable conceptual models to inspire human-centred, biologically informed and interpretable AI systems. The synergy between these fields supports the development of AI that not only performs but also provides explanations - making it more clinically relevant, transparent and ethically sound.

An important prerequisite for this integration is the emergence of large, trustworthy academic AI platforms and open digital ecosystems. These infrastructures have been developed in research institutions and designed for neuroscientific and clinical applications:
-Facilitate the integration of heterogeneous data (e.g. fMRI, EEG, cognitive scores, digital phenotyping),
-Promote reproducible and scalable analysis pipelines and
-Promote collaboration and interdisciplinary workflows between psychologists, neuroscientists, clinicians and data scientists.

By focusing on current priorities such as explainability, fairness and clinical safety, academic AI platforms act as catalysts for translational research and support the real-world use of neuropsychological tools - from assessment and diagnosis to adaptive, personalised rehabilitation.

This convergence enables a more holistic and scalable understanding of cognition, particularly in neurological and psychiatric disorders, through the use of multimodal techniques such as MRI, fMRI, EEG, fNIRS, eye-tracking and psychophysiological measurements (e.g. heart rate, galvanic skin response).

This Research Topic aims to foster interdisciplinary contributions that explore how AI - especially in the context of academic platforms and reproducible ecosystems - can advance both theoretical modelling and clinical application of neuropsychology and cognitive neuroscience.

We particularly welcome studies that emphasise the following:
-The explainability and interpretability of AI models
-The real-life and clinical application and
-The translation of findings from cognitive neuroscience into useful neuropsychological tools.

We invite empirical, theoretical and methodological contributions as well as review papers dealing with the integration of AI with cognitive neuroscience and neuropsychology. Both basic and applied research is welcome.

Topics may include:
-AI-based tools for neuropsychological assessment and early diagnosis
-Predictive modelling of cognitive decline, recovery or cognitive reserve
-Computational modelling of key cognitive domains: Memory, attention, language, executive functions, visuospatial abilities
-Multimodal integration through AI: neuroimaging (MRI, fMRI), electrophysiology (EEG, fNIRS), eye tracking and psychophysiology
-Biologically plausible, interpretable and human-centred AI, inspired by cognitive neuroscience
-Personalised and adaptive cognitive rehabilitation through digital platforms and AI agents
-Generative AI and large language models in cognitive assessment and clinical interaction
-AI-assisted subtyping and stratification of clinical neuropsychological profiles
-Explainable AI (XAI) to improve the understanding of brain-behaviour relationships
-Ethical and governance challenges in AI-driven neuropsychology, including mitigating bias and data transparency
-Interdisciplinary frameworks that bridge theory and practical application in cognitive disorders
-The role of academic AI platforms in promoting open science, reproducibility and clinical scalability.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Neuropsychology, Cognitive neuroscience, Artificial intelligence, Machine learning, Generative AI, Explainable AI, Computational modelling, EEG / fNIRS, Neuroimaging, fMRI, MRI, Cognitive domains, Cognitive assessment, Personalised rehabilitation, Academic AI platforms, Digital Health, Translational Neuroscience

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.