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Brain Sciences
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Artificial Intelligence in Neuropsychology: Advancing Diagnosis, Personalized Treatment, and Predictive...
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Artificial Intelligence in Neuropsychology: Advancing Diagnosis, Personalized Treatment, and Predictive Modeling

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neuropsychology".

Deadline for manuscript submissions: closed (1 November 2025) | Viewed by 1732

Special Issue Editor

Dr. Sara Palermo

E-Mail Website
Guest Editor
1. Department of Psychology, University of Turin, Turin, Italy
2. Neuroradiology Unit, Department of Diagnostic and Technology, Fondazione IRCCS Isttuto Neurologico Carlo Besta, Milan, Italy
3. Neuroscience Institute of Turin, University of Turin, Turin, Italy
Interests: clinical neuropsychology; aging neuroscience; psychogeriatrics; cognitive; affective and social neuroscience; active and healty aging; physical and cognitive frailty; neurodegenerative disorders; movement disorders; placebo and nocebo; pain; neuropsychological testing; brain imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into neuropsychology is transforming the field, offering innovative solutions for diagnosis, treatment, and predictive modeling. AI-driven diagnostic tools enhance the accuracy and efficiency of neuropsychological assessments by analyzing complex cognitive and behavioral data. Personalized treatment approaches leverage AI algorithms to tailor interventions to the unique characteristics of each patient, optimizing therapeutic outcomes. Additionally, predictive modeling techniques, powered by machine learning, allow for early identification of patients at risk and provide insights into treatment efficacy. This Special Issue aims to explore the latest advancements in AI applications for neuropsychology, highlighting methodological innovations, clinical applications, and ethical considerations. We invite contributions on AI-based diagnostic tools, personalized neuropsychological interventions, predictive analytics, and related topics. By bridging AI and neuropsychology, this issue seeks to foster a deeper understanding of how intelligent systems can enhance patient care and clinical decision-making.

Dr. Sara Palermo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI-driven diagnostic tools
  • personalized treatment
  • machine learning predictive modeling
  • data analysis
  • mental health and rehabilitation
  • decision support systems

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Published Papers (1 paper)

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Research

20 pages, 1664 KB  
Article
AI-Driven Prediction of Possible Mild Cognitive Impairment Using the Oculo-Cognitive Addition Test (OCAT)
by Gaurav N. Pradhan, Sarah E. Kingsbury, Michael J. Cevette, Jan Stepanek and Richard J. Caselli
Brain Sci. 2026, 16(1), 70; https://doi.org/10.3390/brainsci16010070 - 3 Jan 2026
Viewed by 945
Abstract
Background/Objectives: Mild cognitive impairment (MCI) affects multiple functional and cognitive domains, rendering it challenging to diagnose. Brief mental status exams are insensitive while detailed neuropsychological testing is time-consuming and presents accessibility issues. By contrast, the Oculo-Cognitive Addition Test (OCAT) is a rapid, [...] Read more.
Background/Objectives: Mild cognitive impairment (MCI) affects multiple functional and cognitive domains, rendering it challenging to diagnose. Brief mental status exams are insensitive while detailed neuropsychological testing is time-consuming and presents accessibility issues. By contrast, the Oculo-Cognitive Addition Test (OCAT) is a rapid, objective tool that measures oculometric features during mental addition tasks under one minute. This study aims to develop artificial intelligence (AI)-derived predictive models using OCAT eye movement and time-based features for the early detection of those at risk for MCI, requiring more thorough assessment. Methods: The OCAT with integrated eye tracking was completed by 250 patients at the Mayo Clinic Arizona Department of Neurology. Raw gaze data analysis yielded time-related and eye movement features. Random Forest and univariate decision trees were the feature selection methods used to identify predictors of Dementia Rating Scale (DRS) outcomes. Logistic regression (LR) and K-nearest neighbors (KNN) supervised models were trained to classify PMCI using three feature sets: time-only, eye-only, and combined. Results: LR models achieved the highest performance using the combined time and eye movement features, with an accuracy of 0.97, recall of 0.91, and an AUPRC of 0.95. The eye-only and time-only LR models also performed well (accuracy = 0.93), though with slightly lower F1-scores (0.87 and 0.86, respectively). Overall, models leveraging both time and eye movement features consistently outperformed those using individual feature sets. Conclusions: Machine learning models trained on OCAT-derived features can reliably predict DRS outcomes (PASS/FAIL), offering a promising approach for early MCI identification. With further refinement, OCAT has the potential to serve as a practical and scalable cognitive screening tool, suitable for use in clinics, at the bedside, or in remote and resource-limited settings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Neuropsychology: Advancing Diagnosis, Personalized Treatment, and Predictive Modeling)
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