RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.

Authors

Mohammad Nami, David Peebles, Fadi Thabtah, Firuz Kamalov

Published in

Brain informatics. Jun 19, 2026. Epub Jun 19, 2026.

Abstract

Explainable Artificial Intelligence (XAI) is gaining popularity in early diagnosis and monitoring of dementia. Herein, we recommend the incorporation of the National Institute of Mental Health's Research Domain Criteria (NIMH-RDoC) framework with XAI-informed diagnostic protocols to help establish diagnosis at early stages of Alzheimer's disease (AD). RDoC has a dimensional structure that extends across units of analysis from genes and molecules to circuits, physiology, behavior, and introspection. By restructuring diverse features as inputs including apolipoprotein E (APOE) genotype, amyloid and tau biomarkers, computational neuroimaging-informed cortical atrophy, Positron Emission Tomography (PET) hypometabolism, quantitative electroencephalography (qEEG) rhythms, cognitive tests, and digital behavioral markers), onto RDoC units provides more insightful and inclusive models. In this context, data-driven approaches such as XAI can achieve not only increased interpretability but also enhance their mechanistic validity. Such an innovative approach places data-driven model outputs within neurobiologically based domains such as Cognitive Systems, Negative Valence, and Arousal/Regulatory Systems. Our synthesis suggests that a 'converging RDoC and XAI' approach may help bolster the coherence of AD biomarkers, promote model exploration in clinical decision-making. This approach is also expected to provide a strategic roadmap for translational neuroscience and personalized medicine. Another major aim of this study is to critically analyze current XAI approaches used in dementia research, particularly the diagnostic and prognostic aspects. By explicitly grounding explanations in RDoC cognitive domains and paradigms, the framework also aims to make model outputs meaningful in terms of specific mental functions (e.g., episodic memory, cognitive control), thereby supporting neuropsychologically-informed diagnosis, categorization, and communication with patients and caregivers.

PMID:
42319634
Bibliographic data and abstract were imported from PubMed on 19 Jun 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.