RDoC-Informed Explainable AI as a Paradigm for Multilevel Alzheimer’s Disease Diagnosis and Progression Prediction: a Systematic Review

Explainable Artificial Intelligence (XAI) is gaining popularity as it has been used to enhance 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 the 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, digital behavioral markers), onto RDoC units, data driven approaches like XIA can now not only achieve 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 to bolster the coherence of AD biomarkers, promote model exploration in clinical decision-making, and provide a strategic roadmap for translational neuroscience and personalized medicine. Another major aim of this research is to critically analyze current approaches related to XIA that have been used in dementia research, particularly diagnosis and prognosis. 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.