AI-enabled resilience modeling for brain health.

Area of Science:

• Neuroscience and Gerontology
• Public Health and Health Services
• Artificial Intelligence in Medicine

Background:

• Growing focus on brain resilience in Alzheimer's Disease and related neurological disorders (ADRD).
• Integration of lifestyle behaviors, socio-ecological determinants, and resilience concepts for brain health.
• Existing literature highlights the role of lifestyle in ADRD prevention and management.

Purpose of the Study:

• To review 'brain health' definitions and integrate them with resilience system models for ADRD.
• To propose a unified model of resilience and aging in the context of ADRD.
• To identify Artificial Intelligence (AI) innovations needed for interdisciplinary data mining in ADRD research.

Main Methods:

• Socio-ecological framework integrating physiological, behavioral, economic, and social determinants.
• Review of 'brain health' definitions and resilience system models.
• Proposal of a unified resilience and aging model for ADRD.

Main Results:

• A unified model of resilience and aging in ADRD is proposed, based on a socio-ecological framework.
• Identified AI innovations crucial for harnessing interdisciplinary data: digital twins, precision health analytics, AI sensors, Multimodal Large Language Models (MLLM), knowledge graphs, and cognitive/decision science modeling.
• The model's potential value, requirements, risks, and challenges are elucidated through research and clinical examples.

Conclusions:

• A resilience-focused, AI-driven approach is essential for advancing ADRD research and clinical practice.
• Innovation in AI, including digital twins and MLLMs, is critical for analyzing complex ADRD data.
• The proposed model provides a framework for future research agendas in brain health and ADRD resilience.