Computational whole-body-exposome models for global precision brain health

Area of Science:

• Neuroscience
• Psychiatry
• Environmental Health

Background:

• Neurological and psychiatric conditions are rising globally, posing significant challenges.
• Current research is fragmented, using limited cohorts and poorly integrated datasets.
• Existing theories fail to unify brain health with extracerebral factors and individual variability.

Purpose of the Study:

• To address the complexity of brain health by integrating diverse data sources.
• To introduce a novel construct for understanding brain health in context.
• To foster personalized predictions and equitable advancements in brain health.

Main Methods:

• Introducing multimodal diversity, a non-linear, causal, and ecological construct.
• Integrating data representation, whole-body, and exposomic factors.
• Utilizing computational modeling for personalized predictions.

Main Results:

• The proposed metamodel integrates global, multilevel data.
• It enables personalized predictions for brain health.
• It fosters population inclusion and diagnostic precision.

Conclusions:

• Multimodal diversity offers a holistic approach to brain health research.
• This framework promotes equitable and context-sensitive advancements.
• It enhances diagnostic precision by integrating diverse health factors.