Linking the exposome to the brain-behaviour phenotype.

Area of Science:

• Neuroscience
• Environmental Health
• Genetics

Background:

• Individual neurocognitive phenotypes result from complex interactions between genetic factors and a lifetime of environmental, lifestyle, and biological exposures.
• The exposome concept captures the totality of these exposures, offering a framework to understand variations in brain-behavior phenotypes and susceptibility to brain diseases.
• Large-scale neuroimaging studies increasingly incorporate extensive exposomic data, yet analyzing these intricate relationships presents significant challenges.

Purpose of the Study:

• To emphasize the exposome as a network of interrelated factors.
• To outline multivariate pattern learning as a method to address exposome complexity.
• To highlight temporality, causal inference challenges, and potential analytical approaches in exposomic research.

Main Methods:

• Utilizing multivariate pattern learning to analyze complex, interrelated exposomic factors.
• Examining temporal dynamics (timing, duration, sequencing) of exposures and their impact.
• Exploring traditional statistical designs, generative models, and causal machine learning for data analysis.

Main Results:

• The exposome concept provides a holistic view of factors influencing neurocognition.
• Multivariate pattern learning can manage the complexity of interacting exposures.
• Temporality significantly impacts exposure-brain-behavior associations, necessitating dynamic analytical approaches.
• Causal inference is challenging due to bidirectional exposome-brain relationships.

Conclusions:

• Advanced analytical methods like multivariate pattern learning and causal machine learning are crucial for deciphering exposome-brain dynamics.
• Addressing the timing and causality of exposures is essential for understanding neurocognitive outcomes.
• Global diversity in brain-behavior exposomic research must be enhanced to benefit global health.