An integrated computational approach for diversity-sensitive personalized medicine

Area of Science:

• Computational neuroscience
• Neuroimaging analysis
• Precision medicine

Background:

• Brain health and disease are influenced by diverse biological, social, and environmental factors.
• Conventional approaches struggle with interindividual variability, leading to reproducibility and translation issues.
• Heterogeneous mechanisms underlie overlapping clinical phenotypes in brain disorders.

Purpose of the Study:

• To propose an integrative computational framework for precision neuroscience.
• To address limitations of population-averaged approaches in capturing individual brain variability.
• To tailor assessment, understanding, and treatment of brain disorders to individual profiles.

Main Methods:

• Utilizing normative models of brain aging (brain clocks) for individualized brain health scores.
• Analyzing high-order functional interactions beyond pairwise connectivity for sensitive biomarkers.
• Employing whole-brain modeling for simulating individual brain dynamics and inferring mechanisms.

Main Results:

• The synergistic approach integrates personalized baselines, sensitive biomarkers, and causal insights.
• The framework embeds inter-individual variability and contextual diversity into computational layers.
• This moves towards tailored interventions and a deeper understanding of brain function.

Conclusions:

• The proposed integrative framework facilitates precision neuroscience.
• It enhances the characterization of brain aging and neurodegeneration by embracing individual diversity.
• This approach supports the development of targeted interventions for brain health and disease.