From early to contemporary normative modeling: Mapping individual differences in neurophysiological signals

Area of Science:

• Computational Neuroscience
• Electrophysiology
• Machine Learning

Background:

• Normative modeling is crucial for identifying individual brain function deviations.
• Its use in electrophysiology has seen a revival due to machine learning and large datasets.
• Early models were small and site-specific, evolving towards harmonized, integrated approaches.

Purpose of the Study:

• To review the historical development of normative modeling in brain electrophysiology.
• To compare key studies regarding methodology, cohort composition, and validation.
• To identify current challenges and future directions for the field.

Main Methods:

• Literature review tracing the evolution of normative modeling in electrophysiology.
• Comparison of key studies based on modeling strategies and validation.
• Analysis of methodological progress and integration of innovations.

Main Results:

• The field has shifted from simple age-based models to complex, harmonized approaches.
• Significant challenges include lack of standardization and limited comparability.
• Advancements are driven by machine learning and large-scale data.

Conclusions:

• Scaling and unifying efforts through international collaboration and standardized pipelines are essential.
• Integrating novel features and complex neurophysiological signals will enhance precision.
• Future applications include individualized brain mapping and clinical monitoring for brain disorders.