# Independent Component Analysis (ICA) With Covariates Strengthens Behavioral Links in Electroencephalography (EEG) Connectivity

## TL;DR

This paper introduces a new method that combines brain activity data with cognitive test scores to better understand how brain connectivity relates to behavior.

## Contribution

The novel approach integrates behavioral assessments into ICA to enhance EEG connectivity analysis and cognitive performance correlations.

## Key findings

- Integrating behavioral data into ICA improves the significance of EEG-cognition correlations.
- The augmented ICA method shows robust results in independent test datasets.
- This approach offers a multivariate framework for uncovering brain-behavior relationships.

## Abstract

The search for reliable biomarkers of clinical and cognitive deficits remains the holy grail of clinical neuroscience, with improved predictive methods and neural correlates paving the way for more effective treatments. Independent component analysis (ICA) has been widely applied in electroencephalography (EEG) signal processing to isolate neural activity from artifacts and noise. This study introduces a novel approach by integrating clinical covariates - behavioral assessments from the Woodcock-Johnson Cognitive Abilities Test III (WJ) Tests - into ICA, enabling the simultaneous decomposition of EEG connectivity patterns and cognitive performance metrics. Using functional connectivity measures as input, we applied two ICA methodologies to a dataset of 175 patients: (1) conventional ICA on EEG connectivity data, followed by correlation analysis with WJ scores, and (2) an augmented ICA approach incorporating both EEG connectivity and WJ measures. Our findings demonstrate that integrating behavioral data into ICA decomposition enhances the significance and robustness of correlations between EEG connectivity and cognitive performance in independent test datasets. These results underscore the potential of ICA with integrated covariates as a powerful multivariate framework for uncovering brain-behavior relationships, offering new insights for clinical and cognitive neuroscience research.