Cripe Curtis T, Delorme Arnaud
Graduate School of Social Service, Fordham University, New York City, USA.
Research, Independent Consultancy, La Jolla, USA.
Cureus. 2025 Jun 22;17(6):e86533. doi: 10.7759/cureus.86533. eCollection 2025 Jun.
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.
寻找临床和认知缺陷的可靠生物标志物仍然是临床神经科学的圣杯，改进的预测方法和神经关联为更有效的治疗铺平了道路。独立成分分析（ICA）已广泛应用于脑电图（EEG）信号处理，以从伪迹和噪声中分离神经活动。本研究引入了一种新方法，即将临床协变量——来自伍德库克-约翰逊认知能力测试第三版（WJ）测试的行为评估——整合到ICA中，从而能够同时分解EEG连接模式和认知表现指标。以功能连接测量作为输入，我们将两种ICA方法应用于175名患者的数据集：（1）对EEG连接数据进行传统ICA，然后与WJ分数进行相关分析；（2）一种结合EEG连接和WJ测量的增强ICA方法。我们的研究结果表明，将行为数据整合到ICA分解中可增强独立测试数据集中EEG连接与认知表现之间相关性的显著性和稳健性。这些结果强调了带有整合协变量的ICA作为揭示脑-行为关系的强大多变量框架的潜力，为临床和认知神经科学研究提供了新见解。
