Integrating memory-guided saccades and EEG for MCI screening: a multimodal LASSO modeling approach.

Background: We investigated differences in memory-guided saccades (MGS) and electroencephalographic (EEG) spectral ratios between cognitively normal older adults and patients with mild cognitive impairment (MCI). A multimodal predictive model was developed to evaluate its potential for early MCI identification. Methods: We enrolled 60 participants (37 MCI, 23 controls) who underwent cognitive assessments, 32-channel resting-state EEG, and MGS testing. EEG spectral ratios, including the Delta-to-Alpha Ratio (DAR), Theta-to-Alpha Ratio (TAR), and (Delta+Theta)/(Alpha+Beta) Ratio (DTABR), were extracted along with MGS parameters (latency, accuracy, gain). To prevent data leakage, 21 multimodal features were evaluated using a zero-leakage least absolute shrinkage and selection operator (LASSO) pipeline. A multivariable predictive model was subsequently constructed using Firth's penalized logistic regression, explicitly adjusting for demographic covariates, and internally validated with 1,000 bootstrap resamples. Results: MCI patients exhibited significantly prolonged bilateral saccadic latencies, decreased accuracy, and widespread cortical spectral slowing (elevated DAR, TAR, and DTABR across all regions) (all Conclusion: MCI patients demonstrate coupled oculomotor and electrophysiological abnormalities, reflecting impaired frontoparietal-sensorimotor network efficiency. Our rigorously penalized multimodal model yields robust internal diagnostic performance, providing a promising exploratory proof-of-concept for the early screening of "cognitive-motor decoupling" in clinical neurology, though large-scale external validation is warranted.