Cortical eigenmode coordinates provide compact subject-level signatures across structural MRI, resting-state fMRI, and EEG

A practical barrier in multimodal neuroimaging is that structural MRI, fMRI, and EEG are often analyzed in modality-specific spaces or reduced to atlas- and sensor-based summaries, limiting the construction of common, interpretable subject-level brain signatures. We evaluate cortical Laplace-Beltrami eigenmode coordinates as a shared geometry-aligned language for structural MRI (sMRI), resting-state fMRI (rs-fMRI), and EEG. In this framework, sMRI morphometric fields are represented by cortical eigenmode coefficients, rs-fMRI by covariance among eigenmode time-series coefficients, and EEG by mode-frequency-condition summaries. Using the Max Planck Institute Leipzig Mind-Brain-Body dataset (MPI-LEMON), we compared unimodal eigenmode-coordinate summaries, multimodal cortical eigenmode-coordinate PCA, conventional atlas/sensor-based PCA and ridge representations, and geometric eigenmode multiview factorization (GEMF). GEMF is a structured decomposition that preserves the modality-native organization of the data objects while separating shared from modality-specific variation. Eigenmode-coordinate representations yielded compact subject-level signatures with strong external validity for chronological age and a secondary cognitive outcome. Multimodal eigenmode-coordinate PCA was among the strongest-performing approaches, reached high age-prediction performance at moderate dimension, and consistently outperformed conventional low-dimensional PCA. GEMF selected an even lower-dimensional shared representation and remained competitive with the benefit of providing interpretable shared and modality-specific factors. These findings support cortical eigenmode coordinates as a practical foundation for compact, interpretable, and multimodally aligned subject-level brain signatures.