Spectral Geometry of Infant Resting-State fNIRS Connectivity: Bilingual vs Monolingual

PurposeWe investigate whether bilingual versus monolingual language environments in early infancy are associated with differences in intrinsic functional organization measured from resting-state fNIRS connectivity. ApproachUsing the RS4 infant resting-state fNIRS cohort (HbO), we studied two complementary subject-level representations of resting-state connectivity: correlation-based symmetric positive definite (SPD) operators and learned-graph Laplacian operators. Correlation matrices were estimated over fixed non-overlapping temporal windows, regularized by shrinkage, and aggregated at the subject level using a Jensen- Bregman LogDet (JBLD/Stein) barycentric mean. Dominant eigenspaces were used as compact descriptors of functional organization and compared across subjects through principal angles augmented with spectral jump features. In parallel, learned functional graphs provided a complementary Laplacian-based representation of network structure. All analyses followed a strict leave-one-subject-out protocol on a common subject set (N = 94), with all templates and model parameters estimated from the training fold only. ResultsThe strongest individual branch was the correlation-based spectral-subspace representation (CORR-ANGLES: ROC-AUC = 0.811), while the learned-graph spectral branch also showed clear above-chance performance (LAP-ANGLES: ROC-AUC = 0.785). Fusion improved performance both within representation families and across them. Within-family fusion yielded ROC-AUC = 0.836 for the correlation branch and ROC-AUC = 0.805 for the Laplacian branch, whereas fusion of the two spectral branches reached ROC-AUC = 0.883, supporting the view that covariance-based and learned-graph representations capture complementary aspects of infant functional connectivity. The best overall performance was achieved by the main reported hierarchical four-branch fusion, with balanced accuracy = 0.826, F1 score = 0.781, and ROC-AUC = 0.900. ConclusionsResting-state infant fNIRS contains subtle spectral-geometric structure associated with bilingual exposure. Correlation-based and learned-graph representations provide complementary information, and their hierarchical fusion improves separability under strict cross-subject evaluation.