Robustness of NeuroMark-Derived Functional Networks to fMRI Spatial Normalization Across the Human Lifespan

Abstract NeuroMark is a fully automated hybrid independent component analysis (ICA) framework designed to extract functional network features that are individually resolved and comparable across different cohorts. By integrating a reliable spatial template with spatially constrained ICA that adapts to each scan, NeuroMark retains the advantages of data-driven decomposition while avoiding limitations of fixed region-of-interest approaches. NeuroMark typically employs direct spatial normalization of fMRI data to a standardized adult EPI template; it remains unclear whether this approach is optimal for populations whose anatomy differs substantially from that of adults. We evaluated two normalization strategies in three large datasets spanning infancy, development, and aging: (1) direct normalization to the adult EPI template (EPInorm), and (2) normalization using an age-specific anatomical T1 template followed by transformation to the adult EPI template (T1toEPInorm). Across all cohorts, average intrinsic connectivity networks derived from EPInorm and T1toEPInorm exhibited very high spatial correspondence (mean ± SD: 0.9966 ± 0.0012 in infants; 0.9947 ± 0.0019 in development; 0.9963 ± 0.0012 in aging). The individual level also showed high similarity, though time courses showed slightly higher consistency than spatial maps (average correlations for time courses: 0.7990–0.9931; average correlations for spatial maps: 0.6879–0.9131). Functional network connectivity (FNC) measures were extremely well preserved across scans (95% of FNC with r > 0.9374 in infants; r > 0.8670 in developmental cohorts; r > 0.9219 in aging), demonstrating the robustness of NeuroMark features to different normalization strategies. Together, these results indicate that NeuroMark yields highly stable functional network features irrespective of whether an age-specific intermediate registration step is incorporated. NeuroMark, along with direct normalization to the adult EPI template, thus provides a robust, efficient, and harmonizable approach for large-scale, multisite, and lifespan neuroimaging studies, facilitating broad comparability across datasets while avoiding potential biases introduced by using multiple age-specific templates within a single study. Competing Interest Statement The authors have declared no competing interest.