Neonatal sensory networks at birth predict cognitive, language, and motor outcomes at 18 months

The relationship between neonatal brain activity patterns and later cognitive development has become a central topic in developmental neuroscience. Addressing this question requires whole-brain analytical approaches capable of identifying which large-scale functional systems carry stable and generalizable predictive signals. However, most existing studies remain focused on specific brain regions or localized functional circuits, such as thalamocortical pathways and amygdala-centered emotional networks. While these region-specific investigations have provided important insights, they are inherently limited in terms of robustness and cross-sample generalizability. As a result, systematic evidence identifying which large-scale functional systems reliably support stable and generalizable predictive signals remains scarce. Overcoming the methodological constraints of conventional whole-brain analytical paradigms has therefore become a key bottleneck in advancing our understanding of how early brain activity patterns relate to subsequent cognitive development. Here, using data from 402 infants in the developing Human Connectome Project (278 term-born; 124 preterm-born), we introduce a region-of-interest (ROI)-constrained variant of Connectome-Based Predictive Modeling (CPM) that incorporates ROI-degree-guided feature selection to predict 18-month Bayley-III cognitive, language, and motor outcomes. Model performance declined as progressively lower-degree regions were included, indicating that conventional whole-connectome CPM may obscure robust predictive signals by incorporating low signal-to-noise (SNR) features. Our models robustly predicted cognitive, language, and motor outcomes at 18 months of age. Cohort-specific connectivity patterns emerged. In term-born infants, dominant predictive features were concentrated in visual-auditory interactions, as well as connections between visual and auditory networks and other cortical regions. Interhemispheric and intrahemispheric connections contributed in roughly equal proportions. In contrast, among preterm infants, predictive features were primarily concentrated in connectivity involving auditory and temporoparietal networks, with interhemispheric connections comprising approximately twice the number of intrahemispheric connections. The whole-cohort model (term + preterm) reflected the combined contributions of both term- and preterm-associated connectivity patterns. Predictions generalized across Bayley composite and subscale scores and were supported by permutation testing and held-out validation. These findings identify early sensory hubs--particularly visual and auditory regions--as promising early biomarkers for later neurodevelopmental outcomes. Furthermore, they demonstrate that ROI-constrained CPM can reveal meaningful predictive signals that may be obscured by conventional connectome-wide approaches.