Revisiting KANET in the context of fetal connectomics: defining optimal gestational windows for neurodevelopmental screening

Objectives To introduce the KANET-connectome matrix (KANET-Con) as a conceptual framework linking fetal behaviors observed on four-dimensional (4D) ultrasound to underlying neural network maturation, and to evaluate optimal gestational timing for functional neurodevelopmental screening. Methods A narrative review was conducted using a PRISMA-guided literature identification and screening process. PubMed, Scopus, and Web of Science were searched (January 2000–March 2025) for studies addressing fetal connectomics, fetal neurobehavior, KANET scoring, and developmental neuroimaging. Forty-two peer-reviewed studies met inclusion criteria. Observed fetal behaviors—including facial mimicry, eye blinking, limb movement, and overall gestalt coordination—were aligned with their most plausible neural substrates using evidence from developmental neuroscience and imaging. Results Findings demonstrated clear temporal associations between specific fetal behaviors and neural circuit development. Eye blinking and facial expressions (24–26 weeks) correlated with brainstem-cortical integration; hand-to-face gestures (26–30 weeks) reflected emerging interhemispheric pathways; and complex limb coordination (28–32 weeks) was linked to corticospinal and basal ganglia maturation. Collectively, these data indicate that 24–32 weeks of gestation represents an optimal window for KANET-based neurobehavioral screening. Additionally, emerging artificial intelligence applications show potential to enhance scoring objectivity by detecting subtle movement features such as behavioral entropy, asymmetry, and latency. Conclusions KANET, interpreted through a fetal connectomic lens, provides a functional window into early neural integration. The KANET-Con offers a clinically relevant, globally accessible conceptual model to support early detection of neurodevelopmental deviations and inform risk stratification, particularly in resource-limited settings.