Biology subjects

Spann, M.

Publications and source records attributed to Spann, M..

3 recordsLinked to original sources

bioRxiv2025

Fetal network controllability co-develops with synaptic density and synchronizes with maternal network controllability during pregnancy

White matter undergoes rapid changes during the fetal period that are foundational for future cognitive functions. However, how these changes contribute to the brains capacity to support its dynamic activities--its controllability--remains largely unknown. Here, we apply network control theory (NCT) to investigate the developmental trajectory of controllability from the second trimester through the first postnatal month. We analyzed structural connectivity data from fetuses and infants as part of the developing Human Connectome Project. We identified a robust, nonlinear U-shaped developmental curve of whole-brain controllability across the perinatal period, with a minimum at approximately 35 weeks of gestation. Preterm birth disrupted these trajectories, leading to greater controllability and earlier minimums compared to age-matched fetuses. Using gene expression microarray data from 18 fetal post-mortem brains, we identified genes implicated in synaptic functions that co-develop with changes in controllability during the fetal period. We then used positron emission tomography in seven pregnant rhesus macaques to quantify changes in fetal synaptic density. Increased synaptic density in non-human primates (NHPs) co-occurred with periods of reduced controllability in humans. Finally, using longitudinal scans of a pregnant woman, we mapped the trajectory of changes in maternal controllability during pregnancy. This trajectory exhibited a U-shaped pattern that inversely correlated with the fetal trajectory, reaching a maximum around 36 weeks. Together, fetal controllability follows a nonlinear trajectory that co-develops with synaptic functions and synchronizes with maternal changes in controllability during pregnancy.

neuroscience↗

bioRxiv2025

White-matter controllability at birth predicts social engagement and language outcomes in toddlerhood

Social engagement and language are connected through early development. Alterations in their development can have a prolonged impact on childrens lives. However, the role of white matter at birth in this ongoing connection is less well-known. Here, we investigate how white matter at birth jointly supports social engagement and language outcomes in 642 infants. We use edge-centric network control theory to quantify edge controllability, or the ability of white-matter connections to drive transitions between diverse brain states, at 1 month. Next, we used connectome-based predictive modeling (CPM) to predict the Quantitative Checklist for Autism in Toddlers (Q-CHAT) for social engagement risks and the Bayley Scales of Infant and Toddler Development (BSID-III) for language skills at 18 months from edge controllability. We created the social engagement network (SEN) to predict Q-CHAT scores and the language network (LAN) to predict BSID-III scores. The SEN and LAN were complex, spanning the whole brain. They also significantly overlapped in anatomy and generalized across measures. Controllability in the SEN at 1 month partially mediated associations between Q-CHAT and BSID-III language scores at 18 months. Further, controllability in the SEN significantly differed between term and preterm infants and predicted Q-CHAT scores in an external sample of preterm infants. Together, our results suggest that the intertwined nature of social engagement and language development is rooted in an infants white-matter controllability. Significance StatementDuring infancy and toddlerhood, social engagement and language emerge together. Delays are often observed in both simultaneously. These interactions persist into later childhood, potentially affecting life quality. We reveal that the interplay between social engagement and language milestones in toddlerhood is rooted in the infants structural connectivity, which may assist in early risk identification of developmental delays. Insights into the early brain foundations for emerging social engagement and language skills may open opportunities for individualized interventions to improve developmental outcomes.

neuroscience↗

bioRxiv2024

Brain age prediction and deviations from normative trajectories in the neonatal connectome

Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brains maturity relative to normative data. The difference between the individuals predicted and chronological age--or brain age gap (BAG)--represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks--even putatively later developing ones--generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

neuroscience↗