Developmental trajectories in functional brain network properties of preterm and at-term neonates

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Abstract Submission 

Nelsiyamid López Guerrero¹, Sarael Alcauter¹

¹Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México

Nelsiyamid López Guerrero  
Instituto de Neurobiología, Universidad Nacional Autónoma de México
Querétaro, México

Sarael Alcauter  
Instituto de Neurobiología, Universidad Nacional Autónoma de México
Querétaro, México

The human brain undergoes rapid growth during the first years of life. Premature infants, born before 37 weeks of gestation can have consequences on development, even when no anatomical lesions are evident (Rogers et al.,2018). Resting state functional (MRI) naturally sleeping babies allows the characterization of the brain functional connectome, showing decreased long range connectivity (Smyser et al., 2010). Preterm infants have shown alterations in connectivity measures globally and in specific networks (Gozdas et al., 2018). In this work, we characterize the developmental trajectories in the functional brain network in preterm and term neonates.

We included 454 preprocessed structural-functional datasets from the developing Human Connectome Project (Hughes et al., 2017), acquired between 26 -44 weeks of postmenstrual age (PMA) and with no radiological signs of white matter lesions. For each subject, we estimated the connectivity matrix as the correlation of the BOLD time series between all possible pairs of the 90 regions within the neonate AAL atlas (Shi et al., 2011). Subsequently, these matrices were thresholded to keep only the ten percent of the highest connections. From these thresholded matrices, we computed graph theory measures as clustering coefficient, node strength, global efficiency and shortest path length, using the Brain Connectivity Toolbox. To characterize the developmental trajectories of the graph theory properties here explored, linear, quadratic, and log-linear mixed models were constructed with gestational age at scan as an independent fixed-effect variable. Random effects were added for the intercept and subject ID. Significance was defined as p <0.05, and the model with the lowest Akaike Information Criterion (AIC) was selected as the best model to describe the data.

The best-fitting models showed non-linear trajectories for all the properties in preterm neonates and two of them in at-term neonates (Figure 1 and Figure 2). When compared by sex, at-term infants showed no significant differences between males and females; females preterm showed increased connectivity in clustering coefficient (p < 0.02), node strength (p < 0.01) and global efficiency (p < 0.01).

Overall, our results confirm that the functional connectivity, integration and segregation properties of the preterm brain follow nonlinear trajectories with a clear sexual dimorphism for these brain network properties.

Neurodevelopmental/ Early Life (eg. ADHD, autism) ¹

Connectivity (eg. functional, effective, structural) ²

fMRI Connectivity and Network Modeling

BOLD fMRI

Development

FUNCTIONAL MRI