Very preterm infants (≤ 31 weeks gestational age) are at high risk for brain injury and delayed development. Applying functional connectivity and graph theory methods to resting state MRI data (fcMRI), we tested the hypothesis that preterm infants would demonstrate alterations in connectivity measures both globally and in specific networks related to motor, language and cognitive function, even when there is no anatomical imaging evidence of injury. Fifty-one healthy full-term controls and 24 very preterm infants without significant neonatal brain injury, were evaluated at term-equivalent age with fcMRI. Preterm subjects showed lower functional connectivity from regions associated with motor, cognitive, language and executive function, than term controls. Examining brain networks using graph theory measures of functional connectivity, very preterm infants also exhibited lower rich-club coefficient and assortativity but higher small-worldness and no significant difference in modularity when compared to term infants. The findings provide evidence that functional connectivity exhibits deficits soon after birth in very preterm infants in key brain networks responsible for motor, language and executive functions, even in the absence of anatomical lesions. These functional network measures could serve as prognostic biomarkers for later developmental disabilities and guide decisions about early interventions.

Introduction: Reading is an acquired-developmental ability that relies on intact language and executive function skills. Verbal fluency tasks (such as verb generation) also engage language and executive function skills. Performance of such tasks matures with normal language development, and is independent of reading proficiency. In this longitudinal fMRI study, we aim to examine the association between maturation of neural-circuits supporting both executive functions and language (assessed using verb generation) with reading proficiency achieved in adolescence with a focus on left-lateralization typical for language proficiency.Methods: Normalized fMRI data from the verb generation task was collected from 16 healthy children at ages 7, 11, and 17 years and was correlated with reading scores at 17 years of age. Lateralization indices were calculated in key language, reading, and executive function-related regions in all age groups.Results: Typical development was associated with (i) increasingly left-lateralized patterns in language regions (ii) more profound left-lateralized activation for reading and executive function-related regions when correlating with reading scores, (iii) greater involvement of frontal and parietal regions (in older children), and of the anterior frontal cortex (in younger children).Conclusion: We suggest that reading and verb generation share mutual neural-circuits during development with major reliance on regions related to executive functions and reading. The results are discussed in the context of the dual-networks architecture model.

Survivors following very premature birth (i.e., ≤ 32 weeks gestational age) remain at high risk for neurodevelopmental impairments. Recent advances in deep learning techniques have made it possible to aid the early diagnosis and prognosis of neurodevelopmental deficits. Deep learning models typically require training on large datasets, and unfortunately, large neuroimaging datasets with clinical outcome annotations are typically limited, especially in neonates. Transfer learning represents an important step to solve the fundamental problem of insufficient training data in deep learning. In this work, we developed a multi-task, multi-stage deep transfer learning framework using the fusion of brain connectome and clinical data for early joint prediction of multiple abnormal neurodevelopmental (cognitive, language and motor) outcomes at 2 years corrected age in very preterm infants. The proposed framework maximizes the value of both available annotated and non-annotated data in model training by performing both supervised and unsupervised learning. We first pre-trained a deep neural network prototype in a supervised fashion using 884 older children and adult subjects, and then re-trained this prototype using 291 neonatal subjects without supervision. Finally, we fine-tuned and validated the pre-trained model using 33 preterm infants. Our proposed model identified very preterm infants at high-risk for cognitive, language, and motor deficits at 2 years corrected age with an area under the receiver operating characteristic curve of 0.86, 0.66 and 0.84, respectively. Employing such a deep learning model, once externally validated, may facilitate risk stratification at term-equivalent age for early identification of long-term neurodevelopmental deficits and targeted early interventions to improve clinical outcomes in very preterm infants.

Investigation of the brain connectome using functional magnetic resonance imaging (fMRI) and measures derived from graph theory analysis has emerged as a new approach to study brain development, cognitive function, and neurophysiological disorders. Here we use graph theory analysis to examine the influence of age, sex, and neurocognitive measures on developmental changes to the global and regional topology of functional brain networks derived from fMRI data recorded in 189 healthy subjects from the age of 0-18 years during rest. We observed that Global Efficiency and Rich-Club coefficient increased with age and Local Efficiency and Small-Worldness decreased with age, while Modularity at the global level showed an inverted U-shaped trajectory during development. Marginally significant differences were observed in Local Efficiency, Small-Worldness, and Modularity at a global level between boys and girls throughout development. We also examine the effects of neurocognitive measures in boys and girls globally and locally. Our results provide new insight to understand brain maturation of functional brain connectome and its relation to cognitive development from birth through adolescence. K E Y W O R D S brain, connectivity, fMRI, imaging, network