Characterising abnormal early brain development and links to neuropsychiatric disorders using diffusion MRI and machine learning

Emerging evidences suggest that a wide range of neuropsychiatric diseases, irrespective of their age of onset, could arise from the disruptions of early developmental pathways (1). For example, transcriptomic analyses of developing human brain have found genes significantly associated with the disorders to express highly across the foetal cerebral cortex (2). Since the brain plasticity is a long-lasting and continuous feature with the highest activity soon after birth (3), the neuronal circuits expressed in the cognitively impaired adults could result from damages during sensitive periods of the brain development. Indeed, cortical mapping comparison of individuals between the first and ninth decades of life has demonstrated the trajectories of maturational changes can vary across regions and at different rates (4). In addition to the risk genes, environmental factors found during the perinatal period including maternal stress, viral infection, malnutrition, and obstetric complications can also contribute to the risk of behavioural abnormalities seen in schizophrenia and bipolar disorders (5, 6). Thus, identification of critical windows, during which the brain is highly vulnerable to both genetic and environmental perturbations, could provide novel insights to potential therapeutic targets and early intervention to later cognitive deficits. However, since most of the current knowledge on the human brain maturation is extrapolated primarily from post-mortem and animal models, there remains a possibility of clinical ineffectiveness when translating such findings in human subjects. With advancement of non-invasive imaging techniques, it is now feasible to examine the developmental processes with great details in neonates and young children long before the onset of the neuropsychiatric diseases.

Preterm births, often characterised by very low birth weights, are those that take place at less than 37 weeks' gestational age (GA); although accounting for only 5-12% of all deliveries in the developed countries, they make up almost 75% of all perinatal deaths and have 4-fold higher risks of developing neurodevelopmental disorders including schizophrenia, attention deficit/hyperactivity disorder, autism spectrum disorder and emotional disorders compared with the term-born peers (7, 8). Indeed, epidemiologic studies have suggested the occurrence of psychiatric morbidity to increase as birthweight and GA decrease (9). Additionally, since many of the risk determinants of preterm labour including maternal infection, inflammation, stress and haemorrhage overlap with previously mentioned environmental risk factors for neurodevelopmental disorders, preterm neonates present attractive model to bridge the gap in neurobiological knowledge between animal and human studies. Therefore, examining aetiology of structural and functional abnormalities in the premature brain could provide more insight into the potential therapeutic windows of psychiatric diseases.

By leveraging neurobiological knowledge and machine learning approaches, the study aims to identify neuroimaging markers derived from diffusion MRI that could associate with genetic risk factors for psychiatric diseases and long-term outcome in preterm neonates. The result from this work could provide further insight into neurodevelopmental consequence of perinatal brain injury, bridge the gap between animal and human research and contribute to novel therapeutic interventions in early critical windows.

To achieve this aim, the study will attempt to carry out the following objectives:

Identify potential neurodevelopmental pathways that correlate with neuropsychiatric diseases in preterm neonates.

Build a framework for statistical inference of small associations between many variables (genetic variants) to many variables (d-MRI markers).

Perform validation of such pipeline on the identified pathways using existing neonatal dataset.