The restoration of myelin sheaths on demyelinated axons remains a major obstacle in the treatment of multiple sclerosis (MS). Currently approved therapies work by modulating the immune system to reduce the number and rate of lesion formation but are only partially effective since they are not able to restore lost myelin. In the healthy CNS, myelin continues to be generated throughout life and spontaneous remyelination occurs readily in response to insults. In patients with MS, however, remyelination eventually fails, at least in part as a result of a failure of oligodendrocyte precursor cell (OPC) differentiation and the subsequent production of new myelin. A better understanding of the molecular mechanisms and signaling pathways that drive the process of myelin sheath formation is therefore important in order to speed the development of novel therapeutics designed to target remyelination. Here we review data supporting critical roles for three highly conserved intracellular signaling pathways: Wnt/β-catenin, PI3K/AKT/mTOR, and ERK/MAPK in the regulation of OPC differentiation and myelination both during development and in remyelination. Potential points of crosstalk between the three pathways and important areas for future research are also discussed.

Marked by incomplete division of the embryonic forebrain, holoprosencephaly is one of the most common human developmental disorders. Despite decades of phenotype-driven research, 80–90% of aneuploidy-negative holoprosencephaly individuals with a probable genetic aetiology do not have a genetic diagnosis. Here we report holoprosencephaly associated with variants in the two X-linked cohesin complex genes, STAG2 and SMC1A, with loss-of-function variants in 10 individuals and a missense variant in one. Additionally, we report four individuals with variants in the cohesin complex genes that are not X-linked, SMC3 and RAD21. Using whole mount in situ hybridization, we show that STAG2 and SMC1A are expressed in the prosencephalic neural folds during primary neurulation in the mouse, consistent with forebrain morphogenesis and holoprosencephaly pathogenesis. Finally, we found that shRNA knockdown of STAG2 and SMC1A causes aberrant expression of HPE-associated genes ZIC2, GLI2, SMAD3 and FGFR1 in human neural stem cells. These findings show the cohesin complex as an important regulator of median forebrain development and X-linked inheritance patterns in holoprosencephaly.

Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance. Volumetric information alone is insufficient for diagnosis. In this study, we developed a computational framework for the automated classification of brain dysmaturation from neonatal MRI, by combining a specific deep neural network implementation with neonatal structural brain segmentation as a method for both clinical pattern recognition and data-driven inference into the underlying structural morphology. We implemented three-dimensional convolution neural networks (3D-CNNs) to specifically classify dysplastic cerebelli, a subset of surface-based subcortical brain dysmaturation, in term infants born with congenital heart disease. We obtained a 0.985 ± 0. 0241-classification accuracy of subtle cerebellar dysplasia in CHD using 10-fold cross-validation. Furthermore, the hidden layer activations and class activation maps depicted regional vulnerability of the superior surface of the cerebellum, (composed of mostly the posterior lobe and the midline vermis), in regards to differentiating the dysplastic process from normal tissue. The posterior lobe and the midline vermis provide regional differentiation that is relevant to not only to the clinical diagnosis of cerebellar dysplasia, but also genetic mechanisms and neurodevelopmental outcome correlates. These findings not only contribute to the detection and classification of a subset of neonatal brain dysmaturation, but also provide insight to the pathogenesis of cerebellar dysplasia in CHD. In addition, this is one of the first examples of the application of deep learning to a neuroimaging dataset, in which the hidden layer activation revealed diagnostically and biologically relevant features about the clinical pathogenesis. The code developed for this project is open source, published under the BSD License, and designed to be generalizable to applications both within and beyond neonatal brain imaging.

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