OBJECTIVE: Congenital hydrocephalus (CH) is one of the most frequent CNS congenital malformations, representing an entity with serious pathological consequences. Although several studies have previously assessed child-related risk factors associated with CH development, there is a gap of knowledge on maternal environmental risk factors related to CH. The authors have systematically assessed extrinsic factors in the maternal environment that potentially confer an increased risk of CH development. METHODS: The Cochrane Library, MEDLINE, and EMBASE were systematically searched for works published between 1966 and December 2015 to identify all relevant articles published in English. Only studies that investigated environmental risk factors concerning the mother—either during gestation or pregestationally—were included. RESULTS: In total, 13 studies (5 cohorts, 3 case series, 3 case-control studies, 1 meta-analysis, and 1 case report) meeting the inclusion criteria were identified. Maternal medication or alcohol use during gestation; lifestyle modifiable maternal pathologies such as obesity, diabetes, or hypertension; lack of prenatal care; and a low socioeconomic status were identified as significant maternal environmental risk factors for CH development. Maternal infections and trauma to the mother during pregnancy have also been highlighted as potential mother-related risk factors for CH. CONCLUSIONS: Congenital hydrocephalus is an important cause of serious infant health disability that can lead to health inequalities among adults. The present study identified several maternal environmental risk factors for CH, thus yielding important scientific information relevant to prevention of some CH cases. However, further research is warranted to confirm the impact of the identified factors and examine their underlying behavioral and/or biological basis, leading to the generation of suitable prevention strategies.

The somato-cognitive action network (SCAN) consists of three nodes interspersed within Penfield’s motor effector regions. The configuration of the somato-cognitive action network nodes resembles the one of the ‘plis de passage’ of the central sulcus: small gyri bridging the precentral and postcentral gyri. Thus, we hypothesize that these may provide a structural substrate of the somato-cognitive action network. Using microdissections of 16 human hemispheres, we consistently identified a chain of three distinct plis de passage with increased underlying white matter in locations analogous to the somato-cognitive action network nodes. We mapped localizations of plis de passage into standard stereotactic space to seed functional MRI connectivity across 9000 resting-state functional MRI scans, which demonstrated the connectivity of these sites with the somato-cognitive action network. Intraoperative recordings during direct electrical central sulcus stimulation further identified inter-effector regions corresponding to plis de passage locations. This work provides a critical step towards an improved understanding of the somato-cognitive action network in both structural and functional terms. Furthermore, our work has the potential to guide the development of refined motor cortex stimulation techniques for treating brain disorders and operative resective techniques for complex surgery of the motor cortex.

Brain gliomas are highly infiltrative and heterogenous tumors, whose early and accurate detection as well as therapeutic management are challenging. Artificial intelligence (AI) has the potential to redefine the landscape in neuro-oncology and can enhance glioma detection, imaging segmentation, and non-invasive molecular characterization better than conventional diagnostic modalities through deep learning-driven radiomics and radiogenomics. AI algorithms have been shown to predict genotypic and phenotypic glioma traits with remarkable accuracy and facilitate patient-tailored therapeutic decision-making. Such algorithms can be incorporated into surgical planning to optimize resection extent while preserving eloquent cortical structures through preoperative imaging fusion and intraoperative augmented reality-assisted navigation. Beyond resection, AI may assist in radiotherapy dose distribution optimization, thus ensuring maximal tumor control while minimizing surrounding tissue collateral damage. AI-guided molecular profiling and treatment response prediction models can facilitate individualized chemotherapy regimen tailoring, especially for glioblastomas with MGMT promoter methylation. Applications in immunotherapy are emerging, and research is focusing on AI to identify tumor microenvironment signatures predictive of immune checkpoint inhibition responsiveness. AI-integrated prognostic models incorporating radiomic, histopathologic, and clinical variables can additionally improve survival stratification and recurrence risk prediction remarkably, to refine follow-up strategies in high-risk patients. However, data heterogeneity, algorithmic transparency concerns, and regulatory challenges hamstring AI implementation in neuro-oncology despite its transformative potential. It is therefore imperative for clinical translation to develop interpretable AI frameworks, integrate multimodal datasets, and robustly validate externally. Future research should prioritize the creation of generalizable AI models, combine larger and more diverse datasets, and integrate multimodal imaging and molecular data to overcome these obstacles and revolutionize AI-assisted patient-specific glioma management.