Neuroanatomy education is a challenging field which could benefit from modern innovations, such as augmented reality (AR) applications. This study investigates the differences on test scores, cognitive load, and motivation after neuroanatomy learning using AR applications or using cross‐sections of the brain. Prior to two practical assignments, a pretest (extended matching questions, double‐choice questions and a test on cross‐sectional anatomy) and a mental rotation test (MRT) were completed. Sex and MRT scores were used to stratify students over the two groups. The two practical assignments were designed to study (1) general brain anatomy and (2) subcortical structures. Subsequently, participants completed a posttest similar to the pretest and a motivational questionnaire. Finally, a focus group interview was conducted to appraise participants’ perceptions. Medical and biomedical students (n = 31); 19 males (61.3%) and 12 females (38.7%), mean age 19.2 ± 1.7 years participated in this experiment. Students who worked with cross‐sections (n = 16) showed significantly more improvement on test scores than students who worked with GreyMapp‐AR (P = 0.035) (n = 15). Further analysis showed that this difference was primarily caused by significant improvement on the cross‐sectional questions. Students in the cross‐section group, moreover, experienced a significantly higher germane (P = 0.009) and extraneous cognitive load (P = 0.016) than students in the GreyMapp‐AR group. No significant differences were found in motivational scores. To conclude, this study suggests that AR applications can play a role in future anatomy education as an add‐on educational tool, especially in learning three‐dimensional relations of anatomical structures.

The use of Augmented Reality (AR) in anatomical education has been promoted by numerous authors. Next to financial and ethical advantages, AR has been described to decrease cognitive load while increasing student motivation and engagement. Despite these advantages, the effects of AR on learning outcome varies in different studies and an overview and aggregated outcome on learning anatomy is lacking. Therefore, a meta-analysis on the effect of AR vs. traditional anatomical teaching methods on learning outcome was performed. Systematic database searches were conducted by two independent investigators using predefined inclusion and exclusion criteria. This yielded five papers for meta-analysis totaling 508 participants; 240 participants in the AR-groups and 268 participants in the control groups. (306 females/202 males). Meta-analysis showed no significant difference in anatomic test scores between the AR group and the control group (− 0.765 percentage-points (%-points); P = 0.732). Sub analysis on the use of AR vs. the use of traditional 2D teaching methods showed a significant disadvantage when using AR (− 5.685%-points; P = 0.024). Meta-regression analysis showed no significant co-relation between mean difference in test results and spatial abilities (as assessed by the mental rotations test scores). Student motivation and/or engagement could not be included since studies used different assessment tools. This meta-analysis showed that insufficient evidence is present to conclude AR significantly impacts learning outcome and that outcomes are significantly impacted by students’ spatial abilities. However, only few papers were suitable for meta-analysis, indicating that there is a need for more well-designed, randomized-controlled trials on AR in anatomy education research.

Objectives: Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods. Methods: A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033). Results: After the literature search (n = 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86). In addition, a DSC score of 0.83 (95% CI: 0.80–0.87) and 0.82 (95% CI: 0.78–0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed. Conclusion: MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set. Key Points: • MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86), indicating a good performance. • MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas. • For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.

Various studies reported changes in grey matter volumes and modifications in functional connectivity of cortical and subcortical structures in patients suffering from trigeminal neuralgia (TN) and trigeminal neuropathic pain (TNP). This study meta-analyzed the concordant structural and functional changes in foci and provide further understanding of the anatomy and biology of TN/TNP. Relevant articles on magnetic resonance imaging (MRI) and functional MRI in TN/TNP, published before August 2018, were searched for on PubMed and Embase. Following exclusion of unsuitable studies, a meta-analysis was performed using activation likelihood estimation (ALE). In total, 322 paper were identified, 11 of which could be included based on the predefined inclusion and exclusion criteria. Eight papers, totaling 279 subjects, discussing structural changes and four papers, totaling 102 subjects, discussing functional changes were included (i.e., one paper investigated both structural and functional alterations). ALE analysis showed that in TN/TNP, grey matter decreases are found in the thalamus, (anterior) cingulate gyrus, bilateral striatum, the superior-, middle- and transverse temporal gyrus, subcallosal gyrus, the bilateral insular cortex, the pre- and postcental gyrus, the middle frontal gyrus bilaterally and the anterior cerebellar lobe. Grey matter increases were seen in the periaqueductal grey (PAG). Increased resting state functional organization was found within the bilateral middle- and superior frontal gyri, the (posterior) cingulate cortex and the thalamus/pulvinar. Structural and functional changes meta-analyzed in this paper may contribute to elucidating the central pathophysiological mechanisms involved in TN/TNP. These results may be used as biomarkers to predict the response to medication and, ideally, in the future to offer personalized treatments.

BACKGROUND: Predicting outcome after aneurysmal subarachnoid hemorrhage (aSAH) is known to be challenging and complex. Machine learning approaches, of which feedforward artificial neural networks (ffANNs) are the most widely used, could contribute to the patient-specific outcome prediction. OBJECTIVE: To investigate the prediction capacity of an ffANN for the patient-specific clinical outcome and the occurrence of delayed cerebral ischemia (DCI) and compare those results with the predictions of 2 internationally used scoring systems. METHODS: A prospective database was used to predict (1) death during hospitalization (ie, mortality) (n = 451), (2) unfavorable modified Rankin Scale (mRS) at 6 mo (n = 413), and (3) the occurrence of DCI (n = 362). Additionally, the predictive capacities of the ffANN were compared to those of Subarachnoid Haemorrhage International Trialists (SAHIT) and VASOGRADE to predict clinical outcome and occurrence of DCI. RESULTS: The area under the curve (AUC) of the ffANN showed to be 88%, 85%, and 72% for predicting mortality, an unfavorable mRS, and the occurrence of DCI, respectively. Sensitivity/specificity rates of the ffANN for mortality, unfavorable mRS, and the occurrence of DCI were 82%/80%, 94%/80%, and 74%/68%. The ffANN and SAHIT calculator showed similar AUCs for predicting personalized outcome. The presented ffANN and VASOGRADE were found to perform equally with regard to personalized prediction of occurrence of DCI. CONCLUSION: The presented ffANN showed equal performance when compared with VASOGRADE and SAHIT scoring systems while using less individual cases. The web interface launched simultaneously with the publication of this manuscript allows for usage of the ffANN-based prediction tool for individual data (https://nutshell-tool.com/).

Background: Non-invasive stimulation of the vagus nerve has been proposed as a new neuromodulation therapy to treat primary headache disorders, as the vagus nerve is hypothesized to modulate the headache pain pathways in the brain. Vagus nerve stimulation can be performed by placing an electrode on the ear to stimulate the tragus nerve, which contains about 1% of the vagus fibers. Non-invasive vagus nerve stimulation (nVNS) conventionally refers to stimulation of the cervical branch of the vagus nerve, which is made up entirely of vagal nerve fibers. While used interchangeably, most of the research to date has been performed with nVNS or an implanted vagus nerve stimulation device. However, the exact mechanism of action of nVNS remains hypothetical and no clear overview of the effectiveness of nVNS in primary headache disorders is available.Methods: In the present study, the clinical trials that investigated the effectiveness, tolerability and safety of nVNS in primary headache disorders were systematically reviewed. The second part of this study reviewed the central connections of the vagus nerve. Papers on the clinical use of nVNS and the anatomical investigations were included based on predefined criteria, evaluated, and results were reported in a narrative way.Results: The first part of this review shows that nVNS in primary headache disorders is moderately effective, safe and well-tolerated. Regarding the anatomical review, it was reported that fibers from the vagus nerve intertwine with fibers from the trigeminal, facial, glossopharyngeal and hypoglossal nerves, mostly in the trigeminal spinal tract. Second, the four nuclei of the vagus nerve (nuclei of the solitary tract, nucleus ambiguus, spinal nucleus of the trigeminal nerve and dorsal motor nucleus (DMX)) show extensive interconnections. Third, the efferents from the vagal nuclei that receive sensory and visceral input (i.e. nuclei of the solitary tract and spinal nucleus of the trigeminal nerve) mainly course towards the main parts of the neural pain matrix directly or indirectly via other vagal nuclei.Conclusion: The moderate effectiveness of nVNS in treating primary headache disorders can possibly be linked to the connections between the trigeminal and vagal systems as described in animals.