Mechanisms of cerebellar tonsil herniation in patients with Chiari malformations as guide to clinical management

Background The pathogenesis of Chiari malformations is incompletely understood. We tested the hypothesis that different etiologies have different mechanisms of cerebellar tonsil herniation (CTH), as revealed by posterior cranial fossa (PCF) morphology. Methods In 741 patients with Chiari malformation type I (CM-I) and 11 patients with Chiari malformation type II (CM-II), the size of the occipital enchondrium and volume of the PCF (PCFV) were measured on reconstructed 2D-CT and MR images of the skull. Measurements were compared with those in 80 age- and sex-matched healthy control individuals, and the results were correlated with clinical findings. Results Significant reductions of PCF size and volume were present in 388 patients with classical CM-I, 11 patients with CM-II, and five patients with CM-I and craniosynostosis. Occipital bone size and PCFV were normal in 225 patients with CM-I and occipitoatlantoaxial joint instability, 55 patients with CM-I and tethered cord syndrome (TCS), 30 patients with CM-I and intracranial mass lesions, and 28 patients with CM-I and lumboperitoneal shunts. Ten patients had miscellaneous etiologies. The size and area of the foramen magnum were significantly smaller in patients with classical CM-I and CM-I occurring with craniosynostosis and significantly larger in patients with CM-II and CM-I occurring with TCS. Conclusions Important clues concerning the pathogenesis of CTH were provided by morphometric measurements of the PCF. When these assessments were correlated with etiological factors, the following causal mechanisms were suggested: (1) cranial constriction; (2) cranial settling; (3) spinal cord tethering; (4) intracranial hypertension; and (5) intraspinal hypotension

Seizure prediction : ready for a new era

Acknowledgements: The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.Peer reviewedPostprin

Review of current microsurgical management of insular gliomas

The insular lobe is a functionally complex structure, harbouring peculiar anatomical and vascular features and specific neuronal connectivity with surrounding cerebral structures. It is situated in the depth of the Sylvian fissure and can be affected by either low-grade or high-grade gliomas. Because of its complexity, surgery of insular tumours has been traditionally regarded as hazardous. Nonetheless, currently improved diagnostic, neurophysiological and surgical tools allow the neurosurgeon to perform surgery of insular gliomas in a safer way, thus bringing forward the pioneering work performed by neurosurgeons in the past two decades.The aim of this paper is to provide the reader with an updated review of the anatomy, the clinical picture, diagnosis and surgical management of insular gliomas. © 2009 Springer-Verlag

A neuroimaging model based on MRI, DTI, and spect findings for lateralization of temporal lobe epilepsy

Purpose: Temporal lobe epilepsy (TLE) is the most widespread type of epilepsy with the most successful resection outcome. Interhemispheric variations detected in the images of T1-weighted and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI), and ictal and interictal single photon emission computed tomography (SPECT), and in the indices of mean diffusivity (MD) and fractional anisotropy (FA) of diffusion tensor imaging (DTI), are within the established markers ofTLE laterality. However, current non-quantitative imaging evaluations may not optimally incorporate the imaging information into the decision-making process prior to resection of mesial temporalstructures. We hypothesize that quantitative TLE lateralization response models of MRI, DTI, and SPECTneuroimaging attributes will optimize the selection ofsurgical candidates and reduce, in some cases, the need for extraoperative electrocorticography (eECoG). Method: Neuroimaging features of 138 retrospective TLE patients with Engel class l surgical outcomes were extracted, including the hippocampal volumes, normalized ictal-interictal SPECT and FLAIR intensities, and mean diffusivity, along with the cingulate and forniceal fractional anisotropy (FA). Using logistic function regression, univariate and multivariate models were developed. Results: The model incorporating all multivariate attributes for138 TLE cases that had at least one imaging attribute and imputing the missing attributes with the mean values of the corresponding attributes measured oncontrol cohort reached the probability of detection and false alarm of 0.83 and 0.17 for all cases, and 0.90 and 0.10 for the patients who underwent eECoG. Conclusion: Increased reliability in lateralizing TLE cases using the proposed response model involving the incorporation of the multivariate attributes reinforces the notion that eECoG in a number ofcases may be circumvented. The proposed response model can be further generalized by integrating attributes of additional neuroclinical, neurophysiological, neuropsychological, and neuroimaging attributes into the presurgical decision making process

In Vivo Growth of C6 Glioma Cells Transfected with Connexin43 cDNA

In order to examine the possible role of intercellular communication via gap junctions in the control of tumor growth, we have transfected C6 glioma cells with connexin43 cDNA. We obtained several clones with variable expression of connexin43. The growth rate of these clones in culture was inversely related to the degree of expression of the transfected cDNA. To examine the growth of these transfected cells in vivo, cells were grown in spinner culture flasks to form spheroids 250–300 µm in diameter. Spheroids of nontransfected C6 cells produced large gliomas. Immunohistochemical and in situ hybridization analyses revealed relatively high levels of connexin43 protein and mRNA in the host tissue, while little of this protein was detected in the glioma. In contrast, spheroids of connexin43-transfected cells grew more slowly and exhibited elevated levels of connexin43 protein and mRNA. These findings suggest that the expression of connexin43 may be associated with the control of brain tumor growth in vivo

Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing

Background and objective: Multimodal data analysis and large-scale computational capability is entering medicine in an accelerative fashion and has begun to influence investigational work in a variety of disciplines. It is also informing us of therapeutic interventions that will come about with such development. Epilepsy is a chronic brain disorder in which functional changes may precede structural ones and which may be detectable using existing modalities. Methods: Functional connectivity analysis using electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI) has provided such meaningful input in cases of epilepsy. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for monitoring, evaluation, and regulation of the epileptic brain. First, an autonomic edge computing framework is proposed for the processing of big data as part of a decision support system for surgical candidacy. Second, a multimodal data analysis using independently acquired EEG and rs-fMRI is presented for estimation and prediction of the epileptogenic network. Third, an unsupervised feature extraction model is developed for EEG analysis and seizure prediction based on a Convolutional deep learning (CNN) structure for distinguishing preictal (pre-seizure) state from non-preictal periods by support vector machine (SVM) classifier. Results: Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods. Conclusions: The combination of rs-fMRI and EEG/iEEG can reveal more information about dynamic functional connectivity. However, simultaneous fMRI and EEG data acquisition present challenges. We have proposed system models for leveraging and processing independently acquired fMRI and EEG data