Long-Term Survival of a Patient with Giant Cell Glioblastoma: Case Report and Review of the Literature

Glioblastoma multiforme (GBM) is the most common glial tumor of the central nervous system. Overall survival is less than a year in most of the cases in spite of multimodal treatment approaches. A 45-year-old female with histologically confirmed giant cell GBM was treated at our institution. Subtotal excision of the lesion situated in the right precentral area was performed during the initial stay in August 2005. The patient improved after the procedure with no hypertension and additional neurological deficit. Radiotherapy plus concomitant and adjuvant temozolomide was performed. The patient was symptom-free for 35 months after initial surgery. From July 2008 the patient developed partial motor seizures in the left side of the body and progressive hemiparesis. Local tumor progression was demonstrated on the neuroimaging studies. In December 2008, a second operative intervention was performed with subtotal excision of the tumor. Forty-five months after the initial diagnosis the patient is still alive with moderate neurological deficit. Microarray analysis of the tumor found the following numeric chromosomal aberrations: monosomy 8, 10, 13, 22, and trisomy 21, as well as amplifications in 4q34.1, 4q28.2, 6q16.3, 7q36.1, 7p21.3, and deletions in 1q42.12, 1q32.2, 1q25.2, 1p33, 2q37.2, 18q22.3, 19p13.2, Xq28, and Xq27.3. GBMs seem to be a heterogeneous group of glial tumors with different clinical course and therapeutic response. Microarray analysis is a useful method to establish a number of possible molecular predictors

Neck atonia with a focal stimulation-induced seizure arising from the SMA: pathophysiological considerations.

A 28-year-old patient with pharmacoresistant non-lesional right frontal epilepsy underwent extra-operative intracranial EEG recordings and electrical cortical stimulation (ECS) to map eloquent cortex. Right supplementary motor area (SMA) ECS induced a brief seizure with habitual symptoms involving neck tingling followed by asymmetric tonic posturing. An additional feature was neck atonia. During atonia and sensory aura, discharges were seen in the mesial frontal electrodes and precentral gyrus. Besides motor signs, atonia, although rare and not described in the neck muscles, and sensations have been reported with SMA stimulation. The mechanisms underlying neck atonia in seizures arising from the SMA can be explained by supplementary negative motor area (SNMA) - though this was not mapped in electrodes overlying the ictal onset zone in our patient - or primary sensorimotor cortex activation through rapid propagation. Given the broad spectrum of signs elicited by SMA stimulation and rapid spread of seizures arising from the SMA, caution should be taken to not diagnose these as non-epileptic, as had previously occurred in this patient

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Characterizing acute service demand is critical for neurosurgery and other emergency-dominant specialties in order to dynamically distribute resources and ensure timely access to treatment. This is especially important in the post-Covid 19 pandemic period, when healthcare centers are grappling with a record backlog of pending surgical procedures and rising acute referral numbers. Healthcare dashboards are well-placed to analyze this data, making key information about service and clinical outcomes available to staff in an easy-to-understand format. However, they typically provide insights based on inference rather than prediction, limiting their operational utility. We retrospectively analyzed and prospectively forecasted acute neurosurgical referrals, based on 10,033 referrals made to a large volume tertiary neurosciences center in London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021 through the use of a novel AI-enabled predictive dashboard. As anticipated, weekly referral volumes significantly increased during this period, largely owing to an increase in spinal referrals (p < 0.05). Applying validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point, with Prophet demonstrating the best test and computational performance. Using a mixed-methods approach, we determined that a dashboard approach was usable, feasible, and acceptable among key stakeholders

Normal Pressure Hydrocephalus as an Unusual Presentation of Supratentorial Extraventricular Space-Occupying Processes: Report on Two Cases

Normal pressure hydrocephalus (NPH) is a clinical and radiographic syndrome characterized by ventriculomegaly, abnormal gait, urinary incontinence, and dementia. The condition may occur due to a variety of secondary causes but may be idiopathic in approximately 50% of patients. Secondary causes may include head injury, subarachnoid hemorrhage, meningitis, and central nervous system tumor. Here, we describe two extremely rare cases of supratentorial extraventricular space-occupying processes: meningioma and glioblastoma multiforme, which initially presented with NPH

Complexes of Zinc With Picolinic and Aspartic Acids Inactivate Free Varicella-Zoster Virions

Zn(II) picolinate and aspartate, Zn(pic)2 and Zn(asp)2, have been shown to inhibit key steps of the replication of HSV-1. In the present study we describe the effect of Zn(pic)2 and Zn(asp)2 on the replication of VZV and on the infectivity of free virions. The experiments are done using BHK-21 cells, a clinical isolate of VZV and Zn-complexes in concentration of 10 μM. When Zn-complexes are present during the whole period of infection, the yield of infectious virus progeny decreases up to 98%. The infectivity of VZV is completely restored after the removal of zinc. The virucidal effect is manifested at the 2nd h of contact, when 90% of the virions are inactivated. The results show that both Zn(pic)2 and Zn(asp)2 specifically inactivate free VZV virions with no effect on viral replication

Denoising diffusion models for out-of-distribution detection

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck-such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://github.com/marksgraham/ddpm-ood

Hierarchical Brain Parcellation with Uncertainty

Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods tend to ignore this structure and treat labels as if they are ‘flat’. We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree. We further show how this method can be used to model uncertainty separately for every branch in this label tree. Our method exceeds the performance of flat uncertainty methods, whilst also providing decomposed uncertainty estimates that enable us to obtain self-consistent parcellations and uncertainty maps at any level of the label hierarchy. We demonstrate a simple way these decision-specific uncertainty maps may be used to provided uncertainty-thresholded tissue maps at any level of the label tree

Machine phenotyping of cluster headache and its response to verapamil

Cluster headache is characterized by recurrent, unilateral attacks of excruciating pain associated with ipsilateral cranial autonomic symptoms. Although a wide array of clinical, anatomical, physiological, and genetic data have informed multiple theories about the underlying pathophysiology, the lack of a comprehensive mechanistic understanding has inhibited, on the one hand, the development of new treatments and, on the other, the identification of features predictive of response to established ones. The first-line drug, verapamil, is found to be effective in only half of all patients, and after several weeks of dose escalation, rendering therapeutic selection both uncertain and slow. Here we use high-dimensional modelling of routinely acquired phenotypic and MRI data to quantify the predictability of verapamil responsiveness and to illuminate its neural dependants, across a cohort of 708 patients evaluated for cluster headache at the National Hospital for Neurology and Neurosurgery between 2007 and 2017. We derive a succinct latent representation of cluster headache from non-linear dimensionality reduction of structured clinical features, revealing novel phenotypic clusters. In a subset of patients, we show that individually predictive models based on gradient boosting machines can predict verapamil responsiveness from clinical (410 patients) and imaging (194 patients) features. Models combining clinical and imaging data establish the first benchmark for predicting verapamil responsiveness, with an area under the receiver operating characteristic curve of 0.689 on cross-validation (95% confidence interval: 0.651 to 0.710) and 0.621 on held-out data. In the imaged patients, voxel-based morphometry revealed a grey matter cluster in lobule VI of the cerebellum (–4, –66, –20) exhibiting enhanced grey matter concentrations in verapamil non-responders compared with responders (familywise error-corrected P = 0.008, 29 voxels). We propose a mechanism for the therapeutic effect of verapamil that draws on the neuroanatomy and neurochemistry of the identified region. Our results reveal previously unrecognized high-dimensional structure within the phenotypic landscape of cluster headache that enables prediction of treatment response with modest fidelity. An analogous approach applied to larger, globally representative datasets could facilitate data-driven redefinition of diagnostic criteria and stronger, more generalizable predictive models of treatment responsiveness

An MRF-UNet Product of Experts for Image Segmentation

While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribution data. Markov random fields (MRFs) on the other hand, encode simpler distributions over labels that, although less flexible than UNets, are less prone to over-fitting. In this paper, we propose to fuse both strategies by computing the product of distributions of a UNet and an MRF. As this product is intractable, we solve for an approximate distribution using an iterative mean-field approach. The resulting MRF-UNet is trained jointly by back-propagation. Compared to other works using conditional random fields (CRFs), the MRF has no dependency on the imaging data, which should allow for less over-fitting. We show on 3D neuroimaging data that this novel network improves generalisation to out-of-distribution samples. Furthermore, it allows the overall number of parameters to be reduced while preserving high accuracy. These results suggest that a classic MRF smoothness prior can allow for less over-fitting when principally integrated into a CNN model. Our implementation is available at https://github.com/balbasty/nitorch

Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models

We describe CounterSynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesised counterfactuals with voxel-based morphometry, classification and regression of the conditioning attributes, and the Fréchet inception distance. Examining downstream discriminative performance in the context of engineered demographic imbalance and confounding, we use UK Biobank and OASIS magnetic resonance imaging data to benchmark CounterSynth augmentation against current solutions to these problems. We achieve state-of-the-art improvements, both in overall fidelity and equity. The source code for CounterSynth is available at https://github.com/guilherme-pombo/CounterSynth