Therapeutic approach in glioblastoma multiforme with primitive neuroectodermal tumor components: case report and review of the literature

Glioblastoma multiforme (GBM) is the most common and aggressive malignant glioma that is treated with first-line therapy, using surgical resection followed by local radiotherapy and concomitant/adjuvant temozolomide (TMZ) treatment. GBM is characterised by a high local recurrence rate and a low response to therapy. Primitive neuroectodermal tumour (PNET) of the brain revealed a low local recurrence rate; however, it also exhibited a high risk of cerebrospinal fluid (CSF) dissemination. PNET is treated with surgery followed by craniospinal irradiation (CSI) and platinum-based chemotherapy in order to prevent CSF dissemination. GBM with PNET-like components (GBM/PNET) is an emerging variant of GBM, characterised by a PNET-like clinical behaviour with an increased risk of CSF dissemination; it also may benefit from platinum-based chemotherapy upfront or following failure of GBM therapy. The results presented regarding the management of GBM/PNET are based on case reports or case series, so a standard therapeutic approach for GBM/PNET is not defined, constituing a challenging diagnostic and therapeutic dilemma. In this report, a case of a recurrent GBM/PNET treated with surgical resection and radiochemotherapy as Stupp protocol, and successive platinum-based chemotherapy due to the development of leptomeningeal dissemintation and an extracranial metastasis, is discussed. A review of the main papers regarding this rare GBM variant and its therapeutic approach are also reported. In conclusion, GBM/PNET should be treated with a multimodal approach including surgery, chemoradiotherapy, and/or the early introduction of CSI and platinum-based chemotherapy upfront or at recurrence

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Full Issue: Volume 13, Issue 1 - Winter 2018

Full Issue: Volume 13, Issue 1 - Winter 201

Electrical Stimulation of the Human Cerebral Cortex by Extracranial Muscle Activity: Effect Quantification With Intracranial EEG and FEM Simulations

Objective: Electric fields (EF) of approx. 0.2 V/m have been shown to be sufficiently strong to both modulate neuronal activity in the cerebral cortex and have measurable effects on cognitive performance. We hypothesized that the EF caused by the electrical activity of extracranial muscles during natural chewing may reach similar strength in the cerebral cortex and hence might act as an endogenous modality of brain stimulation. Here, we present first steps toward validating this hypothesis. Methods: Using a realistic volume conductor head model of an epilepsy patient having undergone intracranial electrode placement and utilizing simultaneous intracranial and extracranial electrical recordings during chewing, we derive predictions about the chewing-related cortical EF strength to be expected in healthy individuals. Results: We find that in the region of the temporal poles, the expected EF strength may reach amplitudes in the order of 0.1-1 V/m. Conclusion: The cortical EF caused by natural chewing could be large enough to modulate ongoing neural activity in the cerebral cortex and influence cognitive performance. Significance: The present study lends first support for the assumption that extracranial muscle activity might represent an endogenous source of electrical brain stimulation. This offers a new potential explanation for the puzzling effects of gum chewing on cognition, which have been repeatedly reported in the literature

Immunoadsorption for Treatment of Patients with Suspected Alzheimer Dementia and Agonistic Autoantibodies against Alpha1a-Adrenoceptor—Rationale and Design of the IMAD Pilot Study

Background: agonistic autoantibodies (agAABs) against G protein-coupled receptors (GPCR) have been linked to cardiovascular disease. In dementia patients, GPCR-agAABs against the α1- and ß2-adrenoceptors (α1AR- and ß2AR) were found at a prevalence of 50%. Elimination of agAABs by immunoadsorption (IA) was successfully applied in cardiovascular disease. The IMAD trial (Efficacy of immunoadsorption for treatment of persons with Alzheimer dementia and agonistic autoantibodies against alpha1A-adrenoceptor) investigates whether the removal of α1AR-AABs by a 5-day IA procedure has a positive effect (improvement or non-deterioration) on changes of hemodynamic, cognitive, vascular and metabolic parameters in patients with suspected Alzheimer’s clinical syndrome within a one-year follow-up period. Methods: the IMAD trial is designed as an exploratory monocentric interventional trial corresponding to a proof-of-concept phase-IIa study. If cognition capacity of eligible patients scores 19–26 in the Mini Mental State Examination (MMSE), patients are tested for the presence of agAABs by an enzyme-linked immunosorbent assay (ELISA)-based method, followed by a bioassay-based confirmation test, further screening and treatment with IA and intravenous immunoglobulin G (IgG) replacement. We aim to include 15 patients with IA/IgG and to complete follow-up data from at least 12 patients. The primary outcome parameter of the study is uncorrected mean cerebral perfusion measured in mL/min/100 gr of brain tissue determined by magnetic resonance imaging with arterial spin labeling after 12 months. Conclusion: IMAD is an important pilot study that will analyze whether the removal of α1AR-agAABs by immunoadsorption in α1AR-agAAB-positive patients with suspected Alzheimer’s clinical syndrome may slow the progression of dementia and/or may improve vascular functional parameters

The design of brainstem interfaces: characterisation of physiological artefacts and implications for closed-loop algorithms

Surgical neuromodulation through implantable devices allows for stimulation delivery to subcortical regions, crucial for symptom control in many debilitating neurological conditions. Novel closed-loop algorithms deliver therapy tailor-made to endogenous physiological activity, however rely on precise sensing of signals such as subcortical oscillations. The frequency of such intrinsic activity can vary depending on subcortical target nucleus, while factors such as regional anatomy may also contribute to variability in sensing signals. While artefact parameters have been explored in more ‘standard’ and commonly used targets (such as the basal ganglia, which are implanted in movement disorders), characterisation in novel candidate nuclei is still under investigation. One such important area is the brainstem, which contains nuclei crucial for arousal and autonomic regulation. The brainstem provides additional implantation targets for treatment indications in disorders of consciousness and sleep, yet poses distinct anatomical challenges compared to central subcortical targets. Here we investigate the region-specific artefacts encountered during activity and rest while streaming data from brainstem implants with a cranially-mounted device in two patients. Such artefacts result from this complex anatomical environment and its interactions with physiological parameters such as head movement and cardiac functions. The implications of the micromotion-induced artefacts, and potential mitigation, are then considered for future closed-loop stimulation methods

Deep learning pipeline for quality filtering of MRSI spectra.

With the rise of novel 3D magnetic resonance spectroscopy imaging (MRSI) acquisition protocols in clinical practice, which are capable of capturing a large number of spectra from a subject's brain, there is a need for an automated preprocessing pipeline that filters out bad-quality spectra and identifies contaminated but salvageable spectra prior to the metabolite quantification step. This work introduces such a pipeline based on an ensemble of deep-learning classifiers. The dataset consists of 36,338 spectra from one healthy subject and five brain tumor patients, acquired with an EPSI variant, which implemented a novel type of spectral editing named SLOtboom-Weng (SLOW) editing on a 7T MR scanner. The spectra were labeled manually by an expert into four classes of spectral quality as follows: (i) noise, (ii) spectra greatly influenced by lipid-related artifacts (deemed not to contain clinical information), (iii) spectra containing metabolic information slightly contaminated by lipid signals, and (iv) good-quality spectra. The AI model consists of three pairs of networks, each comprising a convolutional autoencoder and a multilayer perceptron network. In the classification step, the encoding half of the autoencoder is kept as a dimensionality reduction tool, while the fully connected layers are added to its output. Each of the three pairs of networks is trained on different representations of spectra (real, imaginary, or both), aiming at robust decision-making. The final class is assigned via a majority voting scheme. The F1 scores obtained on the test dataset for the four previously defined classes are 0.96, 0.93, 0.82, and 0.90, respectively. The arguably lower value of 0.82 was reached for the least represented class of spectra mildly influenced by lipids. Not only does the proposed model minimise the required user interaction, but it also greatly reduces the computation time at the metabolite quantification step (by selecting a subset of spectra worth quantifying) and enforces the display of only clinically relevant information

A knowledge-guided active model method of skull segmentation on T1-weighted MR images

Skull is the anatomic landmark for patient set up of head radiation therapy. Skull is generally segmented from CT images because CT provides better definition of skull than MR imaging. In the mean time, radiation therapy is planned on MR images for soft tissue information. This study utilized a knowledge-guided active model (KAM) method to segmented skull on MR images in order to enable radiation therapy planning with MR images as the primary planning dataset. KAM utilized age-specific skull mesh models that segmented from CT images using a conditional region growing algorithm. Skull models were transformed to given MR images using an affine registration algorithm based on normalized mutual information. The transformed mesh models actively located skull boundaries by minimizing their total energy. The preliminary validation was performed on MR and CT images from five patients. The KAM segmented skulls were compared with those segmented from CT images. The average image similarity (kappa index) was 0.57. The initial validation showed that it was promising to segment skulls directly on MR images using KAM