Journal de recherche sur le cerveau

Glioblastoma (GBM) is the most aggressive and lethal primary brain tumor in adults. Despite significant advances in medical science, the prognosis for GBM patients remains poor, with a median survival of around 15 months. One of the primary challenges in treating GBM is the invasive nature of the tumor, leading to tumor cells infiltrating surrounding healthy brain tissue beyond the visible tumor mass. This region surrounding the tumor, known as the peritumoural brain zone, plays a crucial role in the progression and recurrence of GBM. Recent research has shown that several genetic factors, including CDK4 and EXT2, could play vital roles in driving malignancy within this zone. This article aims to shed light on the importance of understanding the peritumoural brain zone of GBM and explore the potential implications of CDK4 and EXT2 in the tumor's malignancy.

Introduction: More than 28,000 people are diagnosed with brain tumor every year in India, out of which 80% are cancerous and more than 24,000 people die due to delayed treatment of tumor and cancer identification. This work is focused on "Brain Tumor Detection Using Deep Convolutional Neural Network” and it proposes a method for accurately detecting brain tumours with deep Convolutional Neural Networks (CNNs), eliminating the need of a radiologist to confirm the presence and identifying the type of tumor, thereby putting more focus on immediate treatment and cancer identification using biopsy and reducing the duration of the tumor detection process. This work highlights the importance of early and accurate detection of tumors in brain, as it plays critical role in the successful treatment and management of a brain cancer. It is mentioned that traditional methods for brain tumor detection, such as manual segmentation and feature extraction are timeconsuming and subject to human error. Hence, deep learning-based approach using 23 layers CNN architecture and transfer learning based VGG16 architecture has been proposed, which have shown remarkable success in a various image recognition tasks.

Methodology: The methodology used in the research, involves several steps. First step involves collection of brain MRI (Magnetic Resonance Imaging) data from a publicly available dataset- Harvard Medical Dataset and Figshare. The data by resizing the images to a consistent resolution and normalizing the pixel values. Then, the dataset is split into training, and testing sets, in the ratio of 7:3 fora training and evaluating the proposed CNN models. Ab CNN architecture is designed, which consists of multiple convolutional and pooling layers followed by a fully connected layer. Rectified Linear Unit (ReLU) activation functions are used to introduce non-linearity and batch normalization to improve the training stability. Dropout regularization is employed to prevent overfitting, which is a common issue in deep learning models.

Discussion: CNN models are trained using the training set and optimized using Stochastic Gradient Descent (SGD) with a categorical cross-entropy loss function. Experimentations with different hyper parameters, such as learning rate and batch size, were carried out to find the optimal settings for the models. Data augmentation techniques were performed, such as rotation, flipping, and scaling, to increase the diversity of training data and improve the model’s generalization ability. Once the training was completed, the CNN model was evaluated on the testing sets. Various performance metrics were reported, such as accuracy, precision, recall, and F1-score, true positive rate and true negative rate to assess the effectiveness of the models in detecting brain tumors accurately. The results were compared with existing methods in the literature and it was observed that the proposed CNN models outperform all of them in terms of accuracy and other performance measures.

Conclusion: Furthermore, additional experiments are carried out to analyze the robustness of the CNN models against different types of brain tumors, such as benign and malignant tumors, as well as different tumor sizes. Ablation studies are performed to investigate the impact of different components of the CNN architecture on the model's performance. In addition, Different kernel sizes, which refer to the width height of the filter mask here, are integrated with the model to extract the complex features from the MRI images to make the model more robust and adaptive. The radiologist uses different experimental procedures for diagnosing brain tumors, including biopsy, Cerebrospinal Fluid (CSF) analysis, and X-ray analysis. The biopsy process introduces many risks including inflammation and severe bleeding. It also has just 49.1% accuracy.

CSF Analysis, similar to biopsy, it introduces many risks including bleeding from the incision site to the bloodstream and perhaps an allergic reaction after the treatment. Similarly, using X-rays on the skull can lead to an increase in the risk of cancer due to the radiation. The results of the research show that the proposed CNN-based approach achieves high accuracy in detecting brain tumors, with promising performance metrics across different types and sizes of tumors. The implications of the findings, includes the potential clinical applications of the approach in real-world scenarios fora assisting radiologists in accurate brain tumor diagnosis. The limitation of the study, is that only a single dataset is used for testing, however testing should have been performed on a diverse dataset of real clinical images.

Background: It was the goal of this research to identify individual parameters of criteria for synchronization processes that occurred in various functional body states in athletes who played acyclic sports requiring strong capability for mobilizing body resource.

Materials and methods: 15 athletes have been examined, using Electroencephalography (EEG); 17 subjects of the same age, who practice psychophysical self-regulation relaxation and 19 controls.

Results: Regular synchronization periods have been found on EEG in all the subjects in more than 50% of the deflections, with a synchronization pattern similar to that of a neural network visually detectible. The synchronization periodicity ranged between 5 and 70 sec, varied between subjects belonging to specific groups and depended on their state (functional test results). The Athlete Group had the highest periodicity, while the Control Group had the lowest one. It is worth noting that accelerated synchronization was detected as subject were doing cognitive test (mental subtraction), with numbers of deflections involved being increased. Alpha-wave generalization that periodically occurred in the wake of a synchronization pattern at the same frequency was observed in all of the subjects when both open-eye and closed-eye EEGs were being recorded. The said generalization was most commonly found in the athletes: synchronization being predominant in the fronto-centro-temporal deflections; the shortest generalization pattern period, prolonged alpha-wave generalization after a pattern, both of the brain hemispheres being equally involved, etc. We have been first to demonstrate that most of the criteria found in the relaxation groups are parametrically similar to those found in the athletes.

Conclusion: We recommend using our research findings for functional diagnostics and performance prognostication in, e.g., sports. E.g., better resource mobilization found in the Athlete Group by EEG came hand in hand with improved performance in various activities.

Addictive behaviours treatment networks are composed by harm reduction services and recovery oriented programmes. Recovery is a perspective in addictive behaviours intervention based on empowerment, competences and life skills of person with addictive behaviours. Recovery oriented programmes have advanced from traditional therapeutic communities to actual integral services, which are integrated in main international standards and manuals about addictive behaviours intervention. There are different experiences valid to define proper recovery treatments. Different methodologies have been used to study addictive behaviours recovery programmes, either quantitative or qualitative strategies. Main conclusions are: recovery oriented programs must be integrated and connected with harm reduction networks, social services, health system and employment service; recovery is based in empowerment and peer social support, so it´s necessary to develop structured programs for these topics; it´s also necessary to create specific actions for several collectives, as develop evaluation systems to validate efficiency and adequacy of recovery oriented programmes. As main conclusion, HERMESS recovery model was developed to be a reference for new recovery- oriented programmes.