Research on the visual patterns that foreshadow migraines may reveal clues on how painful headaches arise from the brain even though it has no pain receptors. Colorful zig-zag lines flash in the corner of an eye, while the tunnel vision makes most of the view obscured. Migraine with aura is usually painless, though in large majority of cases means the real migraine is about to kick in. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links.

Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal cord. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 +- 0.16 (mean +- standard deviation across rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation <= 1.41 %), as well as low inter-session variability (coefficient of variation <= 1.30 %) indicating stable predictions across different MRI vendors, sites, and sessions. The proposed methodology is open-source and readily available in the Spinal Cord Toolbox (SCT) v6.2 and higher.

The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (> 87%). Although our results are preliminary, they indicate that RS and topological analysis together may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps will include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to understand if topological data analysis could support the characterization of AD subtypes.

You wouldn't know it from interacting with Siri or those technical-support, call-center robots, but artificial intelligence has made some incredible advances in a short amount of time. Earlier this year, the tech world was abuzz with various generative AI programs that could, on command, create entirely new, never-before-existing images or works of art. Today, the tech world is abuzz again over ChatGPT, a chat bot that is said to be the most advanced ever made. Just how advanced is ChatGPT? It can create poems, songs, and even computer code.

Abstract: Meningitis is defined as inflammation of the meninges, in almost all cases identified by an abnormal number of white blood cells in the cerebrospinal fluid and specific clinical signs/symptoms. Onset may be acute or chronic, and clinical symptoms of acute disease develop over hours to days. This article reviews the epidemiology, pathophysiology, clinical manifestations, diagnosis, and management of acute meningitis, and provides a list of key points for primary care practitioners. Aseptic and bacterial meningitis vary significantly and are discussed separately. Abstract: Chronic meningitis is an inflammation of the meninges with subacute onset and persisting cerebrospinal fluid (CSF) abnormalities lasting for at least one month.

Aalto University and Bayer in February announced they have expanded their collaboration on artificial intelligence-based solutions for enhancing the safety and efficacy of clinical drug research by embarking on a three-year project with HUS Helsinki University Hospital. The methods and algorithms developed as part of the collaboration will be applied to the patient data of the university hospital. "Combining real-world data and clinical research data involves several challenges," told Jussi Leinonen, principal clinical data scientist at Bayer. "With AI, it can be done much faster, more efficiently and also more reliably." The project partners believe artificial intelligence is a means to address numerous challenges associated with drug development, including its resource-intensive nature.

IMAGE: Example of segmentation produced by the tool which separates the structures in cerebrospinal fluid (red), grey matter (blue) and white matter (yellow) from MRI images T2 (middle column) and T1... view more Canadian scientists have developed an innovative new technique that uses artificial intelligence to better define the different sections of the brain in newborns during a magnetic resonance imaging (MRI) exam. The results of this study -- a collaboration between researchers at Montreal's CHU Sainte-Justine children's hospital and the ÉTS engineering school -- are published today in Frontiers in Neuroscience. "This is one of the first times that artificial intelligence has been used to better define the different parts of a newborn's brain on an MRI: namely the grey matter, white matter and cerebrospinal fluid," said Dr. Gregory A. Lodygensky, a neonatologist at CHU Sainte-Justine and professor at Université de Montréal. "Until today, the tools available were complex, often intermingled and difficult to access," he added. In collaboration with Professor Jose Dolz, an expert in medical image analysis and machine learning at ÉTS, the researchers were able to adapt the tools to the specificities of the neonatal setting and then validate them. This new technique allows babies' brains to be examined quickly, accurately and reliably.

Conventional automated segmentation of the human head distinguishes different tissues based on image intensities in an MRI volume and prior tissue probability maps (TPM). This works well for normal head anatomies, but fails in the presence of unexpected lesions. Deep convolutional neural networks leverage instead volumetric spatial patterns and can be trained to segment lesions, but have thus far not integrated prior probabilities. Here we add to a three-dimensional convolutional network spatial priors with a TPM, morphological priors with conditional random fields, and context with a wider field-of-view at lower resolution. The new architecture, which we call MultiPrior, was designed to be a fully-trainable, three-dimensional convolutional network. Thus, the resulting architecture represents a neural network with learnable spatial memories. When trained on a set of stroke patients and healthy subjects, MultiPrior outperforms the state-of-the-art segmentation tools such as DeepMedic and SPM segmentation. The approach is further demonstrated on patients with disorders of consciousness, where we find that cognitive state correlates positively with gray-matter volumes and negatively with the extent of ventricles. We make the code and trained networks freely available to support future clinical research projects.

A simple blood test could detect the onset of dementia almost two decades before symptoms are noticed, according to a new study. Researchers have long known that dementia sufferers have higher levels of a certain protein that leaks into the cerebrospinal fluid after brain cells die. But they could not work out how to measure it without invasive, expensive spinal taps. In a new study, scientists say they can now detect this protein in the blood and that levels of it rise at the same speed that the brain loses neurons and begins shrinking. The blood test that looks for the protein would be performed in middle-age, well before most are diagnosed with Alzheimer's disease.

However, acquiring manual labels as ground truth is both labor intensive and time consuming. Furthermore, non-standardized manual segmentation protocols and inter-and intra-observer variability add another factor of variation to an already complex problem. Instead of increasing the number of manual labels, we propose to improve generalization by teaching a neural network to minimize an undesirable form of variation, namely acquisitionbased variation. The proposed network learns a representation [1] in which for example gray matter patches acquired with a 1.5T scanner and a 3T scanner are considered similar. Therefore it has the potential to fully exploit a 1.5T data set with fully labeled brain tissues for segmenting an unlabelled 3T data set. Overcoming acquisition-variation is a relatively new challenge in medical imaging. Transfer classifiers have been proposed that focus on weighting classifiers based on how well their training data matches the test data, such as weighted SVM's [2] and weighted ensembles [3].