Explore the vast range of titles available.

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials. Multiple sclerosis is a heterogeneous progressive disease. Here, the authors use an unsupervised machine learning algorithm to determine multiple sclerosis subtypes, progression, and response to potential therapeutic treatments based on neuroimaging data.

Visual rating scales, developed to assess atrophy in patients with cognitive impairment, offer a cost-effective diagnostic tool that is ideally suited for implementation in clinical practice. By focusing attention on brain regions susceptible to change in dementia and enforcing structured reporting of these findings, visual rating can improve the sensitivity, reliability and diagnostic value of radiological image interpretation. Brain imaging is recommended in all current diagnostic guidelines relating to dementia, and recent guidelines have also recommended the application of medial temporal lobe atrophy rating. Despite these recommendations, and the ease with which rating scales can be applied, there is still relatively low uptake in routine clinical assessments. Careful consideration of atrophy rating scales is needed to verify their diagnostic potential and encourage uptake among clinicians. Determining the added value of combining scores from visual rating in different brain regions may also increase the diagnostic value of these tools.

In patients presenting with a clinically isolated syndrome, MRI can support and substitute clinical information in the diagnosis of multiple sclerosis by showing disease dissemination in space and time and by helping to exclude disorders that can mimic multiple sclerosis. MRI criteria were first included in the diagnostic work-up for multiple sclerosis in 2001, and since then several modifications to the criteria have been proposed in an attempt to simplify lesion-count models for showing disease dissemination in space, change the timing of MRI scanning to show dissemination in time, and increase the value of spinal cord imaging. Since the last update of these criteria, new data on the use of MRI to establish dissemination in space and time have become available, and MRI technology has improved. State-of-the-art MRI findings in these patients were discussed in a MAGNIMS workshop, the goal of which was to provide an evidence-based and expert-opinion consensus on proposed modifications to MRI criteria for the diagnosis of multiple sclerosis.

Early evaluation of treatment response and prediction of disease evolution are key issues in the management of people with multiple sclerosis (MS). In the past 20 years, MRI has become the most useful paraclinical tool in both situations and is used clinically to assess the inflammatory component of the disease, particularly the presence and evolution of focal lesions — the pathological hallmark of MS. However, diffuse neurodegenerative processes that are at least partly independent of inflammatory mechanisms can develop early in people with MS and are closely related to disability. The effects of these neurodegenerative processes at a macroscopic level can be quantified by estimation of brain and spinal cord atrophy with MRI. MRI measurements of atrophy in MS have also been proposed as a complementary approach to lesion assessment to facilitate the prediction of clinical outcomes and to assess treatment responses. In this Consensus statement, the Magnetic Resonance Imaging in MS (MAGNIMS) study group critically review the application of brain and spinal cord atrophy in clinical practice in the management of MS, considering the role of atrophy measures in prognosis and treatment monitoring and the barriers to clinical use of these measures. On the basis of this review, the group makes consensus statements and recommendations for future research.In this Consensus statement, the Magnetic Resonance Imaging in MS (MAGNIMS) study group reviews the application of brain and spinal cord atrophy in clinical practice in the management of MS and makes consensus statements and recommendations for future research.

The 2010 McDonald criteria for the diagnosis of multiple sclerosis are widely used in research and clinical practice. Scientific advances in the past 7 years suggest that they might no longer provide the most up-to-date guidance for clinicians and researchers. The International Panel on Diagnosis of Multiple Sclerosis reviewed the 2010 McDonald criteria and recommended revisions. The 2017 McDonald criteria continue to apply primarily to patients experiencing a typical clinically isolated syndrome, define what is needed to fulfil dissemination in time and space of lesions in the CNS, and stress the need for no better explanation for the presentation. The following changes were made: in patients with a typical clinically isolated syndrome and clinical or MRI demonstration of dissemination in space, the presence of CSF-specific oligoclonal bands allows a diagnosis of multiple sclerosis; symptomatic lesions can be used to demonstrate dissemination in space or time in patients with supratentorial, infratentorial, or spinal cord syndrome; and cortical lesions can be used to demonstrate dissemination in space. Research to further refine the criteria should focus on optic nerve involvement, validation in diverse populations, and incorporation of advanced imaging, neurophysiological, and body fluid markers.

Various biomarkers are available to support the diagnosis of neurodegenerative diseases in clinical and research settings. Among the molecular imaging biomarkers, amyloid-PET, which assesses brain amyloid deposition, and 18F-fluorodeoxyglucose (18F-FDG) PET, which assesses glucose metabolism, provide valuable and complementary information. However, uncertainty remains regarding the optimal timepoint, combination, and an order in which these PET biomarkers should be used in diagnostic evaluations because conclusive evidence is missing. Following an expert panel discussion, we reached an agreement on the specific use of the individual biomarkers, based on available evidence and clinical expertise. We propose a diagnostic algorithm with optimal timepoints for these PET biomarkers, also taking into account evidence from other biomarkers, for early and differential diagnosis of neurodegenerative diseases that can lead to dementia. We propose three main diagnostic pathways with distinct biomarker sequences, in which amyloid-PET and 18F-FDG-PET are placed at different positions in the order of diagnostic evaluations, depending on clinical presentation. We hope that this algorithm can support diagnostic decision making in specialist clinical settings with access to these biomarkers and might stimulate further research towards optimal diagnostic strategies.

PurposeH3K27M-mutant associated brainstem glioma (BSG) carries a very poor prognosis. We aimed to predict H3K27M mutation status by amide proton transfer–weighted (APTw) imaging and radiomic features.MethodsEighty-one BSG patients with APTw imaging at 3T MR and known H3K27M status were retrospectively studied. APTw values (mean, median, and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student’s T/Mann–Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine learning algorithm (support vector machine) in randomly selected train (n = 64) and test (n = 17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks, and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm.ResultsBSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, sensitivity of 0.92, and specificity of 0.80 in the test set. Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71–0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, sensitivity of 0.88, and specificity of 0.85 for predicting H3K27M-mutation status.ConclusionBSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTw-derived radiomics could accurately predict a H3K27M-mutant status in BSG patients.

The mechanisms underlying the pathogenesis of multiple sclerosis induce the changes that underpin relapse-associated and progressive disability. Disease mechanisms can be investigated in preclinical models and patients with multiple sclerosis by molecular and metabolic imaging techniques. Many insights have been gained from such imaging studies: persisting inflammation in the absence of a damaged blood–brain barrier, activated microglia within and beyond lesions, increased mitochondrial activity after acute lesions, raised sodium concentrations in the brain, increased glutamate in acute lesions and normal-appearing white matter, different degrees of demyelination in different patients and lesions, early neuronal damage in grey matter, and early astrocytic proliferation and activation in lesions and white matter. Clinical translation of molecular and metabolic imaging and extension of these techniques will enable the assessment of novel drugs targeted at these disease mechanisms, and have the potential to improve health outcomes through the stratification of patients for treatments.

The identification of pathological processes that could be targeted by therapeutic interventions is a major goal of research into multiple sclerosis (MS). Pathological assessment is the gold standard for such identification, but has intrinsic limitations owing to the limited availability of autopsy and biopsy tissue. MRI has gained a leading role in the assessment of MS because it allows doctors to obtain an ante mortem picture of the degree of CNS involvement. A number of correlative pathological and MRI studies have helped to define in vivo the pathological substrates of MS in focal lesions and normal-appearing white matter, not only in the brain, but also in the spinal cord. These studies have resulted in the identification of aspects of pathophysiology that were previously neglected, including grey matter involvement and vascular pathology. Despite these important achievements, numerous open questions still need to be addressed to resolve controversies about how the pathology of MS results in fixed neurological disability.

Several studies have reported alterations in gut microbiota composition of Alzheimer's disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD). We included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE. The machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of and lower abundance of group spp., spp., spp., spp., spp., , and spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of spp., spp., and and higher odds of positive p-tau status. Gut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.