Time-optimized high-resolution readout-segmented diffusion tensor imaging

Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1×1×2.5 mm3) diffusion tensor imaging of the entire brain applicable in a clinical context

Periventricular magnetisation transfer abnormalities in early multiple sclerosis

OBJECTIVE: Recent studies suggested that CSF-mediated factors contribute to periventricular (PV) T2-hyperintense lesion formation in multiple sclerosis (MS) and this in turn correlates with cortical damage. We thus investigated if such PV-changes are observable microstructurally in early-MS and if they correlate with cortical damage. METHODS: We assessed the magnetisation transfer ratio (MTR) in PV normal-appearing white matter (NAWM) and in MS lesions in 44 patients with a clinically isolated syndrome (CIS) suggestive of MS and 73 relapsing-remitting MS (RRMS) patients. Band-wise MTR values were related to cortical mean thickness (CMT) and compared with 49 healthy controls (HCs). For each band, MTR changes were assessed relative to the average MTR values of all HCs. RESULTS: Relative to HCs, PV-MTR was significantly reduced up to 2.63% in CIS and 5.37% in RRMS (p<0.0001). The MTR decreased towards the lateral ventricles with 0.18%/mm in CIS and 0.31%/mm in RRMS patients, relative to HCs. In RRMS, MTR-values adjacent to the ventricle and in PV-lesions correlated positively with CMT and negatively with EDSS. CONCLUSION: PV-MTR gradients are present from the earliest stage of MS, consistent with more pronounced microstructural WM-damage closer to the ventricles. The positive association between reduced CMT and lower MTR in PV-NAWM suggests a common pathophysiologic mechanism. Together, these findings indicate the potential use of multimodal MRI as refined marker for MS-related tissue changes

Social cognition in multiple sclerosis:A systematic review and meta-analysis

Objective: To quantify the magnitude of deficits in theory of mind (ToM) and facial emotion recognition among patients with multiple sclerosis (MS) relative to healthy controls. Methods: An electronic database search of Ovid MEDLINE, PsycINFO, and Embase was conducted from inception to April 1, 2016. Eligible studies were original research articles published in peer-reviewed journals that examined ToM or facial emotion recognition among patients with a diagnosis of MS and a healthy control comparison group. Data were independently extracted by 2 authors. Effect sizes were calculated using Hedges g. Results: Twenty-one eligible studies were identified assessing ToM (12 studies) and/or facial emotion recognition (13 studies) among 722 patients with MS and 635 controls. Deficits in both ToM (g -0.71, 95% confidence interval [CI] -0.88 to -0.55, p < 0.001) and facial emotion recognition (g -0.64, 95% CI -0.81 to -0.47, p < 0.001) were identified among patients with MS relative to healthy controls. The largest deficits were observed for visual ToM tasks and for the recognition of negative facial emotional expressions. Older age predicted larger emotion recognition deficits. Other cognitive domains were inconsistently associated with social cognitive performance. Conclusions: Social cognitive deficits are an overlooked but potentially important aspect of cognitive impairment in MS with potential prognostic significance for social functioning and quality of life. Further research is required to clarify the longitudinal course of social cognitive dysfunction, its association with MS disease characteristics and neurocognitive impairment, and the MS-specific neurologic damage underlying these deficits

Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis

Imaging; Multiple sclerosis; Quantitative MRIImatges; Esclerosi múltiple; Ressonància magnètica quantitativaImágenes; Esclerosis múltiple; Resonancia magnética cuantitativaQuantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.C.G. is supported by the Swiss National Science Foundation (SNSF) grant PP00P3_176984, the Stiftung zur Förderung der gastroenterologischen und allgemeinen klinischen Forschung and the EUROSTAR E! 113682 HORIZON2020. F.B. is supported by the National Institute for Health Research biomedical research center at University College London Hospitals. J.W. is supported by the EU Horizon2020 research and innovation grant (FORCE, 668039). D.S.R. is supported by the Intramural Research Program of National Institute of Neurological Disorders and Stroke, National Institutes of Health. A.T.T. is supported by an Medical Research Council grant (MR/S026088/1). S.R. is supported by the Austrian Science Foundation (FWF) grant I-3001. P.S. is supported by the Intramural Research Program of National Institute of Neurological Disorders and Stroke, National Institutes of Health. H.V. is supported by the Dutch multiple sclerosis Research Foundation, ZonMW and HealthHolland

Are morphologic features of recent small subcortical infarcts related to specific etiologic aspects?

Background: Recent small subcortical infarcts (RSSIs) mostly result from the occlusion of a single, small, brain artery due to intrinsic cerebral small-vessel disease (CSVD). Some RSSIs may be attributable to other causes such as cardiac embolism or large-artery disease, and their association with coexisting CSVD and vascular risk factors may vary with morphological magnetic resonance imaging (MRI) features. Methods: We retrospectively identified all inpatients with a single symptomatic MRI-confirmed RSSI between 2008 and 2013. RSSIs were rated for size, shape, location (i.e. anterior: basal ganglia and centrum semiovale posterior cerebral circulation: thalamus and pons) and MRI signs of concomitant CSVD. In a further step, clinical data, including detailed diagnostic workup and vascular risk factors, were analyzed with regard to RSSI features. Results: Among 335 RSSI patients (mean age 71.1 ± 12.1 years), 131 (39%) RSSIs were >15 mm in axial diameter and 66 (20%) were tubular shaped. Atrial fibrillation (AF) was present in 44 (13.1%) and an ipsilateral vessel stenosis > 50% in 30 (9%) patients. Arterial hypertension and CSVD MRI markers were more frequent in patients with anterior-circulation RSSIs, whereas diabetes was more prevalent in posterior-circulation RSSIs. Larger RSSIs occurred more frequently in the basal ganglia and pons, and the latter were associated with signs of large-artery atherosclerosis. Patients with concomitant AF had no specific MRI profile. Conclusion: Our findings suggest the contribution of different pathophysiological mechanisms to the occurrence of RSSIs in the anterior and posterior cerebral circulation. While there appears to be some general association of larger infarcts in the pons with large-artery disease, we found no pattern suggestive of AF in RSSIs

SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis

Esclerosi múltiple; Classificació d'aprenentatge automàtic; Selecció de funcionsEsclerosis múltiple; Clasificación de aprendizaje automático; Selección de característicasMultiple sclerosis; Machine learning classification; Feature selectionMachine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification. We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together. The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together. Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients.This project received funding from the European Union's Horizon2020 Research and Innovation Program EuroPOND under grant agreement number 666992, and it was supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. We thank all participating partners of the MAGNIMS study group for sharing their data with us

Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer's Disease

The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer's disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited

Results From the Austrian Stroke Unit Registry

Evaluation of: Gattringer T, Ferrari J, Knoflach M et al. Sex-related differences of acute stroke unit care results from an Austrian stroke unit registry. Stroke 45, 1632–1638 (2014). The authors analyzed data from 47,209 patients diagnosed with ischemic stroke or transient ischemic attack from January 2005 to December 2012. In this study, epidemiological data, stroke type, diagnostics and clinical scores were analyzed for age-adjusted preclinical and clinical characteristics as well as quality of acute stroke care. Moreover, outcome at 3 months was included in a multivariate model corrected for demographic and clinical confounders. While there were no reported sex differences in stroke care and thrombolysis rates, males more often received magnetic resonance imaging (MRI) brain scans. From follow-up data, a worse functional outcome was observed for females in univariate and multivariate analysis. In fact, females were less likely to be prescribed statins and more likely to receive antiplatelet therapy. A..

Predictors of Lesion Cavitation After Recent Small Subcortical Stroke

The Wellcome Trust (WT088134/Z/09/A) and Row Fogo Charitable Trust funded the Mild Stroke Study 2 from which the patients were selected. We thank the European Union Horizon 2020, PHC-03-15, project no. 666881, ‘SVDs@Target’, the Fondation Leducq Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease, ref. no. 16 CVD 05, and the MRC UK Dementia Research Institute for support.Peer reviewedPublisher PD