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In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified.

Background Multiple Sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging, and multimodal biomarkers to define the risk of disease activity. Methods We have analyzed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centers, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Validation was conducted in an independent prospective cohort of 271 MS patients from a single center. Results We found algorithms for predicting confirmed disability accumulation for the different scales, No Evidence of Disease Activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors by using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in the discovery and validation cohorts. Conclusion Combining clinical, imaging, and omics data with machine learning helps to identify MS patients at risk of disability worsening.

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.

Altres ajuts: Fondo Europeo de Desarrollo Regional (FEDER, "Otra manera de hacer Europa", "Investing in your future"); Red Española de Esclerosis Múltiple (REEM - RD16/0015/0002, RD16/0015/0003, RD12/0032/0002, RD12/0060/01-02); TEVA Spain; Fundación Merck Salud (Ayudas Merck de Investigación 2017); Proyecto Societat Catalana Neurologia 2017; CIBERNED program (Program 1, Alzheimer Disease and SIGNAL study); National Institutes of Health (NIA grants 1R01AG056850-01A1, R21AG056974, R01AG061566;, Fundació La Marató de TV3 (20142030, 20141210); Fundació Catalana Síndrome de Down; Fundació Víctor Grífols i Lucas; Generalitat de Catalunya (SLT006/17/00119); Universitat de Barcelona (APIF Pre-doctoral grant); Hospital Clinic Emili Letang). The spatio-temporal characteristics of grey matter (GM) impairment in multiple sclerosis (MS) are poorly understood. We used a new surface-based diffusion MRI processing tool to investigate regional modifications of microstructure, and we quantified volume loss in GM in a cohort of patients with MS classified into three groups according to disease duration. Additionally, we investigated the relationship between GM changes with disease severity. We studied 54 healthy controls and 247 MS patients classified regarding disease duration: MS1 (less than 5 years, n = 67); MS2 (5-15 years, n = 107); and MS3 (more than15 years, n = 73). We compared GM mean diffusivity (MD), fractional anisotropy (FA) and volume between groups, and estimated their clinical associations. Regional modifications in diffusion measures (MD and FA) and volume did not overlap early in the disease, and became widespread in later phases. We found higher MD in MS1 group, mainly in the temporal cortex, and volume reduction in deep GM and left precuneus. Additional MD changes were evident in cingulate and occipital cortices in the MS2 group, coupled to volume reductions in deep GM and parietal and frontal poles. Changes in MD and volume extended to more than 80% of regions in MS3 group. Conversely, increments in FA, with very low effect size, were observed in the parietal cortex and thalamus in MS1 and MS2 groups, and extended to the frontal lobe in the later group. MD and GM changes were associated with white matter lesion load and with physical and cognitive disability. Microstructural integrity loss and atrophy present differential spatial predominance early in MS and accrual over time, probably due to distinct pathogenic mechanisms that underlie tissue damage.

Background To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to $$T_1$$ T 1 , $${T_2}^*$$ T 2 ∗ , NAWM, and GM- probability maps. Methods We performed MRF-EPI measurements in 42 patients with multiple sclerosis and 6 healthy volunteers along two sites. A U-net was trained to reconstruct the denoised and distortion corrected $$T_1$$ T 1 and $${T_2}^*$$ T 2 ∗ maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps. Results WM lesions were predicted with a dice coefficient of $$0.61\pm 0.09$$ 0.61 ± 0.09 and a lesion detection rate of $$0.85\pm 0.25$$ 0.85 ± 0.25 for a threshold of 33%. The network jointly enabled accurate $$T_1$$ T 1 and $${T_2}^*$$ T 2 ∗ times with relative deviations of 5.2% and 5.1% and average dice coefficients of $$0.92\pm 0.04$$ 0.92 ± 0.04 and $$0.91\pm 0.03$$ 0.91 ± 0.03 for NAWM and GM after binarizing with a threshold of 80%. Conclusion DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.

Purpose: To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. Methods: MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of (Formula presented.) and (Formula presented.) in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF (Formula presented.) and (Formula presented.) parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the (Formula presented.) and (Formula presented.) parametric maps, and the WM and GM probability maps. Results: Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for (Formula presented.) (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for (Formula presented.) (deviations 6.0%). Conclusions: MRF is a fast and robust tool for quantitative (Formula presented.) and (Formula presented.) mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.

Brain structural network modifications in multiple sclerosis (MS) seem to be clinically relevant. The discriminative ability of those changes to identify MS patients or their cognitive status remains unknown. Therefore, this study aimed to investigate connectivity changes in MS patients related to their cognitive status, and to define an automatic classification method to classify subjects as patients and healthy volunteers (HV) or as cognitively preserved (CP) and impaired (CI) patients. We analysed structural brain connectivity in 45 HV and 188 MS patients (104 CP and 84 CI). A support vector machine with k-fold cross-validation was built using the graph metrics features that best differentiate the groups (p

Multisite machine-learning neuroimaging studies, such as those conducted by the ENIGMA Consortium, need to remove the differences between sites to avoid effects of the site (EoS) that may prevent or fraudulently help the creation of prediction models, leading to impoverished or inflated prediction accuracy. Unfortunately, we have shown earlier that current Methods Aiming to Remove the EoS (MAREoS, e.g., ComBat) cannot remove complex EoS (e.g., including interactions between regions). And complex EoS may bias the accuracy. To overcome this hurdle, groups worldwide are developing novel MAREoS. However, we cannot assess their effectiveness because EoS may either inflate or shrink the accuracy, and MAREoS may both remove the EoS and degrade the data. In this work, we propose a strategy to measure the effectiveness of a MAREoS in removing different types of EoS. FOR MAREOS DEVELOPERS, we provide two multisite MRI datasets with only simple true effects (i.e., detectable by most machine-learning algorithms) and two with only simple EoS (i.e., removable by most MAREoS). First, they should use these datasets to fit machine-learning algorithms after applying the MAREoS. Second, they should use the formulas we provide to calculate the relative accuracy change associated with the MAREoS in each dataset and derive an EoS-removal effectiveness statistic. We also offer similar datasets and formulas for complex true effects and EoS that include first-order interactions. FOR MACHINE-LEARNING RESEARCHERS, we provide an extendable benchmark website to show: a) the types of EoS they should remove for each given machine-learning algorithm and b) the effectiveness of each MAREoS for removing each type of EoS. Relevantly, a MAREoS only able to remove the simple EoS may suffice for simple machine-learning algorithms, whereas more complex algorithms need a MAREoS that can remove more complex EoS. For instance, ComBat removes all simple EoS as needed for predictions based on simple lasso algorithms, but it leaves residual complex EoS that may bias the predictions based on standard support vector machine algorithms.

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/. The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord., Measurement(s)spinal cordTechnology Type(s)magnetic resonance imagingFactor Type(s)manufacturer • siteSample Characteristic - OrganismHomo sapiensSample Characteristic - LocationCanada • Switzerland • Australia • United States of America • United Kingdom • Germany • French Republic • Czech Republic • Italy • Japan • Kingdom of Spain • China Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14052269

The spatio-temporal characteristics of grey matter (GM) impairment in multiple sclerosis (MS) are poorly understood. We used a surface-based diffusion MRI processing tool to investigate regional modifications of microstructure and volume loss in GM at different time-points of the disease, and their relationship to disability. We studied 54 healthy controls and 247 MS patients classified according to disease duration: MS1 (less than 5 years, n=67); MS2 (5-15 years, n=107); and MS3 (more than15 years, n=73). We compared GM mean diffusivity (MD) and volume between groups, and estimated their clinical correlations. Regional modifications in MD and volume did not overlap early in the disease, and became widespread in later phases. We found higher MD in MS1 group, mainly in the temporal cortex, and volume reduction in right putamen. Additional MD changes were evident in cingulate and occipital cortices in the MS2 group, coupled to volume reductions in deep GM and parietal and frontal poles. Changes extended to more than 80% of regions in MS3 group. Clinical disability was correlated with GM changes. Microstructural integrity loss and atrophy present differential spatial predominance early in MS and accrual over time, probably due to distinct pathogenic mechanisms that underlie tissue damage.

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.

ObjectiveTo investigate the incidence of coronavirus disease 2019 (COVID-19) in a single-center cohort of patients with MS and to explore the contribution of their comorbidities and therapies to the outcome.MethodsA cross-sectional mixed-method study was conducted involving an email-based, self-administered questionnaire sent on May 21, 2020, to 586 patients with MS followed at the MS Unit of Hospital Clinic, University of Barcelona, along with telephone interview, and review of electronic medical records until June 18, 2020. The cumulative incidence of confirmed COVID-19 (positive PCR or antibody test) and all COVID-19 cases (confirmed and suspected) from the start of the pandemic was compared with the population estimates for Barcelona.ResultsA total of 407 patients (69.5%) completed the survey. Most of the responders (67%) were female. The responders had a median age of 48 years (range 19–86), relapsing-remitting disease (84%), at least 1 comorbidity (45%), and were on disease-modifying therapy (DMT; 74.7%). COVID-19 was confirmed in 5 patients (1.2%) and suspected in 46 (11.3%). The cumulative incidence of confirmed COVID-19 cases was similar to that of the general population but was almost 2-fold higher when all cases were considered (p < 0.001). Six patients (11.7%) were hospitalized, of which 5 had good recovery and 1 died. Hospitalized patients were more frequently male, had diabetes and had progressive forms of MS (p < 0.05). DMT was not associated with the risk of infection or the outcome.ConclusionsIn the studied MS cohort, the incidence of COVID-19 was higher than that of the general population; however, most patients did not require hospitalization and had a good outcome despite the frequent presence of comorbidities and treatment with DMT.

Quadrantanopia caused by inadvertent severing of Meyer's Loop of the optic radiation is a well‐recognised complication of temporal lobectomy for conditions such as epilepsy. Dissection studies indicate that the anterior extent of Meyer's Loop varies considerably between individuals. Quantifying this for individual patients is thus an important step to improve the safety profile of temporal lobectomies. Previous attempts to delineate Meyer's Loop using diffusion MRI tractography have had difficulty estimating its full anterior extent, required manual ROI placement, and/or relied on advanced diffusion sequences that cannot be acquired routinely in most clinics. Here we present CONSULT: a pipeline that can delineate the optic radiation from raw DICOM data in a completely automated way via a combination of robust pre‐processing, segmentation, and alignment stages, plus simple improvements that bolster the efficiency and reliability of standard tractography. We tested CONSULT on 696 scans of predominantly healthy participants (539 unique brains), including both advanced acquisitions and simpler acquisitions that could be acquired in clinically acceptable timeframes. Delineations completed without error in 99.4% of the scans. The distance between Meyer's Loop and the temporal pole closely matched both averages and ranges reported in dissection studies for all tested sequences. Median scan‐rescan error of this distance was 1 mm. When tested on two participants with considerable pathology, delineations were successful and realistic. Through this, we demonstrate not only how to identify Meyer's Loop with clinically feasible sequences, but also that this can be achieved without fundamental changes to tractography algorithms or complex post‐processing methods., Quadrantanopia caused by inadvertent severing of Meyer's Loop of the optic radiation is a well‐recognised complication of temporal lobectomy. We demonstrated a fully automated pipeline that could delineate this structure reliably and realistically on more than 500 unique brains, including both advanced and more clinically‐accessible acquisitions. Results were in line with historical dissections studies and median scan‐rescan error was 1 mm.

Altres ajuts: SRB: The Australian Schizophrenia Research Bank (ASRB) was supported by the National Health and Medical Research Council of Australia (NHMRC) (Enabling Grant, ID 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief Investigators for ASRB were Carr, V., Schall, U., Scott, R., Jablensky, A., Mowry, B., Michie, P., Catts, S., Henskens, F., Pantelis, C. We thank Loughland, C., the ASRB Manager, and acknowledge the help of Jason Bridge for ASRB database queries. CP was supported by NHMRC Senior Principal Research Fellowships (IDs: 628386 & 1105825); GC was supported by the Schizophrenia Research Institute utilizing infrastructure funding from the New South Wales Ministry of Health and New South Wales Ministry of Trade and Investment (Australia); JMF was supported by NHMRC project grant (1063960) and the Janette Mary O'Neil Research Fellowship; MJG was supported by NHMRC as an R.D. Wright Biomedical Career Development Fellow (1061875). MJC was supported by NHMRC Senior Research Fellowship (1121474). CASSI: CSW is funded by the NSW Ministry of Health, Office of Health and Medical Research. CSW is a recipient of a National Health and Medical Research Council (Australia) Principal Research Fellowship (PRF) (#1117079). CIAM: The CIAM study (FMH - PI) was supported by the University Research Committee, University of Cape Town and South African funding bodies National Research Foundation and Medical Research Council. COBRE: The COBRE dataset and investigators were supported by NIH grants R01EB006841 & P20GM103472, as well as NSF grant 1539067. JT (senior author) and VDC are supported by 5R01MH094524. JMS is supported by R01 AA021771 and P50 AA022534. EONCKS: This work was supported by a New Partnership for Africa's Development (NEPAD) grant through the Department of Science and Technology of South Africa, the Medical Research Council of South Africa (grant number 65174). ESO: The ESO study was funded by NPU I - LO1611 and Ministry of Health, Czech Republic - Conceptual Development of Research Organization 00023001 (IKEM). FIDMAG/Project: This work was supported by the Catalan Government and several grants from the Instituto de Salud Carlos III and co-funded by European Union (ERDF/ESF, 'Investing in your future'): Miguel Servet Research Contracts and Research Project Grants. FOR2107 Marburg: The FOR2107 Marburg study was funded by the German Research Foundation (DFG), Tilo Kircher (speaker FOR2107; DFG grant numbers KI588/14-1, KI588/14-2), Axel Krug (KR 3822/5-1, KR 3822/7-2), Igor Nenadic (NE 2254/1-2), Carsten Konrad (KO 4291/3-1). FOR2107 Muenster: The FOR2107 Muenster study was funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5-1 and DA1151/5-2 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD). TH was supported by grants from the German Research Foundation (DFG grants HA7070/2-2, HA7070/3, HA7070/4). Frankfurt: MRI was performed at the Frankfurt Brain Imaging Center, supported by the German Research Council (DFG) and the German Ministry for Education and Research (BMBF; Brain Imaging Center Frankfurt/Main, DLR 01GO0203). GIPSI: This study was supported by Colciencias PRISMA-U.T. Huilong1 & Huilong2: This study was funded by the National Natural Science Foundation of China (81761128021; 31671145; 81401115; 81401133), Beijing Municipal Science and Technology Commission grant (Z141107002514016) and Beijing Natural Science Foundation(7162087, Beijing Municipal Administration of Hospitals Clinical medicine Development of special funding (XMLX201609; zylx201409). IGP: This study was funded by Project Grants from the Australian National Health and Medical Research Council of Australia (NHMRC; APP630471 and APP1081603), the Macquarie University's Australian Research Council Centre of Excellence in Cognition and its Disorders (CE110001021). Johns Hopkins: Supported by National Institutes of Health Grant Nos. MH-092443, MH-094268 (Silvio O. Conte Center), MH-105660, and MH-107730; foundation grants from Stanley, RUSK/S-R, and NARSAD/Brain and Behavior Research Foundation. Madrid: Supported by the Spanish Ministry of Science, Innovation and Universities, Instituto de Salud Carlos III, co-financed by ERDF Funds from the European Commission, "A way of making Europe", CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds and European Union Seventh Framework Program and H2020 Program; Fundación Familia Alonso, Fundación Alicia Koplowitz and Fundación Mutua Madrileña. MPRC1 & MPRC2: Support was received from NIH grants U01MH108148, 2R01EB015611, R01MH112180, R01DA027680, R01MH085646, P50MH103222 and T32MH067533, a State of Maryland contract (M00B6400091) and NSF grant (1620457). OLIN: The Olin study was supported by NIH grants R37MH43375 and R01MH074797. Oxford: The Oxford study MRC G0500092. SLF Rome: Support from the Italian Ministry of Health grants RC-12-13-14-15-16-17-18-19/A. RSCZ: RSCZ data collection was supported by RFBR 15-06-05758 grant. SCORE: This study was supported in part by grant 3232BO_119382 from the Swiss National Science Foundation. We thank the FePsy (Frueherkennung von Psychosen; early detection of psychosis) Study Group from the University of Basel, Department of Psychiatry, Switzerland, for the recruitment of the study participants. The FePsy Study was supported in part by grant No. SNF 3200-057216/1, ext./2, ext./3. Singapore: This study was supported by research grants from the National Healthcare Group, Singapore (SIG/05004; SIG/11003), and the Singapore Bioimaging Consortium (RP C-009/2006) research grants awarded to KS. SNUH: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (Grant no. 2013R1A2A1A03071089 and 2017M3C7A1029610). UCISZ: The UCISZ study was supported by the National Institutes of Mental Health grant number R21MH097196 to TGMvE. UCISZ data were processed by the UCI High Performance Computing cluster supported by Joseph Farran, Harry Mangalam, and Adam Brenner and the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR000153. UNIBA: The UNIBA study was supported by grant funding from the Italian Ministry of Health (PE-2011-02347951). UNIMAAS: The study was supported by Dutch Organization for Health Research and Development (ZonMw 91112002) and a personal grant to Thérèse van Amelsvoort (ZonMw-VIDI: 91712394). The data was collected in a clinical trial registered in the Dutch clinical trial registry under ID: NTR5094 (http://www.trialregister.nl). UPenn: This study was supported by the National Institute of Mental Health grants MH064045, MH 60722, MH019112, MH085096 (DHW), and R01MH112847 (RTS and TDS). Zurich: This study was supported by the Swiss National Science Foundation (105314_140351 to S.K.). Matthias Kirschner acknowledges support from the National Bank Fellowship (McGill University) and the Swiss National Foundation (P2SKP3_178175). Research reported in this publication was also supported by the following National Institutes of Health grants: U54 EB020403 to PMT, R01 MH116147, U24 RR21992, R21MH097196, and TR000153 to TGMvE, S10 OD023696 and R01EB015611 to PK, T32 AG058507and 5T32 MH073526 to CRKC, R01 MH117601 to NJ, ENIGMA's NIH Big Data to Knowledge (BD2K) initiative U54 EB020403, ENIGMA Sex Differences R01MH116147, and ENIGMA-COINSTAC: Advanced World-wide Transdiagnostic Analysis of Valence System Brain Circuits R01MH121246. A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

Understanding of the role of focal inflammation, a treatable feature, on neuro-axonal injury, is paramount to optimize neuroprotective strategy in MS. To quantify the impact of focal inflammatory activity on the rate of neuro-axonal injury over the MS course. We quantified the annualized rates of change in peripapillary retinal nerve fiber layer, ganglion cell plus inner plexiform layer (GCIPL), whole-brain, gray matter and thalamic volumes in patients with and without focal inflammatory activity in 161 patients followed over 5 years. We used mixed models including focal inflammatory activity (the presence of at least one relapse or a new/enlarging T2-FLAIR or gadolinium- enhancing lesion), and its interaction with time adjusted by age, sex, use of disease-modifying therapies and steroids, and prior optic neuritis. The increased rate of neuro-axonal injury during the first five years after onset was more prominent among active patients, as reflected by the changes in GCIPL thickness (p = 0.02), whole brain (p = 0.002) and thalamic volumes (p

1AbstractQuadrantanopia caused by inadvertent severing of Meyer’s Loop of the optic radiation is a well-recognised complication of temporal lobectomy for conditions such as epilepsy. Dissection studies indicate that the anterior extent of Meyer’s Loop varies considerably between individuals. Quantifying this for individual patients is thus an important step to improve the safety profile of temporal lobectomies. Previous attempts to delineate Meyer’s Loop using diffusion MRI tractography have had difficulty estimating its full anterior extent, required manual ROI placement, and/or relied on advanced diffusion sequences that cannot be acquired routinely in most clinics. Here we present CONSULT – a pipeline that can delineate the optic radiation from raw DICOM data in a completely automated way via a combination of robust preprocessing, segmentation, and alignment stages, plus simple improvements that bolster the efficiency and reliability of standard tractography. We tested CONSULT on 694 scans of predominantly healthy participants (538 unique brains), including both advanced acquisitions and simpler acquisitions that could be acquired in clinically acceptable timeframes. Delineations completed without error in 99.4% of the scans. The distance between Meyer’s Loop and the temporal pole closely matched both averages and ranges reported in dissection studies for all tested sequences. Median scan-rescan error of this distance was 1mm. When tested on two participants with considerable pathology, delineations were successful and realistic. Through this, we demonstrate not only how to identify Meyer’s Loop with clinically accessible sequences, but also that this can be achieved without fundamental changes to tractography algorithms or complex post-processing methods.HighlightsWe propose a fully automated means of delineating the optic radiation using diffusion MRI from DICOM dataAnatomical measurements from tractography of over 500 brains align well with previous dissection studiesThe proposed pipeline works well with clinically accessible and advanced acquisitionsMedian scan-rescan error was 1mmPlausible tractography was generated when pathology was present

Patients with multiple sclerosis (MS) display reduced structural connectivity among brain regions, but the pathogenic mechanisms underlying network disruption are still unknown. We aimed to investigate the association between the loss of diffusion-based structural connectivity, measured with graph theory metrics, and magnetic resonance (MR) markers of microstructural damage. Moreover, we evaluated the cognitive consequences of connectivity changes. We analysed the frontoparietal network in 102 MS participants and 25 healthy volunteers (HV). MR measures included radial diffusivity (RD), as marker of demyelination, and ratios of myo-inositol, N-acetylaspartate and glutamate+glutamine with creatine in white (WM) and grey matter as markers of astrogliosis, neuroaxonal integrity and glutamatergic neurotoxicity. Patients showed decreased global and local efficiency, and increased assortativity (p, Highlights • The pathogenic mechanisms underlying structural network disruption in MS are unknown. • The microstructural damage of the white matter contributes to network impairment. • The worsening of global efficiency is associated with markers of neuroaxonal burden. • Local efficiency impairment is related to MR measures of demyelination. • The described network modifications contribute to cognitive dysfunction.

Aim To evaluate the reliability of synthetic magnetic resonance imaging (SyMRI) for detecting complications associated with subarachnoid haemorrhage (SAH), such as ischaemic lesions, hydrocephalus, or bleeding complications. Materials and methods Twenty patients with SAH, who underwent a conventional brain MRI and a SyMRI on a 3 T MRI machine. Comparable conventional and synthetic T2-weighted fluid attenuated inversion recovery (FLAIR) images were acquired. The presence of ischaemic lesions, hydrocephalus, extra-axial blood collections as well as the volumes of grey matter (GMv), white matter (WMv), and cerebrospinal (CSFv) were compared. The acquisition times of both sequences was also analysed. Results The concordance between the two techniques was excellent for the detection of ischaemic lesions and extra-axial collections (kappa = 0.80 and 0.88 respectively) and good for the detection of hydrocephalus (kappa = 0.69). No significant differences were detected in the number of ischaemic lesions (p=0.31) or in the Evans index (p=0.11). The WMv and CSFv measures were also similar (p=0.18 and p=0.94, respectively), as well as the volume of ischaemic lesions (p=0.79). Compared to conventional MRI, the SyMRI acquisition time was shorter regardless of the number of sections (32% and 6% time reduction for 4 or 3 mm section thickness, respectively). Conclusions SyMRI allows the detection of potential complications of SAH in a similar way to conventional MRI with a shorter acquisition time.

Attention and executive deficits are disabling symptoms in multiple sclerosis (MS) that have been related to disconnection mechanisms. We aimed to investigate changes in structural connectivity in MS and their association with attention and executive performance applying an improved framework that combines high order probabilistic tractography and anatomical exclusion criteria postprocessing. We compared graph theory metrics of structural networks and fractional anisotropy (FA) of white matter (WM) connections or edges between 72 MS subjects and 38 healthy volunteers (HV) and assessed their correlation with cognition. Patients displayed decreased network transitivity, global efficiency and increased path length compared with HV (p, Highlights • High order tractography and anatomical exclusion criteria improve connectivity analyses. • Structural connectivity is less efficient in multiple sclerosis. • Attentional and executive functions relate to integrity of strategic networks. • Increased connectivity suggests structural reorganization mechanisms.

Highlights • Macroscopic and microscopic diffusion properties discriminate between MS lesion types. • The number and volume of lesions with larger diffusion changes are associated with worse clinical outcomes. • Diffusion MRI provides useful information of the pathological heterogeneity in plaques., Diffusion magnetic resonance imaging can reveal quantitative information about the tissue changes in multiple sclerosis. The recently developed multi-compartment spherical mean technique can map different microscopic properties based only on local diffusion signals, and it may provide specific information on the underlying microstructural modifications that arise in multiple sclerosis. Given that the lesions in multiple sclerosis may reflect different degrees of damage, we hypothesized that quantitative diffusion maps may help characterize the severity of lesions “in vivo” and correlate these to an individual’s clinical profile. We evaluated this in a cohort of 59 multiple sclerosis patients (62% female, mean age 44.7 years), for whom demographic and disease information was obtained, and who underwent a comprehensive physical and cognitive evaluation. The magnetic resonance imaging protocol included conventional sequences to define focal lesions, and multi-shell diffusion imaging was used with b-values of 1000, 2000 and 3000 s/mm2 in 180 encoding directions. Quantitative diffusion properties on a macro- and micro-scale were used to discriminate distinct types of lesions through a k-means clustering algorithm, and the number and volume of those lesion types were correlated with parameters of the disease. The combination of diffusion tensor imaging metrics (fractional anisotropy and radial diffusivity) and multi-compartment spherical mean technique values (microscopic fractional anisotropy and intra-neurite volume fraction) differentiated two type of lesions, with a prediction strength of 0.931. The B-type lesions had larger diffusion changes compared to the A-type lesions, irrespective of their location (P

BACKGROUND: Herpes simplex encephalitis can trigger autoimmune encephalitis that leads to neurological worsening. We aimed to assess the frequency, symptoms, risk factors, and outcomes of this complication. METHODS: We did a prospective observational study and retrospective analysis. In the prospective observational part of this study, we included patients with herpes simplex encephalitis diagnosed by neurologists, paediatricians, or infectious disease specialists in 19 secondary and tertiary Spanish centres (Cohort A). Outpatient follow-up was at 2, 6, and 12 months from onset of herpes simplex encephalitis. We studied another group of patients retrospectively, when they developed autoimmune encephalitis after herpes simplex encephalitis (Cohort B). We compared demographics and clinical features of patients who developed autoimmune encephalitis with those who did not, and in patients who developed autoimmune encephalitis we compared these features by age group (patients =4 years compared with patients >4 years). We also used multivariable binary logistic regression models to assess risk factors for autoimmune encephalitis after herpes simplex encephalitis. FINDINGS: Between Jan 1, 2014, and Oct 31, 2017, 54 patients with herpes simplex encephalitis were recruited to Cohort A, and 51 were included in the analysis (median age 50 years [IQR 5-68]). At onset of herpes simplex encephalitis, none of the 51 patients had antibodies to neuronal antigens; during follow-up, 14 (27%) patients developed autoimmune encephalitis and all 14 (100%) had neuronal antibodies (nine [64%] had NMDA receptor [NMDAR] antibodies and five [36%] had other antibodies) at or before onset of symptoms. The other 37 patients did not develop autoimmune encephalitis, although 11 (30%) developed antibodies (n=3 to NMDAR, n=8 to unknown antigens; p

Magnetic resonance fingerprinting (MRF) is a promising method for fast simultaneous quantification of multiple tissue parameters. The objective of this study is to improve the coverage of MRF based on echo-planar imaging (MRF-EPI) by using a slice-interleaved acquisition scheme. For this, the MRF-EPI is modified to acquire several slices in a randomized interleaved manner, increasing the effective repetition time of the spoiled gradient echo readout acquisition in each slice. Per-slice matching of the signal-trace to a precomputed dictionary allows the generation of T1 and T2* maps with integrated B1 + correction. Subsequent compensation for the coil sensitivity profile and normalization to the cerebrospinal fluid additionally allows for quantitative proton density (PD) mapping. Numerical simulations are performed to optimize the number of interleaved slices. Quantification accuracy is validated in phantom scans and feasibility is demonstrated in-vivo. Numerical simulations suggest the acquisition of four slices as a trade-off between quantification precision and scan-time. Phantom results indicate good agreement with reference measurements (Difference T1: −2.4 ± 1.1%, T2*: −0.5 ± 2.5%, PD: −0.5 ± 7.2%). In-vivo whole-brain coverage of T1, T2* and PD with 32 slices was acquired within 3:36 minutes, resulting in parameter maps of high visual quality and comparable performance with single-slice MRF-EPI at 4-fold scan-time reduction.

Introduction: Cortical mean diffusivity (MD) and free water fraction (FW) changes are proposed biomarkers for Alzheimer's disease (AD). Methods: We included healthy control subjects (N = 254), mild cognitive impairment (N = 41), and AD dementia (N = 31) patients. Participants underwent a lumbar puncture and a 3 T magnetic resonance imaging. Healthy control subjects were classified following National Institute on Aging-Alzheimer's Association stages (stage 0, N = 220; stage 1, N = 25; and stage 2/3, N = 9). We assessed the cortical MD, cortical FW, and cortical thickness (CTh) changes along the AD continuum. Results: Microstructural and macrostructural changes show a biphasic trajectory. Stage 1 subjects showed increased CTh and decreased MD and FW with respect the stage 0 subjects. Stage 2/3 subjects showed decreased CTh and increased cortical MD and FW, changes that were more widespread in symptomatic stages. Discussion: These results support a biphasic model of changes in AD, which could affect the selection of patients for clinical trials and the use of magnetic resonance imaging as a surrogate marker of disease modification. (C) 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Objectives: Our aim was to investigate the impact of gray matter (GM) integrity on cognitive performance in multiple sclerosis (MS), and its relationship with white matter (WM) integrity and presence of lesions. Methods: Sixty-seven patients with MS and 26 healthy controls underwent voxel-based analysis of diffusion tensor images (DTI) in GM and tract-based spatial statistics (TBSS) from WM to identify the regional correlations between cognitive functions and integrity. Lesion probability mapping (LPM) was generated for correlation analysis with cognition. Multiple linear regression analyses were used to identify the imaging measures associated with cognitive scores. Results: Compared with controls, patients showed abnormal DTI indices in several GM regions and in most WM tracts. Impairment in DTI indices in specific GM regions was associated with worse performance of distinct cognitive functions. Those regions showed anatomical correspondence with cognitively relevant tracts in TBSS and LPM. The combination of regional GM and WM DTI and lesion volume accounted for 36–51% of the variance of memory and attention scores. Regional GM DTI explained less than 5% of that variance. Conclusion: GM and WM integrity of specific networks influences cognitive performance in MS. However, GM damage assessed by DTI only adds a small increment to the explained variance by WM in predicting cognitive functioning.

IMPORTANCE: Before using brain volume loss (BVL) as a marker of therapeutic response in multiple sclerosis (MS), certain biological and methodological issues must be clarified. OBJECTIVES: To assess the dynamics of BVL as MS progresses and to evaluate the repeatability and exchangeability of BVL estimates with Jacobian Integration (JI) and Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL) (specifically, the Structural Image Evaluation, Using Normalisation, of Atrophy–Cross-Sectional [SIENA-X] tool or FMRIB’s Integrated Registration and Segmentation Tool [FIRST]). DESIGN, SETTING, AND PARTICIPANTS: A cohort of patients who had either clinically isolated syndrome or MS was enrolled from February 2011 through October 2015. All underwent a series of annual magnetic resonance imaging (MRI) scans. Images from 2 cohorts of healthy volunteers were used to evaluate short-term repeatability of the MRI measurements (n = 34) and annual BVL (n = 20). Data analysis occurred from January to May 2017. MAIN OUTCOMES AND MEASURES: The goodness of fit of different models to the dynamics of BVL throughout the MS disease course was assessed. The short-term test-retest error was used as a measure of JI and FSL repeatability. The correlations (R(2)) of the changes quantified in the brain using JI and FSL, together with the accuracy of the annual BVL cutoffs to discriminate patients with MS from healthy volunteers, were used to measure compatibility of imaging methods. RESULTS: A total of 140 patients with clinically isolated syndrome or MS were enrolled, including 95 women (67.9%); the group had a median (interquartile range) age of 40.7 (33.6-48.1) years. Patients underwent 4 MRI scans with a median (interquartile range) interscan period of 364 (351-379) days. The 34 healthy volunteers (of whom 18 [53%] were women; median [IQR] age, 33.5 [26.2-42.5] years) and 20 healthy volunteers (of whom 10 [50%] were women; median [IQR] age, 33.0 [28.7-39.2] years) underwent 2 MRI scans within a median (IQR) of 24.5 (0.0-74.5) days and 384.5 (366.3-407.8) days for the short-term and long-term MRI follow-up, respectively. The BVL rates were higher in the first 5 years after MS onset (R(2) = 0.65 for whole-brain volume change and R(2) = 0.52 for gray matter volume change) with a direct association with steroids (β = 0.280; P = .02) and an inverse association with age at MS onset, particularly in the first 5 years (β = 0.015; P = .047). The reproducibility of FSL (SIENA) and JI was similar for whole-brain volume loss, while JI gave more precise, less biased estimates for specific brain regions than FSL (SIENA-X and FIRST). The correlation between whole-brain volume loss using JI and FSL was high (R(2) = 0.92), but the same correlations were poor for specific brain regions. The area under curve of the whole-brain volume change to discriminate between patients with MS and healthy volunteers was similar, although the thresholds and accuracy index were distinct for JI and FSL. CONCLUSIONS AND RELEVANCE: The proposed BVL threshold of less than 0.4% per year as a marker of therapeutic efficiency should be reconsidered because of the different dynamics of BVL as MS progresses and because of the limited reproducibility and variability of estimates using different imaging methods.

The optic radiation (OR) is one of the major components of the visual system and a key structure at risk in white matter diseases such as multiple sclerosis (MS). However, it is challenging to perform track reconstruction of the OR using diffusion MRI due to a sharp change of direction in the Meyer's loop and the presence of kissing and crossing fibers along the pathway. As such, we aimed to provide a highly precise and reproducible framework for tracking the OR from thalamic and visual cortex masks. The framework combined the generation of probabilistic streamlines by high order fiber orientation distributions estimated with constrained spherical deconvolution and an automatic post-processing based on anatomical exclusion criteria (AEC) to compensate for the presence of anatomically implausible streamlines. Specifically, those ending in the contralateral hemisphere, cerebrospinal fluid or grey matter outside the visual cortex were automatically excluded. We applied the framework to two distinct high angular resolution diffusion-weighted imaging (HARDI) acquisition protocols on one cohort, comprised of ten healthy volunteers and five MS patients. The OR was successfully delineated in both HARDI acquisitions in the healthy volunteers and MS patients. Quantitative evaluation of the OR position was done by comparing the results with histological reference data. Compared with histological mask, the OR reconstruction into a template (OR-TCT) was highly precise (percentage of voxels within the OR-TCT correctly defined as OR), ranging from 0.71 to 0.83. The sensitivity (percentage of voxels in histological reference mask correctly defined as OR in OR-TCT) ranged from 0.65 to 0.81 and the accuracy (measured by F1 score) was 0.73 to 0.77 in healthy volunteers. When AEC was not applied the precision and accuracy decreased. The absolute agreement between both HARDI datasets measured by the intraclass correlation coefficient was 0.73. This improved framework allowed us to reconstruct the OR with high reliability and accuracy independently of the acquisition parameters. Moreover, the reconstruction was possible even in the presence of tissue damage due to MS. This framework could also be applied to other tracts with complex configuration.

OBJECTIVE: Uncontrolled studies of mesenchymal stem cells (MSCs) in multiple sclerosis suggested some beneficial effect. In this randomized, double-blind, placebo-controlled, crossover phase II study we investigated their safety and efficacy in relapsing-remitting multiple sclerosis patients. Efficacy was evaluated in terms of cumulative number of gadolinium-enhancing lesions (GEL) on magnetic resonance imaging (MRI) at 6 months and at the end of the study. METHODS: Patients unresponsive to conventional therapy, defined by at least 1 relapse and/or GEL on MRI scan in past 12 months, disease duration 2 to 10 years and Expanded Disability Status Scale (EDSS) 3.0-6.5 were randomized to receive IV 1-2×10(6) bone-marrow-derived-MSCs/Kg or placebo. After 6 months, the treatment was reversed and patients were followed-up for another 6 months. Secondary endpoints were clinical outcomes (relapses and disability by EDSS and MS Functional Composite), and several brain MRI and optical coherence tomography measures. Immunological tests were explored to assess the immunomodulatory effects. RESULTS: At baseline 9 patients were randomized to receive MSCs (n = 5) or placebo (n = 4). One patient on placebo withdrew after having 3 relapses in the first 5 months. We did not identify any serious adverse events. At 6 months, patients treated with MSCs had a trend to lower mean cumulative number of GEL (3.1, 95% CI = 1.1-8.8 vs 12.3, 95% CI = 4.4-34.5, p = 0.064), and at the end of study to reduced mean GEL (-2.8±5.9 vs 3±5.4, p = 0.075). No significant treatment differences were detected in the secondary endpoints. We observed a non-significant decrease of the frequency of Th1 (CD4+ IFN-γ+) cells in blood of MSCs treated patients. CONCLUSION: Bone-marrow-MSCs are safe and may reduce inflammatory MRI parameters supporting their immunomodulatory properties. ClinicalTrials.gov NCT01228266.

Objectives: To assess the association of primary retinal inflammation, namely retinal periphlebitis (RP) and microcystic macular edema, with clinical, brain, and retinal imaging biomarkers of multiple sclerosis (MS) severity. Methods: One hundred patients with MS underwent a neurologic and ophthalmic examination, MRI, and optical coherence tomography. Disability was assessed using the Expanded Disability Status Scale at baseline and after a 1-year follow-up. The normalized brain volume, the normal-appearing gray matter volume, and T1 lesion volume were assessed at baseline as radiologic biomarkers of disease severity. Retinal nerve fiber layer thickness and macular volume at baseline were used as surrogate markers of axonal damage. We used general linear models adjusted for sex, age, disease duration, and MS treatment to compared adjusted means of these parameters among patients with RP and patients without primary retinal inflammation. Results: Five patients showed RP, 2 showed microcystic macular edema, and the retina was normal in the remaining 93. Patients with RP had a tendency toward a higher adjusted-mean Expanded Disability Status Scale score at baseline and disability progression after a 1-year follow-up compared with patients without primary retinal inflammation. These patients also had a higher adjusted-mean T1 lesion volume (adjusted differences: 10.4, 95% confidence interval [CI]: 0.6 to 20.2; p = 0.038) and lower T1 brain volume (adjusted differences: −68, 95% CI: −139 to 2; p = 0.059). Patients with RP had a lower adjusted-mean retinal nerve fiber layer thickness (adjusted differences: −13.4, 95% CI: −24.4 to −2.3; p = 0.018) and a trend toward lower macular volume. Conclusions: These results support the role of RP as a biomarker of MS severity.

Brain lesions in patients with multiple sclerosis may lead to abnormal excitability of brainstem reflex circuits because of impairment of descending control pathways. We hypothesized that such abnormality should show in the analysis of blink reflex responses in the form of asymmetries in response size. The study was done in 20 patients with relapsing-remitting multiple sclerosis and 12 matched healthy subjects. We identified first patients with latency abnormalities (AbLat). Then, we analyzed response size by calculating the R2c/R2 ratio to stimulation of either side and the mean area of the R2 responses obtained in the same side. Patients with significantly larger response size with respect to healthy subjects in at least one side were considered to have abnormal response excitability (AbEx). We also examined the blink reflex excitability recovery (BRER) and prepulse inhibition (BRIP) of either side in search for additional indices of asymmetry in response excitability. Neurophysiological data were correlated with MRI-determined brain lesion-load and volume. Eight patients were identified as AbLat (median Expanded Disability Status Scale-EDSS = 2.75) and 7 of them had ponto-medullary lesions. Nine patients were identified as AbEx (EDSS = 1.5) and only 2 of them, who also were AbLat, had ponto-medullary lesions. In AbEx patients, the abnormalities in response size were confined to one side, with a similar tendency in most variables (significantly asymmetric R1 amplitude, BRER index and BRIP percentage). AbEx patients had asymmetric distribution of hemispheral lesions, in contrast with the symmetric pattern observed in AbLat. The brainstem lesion load was significantly lower in AbEx than in AbLat patients (p = 0.04). Asymmetric abnormalities in blink reflex response excitability in patients with multiple sclerosis are associated with lesser disability and lower tissue loss than abnormalities in response latency. Testing response excitability could provide a reliable neurophysiological index of dysfunction in early stages of multiple sclerosis.

Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols . The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition.

BackgroundCorpus callosum (CC) is a common target for multiple sclerosis (MS) pathology. We investigated the influence of CC damage on physical disability and cognitive dysfunction using a multimodal approach.MethodsTwenty-one relapsing-remitting MS patients and 13 healthy controls underwent structural MRI and diffusion tensor of the CC (fractional anisotropy; mean diffusivity, MD; radial diffusivity, RD; axial diffusivity). Interhemisferic transfer of motor inhibition was assessed by recording the ipsilateral silent period (iSP) to transcranial magnetic stimulation. We evaluated cognitive function using the Brief Repeatable Battery and physical disability using the Expanded Disability Status Scale (EDSS) and the MS Functional Composite (MSFC) z-score.ResultsThe iSP latency correlated with physical disability scores (r ranged from 0.596 to 0.657, P values from 0.004 to 0.001), and with results of visual memory (r = -0.645, P = 0.002), processing speed (r = -0.51, P = 0.018) and executive cognitive domain tests (r = -0.452, P = 0.039). The area of the rostrum correlated with the EDSS (r = -0.442, P = 0.045). MD and RD correlated with cognitive performance, mainly with results of visual and verbal memory tests (r ranged from -0.446 to -0.546, P values from 0.048 to 0.011). The iSP latency correlated with CC area (r = -0.345, P = 0.049), volume (r = -0.401, P = 0.002), MD (r = 0.404, P = 0.002) and RD (r = 0.415, P = 0.016).ConclusionsWe found evidence for structural and microstructural CC abnormalities associated with impairment of motor callosal inhibitory conduction in MS. CC damage may contribute to cognitive dysfunction and in less extent to physical disability likely through a disconnection mechanism.

BACKGROUND: Multiple Sclerosis (MS) is an immune-mediated disease of the Central Nervous System with two major underlying etiopathogenic processes: inflammation and neurodegeneration. The latter determines the prognosis of this disease. MS is the main cause of non-traumatic disability in middle-aged populations. FINDINGS: The MS-VisualPath Cohort was set up to study the neurodegenerative component of MS using advanced imaging techniques by focusing on analysis of the visual pathway in a middle-aged MS population in Barcelona, Spain. We started the recruitment of patients in the early phase of MS in 2010 and it remains permanently open. All patients undergo a complete neurological and ophthalmological examination including measurements of physical and disability (Expanded Disability Status Scale; Multiple Sclerosis Functional Composite and neuropsychological tests), disease activity (relapses) and visual function testing (visual acuity, color vision and visual field). The MS-VisualPath protocol also assesses the presence of anxiety and depressive symptoms (Hospital Anxiety and Depression Scale), general quality of life (SF-36) and visual quality of life (25-Item National Eye Institute Visual Function Questionnaire with the 10-Item Neuro-Ophthalmic Supplement). In addition, the imaging protocol includes both retinal (Optical Coherence Tomography and Wide-Field Fundus Imaging) and brain imaging (Magnetic Resonance Imaging). Finally, multifocal Visual Evoked Potentials are used to perform neurophysiological assessment of the visual pathway. DISCUSSION: The analysis of the visual pathway with advance imaging and electrophysilogical tools in parallel with clinical information will provide significant and new knowledge regarding neurodegeneration in MS and provide new clinical and imaging biomarkers to help monitor disease progression in these patients.