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In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early or distinct disease processes. The role of MRI-observed DAWM is understudied due to a lack of automated assessment methods. Supervised deep learning (DL) methods are highly capable in this domain, but require large sets of labeled data. To overcome this challenge, a DL-based network (DAWM-Net) was trained using semi-supervised learning on a limited set of labeled data for segmentation of DAWM, focal lesions, and normal-appearing brain tissues on multiparametric MRI. DAWM-Net segmentation performance was compared to a previous intensity thresholding-based method on an independent test set from expert consensus (N = 25). Segmentation overlap by Dice Similarity Coefficient (DSC) and Spearman correlation of DAWM volumes were assessed. DAWM-Net showed DSC > 0.93 for normal-appearing brain tissues and DSC > 0.81 for focal lesions. For DAWM-Net, the DAWM DSC was 0.49 ± 0.12 with a moderate volume correlation (ρ = 0.52, p < 0.01). The previous method showed lower DAWM DSC of 0.26 ± 0.08 and lacked a significant volume correlation (ρ = 0.23, p = 0.27). These results demonstrate the feasibility of DL-based DAWM auto-segmentation with semi-supervised learning. This tool may facilitate future investigation of the role of DAWM in MS., (© 2024. The Author(s).)

Objective: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients., Methods: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing-remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume., Results: The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm 3 ) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size., Conclusion: Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm 3 . The best performance was achieved when the input included all five multispectral image sets.

Objective: To investigate the performance of deep learning (DL) based on fully convolutional neural network (FCNN) in segmenting brain tissues in a large cohort of multiple sclerosis (MS) patients., Methods: We developed a FCNN model to segment brain tissues, including T2-hyperintense MS lesions. The training, validation, and testing of FCNN were based on ~1000 magnetic resonance imaging (MRI) datasets acquired on relapsing-remitting MS patients, as a part of a phase 3 randomized clinical trial. Multimodal MRI data (dual-echo, FLAIR, and T1-weighted images) served as input to the network. Expert validated segmentation was used as the target for training the FCNN. We cross-validated our results using the leave-one-center-out approach., Results: We observed a high average (95% confidence limits) Dice similarity coefficient for all the segmented tissues: 0.95 (0.92-0.98) for white matter, 0.96 (0.93-0.98) for gray matter, 0.99 (0.98-0.99) for cerebrospinal fluid, and 0.82 (0.63-1.0) for T2 lesions. High correlations between the DL segmented tissue volumes and ground truth were observed ( R 2  > 0.92 for all tissues). The cross validation showed consistent results across the centers for all tissues., Conclusion: The results from this large-scale study suggest that deep FCNN can automatically segment MS brain tissues, including lesions, with high accuracy.

Background: The dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known., Purpose: To determine the required training size for a desired accuracy in brain MRI segmentation in multiple sclerosis (MS) using DL., Study Type: Retrospective analysis of MRI data acquired as part of a multicenter clinical trial., Study Population: In all, 1008 patients with clinically definite MS., Field Strength/sequence: MRIs were acquired at 1.5T and 3T scanners manufactured by GE, Philips, and Siemens with dual turbo spin echo, FLAIR, and T 1 -weighted turbo spin echo sequences., Assessment: Segmentation results using an automated analysis pipeline and validated by two neuroimaging experts served as the ground truth. A DL model, based on a fully convolutional neural network, was trained separately using 16 different training sizes. The segmentation accuracy as a function of the training size was determined. These data were fitted to the learning curve for estimating the required training size for desired accuracy., Statistical Tests: The performance of the network was evaluated by calculating the Dice similarity coefficient (DSC), and lesion true-positive and false-positive rates., Results: The DSC for lesions showed much stronger dependency on the sample size than gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). When the training size was increased from 10 to 800 the DSC values varied from 0.00 to 0.86 ± 0.016 for T 2 lesions, 0.87 ± 009 to 0.94 ± 0.004 for GM, 0.86 ± 0.08 to 0.94 ± 0.005 for WM, and 0.91 ± 0.009 to 0.96 ± 0.003 for CSF., Data Conclusion: Excellent segmentation was achieved with a training size as small as 10 image volumes for GM, WM, and CSF. In contrast, a training size of at least 50 image volumes was necessary for adequate lesion segmentation., Level of Evidence: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1487-1496., (© 2019 International Society for Magnetic Resonance in Medicine.)

Background Enhancing lesions on MRI scans obtained after contrast material administration are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable to develop methods that can predict enhancing lesions without the use of contrast material. Purpose To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods This study involved prospective analysis of existing MRI data. A convolutional neural network was used for classification of enhancing lesions on unenhanced MRI scans. This classification was performed for each slice, and the slice scores were combined by using a fully connected network to produce participant-wise predictions. The network input consisted of 1970 multiparametric MRI scans from 1008 patients recruited from 2005 to 2009. Enhanced lesions on postcontrast T1-weighted images served as the ground truth. The network performance was assessed by using fivefold cross-validation. Statistical analysis of the network performance included calculation of lesion detection rates and areas under the receiver operating characteristic curve (AUCs). Results MRI scans from 1008 participants (mean age, 37.7 years ± 9.7; 730 women) were analyzed. At least one enhancing lesion was observed in 519 participants. The sensitivity and specificity averaged across the five test sets were 78% ± 4.3 and 73% ± 2.7, respectively, for slice-wise prediction. The corresponding participant-wise values were 72% ± 9.0 and 70% ± 6.3. The diagnostic performances (AUCs) were 0.82 ± 0.02 and 0.75 ± 0.03 for slice-wise and participant-wise enhancement prediction, respectively. Conclusion Deep learning used with conventional MRI identified enhanced lesions in multiple sclerosis from images from unenhanced multiparametric MRI with moderate to high accuracy. © RSNA, 2019.

Background: Magnetic resonance images with multiple contrasts or sequences are commonly used for segmenting brain tissues, including lesions, in multiple sclerosis (MS). However, acquisition of images with multiple contrasts increases the scan time and complexity of the analysis, possibly introducing factors that could compromise segmentation quality., Objective: To investigate the effect of various combinations of multi-contrast images as input on the segmented volumes of gray (GM) and white matter (WM), cerebrospinal fluid (CSF), and lesions using a deep neural network., Methods: U-net, a fully convolutional neural network was used to automatically segment GM, WM, CSF, and lesions in 1000 MS patients. The input to the network consisted of 15 combinations of FLAIR, T1-, T2-, and proton density-weighted images. The Dice similarity coefficient (DSC) was evaluated to assess the segmentation performance. For lesions, true positive rate (TPR) and false positive rate (FPR) were also evaluated. In addition, the effect of lesion size on lesion segmentation was investigated., Results: Highest DSC was observed for all the tissue volumes, including lesions, when the input was combination of all four image contrasts. All other input combinations that included FLAIR also provided high DSC for all tissue classes. However, the quality of lesion segmentation showed strong dependence on the input images. The DSC and TPR values for inputs with the four contrast combination and FLAIR alone were very similar, but FLAIR showed a moderately higher FPR for lesion size <100 μl. For lesions smaller than 20 μl all image combinations resulted in poor performance. The segmentation quality improved with lesion size., Conclusions: Best performance for segmented tissue volumes was obtained with all four image contrasts as the input, and comparable performance was attainable with FLAIR only as the input, albeit with a moderate increase in FPR for small lesions. This implies that acquisition of only FLAIR images provides satisfactory tissue segmentation. Lesion segmentation was poor for very small lesions and improved rapidly with lesion size., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Deep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI., Purpose: To automatically evaluate the quality of multicenter structural brain MRI images using an ensemble DL model based on deep convolutional neural networks (DCNNs)., Study Type: Retrospective., Population: The study included 1064 brain images of autism patients and healthy controls from the Autism Brain Imaging Data Exchange (ABIDE) database. MRI data from 110 multiple sclerosis patients from the CombiRx study were included for independent testing., Sequence: T 1 -weighted MR brain images acquired at 3T., Assessment: The ABIDE data were separated into training (60%), validation (20%), and testing (20%) sets. The ensemble DL model combined the results from three cascaded networks trained separately on the three MRI image planes (axial, coronal, and sagittal). Each cascaded network consists of a DCNN followed by a fully connected network. The quality of image slices from each plane was evaluated by the DCNN and the resultant image scores were combined into a volumewise quality rating using the fully connected network. The DL predicted ratings were compared with manual quality evaluation by two experts., Statistical Tests: Receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, and positive (PPV) and negative (NPV) predictive values., Results: The AUC, sensitivity, specificity, accuracy, PPV, and NPV for image quality evaluation of the ABIDE test set using the ensemble model were 0.90, 0.77, 0.85, 0.84, 0.42, and 0.96, respectively. On the CombiRx set the same model achieved performance of 0.71, 0.41, 0.84, 0.73, 0.48, and 0.80., Data Conclusion: This study demonstrated the high accuracy of DL in evaluating image quality of structural brain MRI in multicenter studies., Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1260-1267., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: To improve the conspicuity of white matter lesions (WMLs) in multiple sclerosis (MS) using patient-specific optimization of single-slab 3D fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI)., Materials and Methods: Sixteen MS patients were enrolled in a prospective 3.0T MRI study. FLAIR inversion time and echo time were automatically optimized for each patient during the same scan session based on measurements of the relative proton density and relaxation times of the brain tissues. The optimization criterion was to maximize the contrast between gray matter (GM) and white matter (WM), while suppressing cerebrospinal fluid. This criterion also helps increase the contrast between WMLs and WM. The performance of the patient-specific 3D FLAIR protocol relative to the fixed-parameter protocol was assessed both qualitatively and quantitatively., Results: Patient-specific optimization achieved a statistically significant 41% increase in the GM-WM contrast ratio (P < 0.05) and 32% increase in the WML-WM contrast ratio (P < 0.01) compared with fixed-parameter FLAIR. The increase in WML-WM contrast ratio correlated strongly with echo time (P < 10 -11 ). Two experienced neuroradiologists indicated substantially higher lesion conspicuity on the patient-specific FLAIR images over conventional FLAIR in 3-4 cases (intrarater correlation coefficient ICC = 0.72). In no case was the image quality of patient-specific FLAIR considered inferior to conventional FLAIR by any of the raters (ICC = 0.32)., Conclusion: Changes in proton density and relaxation times render fixed-parameter FLAIR suboptimal in terms of lesion contrast. Patient-specific optimization of 3D FLAIR increases lesion conspicuity without scan time penalty, and has potential to enhance the detection of subtle and small lesions in MS., Level of Evidence: 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:557-564., (© 2016 International Society for Magnetic Resonance in Medicine.)

Purpose: We present a platform, GRAphical Pipeline Environment (GRAPE), to facilitate the development of patient-adaptive magnetic resonance imaging (MRI) protocols., Methods: GRAPE is an open-source project implemented in the Qt C++ framework to enable graphical creation, execution, and debugging of real-time image analysis algorithms integrated with the MRI scanner. The platform provides the tools and infrastructure to design new algorithms, and build and execute an array of image analysis routines, and provides a mechanism to include existing analysis libraries, all within a graphical environment. The application of GRAPE is demonstrated in multiple MRI applications, and the software is described in detail for both the user and the developer., Results: GRAPE was successfully used to implement and execute three applications in MRI of the brain, performed on a 3.0-T MRI scanner: (i) a multi-parametric pipeline for segmenting the brain tissue and detecting lesions in multiple sclerosis (MS), (ii) patient-specific optimization of the 3D fluid-attenuated inversion recovery MRI scan parameters to enhance the contrast of brain lesions in MS, and (iii) an algebraic image method for combining two MR images for improved lesion contrast., Conclusions: GRAPE allows graphical development and execution of image analysis algorithms for inline, real-time, and adaptive MRI applications.

Purpose: Postacquisition combination of three-dimensional T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) images can improve the visualization of brain lesions in multiple sclerosis (MS). However, an optimal way to combine these images has not been described so far. The main objective of this study is to investigate an optimal combination of T2w and FLAIR to improve the conspicuity of MS lesions., Materials and Methods: We determined the parameters for a generalized multiplicative image combination which maximize the contrast-to-noise ratio (CNR) between lesions and normal-appearing brain tissue through simulations and verified experimentally. MRI data from 11 MS patients acquired at 3 Tesla were retrospectively analyzed using the proposed approach and compared with conventional FLAIR, and to images obtained by direct multiplication of T2w and FLAIR (FLAIR 2 ). Image quality was assessed by region-of-interest analysis. In addition, to evaluate the degree of cerebrospinal fluid (CSF) suppression, CSF-to-gray matter (CSF/GM) ratio was calculated. Reduction in global image contrast was assessed by computing the reduction in the contrast of mid-level intensity values., Results: An optimal combination was found to be the third order expression: FLAIR 3 = FLAIR 1.55 × T2w 1.45 . Compared with FLAIR, the lesion CNR was significantly increased by 1.9× (P < 0.005) and 2.5× (P < 0.001) using FLAIR 2 and FLAIR 3 , respectively. CSF/GM ratio was increased by 1.7× in FLAIR 2 (P < 0.001) compared with FLAIR, while it was reduced to 0.7× on FLAIR 3 (P < 0.05). The mid-intensity contrast was preserved on FLAIR 2 (P = 0.2), and decreased by 29% on FLAIR 3 (P < 0.001)., Conclusion: These results show that the optimized combination of FLAIR and T2w can improve MS lesion conspicuity. J. Magn. Reson. Imaging 2016;44:1293-1300., (© 2016 International Society for Magnetic Resonance in Medicine.)

A comprehensive analysis of the effect of lesion in-painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing-remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in-painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in-painting. The effect of in-painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in-painting than without. The effect of in-painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in-painting is ∼2%. Based on these results, it appears that in-painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749-3760, 2015. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)

In this retrospective study we tested the hypothesis that the net effect of impaired electrical conduction and therefore increased heat dissipation in multiple sclerosis (MS) results in elevated lateral ventricular (LV) cerebrospinal fluid (CSF) diffusivity as a measure of brain temperature estimated in vivo using diffusion tensor imaging (DTI). We used validated DTI-based segmentation methods to obtain normalized LV-CSF volume and its corresponding CSF diffusivity in 108 MS patients and 103 healthy controls in the age range of 21-63 years. The LV CSF diffusivity was ~2% higher in MS compared to controls that correspond to a temperature rise of ~1°C that could not be explained by changes in the CSF viscosity due to altered CSF protein content in MS. The LV diffusivity decreased with age in healthy controls (r=-0.29; p=0.003), but not in MS (r=0.15; p=0.11), possibly related to MS pathology. Age-adjusted LV diffusivity increased with lesion load (r=0.518; p=1×10(-8)). Our data suggest that the total brain lesion load is the primary contributor to the increase in LV CSF diffusivity in MS. These findings suggest that LV diffusivity is a potential in vivo biomarker of the mismatch between heat generation and dissipation in MS. We also discuss limitations and possible confounders., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Longitudinal magnetic resonance imaging (MRI) studies show that a fraction of the multiple sclerosis (MS) T2-lesions contain T1-hypointense components that may persist to represent severe, irreversible tissue damage. It is not known why certain lesions convert to persistent T1-hypointense lesions., Objective: We hypothesized that the T1-hypointense lesions disproportionately distribute in the more hypoperfused areas of the brain. Here we investigated the association between hypoperfusion and T1-hypointense lesion distributions., Methods: MRI and cerebral blood flow (CBF) data were acquired on 45 multiple sclerosis (MS) patients and 20 healthy controls. CBF maps were generated using pseudo-continuous arterial spin labeling technique. The lesion probability distribution maps were superimposed on the CBF maps., Results: Two distinct CBF clusters were observed in the white matter (WM) both in healthy controls and MS patients. An overall reduction in CBF was observed in MS patients compared to healthy controls. The majority of the T1-hypointense lesions were concentrated almost exclusively in the WM regions with lower CBF. The T2-hyperintense lesions were more generally distributed in both higher and lower perfused WM., Conclusion: This study suggests an association between hypoperfusion and T1-hypointense lesions.

Background: Chronic cerebrospinal venous insufficiency (CCSVI) was implicated in the pathophysiology of multiple sclerosis (MS)., Objective: We evaluated neurosonography (NS), magnetic resonance venography (MRV), and transluminal venography (TLV) in subsets of MS patients drawn from a single-center, prospective, case-control study of 206 MS and 70 non-MS volunteers., Methods: As previously reported, findings on high-resolution B-mode NS imaging with color and spectral Doppler of the extracranial and intracranial venous drainage consistent with CCSVI were similar among MS and non-MS volunteers (3.88% vs 7.14%; p = 0.266). Ninety-nine MS participants consented to intravascular contrast-enhanced 3D MRV to assess their major systemic and intracranial venous circulation, and 40 advanced to TLV that included pressure measurements of the superior vena cava, internal jugular, brachiocephalic, and azygous veins., Results: NS findings and MRV patterns were discrepant for 26/98 evaluable subjects, including four with abnormal findings on NS that had normal venous anatomy by MRV. In no instance were TLV pressure gradients indicative of clinically significant functional stenosis encountered. The three imaging approaches provided generally consistent data with discrepancies referable to inherent technique properties., Conclusions: Our findings lend no support for altered venous outflow dynamics as common among MS patients, nor do they likely contribute to the disease process.

Objective: Chronic cerebrospinal venous insufficiency (CCSVI) has been implicated in the pathophysiology of multiple sclerosis (MS). We sought to determine whether neurosonography (NS) provides reliable information on cerebral venous outflow patterns specific to MS., Methods: This was a single-center, prospective case-control study of volunteer MS and non-MS participants. A neurosonologist, blind to the subjects' diagnosis, used high-resolution B-mode imaging with color and spectral Doppler to systematically investigate, capture, and record extracranial and intracranial venous drainage. These neuroimaging results were evaluated and scored by an expert blinded to subjects' information and with no interactions with the participants., Results: Altogether, 276 subjects were studied: 206 with MS and 70 non-MS. MS patients were older than non-MS subjects (48.3±9.9 vs 44.3±11.8 years, p<0.007), with durations from first symptoms and diagnosis of 13.7±10 and 9.9±7.8 years, and Expanded Disability Status Scale of 2.6±2.0. Overall, 82 subjects (29.7%) fulfilled 1 of 5 NS criteria proposed for CCSVI; 13 (4.7%) fulfilled 2 criteria required for diagnosis, and none fulfilled >2 criteria. The distribution of subjects with 0, 1, or 2 criteria did not differ significantly across all diagnostic groupings, between MS and non-MS subjects, or within MS subgroups. CCSVI was present in 7.14% of non-MS and 3.88% of MS patients (p=0.266). No significant differences emerged between MS and non-MS subjects for extracranial or intracranial venous flow rates., Interpretation: NS findings described as CCSVI are much less prevalent than initially reported, and do not distinguish MS from other subjects. Our findings do not support the hypothesis that CCSVI is causally associated with MS., (© 2013 American Neurological Association.)

Objective: A double-blind, randomized, controlled study was undertaken to determine whether combined use of interferon β-1a (IFN) 30 μg intramuscularly weekly and glatiramer acetate (GA) 20 mg daily is more efficacious than either agent alone in relapsing-remitting multiple sclerosis., Methods: A total of 1,008 participants were randomized and followed until the last participant enrolled completed 3 years. The primary endpoint was reduction in annualized relapse rate utilizing a strict definition of relapse. Secondary outcomes included time to confirmed disability, Multiple Sclerosis Functional Composite (MSFC) score, and magnetic resonance imaging (MRI) metrics., Results: Combination IFN+GA was not superior to the better of the single agents (GA) in risk of relapse. Both the combination therapy and GA were significantly better than IFN in reducing the risk of relapse. The combination was not better than either agent alone in lessening confirmed Expanded Disability Status Scale progression or change in MSFC over 36 months. The combination was superior to either agent alone in reducing new lesion activity and accumulation of total lesion volumes. In a post hoc analysis, combination therapy resulted in a higher proportion of participants attaining disease activity-free status (DAFS) compared to either single arm, driven by the MRI results., Interpretation: Combining the 2 most commonly prescribed therapies for multiple sclerosis did not produce a significant clinical benefit over 3 years. An effect was seen on some MRI metrics. In a test of comparative efficacy, GA was superior to IFN in reducing the risk of exacerbation. The extension phase for CombiRx will address whether the observed differences in MRI and DAFS findings predict later clinical differences., (Copyright © 2013 American Neurological Association.)

Multiple sclerosis (MS) is the most common immune-mediated disabling neurological disease of the central nervous system. The pathogenesis of MS is not fully understood. Histopathology implicates both demyelination and axonal degeneration as the major contributors to the accumulation of disability. The application of several in vivo quantitative magnetic resonance imaging (MRI) methods to both lesioned and normal-appearing brain tissue has not yet provided a solid conclusive support of the hypothesis that MS might be a diffuse disease. In this work, we adopted FreeSurfer to provide standardized macrostructure or volumetry of lesion free normal-appearing brain tissue in combination with multiple quantitative MRI metrics (T(2) relaxation time, diffusion tensor anisotropy and diffusivities) that characterize tissue microstructural integrity. By incorporating a large number of healthy controls, we have attempted to separate the natural age-related change from the disease-induced effects. Our work shows elevation in diffusivity and relaxation times and reduction in volume in a number of normal-appearing white matter and gray matter structures in relapsing-remitting multiple sclerosis patients. These changes were related in part with the spatial distribution of lesions. The whole brain lesion load and age-adjusted expanded disability status score showed strongest correlations in regions such as corpus callosum with qMRI metrics that are believed to be specific markers of axonal dysfunction, consistent with histologic data of others indicating axonal loss that is independent of focal lesions. Our results support that MS at least in part has a neurodegenerative component., (Copyright © 2011 Elsevier B.V. All rights reserved.)

The human brain thalami play essential roles in integrating cognitive, sensory, and motor functions. In multiple sclerosis (MS), quantitative magnetic resonance imaging (qMRI) measurements of the thalami provide important biomarkers of disease progression, but late development and aging confound the interpretation of data collected from patients over a wide age range. Thalamic tissue volume loss due to natural aging and its interplay with lesion-driven pathology has not been investigated previously. In this work, we used standardized thalamic volumetry combined with diffusion tensor imaging, T2 relaxometry, and lesion mapping on large cohorts of controls (N = 255, age range = 6.2-69.1 years) and MS patients (N = 109, age range = 20.8-68.5 years) to demonstrate early age- and lesion-independent thalamic neurodegeneration.

Background: Accurate classification of multiple sclerosis (MS) lesions in the brain cortex may be important in understanding their impact on cognitive impairment (CI). Improved accuracy in identification/classification of cortical lesions was demonstrated in a study combining two magnetic resonance imaging (MRI) sequences: double inversion recovery (DIR) and T1-weighted phase-sensitive inversion recovery (PSIR)., Objective: To evaluate the role of intracortical lesions (IC) in MS-related CI and compare it with the role of mixed (MX), juxtacortical (JX), the sum of IC + MX and with total lesions as detected on DIR/PSIR images. Correlations between CI and brain atrophy, disease severity and disease duration were also sought., Methods: A total of 39 patients underwent extensive neuropsychological testing and were classified into normal and impaired groups. Images were obtained on a 3T scanner and cortical lesions were assessed blind to the cognitive status of the subjects., Results: Some 238 cortical lesions were identified (130 IC, 108 MX) in 82% of the patients; 39 JX lesions were also identified. Correlations between CI and MX lesions alone (p = 0.010) and with the sum of IC + MX lesions (p = 0.030) were found. A correlation between severity of CI and Expanded Disability Status Scale was also seen (p = 0.009)., Conclusion: Cortical lesions play an important role in CI. However, our results suggest that lesions that remain contained within the cortical ribbon do not play a more important role than ones extending into the adjacent white matter; furthermore, the size of the cortical lesion, and not the tissue-specific location, may better explain their correlation with CI.

Proton magnetic resonance spectroscopy ((1)H-MRS) provides tissue metabolic information in vivo. This article reviews the role of MRS-determined metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord in advancing our knowledge of pathologic changes in multiple sclerosis (MS). In addition, the role of MRS in objectively evaluating therapeutic efficacy is reviewed. This potential metabolic information makes MRS a unique tool to follow MS disease evolution, understand its pathogenesis, evaluate the disease severity, establish a prognosis, and objectively evaluate the efficacy of therapeutic interventions.

Purpose: To investigate the utility of caudate nuclei (CN) macro- and microstructural metrics as markers of gray matter degeneration in healthy adults and relapsing-remitting multiple sclerosis (RRMS) patients., Materials and Methods: The normal age- and pathology-related changes in caudate nuclei volume (CNV), the corresponding diffusion tensor metrics, and the T(2) relaxation times were measured in a cohort of 32 healthy adults (12 men/20 women; age range 21-59 years) and 32 age-matched RRMS patients (8 men/34 women; age range 21-57 years)., Results: Smaller values in both the absolute CNV and the caudate volume ratio relative to the total intracranial volume (CNVp) were observed in the RRMS group relative to healthy controls. The fractional anisotropy (FA), based on the diffusion tensor imaging (DTI) of the CN increased with age in healthy adults (r = 0.52; P = 0.003) but not in patients (r = 0.28; P = 0.12). The caudate FA value was approximately 9% larger in RRMS patients relative to controls (P = 0.001). The mean diffusivity of the CN was greater in the RRMS group compared to controls (P = 0.02). The caudate T(2) relaxation times were smaller in the RRMS group relative to the control group (3% reduction, P = 0.05). T(2) relaxation times did not exhibit age-related changes (P > 0.35) in either cohort. Strong and significant correlations between CNVp and whole-brain lesion load (r = -0.48; P = 0.005) and whole-brain CSF fraction (r = -0.46; P = 0.01) were also noted., Conclusion: These preliminary findings indicate that caudate DTI-derived metrics can serve as potential quantitative radiological markers of MS pathology.

Purpose: To prospectively perform a direct measurement of fractional anisotropy (FA) and mean diffusivity (MD) in cortical lesions of patients with multiple sclerosis (MS)., Materials and Methods: The study was approved by the institutional review board and was HIPAA compliant; informed consent was obtained. Magnetic resonance (MR) images, including double inversion-recovery (DIR), phase-sensitive inversion-recovery (PSIR), and diffusion-tensor images, were acquired from nine MS patients with cortical lesions (five women, four men; median age, 47 years) and nine age- and sex-matched volunteer control subjects. Following nonlinear elastically constrained image registration for aligning diffusion-weighted images to DIR images, maps of FA and MD were computed for each subject. Cortical lesions were identified on DIR images and validated by using PSIR images. The diffusion-tensor imaging maps were then overlaid on the coregistered DIR images, and mean FA and MD values were measured in regions of interest drawn on the cortical lesions. Differences between normal gray matter (GM) and cortical lesions were evaluated by using the generalized estimating equation. FA and MD histograms of whole brain and GM (global analysis) in healthy control subjects and MS patients were also computed for comparison with those in previously published studies., Results: FA and MD values were significantly higher in cortical lesions compared with similar regions in healthy control subjects. Histogram peak FA was significantly decreased and peak MD was significantly increased in patients relative to control subjects., Conclusion: DIR and PSIR combined with nonlinear image registration allowed direct focal measurement of FA and MD in cortical lesions., ((c) RSNA, 2008.)

A technique that involves minimal operator intervention was developed and implemented for identification and quantification of black holes on T1-weighted magnetic resonance images (T1 images) in multiple sclerosis (MS). Black holes were segmented on T1 images based on grayscale morphological operations. False classification of black holes was minimized by masking the segmented images with images obtained from the orthogonalization of T2-weighted and T1 images. Enhancing lesion voxels on postcontrast images were automatically identified and eliminated from being included in the black hole volume. Fuzzy connectivity was used for the delineation of black holes. The performance of this algorithm was quantitatively evaluated on 14 MS patients.

The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)- and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations, and ratio maps of PD- and T2-weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field-expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under, and correct estimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low lesion load and 93% of the lesions in those cases with high lesion load.

In addition to providing information on tissue structure, magnetic resonance (MR) technology offers the potential to investigate tissue metabolism and function. MR spectroscopy (MRS) offers a wealth of data on the biochemistry of a selected brain tissue volume, which represent potential surrogate markers for the pathology underlying multiple sclerosis (MS). In particular, the N-acetylaspartate peak in an MR spectrum is a putative marker of neuronal and axonal integrity, and the choline peak appears to reflect cell-membrane metabolism. On this basis, a diminished N-acetylaspartate peak is interpreted to represent neuronal/axonal dysfunction or loss, and an elevated choline peak represents heightened cell-membrane turnover, as seen in demyelination, remyelination, inflammation, or gliosis. Therefore, MRS may provide a unique tool to evaluate the severity of MS, establish a prognosis, follow disease evolution, understand its pathogenesis, and evaluate the efficacy of therapeutic interventions, which complements the information obtained from the various forms of assessment made by conventional MR imaging.

A method for automatic identification and delineation of contrast-enhanced multiple sclerosis (MS) lesions on brain magnetic resonance images is described. This method relies on adaptive local segmentation derived from the morphological "open" and "reconstruction" operations on gray scale images for identification of both lesion and nonlesion enhancements. Nonlesion enhancements from vasculature and extrameningeal tissues are identified by exploiting their topologic relationship to the brain mask. Enhancing structures without a blood-brain-barrier, such as choroid plexus, are identified and eliminated by spatially mapping the locations of the MS lesions visualized on dual echo images onto the post-contrast images. Delineation of enhancements is realized using fuzzy connectivity. Both the detection and delineation results are validated using statistical methods.

Magnetic resonance spectroscopy provides noninvasive insight into the regional and global biochemical alterations that are concomitants of the dynamic processes that underlie the evolution of fundamental pathologic changes in multiple sclerosis. These include now well-recognized alterations of neuronal biochemical markers that accompany tissue destruction readily visualized by magnetic resonance imaging, but also dynamic changes in several metabolites that indicate pathological processes that precede the magnetic resonance imaging-defined lesion, or completely escape current high-resolution imaging.

Background: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. Objective: To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. Materials and Methods: Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. Results: LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. Conclusions: NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients. Methods: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing–remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume. Results: The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size. Conclusion: Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets. [ABSTRACT FROM AUTHOR]

Objective: To investigate the performance of deep learning (DL) based on fully convolutional neural network (FCNN) in segmenting brain tissues in a large cohort of multiple sclerosis (MS) patients. Methods: We developed a FCNN model to segment brain tissues, including T2-hyperintense MS lesions. The training, validation, and testing of FCNN were based on ~1000 magnetic resonance imaging (MRI) datasets acquired on relapsing–remitting MS patients, as a part of a phase 3 randomized clinical trial. Multimodal MRI data (dual-echo, FLAIR, and T1-weighted images) served as input to the network. Expert validated segmentation was used as the target for training the FCNN. We cross-validated our results using the leave-one-center-out approach. Results: We observed a high average (95% confidence limits) Dice similarity coefficient for all the segmented tissues: 0.95 (0.92–0.98) for white matter, 0.96 (0.93–0.98) for gray matter, 0.99 (0.98–0.99) for cerebrospinal fluid, and 0.82 (0.63–1.0) for T2 lesions. High correlations between the DL segmented tissue volumes and ground truth were observed (R 2 > 0.92 for all tissues). The cross validation showed consistent results across the centers for all tissues. Conclusion: The results from this large-scale study suggest that deep FCNN can automatically segment MS brain tissues, including lesions, with high accuracy. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment (CI) cannot be diagnosed by magnetic resonance imaging (MRI). Functional magnetic resonance imaging (fMRI) paradigms, such as the immediate/delayed memory task (I/DMT), detect varying degrees of working memory (WM). Preliminary findings using I/DMT showed differences in blood oxygenation level dependent (BOLD) activation between impaired (MSCI, n = 12) and non-impaired (MSNI, n = 9) multiple sclerosis (MS) patients. Objectives: The aim of the study was to confirm CI detection based on I/DMT BOLD activation in a larger cohort of MS patients. The role of T2 lesion volume (LV) and Expanded Disability Status Scale (EDSS) in magnitude of BOLD signal was also sought. Methods: A total of 50 patients (EDSS mean (m) = 3.2, disease duration (DD) m = 12 years, and age m = 40 years) underwent the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) and I/DMT. Working memory activation (WMa) represents BOLD signal during DMT minus signal during IMT. CI was based on MACFIMS. Results: A total of 10 MSNI, 30 MSCI, and 4 borderline patients were included in the analyses. Analysis of variance (ANOVA) showed MSNI had significantly greater WMa than MSCI, in the left prefrontal cortex and left supplementary motor area (p = 0.032). Regression analysis showed significant inverse correlations between WMa and T2 LV/EDSS in similar areas (p = 0.005, 0.004, respectively). Conclusion: I/DMT-based BOLD activation detects CI in MS. Larger studies are needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Purpose To automatically optimize three-dimensional double-inversion recovery (3D-DIR) MRI of the brain on a patient-by-patient basis. Methods DIR is a powerful MRI technique that allows simultaneous suppression of white matter (WM) and cerebrospinal fluid (CSF) in brain imaging. Unfortunately, the tissue suppression is not always consistent across patients. We propose patient-specific optimization of WM suppression for improved gray matter (GM)-WM contrast. Relaxation times were measured in the same scan session, and through real time processing were used for calculating DIR inversion times for maximum tissue contrast. Signal evolution during the variable-flip-angle turbo-spin-echo readout was calculated using the extended phase graph algorithm. Patient-specific optimization was examined in five healthy volunteers and two multiple sclerosis patients. Two volunteers were scanned twice for reproducibility. The contrast ratios, GM signal-to-noise ratio (SNR), and image histogram were used to assess the performance of this patient-specific approach. Results Automated optimization of 3D-DIR was successfully completed in all experiments with processing time of ∼1 min. GM-WM contrast ratio tripled with the optimized DIR sequence, with only a 19% decrease in GM-CSF contrast and 30% SNR penalty. Conclusion Patient-specific optimization is feasible and significantly improves GM-WM contrast on 3D-DIR with a moderate decrease in the GM SNR. Magn Reson Med 75:585-593, 2016. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose To demonstrate the efficacy of contrast enhanced magnetic resonance venography (CEMRV) using gadofosveset trisodium in the comprehensive evaluation of the intracranial and extracranial venous system. Materials and Methods Temporal signal decay, in-plane saturation and flow artifacts were assessed in an institutional review board approved, HIPAA compliant CEMRV study of 99 subjects. In a 39 subject subset, percent diameter narrowing of the internal jugular (IJ), brachiocephalic and azygous veins were coded according to the following ordinal grades for both catheter venography (CV) and CEMRV: grade 0 ≤ 50%, grade 1 >50% and ≤ 75%, grade 2 >75% and <100% and grade 3 = 100% and compared with pressure gradient measurements obtained during CV. Results There was no significant signal decay, in-plane saturation or flow artifacts identified on CEMRV or hemodynamically significant pressure gradients identified on CV. All brachiocephalic and azygous veins had matched grade 0 narrowing on both modalities. Discrepancy between modalities occurred in the IJ veins at the level of thyroid gland where 15% of IJ veins had CEMRV grade ≥ 1 narrowing compared with 4% for CV or below the thyroid gland where 5% of IJ veins had CEMRV grade ≥ 1 narrowing compared with 20% for CV. There was fair agreement (κ = 0.24) between modalities for grade of narrowing in the combined data set of all coded veins. Conclusion CEMRV using gadofosveset trisodium is accurate in the evaluation of the venous system. J. Magn. Reson. Imaging 2014;40:630-640. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Background and objectives: Based on the application of newer magnetic resonance imaging (MRI) acquisition sequences, the detection of cortical lesions (CL) in multiple sclerosis (MS) has significantly improved. Double inversion recovery (DIR) at 3T has increased the detection sensitivity and classification specificity when combined with phase sensitive inversion recovery (PSIR). Previous findings with 3D magnetization prepared rapid acquisition with gradient echo (MPRAGE) sequences, showed improved classification specificity of purely intracortical (IC) and mixed (MX) lesions, compared to the classification based on DIR/PSIR. Direct comparison between the detection of CL by 3D MPRAGE and by DIR/PSIR at 3T has not been evaluated. Methods: Eleven subjects were imaged on a 3T magnet. DIR/PSIR and 3D MPRAGE images were reviewed independently. Each image set was reviewed twice; only lesions detected on both sessions were scored. Review time per scan was ~5min for DIR/PSIR and ~15min for 3D MPRAGE. Results: We identified 141 CL (62 IC+79 MX) based on DIR/PSIR images vs. 93 (38 IC+55 MX) based on MPRAGE from all eleven patients. MPRAGE under-detected the number of CL in seven cases and over-detected the number of CL in three, only one case had the same number of CL on both sets of images. Conclusions: Combination DIR/PSIR at 3T is superior to 3D MPRAGE for detection of cortical gray matter lesions in MS. The contrast-to-noise ratio of CL appears to be inferior on the MPRAGE images relative to DIR/PSIR [Copyright &y& Elsevier]

The article presents a study which investigates the association between hypoperfusion and TI-hypointense lesion distributions in white matter in multiple sclerosis (MS). An overview of the methods of the study is given. It observes cerebral blood flow clusters in the white matter. The association between hypoperfusion and TI-hypointense lesions is suggested.

The article presents a study on the masked multimodal imaging assessment of chronic cerebrospinal venous insufficiency (CCSVI) and its implication in multiple sclerosis (MS). It mentions the evaluation of magnetic resonance venography (MRV) and transluminal venography (TLV) and neurosonography (NS) in the subsets of patients with the autoimmune disease. It also notes the application of contingency tests for assessing agreement and intraobserver reliability within modalities.

The article presents a study which aims to determine the impacts of oral teriflunomide on multiple sclerosis (MS) pathology deduced by magnetic resonance imaging (MRI). Annualized relapse rate and affirmed progression of disability were main and key secondary outcomes. The study shows that teriflunomide offered benefits on brain MRI activity throughout multiple measures, having a dose impact evident on several markers.

Purpose: To investigate the utility of multimodal quantitative MRI (qMRI) and atlas-based methods to identify characteristics of lesion-driven injury and neurodegeneration in relapsing remitting multiple sclerosis (RRMS). Materials and Methods: This work is health insurance portability and accountability act compliant. High resolution T1-weighted, dual echo, and fluid-attenuated inversion recovery and diffusion tensor MRI images were prospectively acquired on 68 RRMS patients (range, 25-58 years) and 68 age-matched controls. The data were analyzed using standardized human brain atlas-based tissue segmentation procedures to obtain regional volumes and their corresponding T2 relaxation times and DTI maps. Results: Group-averaged brain atlas-based qMRI maps of T2, fractional anisotropy and diffusivities are visually presented and compared between controls and RRMS. The analysis shows a widespread injury in RRMS. Atrophy of the corpus callosum (CC) was substantial in RRMS. The qMRI attributes of the neocortex in combination with the CC such as T2 and diffusivities were elevated and correlated with disability. Conclusion: Using a standardized multimodal qMRI acquisition and analyses that accounted for lesion distribution we demonstrate that cerebral pathology is widespread in RRMS. Our analysis of CC and neocortex qMRI metrics in relation to disability points to a neurodegenerative injury component that is independent from lesions. J. Magn. Reson. Imaging 2012;. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: Accurate classification of multiple sclerosis (MS) lesions in the brain cortex may be important in understanding their impact on cognitive impairment (CI). Improved accuracy in identification/classification of cortical lesions was demonstrated in a study combining two magnetic resonance imaging (MRI) sequences: double inversion recovery (DIR) and T1-weighted phase-sensitive inversion recovery (PSIR).Objective: To evaluate the role of intracortical lesions (IC) in MS-related CI and compare it with the role of mixed (MX), juxtacortical (JX), the sum of IC + MX and with total lesions as detected on DIR/PSIR images. Correlations between CI and brain atrophy, disease severity and disease duration were also sought.Methods: A total of 39 patients underwent extensive neuropsychological testing and were classified into normal and impaired groups. Images were obtained on a 3T scanner and cortical lesions were assessed blind to the cognitive status of the subjects.Results: Some 238 cortical lesions were identified (130 IC, 108 MX) in 82% of the patients; 39 JX lesions were also identified. Correlations between CI and MX lesions alone (p = 0.010) and with the sum of IC + MX lesions (p = 0.030) were found. A correlation between severity of CI and Expanded Disability Status Scale was also seen (p = 0.009).Conclusion: Cortical lesions play an important role in CI. However, our results suggest that lesions that remain contained within the cortical ribbon do not play a more important role than ones extending into the adjacent white matter; furthermore, the size of the cortical lesion, and not the tissue-specific location, may better explain their correlation with CI. [ABSTRACT FROM AUTHOR]

In measuring the association between magnetic resonance spectroscopic imaging (MRSI)-observed neurochemicals from multiple sclerosis (MS) patients, the classic correlation coefficients such as the Pearson's r, Spearman's ρ and Kendall's τ do not take into account the spatial dependence of the observations. This paper reports a comparative study on these classic correlation coefficients (Pearson's r, Spearman's ρ and Kendall's τ) and some more recent correlation coefficients (Tjostheim's a, the modified t) that take into account the spatial dependence of the intensities of the concentrations of several neurochemicals in MS patients. Our study indicates that the use of the classic correlation coefficients that ignores the spatial dependence of the observations may overestimate the statistical significance of the results. [ABSTRACT FROM AUTHOR]

Provides information on a study which examined the relationship of urinary myelin basic protein-like material with cranial magnetic resonance imaging in advanced multiple sclerosis (MS). Design and methods of the study; Results; Conclusions. INSET: PATIENTS, MATERIALS, AND METHODS.

Abstract: While the majority of individuals with multiple sclerosis (MS) develop significant clinical disability, a subset experiences a disease course with minimal impairment even in the presence of significant apparent tissue damage on magnetic resonance imaging (MRI). Functional magnetic resonance imaging (fMRI) in MS patients with low disability suggests that increased use of the cognitive control system may limit the clinical manifestation of the disease. The current fMRI studies tested the hypothesis that nondisabled MS patients show increased recruitment of cognitive control regions while performing sensory, motor and cognitive tasks. Twenty two patients with relapsing–remitting MS and an Expanded Disability Status Scale (EDSS) score of ≤1.5 and 23 matched healthy controls were recruited. Subjects underwent fMRI while observing flashing checkerboards, performing right or left hand movements, or executing the 2-back working memory task. Compared to control subjects, patients demonstrated increased activation of the right dorsolateral prefrontal cortex and anterior cingulate cortex during the performance of the working memory task. This pattern of functional recruitment also was observed during the performance of non-dominant hand movements. These results support the mounting evidence of increased functional recruitment of cognitive control regions in the working memory system of MS patients with low disability and provide new evidence for the role of increased cognitive control recruitment in the motor system. [Copyright &y& Elsevier]

Abstract: Tensor based morphometry (TBM) was applied to determine the atrophy of deep gray matter (DGM) structures in 88 relapsing multiple sclerosis (MS) patients. For group analysis of atrophy, an unbiased atlas was constructed from 20 normal brains. The MS brain images were co-registered with the unbiased atlas using a symmetric inverse consistent nonlinear registration. These studies demonstrate significant atrophy of thalamus, caudate nucleus, and putamen even at a modest clinical disability, as assessed by the expanded disability status score (EDSS). A significant correlation between atrophy and EDSS was observed for different DGM structures: (thalamus: r =−0.51, p =3.85×10−7; caudate nucleus: r =−0.43, p =2.35×10−5; putamen: r =−0.36, p =6.12×10−6). Atrophy of these structures also correlated with 1) T2 hyperintense lesion volumes (thalamus: r =−0.56, p =9.96×10−9; caudate nucleus: r =−0.31, p =3.10×10−3; putamen: r =−0.50, p =6.06×10−7), 2) T1 hypointense lesion volumes (thalamus: r =−0.61, p =2.29×10−10; caudate nucleus: r =−0.35, p =9.51×10−4; putamen: r =−0.43, p =3.51×10−5), and 3) normalized CSF volume (thalamus: r =−0.66, p =3.55×10−12; caudate nucleus: r =−0.52, p =2.31×10−7, and putamen: r =−0.66, r =2.13×10−12). More severe atrophy was observed mainly in thalamus at higher EDSS. These studies appear to suggest a link between the white matter damage and DGM atrophy in MS. [Copyright &y& Elsevier]