Brain lesion location and clinical status 20 years after a diagnosis of clinically isolated syndrome suggestive of multiple sclerosis

Early prediction of unfavorable evolution after a first clinical episode suggestive of multiple sclerosis: the EUMUS score

BACKGROUND

Predicting disease progression in patients with the first clinical episode suggestive of multiple sclerosis (MS) is crucial for personalized therapeutic approaches. This study aimed to develop the EUMUS score for accurately estimating the risk of early evidence of disease activity and progression (EDA).

METHODS

Retrospective analysis was conducted on data from 221 patients with a first clinical MS episode collected from four Italian MS centers. Various variables including socio-demographics, clinical features, cerebrospinal fluid analysis, evoked potentials, and brain MRI were considered. A prognostic multivariate regression model was identified to develop the EUMUS score. The optimal cutoff for predicting the transition from no evidence of disease activity (NEDA3) to EDA was determined. The accuracy of the prognostic model and score were tested in a separate UK MS cohort.

RESULTS

After 12 months, 61.54% of patients experienced relapses and/or new MRI lesions. Younger age (OR 0.96, CI 0.93-0.99; p = 0.005), MRI infratentorial lesion(s) at baseline (OR 2.21, CI 1.27-3.87; p = 0.005), positive oligoclonal bands (OR 2.89, CI 1.47-5.69; p = 0.002), and abnormal lower limb somatosensory-evoked potentials (OR 2.77, CI 1.41-5.42; p = 0.003) were significantly associated with increased risk of EDA. The EUMUS score demonstrated good specificity (72%) and correctly classified 80% of patients with EDA in the independent UK cohort.

CONCLUSIONS

The EUMUS score is a simple and useful tool for predicting MS evolution within 12 months of the first clinical episode. It has the potential to guide personalized therapeutic approaches and aid in clinical decision-making.

Susceptibility networks reveal independent patterns of brain iron abnormalities in multiple sclerosis

Brain iron homeostasis is necessary for healthy brain function. MRI and histological studies have shown altered brain iron levels in the brains of patients with multiple sclerosis (MS), particularly in the deep gray matter (DGM). Previous studies were able to only partially separate iron-modifying effects because of incomplete knowledge of iron-modifying processes and influencing factors. It is therefore unclear to what extent and at which stages of the disease different processes contribute to brain iron changes. We postulate that spatially covarying magnetic susceptibility networks determined with Independent Component Analysis (ICA) reflect, and allow for the study of, independent processes regulating iron levels. We applied ICA to quantitative susceptibility maps for 170 individuals aged 9-81 years without neurological disease ("Healthy Aging" (HA) cohort), and for a cohort of 120 patients with MS and 120 age- and sex-matched healthy controls (HC; together the "MS/HC" cohort). Two DGM-associated "susceptibility networks" identified in the HA cohort (the Dorsal Striatum and Globus Pallidus Interna Networks) were highly internally reproducible (i.e. "robust") across multiple ICA repetitions on cohort subsets. DGM areas overlapping both robust networks had higher susceptibility levels than DGM areas overlapping only a single robust network, suggesting that these networks were caused by independent processes of increasing iron concentration. Because MS is thought to accelerate brain aging, we hypothesized that associations between age and the two robust DGM-associated networks would be enhanced in patients with MS. However, only one of these networks was altered in patients with MS, and it had a null age association in patients with MS rather than a stronger association. Further analysis of the MS/HC cohort revealed three additional disease-related networks (the Pulvinar, Mesencephalon, and Caudate Networks) that were differentially altered between patients with MS and HCs and between MS subtypes. Exploratory regression analyses of the disease-related networks revealed differential associations with disease duration and T2 lesion volume. Finally, analysis of ROI-based disease effects in the MS/HC cohort revealed an effect of disease status only in the putamen ROI and exploratory regression analysis did not show associations between the caudate and pulvinar ROIs and disease duration or T2 lesion volume, showing the ICA-based approach was more sensitive to disease effects. These results suggest that the ICA network framework increases sensitivity for studying patterns of brain iron change, opening a new avenue for understanding brain iron physiology under normal and disease conditions.

Evolution from a first clinical demyelinating even to multiple sclerosis in the REFLEX trial Regional susceptibility in the conversion to multiple sclerosis at disease onset and their amenability to subcutaneous interferon beta-1a

Background and purpose

In the REFLEX trial (ClinicalTrials.gov identifier: NCT00404352), patients with a first clinical demyelinating event (FCDE) displayed significantly delayed onset of multiple sclerosis (MS; McDonald criteria) when treated with subcutaneous interferon beta‐1a (sc IFN β‐1a) versus placebo. This post hoc analysis evaluated the effect of sc IFN β‐1a on spatio‐temporal evolution of disease activity, assessed by changes in T2 lesion distribution, in specific brain regions of such patients and its relationship with conversion to MS.

Methods

Post hoc analysis of baseline and 24‐month magnetic resonance imaging data from FCDE patients who received sc IFN β‐1a 44 μg once or three times weekly, or placebo in the REFLEX trial. Patients were grouped according to McDonald MS status (converter/non‐converter) or treatment (sc IFN β‐1a/placebo). For each patient group, a baseline lesion probability map (LPM) and longitudinal new/enlarging and shrinking/disappearing LPMs were created. Lesion location/frequency of lesion occurrence were assessed in the white matter.

Results

At Month 24, lesion frequency was significantly higher in the anterior thalamic radiation (ATR) and corticospinal tract (CST) of converters versus non‐converters (p < 0.05). Additionally, the overall distribution of new/enlarging lesions across the brain at Month 24 was similar in placebo‐ and sc IFN β‐1a‐treated patients (ratio: 0.95). Patients treated with sc IFN β‐1a versus placebo showed significantly lower new lesion frequency in specific brain regions (cluster corrected): ATR (p = 0.025), superior longitudinal fasciculus (p = 0.042), CST (p = 0.048), and inferior longitudinal fasciculus (p = 0.048).

Conclusions

T2 lesion distribution in specific brain locations predict conversion to McDonald MS and show significantly reduced new lesion occurrence after treatment with sc IFN β‐1a in an FCDE population.

Multiple sclerosis lesions in motor tracts from brain to cervical cord: spatial distribution and correlation with disability

Despite important efforts to solve the clinico-radiological paradox, correlation between lesion load and physical disability in patients with multiple sclerosis remains modest. One hypothesis could be that lesion location in corticospinal tracts plays a key role in explaining motor impairment. In this study, we describe the distribution of lesions along the corticospinal tracts from the cortex to the cervical spinal cord in patients with various disease phenotypes and disability status. We also assess the link between lesion load and location within corticospinal tracts, and disability at baseline and 2-year follow-up. We retrospectively included 290 patients (22 clinically isolated syndrome, 198 relapsing remitting, 39 secondary progressive, 31 primary progressive multiple sclerosis) from eight sites. Lesions were segmented on both brain (T2-FLAIR or T2-weighted) and cervical (axial T2- or T2*-weighted) MRI scans. Data were processed using an automated and publicly available pipeline. Brain, brainstem and spinal cord portions of the corticospinal tracts were identified using probabilistic atlases to measure the lesion volume fraction. Lesion frequency maps were produced for each phenotype and disability scores assessed with Expanded Disability Status Scale score and pyramidal functional system score. Results show that lesions were not homogeneously distributed along the corticospinal tracts, with the highest lesion frequency in the corona radiata and between C2 and C4 vertebral levels. The lesion volume fraction in the corticospinal tracts was higher in secondary and primary progressive patients (mean = 3.6 ± 2.7% and 2.9 ± 2.4%), compared to relapsing-remitting patients (1.6 ± 2.1%, both P < 0.0001). Voxel-wise analyses confirmed that lesion frequency was higher in progressive compared to relapsing-remitting patients, with significant bilateral clusters in the spinal cord corticospinal tracts (P < 0.01). The baseline Expanded Disability Status Scale score was associated with lesion volume fraction within the brain (r = 0.31, P < 0.0001), brainstem (r = 0.45, P < 0.0001) and spinal cord (r = 0.57, P < 0.0001) corticospinal tracts. The spinal cord corticospinal tracts lesion volume fraction remained the strongest factor in the multiple linear regression model, independently from cord atrophy. Baseline spinal cord corticospinal tracts lesion volume fraction was also associated with disability progression at 2-year follow-up (P = 0.003). Our results suggest a cumulative effect of lesions within the corticospinal tracts along the brain, brainstem and spinal cord portions to explain physical disability in multiple sclerosis patients, with a predominant impact of intramedullary lesions.

Impact of Lesion Location on Longitudinal Myelin Water Fraction Change in Chronic Multiple Sclerosis Lesions

BACKGROUND AND PURPOSE

To examine the impact of lesion location on longitudinal myelin water fraction (MWF) changes in chronic multiple sclerosis (MS) lesions. Relative hypoxia, due to vascular watershed regions of the cerebrum, has been implicated in lesion development but impact on ongoing demyelination is unknown.

METHODS

Forty-eight patients with relapsing-remitting and secondary progressive MS had two MWF scans with fast acquisition, spiral trajectory, and T2prep (FAST-T2) sequence, at an interval of 2.0 (±.3) years. Lesion location was identified based upon cerebral lobe and relation to the ventricles. Change in MWF was assessed using a mixed effects model, controlling for lesion location and patient covariates.

RESULTS

Average age was 42.3 (±12) years, mean disease duration was 9.7 (±9.1) years, and median Expanded Disability Status Score (EDSS) was 2.5 (±2.3). The majority of 512 chronic lesions was located in the frontal and parietal lobes (75.6%) and more often periventricular (44.7%). All occipital lesions were periventricular. The average lesion MWF decreased from baseline (.07 ± .03) to 2 years (.06 ±.03) P < .01. Lesions within the occipital lobe showed a significant reduction in MWF as compared to other lobes.

CONCLUSIONS

Chronic lesions in the occipital lobe showed the greatest reduction in MWF. Neuroanatomical localization of lesions to the occipital horns of the lateral ventricles, a watershed region, may contribute to ongoing demyelination in this lesion type.

White matter lesion location correlates with disability in relapsing multiple sclerosis

Background

Lesion location is a prognostic factor of disease progression and disability accrual.

Objective

To investigate lesion formation in 11 brain regions, assess correlation between lesion location and physical and cognitive disability measures and investigate treatment effects by region.

Methods

In 2355 relapsing–remitting multiple sclerosis patients from the FREEDOMS and FREEDOMS II studies, we extracted T2-weighted lesion number, volume and density for each brain region; we investigated the (Spearman) correlation in lesion formation between brain regions, studied association between location and disability (at baseline and change over 2 years) using linear/logistic regression and assessed the regional effects of fingolimod versus placebo in negative binomial models.

Results

At baseline, the majority of lesions were found in the supratentorial brain. New and enlarging lesions over 24 months developed mainly in the frontal and sublobar regions and were substantially correlated to pre-existing lesions at baseline in the supratentorial brain (p = 0.37–0.52), less so infratentorially (p = −0.04–0.23). High sublobar lesion density was consistently and significantly associated with most disability measures at baseline and worsening of physical disability over 24 months. The treatment effect of fingolimod 0.5 mg was consistent across the investigated areas and tracts.

Conclusion

These results highlight the role of sublobar lesions for the accrual of disability in relapsing–remitting multiple sclerosis.

Predicting cognitive decline in multiple sclerosis: a 5-year follow-up study

Cognitive decline is common in multiple sclerosis and strongly affects overall quality of life. Despite the identification of cross-sectional MRI correlates of cognitive impairment, predictors of future cognitive decline remain unclear. The objective of this study was to identify which MRI measures of structural damage, demographic and/or clinical measures at baseline best predict cognitive decline, during a 5-year follow-up period. A total of 234 patients with clinically definite multiple sclerosis and 60 healthy control subjects were examined twice, with a 5-year interval (mean = 4.9 years, standard deviation = 0.9). An extensive neuropsychological evaluation was performed at both time points and the reliable change index was computed to evaluate cognitive decline. Both whole-brain and regional MRI (3 T) measures were assessed at baseline, including white matter lesion volume, diffusion-based white matter integrity, cortical and deep grey matter volume. Logistic regression analyses were performed to determine which baseline measures best predicted cognitive decline in the entire sample as well as in early relapsing-remitting (symptom duration <10 years), late relapsing-remitting (symptom duration ≥10 years) and progressive phenotypes. At baseline, patients with multiple sclerosis had a mean disease duration of 14.8 (standard deviation = 8.4) years and 96/234 patients (41%) were classified as cognitively impaired. A total of 66/234 patients (28%) demonstrated cognitive decline during follow-up, with higher frequencies in progressive compared to relapsing-remitting patients: 18/33 secondary progressive patients (55%), 10/19 primary progressive patients (53%) and 38/182 relapsing-remitting patients (21%). A prediction model that included only whole-brain MRI measures (Nagelkerke R2 = 0.22, P < 0.001) showed cortical grey matter volume as the only significant MRI predictor of cognitive decline, while a prediction model that assessed regional MRI measures (Nagelkerke R2 = 0.35, P < 0.001) indicated integrity loss of the anterior thalamic radiation, lesions in the superior longitudinal fasciculus and temporal atrophy as significant MRI predictors for cognitive decline. Disease stage specific regressions showed that cognitive decline in early relapsing-remitting multiple sclerosis was predicted by white matter integrity damage, while cognitive decline in late relapsing-remitting and progressive multiple sclerosis was predicted by cortical atrophy. These results indicate that patients with more severe structural damage at baseline, and especially cortical atrophy, are more prone to suffer from cognitive decline. New studies now need to further elucidate the underlying mechanisms leading to cortical atrophy, evaluate the value of including cortical atrophy as a possible outcome marker in clinical trials as well as study its potential use in individual patient management.

The topographical model of MS: Empirical evaluation of the recapitulation hypothesis

OBJECTIVE

Using the topographical model of multiple sclerosis (MS) to evaluate a longitudinal cohort we (1) test the recapitulation hypothesis, positing that patients' "disease topography" predicts the clinical pattern of disability accumulation; and (2) identify leading indicators of progression.

METHODS

10 patients who transitioned from relapsing-remitting MS to secondary progressive MS (SPMS) were evaluated. Neurologic exams were analyzed from relapses, at time of SPMS diagnosis, and most recent visit. Functional systems (FS), location/laterality, and recovery were recorded. The pyramidal/motor system was the target FS assessing symptom laterality and severity at relapse and SPMS time-points. Each patient's clinical course was mapped using the topographical model software.

RESULTS

Cohort was 80% female, age 31.6 ± 8.6 years at diagnosis, followed average 23.8 ± 8.8 years, mean 3.1 relapses before SPMS. 83.3 ± 0.2% of relapse symptoms were present at transition to SPMS, increasing to 91.0 ± 0.2% at most recent visit. This demonstrates concordance between the topographical distribution of relapse symptoms and deficits from subsequent progression. In the topographical model, progression became apparent 7.75 years earlier than SPMS was diagnosed in practice.

CONCLUSIONS

We demonstrate the model's utility in depicting patients' disease topography as the loci of clinical progression. This could allow for earlier recognition of progressive disease by identifying leading indicators of progression.

Imaging markers of multiple sclerosis prognosis

PURPOSE OF REVIEW

Studies of large longitudinal cohorts of patients with multiple sclerosis (MS) have emphasized the prognostic value of conventional MRI markers, at least during early stages. Advanced imaging metrics derived from quantitative MRI and PET provide relevant information about microstructural damage within and outside visible lesions that may be more sensitive to predict long-term disability. Here, we summarize the most recent findings regarding the prognostic value of imaging markers throughout MS stages.

RECENT FINDINGS

In clinically isolated syndrome, the presence of at least one brain or spinal cord T2 lesion strongly increases the risk of conversion to clinically definite MS (hazard ratio ranging from 5 to 11). Similarly, the occurrence of new white matter lesions is strongly predictive of subsequent relapse rate and response to current disease modifying therapies. Beyond white matter lesions, volumetric changes in the grey matter and normal-appearing tissue damage are more sensitive prognostic markers for physical and cognitive disability, especially in progressive MS.

SUMMARY

Although white matter lesion number and volume still remains the imaging metric used in daily clinical practice, further development of advanced imaging predictors of long-term disability should allow a better stratification of patients in future clinical trials aimed at promoting repair or neuroprotection.

New Insights into Multiple Sclerosis Clinical Course from the Topographical Model and Functional Reserve

Clinical course in multiple sclerosis (MS) is difficult to predict on group and individual levels. We discuss the topographical model of MS as a new approach to characterizing the clinical course, with the potential to personalize disability progression based on each individual patient's pattern of disease burden (eg, lesion location) and reserve. The dynamic clinical threshold depicted in this visual model may help clinicians to educate patients about clinical phenotype and disease burden, and foster an understanding of the difference between relapses and pseudoexacerbations. There is an emphasis on building reserve against cognitive and physical decline, encouraging agency among patients.

Composite MRI measures and short-term disability in patients with clinically isolated syndrome suggestive of MS

BACKGROUND

The use of composite magnetic resonance imaging (MRI) measures has been suggested to better explain disability in patients with multiple sclerosis (MS). However, little is known about the utility of composite scores at the earliest stages of the disease.

OBJECTIVE

To investigate whether, in patients with clinically isolated syndrome (CIS), a composite MRI measure, rather than the single metrics, would explain conversion to MS and would better correlate with disability at baseline and at 1 year of follow-up.

METHODS

Corticospinal tract (CST), corpus callosum (CC) and optic radiation (OR) volume, fractional anisotropy (FA), and mean diffusivity (MD) values were measured in 27 CIS patients and 24 healthy controls (HCs). Z-scores of FA, MD, and tract volume measures were calculated in patients, based on the corresponding measures obtained from HCs, and then combined in a composite score for each tract. Correlations between Z-scores at baseline and both the Expanded Disability Status Scale (EDSS) at baseline and at follow-up (FU-EDSS) were investigated.

RESULTS

Only CST, CC, and OR composite scores as well as the CST volume were significantly associated with FU-EDSS ( p = 0.005, p = 0.007, p = 0.020, and p = 0.010, respectively).

CONCLUSION

The combination of MRI measures rather than the individual metrics better captured the association between tissue damage in both the CC, OR and CST and short-term follow-up disability.

The topographical model of multiple sclerosis: A dynamic visualization of disease course

Relapses and progression contribute to multiple sclerosis (MS) disease course, but neither the relationship between them nor the spectrum of clinical heterogeneity has been fully characterized. A hypothesis-driven, biologically informed model could build on the clinical phenotypes to encompass the dynamic admixture of factors underlying MS disease course. In this medical hypothesis, we put forth a dynamic model of MS disease course that incorporates localization and other drivers of disability to propose a clinical manifestation framework that visualizes MS in a clinically individualized way. The topographical model encapsulates 5 factors (localization of relapses and causative lesions; relapse frequency, severity, and recovery; and progression rate), visualized utilizing dynamic 3-dimensional renderings. The central hypothesis is that, like symptom recrudescence in Uhthoff phenomenon and pseudoexacerbations, progression clinically recapitulates prior relapse symptoms and unmasks previously silent lesions, incrementally revealing underlying lesion topography. The model uses real-time simulation software to depict disease course archetypes and illuminate several well-described but poorly reconciled phenomena including the clinical/MRI paradox and prognostic significance of lesion location and burden on disease outcomes. Utilization of this model could allow for earlier and more clinically precise identification of progressive MS and predictive implications can be empirically tested.

Higher-resolution MR elastography reveals early mechanical signatures of neuroinflammation in patients with clinically isolated syndrome

PURPOSE

To assess if higher-resolution magnetic resonance elastography (MRE) is a technique that can measure the in vivo mechanical properties of brain tissue and is sensitive to early signatures of brain tissue degradation in patients with clinically isolated syndrome (CIS).

MATERIALS AND METHODS

Seventeen patients with CIS and 33 controls were investigated by MRE with a 3T MRI scanner. Full-wave field data were acquired at seven drive frequencies from 30 to 60 Hz. The spatially resolved higher-resolution maps of magnitude |G*| and phase angle φ of the complex-valued shear modulus were obtained in addition to springpot model parameters. These parameters were spatially averaged in white matter (WM) and whole-brain regions and correlated with clinical and radiological parameters.

RESULTS

Spatially resolved MRE revealed that CIS reduced WM viscoelasticity, independent of imaging markers of multiple sclerosis and clinical scores. |G*| was reduced by 14% in CIS (1.4 ± 0.2 kPa vs. 1.7 ± 0.2 kPa, P < 0.001, 95% confidence interval [CI] [-0.4, -0.1] kPa), while φ (0.66 ± 0.04 vs. 0.67 ± 0.04, P = 0.65, 95% CI [-0.04, 0.02]) remained unaltered. Springpot-based shear elasticity showed only a trend of CIS-related reduction (3.4 ± 0.5 kPa vs. 3.7 ± 0.5 kPa, P = 0.06, 95% CI [-0.6, 0.02] kPa) in the whole brain.

CONCLUSION

We demonstrate that CIS leads to significantly reduced elasticity of brain parenchyma, raising the prospect of using MRE as an imaging marker for subtle and diffuse tissue damage in neuroinflammatory diseases. J. Magn. Reson. Imaging 2016;44:51-58.

Update on clinically isolated syndrome

Optic neuritis, myelitis and brainstem syndrome accompanied by a symptomatic MRI T2 or FLAIR hyperintensity and T1 hypointensity are highly suggestive of multiple sclerosis (MS) in young adults. They are called "clinically isolated syndrome" (CIS) and correspond to the typical first multiple sclerosis (MS) episode, especially when associated with other asymptomatic demyelinating lesions, without clinical, radiological and immunological sign of differential diagnosis. After a CIS, the delay of apparition of a relapse, which corresponds to the conversion to clinically definite MS (CDMS), varies from several months to more than 10 years (10-15% of cases, generally called benign RRMS). This delay is generally associated with the number and location of demyelinating lesions of the brain and spinal cord and the results of CSF analysis. Several studies comparing different MRI criteria for dissemination in space and dissemination in time of demyelinating lesions, two hallmarks of MS, provided enough substantial data to update diagnostic criteria for MS after a CIS. In the last revision of the McDonald's criteria in 2010, diagnostic criteria were simplified and now the diagnosis can be made by a single initial scan that proves the presence of active asymptomatic lesions (with gadolinium enhancement) and of unenhanced lesions. However, time to conversion remains highly unpredictable for a given patient and CIS can remain isolated, especially for idiopathic unilateral optic neuritis or myelitis. Univariate analyses of clinical, radiological, biological or electrophysiological characteristics of CIS patients in small series identified numerous risk factors of rapid conversion to MS. However, large series of CIS patients analyzing several characteristics of CIS patients and the influence of disease modifying therapies brought important information about the risk of CDMS or RRMS over up to 20 years of follow-up. They confirmed the importance of the initial MRI pattern of demyelinating lesions and of CSF oligoclonal bands. Available treatments of MS (immunomodulators or immunosuppressants) have also shown unequivocal efficacy to slow the conversion to RRMS after a CIS, but they could be unnecessary for patients with benign RRMS. Beyond diagnostic criteria, knowledge of established and potential risk factors of conversion to MS and of disability progression is essential for CIS patients' follow-up and initiation of disease modifying therapies.

Predicting outcome in clinically isolated syndrome using machine learning

We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clinically isolated syndrome (CIS), on the basis of single patient's lesion features and clinical/demographic characteristics.

Seventy-four patients at onset of CIS were scanned and clinically reviewed after one and three years. CDMS was used as the gold standard against which SVM classification accuracy was tested. Radiological features related to lesional characteristics on conventional MRI were defined a priori and used in combination with clinical/demographic features in an SVM. Forward recursive feature elimination with 100 bootstraps and a leave-one-out cross-validation was used to find the most predictive feature combinations.

30 % and 44 % of patients developed CDMS within one and three years, respectively. The SVMs correctly predicted the presence (or the absence) of CDMS in 71.4 % of patients (sensitivity/specificity: 77 %/66 %) at 1 year, and in 68 % (60 %/76 %) at 3 years on average over all bootstraps. Combinations of features consistently gave a higher accuracy in predicting outcome than any single feature.

Machine-learning-based classifications can be used to provide an “individualised” prediction of conversion to MS from subjects' baseline scans and clinical characteristics, with potential to be incorporated into routine clinical practice.

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SVMs predict the presence (or absence) of a second clinical attack in Multiple Sclerosis at 1- and 3-year follow-ups.

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SVM-based classification reaches 71.4 % accuracy, 77 % sensitivity and 66 % specificity for 1-year follow-up.

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Combinations of features give a higher accuracy than single features.

Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment

Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.

ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS

Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically "mass univariate" and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary choice of the smoothing parameter. Here we present a Bayesian spatial model to accurately model binary lesion maps and to determine if there is spatial dependence between lesion location and subject specific covariates such as MS subtype, age, gender, disease duration and disease severity measures. We apply our model to binary lesion maps derived from T2-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.

MRI in multiple sclerosis: a review of the current literature

PURPOSE OF REVIEW

This review summarizes the recent data pertaining to the use of magnetic resonance imaging (MRI) in assessing brain and spinal cord involvement in multiple sclerosis (MS).

RECENT FINDINGS

Using MRI as a tool, investigators have made progress recently in understanding the substrate and mechanisms underlying the development and evolution of focal lesions and diffuse damage in MS. The application of refined MRI sequences has markedly improved the characterization of focal lesions, in particular cortical lesions. Promising improvements have been made to clarify the pathological specificity and sensitivity of MRI techniques by performing combined histopathologic-MRI correlation studies. The use of high-field (3 T) and ultra-high-field (UHF; >3 T) MRI has further facilitated the detection of both gray matter and white matter microstructural damage, and elucidated the topographic relationship of overt damage to venous blood vessels. The development of advanced MRI postprocessing tools has led to additional progress in detecting clinically relevant regional gray matter and white matter damage.

SUMMARY

MRI continues to play a pivotal role in the investigation of MS. Ongoing advances in MRI technology should further expand the current understanding of pathologic disease mechanisms and improve diagnostic, prognostic, and monitoring ability in patients with MS.

Relevance of brain lesion location to cognition in relapsing multiple sclerosis

OBJECTIVE

To assess the relationship between cognition and brain white matter (WM) lesion distribution and frequency in patients with relapsing-remitting multiple sclerosis (RR MS).

METHODS

MRI-based T2 lesion probability map (LPM) was used to assess the relevance of brain lesion location for cognitive impairment in a group of 142 consecutive patients with RRMS. Significance of voxelwise analyses was p<0.05, cluster-corrected for multiple comparisons. The Rao Brief Repeatable Battery was administered at the time of brain MRI to categorize the MS population into cognitively preserved (CP) and cognitively impaired (CI).

RESULTS

Out of 142 RRMS, 106 were classified as CP and 36 as CI. Although the CI group had greater WM lesion volume than the CP group (p = 0.001), T2 lesions tended to be less widespread across the WM. The peak of lesion frequency was almost twice higher in CI (61% in the forceps major) than in CP patients (37% in the posterior corona radiata). The voxelwise analysis confirmed that lesion frequency was higher in CI than in CP patients with significant bilateral clusters in the forceps major and in the splenium of the corpus callosum (p<0.05, corrected). Low scores of the Symbol Digit Modalities Test correlated with higher lesion frequency in these WM regions.

CONCLUSIONS

Overall these results suggest that in MS patients, areas relevant for cognition lie mostly in the commissural fiber tracts. This supports the notion of a functional (multiple) disconnection between grey matter structures, secondary to damage located in specific WM areas, as one of the most important mechanisms leading to cognitive impairment in MS.

Spatiotemporal distribution of white matter lesions in relapsing-remitting and secondary progressive multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. MS lesions show a typical distribution pattern and primarily affect the white matter (WM) in the periventricular zone and in the centrum semiovale.

OBJECTIVE

To track lesion development during disease progression, we compared the spatiotemporal distribution patterns of lesions in relapsing-remitting MS (RRMS) and secondary progressive MS (SPMS).

METHODS

We used T1 and T2 weighted MR images of 209 RRMS and 62 SPMS patients acquired on two different 1.5 Tesla MR scanners in two clinical centers followed up for 25 (± 1.7) months. Both cross-sectional and longitudinal differences in lesion distribution between RRMS and SPMS patients were analyzed with lesion probability maps (LPMs) and permutation-based inference.

RESULTS

MS lesions clustered around the lateral ventricles and in the centrum semiovale. Cross-sectionally, compared to RRMS patients, the SPMS patients showed a significantly higher regional probability of T1 hypointense lesions (p ≤ 0.03) in the callosal body, the corticospinal tract, and other tracts adjacent to the lateral ventricles, but not of T2 lesions (peak probabilities were RRMS: T1 9%, T2 18%; SPMS: T1 21%, T2 27%). No longitudinal changes of regional T1 and T2 lesion volumes between baseline and follow-up scan were found.

CONCLUSION

The results suggest a particular vulnerability to neurodegeneration during disease progression in a number of WM tracts.