Disease progression in the first 5 years of treatment in multiple sclerosis: Predictive value of early brain and lesion volume changes.

BACKGROUND: Whether the degree of inflammation (and its resolution) and neurodegeneration after treatment initiation predicts disease progression in multiple sclerosis (MS) remains unclear. OBJECTIVES: To assess the predictive value of magnetic resonance imaging (MRI)-derived brain and lesion volume (LV) changes in years 1 and 2 of treatment for disease progression. METHODS: Patients receiving early interferon beta-1a treatment in REFLEX/REFLEXION (N = 262) were included. Predictive regression models included new/enlarging LV (positive activity), disappearing/shrinking LV (negative activity), and global/central atrophy during years 1 and 2. RESULTS: Faster global atrophy and/or pseudo-atrophy and positive lesion activity in years 1 and 2 related to an increased probability and faster conversion to clinically definite multiple sclerosis (CDMS). Negative lesion activity in year 1 and slower central atrophy in year 2 were predictive of confirmed disability progression (9-Hole Peg Test). Positive lesion activity in year 2 was predictive of faster global atrophy, while positive lesion activity in years 1 and 2 was predictive of faster central atrophy. CONCLUSIONS: A higher degree of global atrophy and/or pseudo-atrophy in year 1 was predictive of CDMS. Positive lesion activity in any year was related to CDMS and neurodegeneration. Disability was related to negative lesion activity in year 1 and slower central atrophy in year 2.

Spinal cord lesions and brain grey matter atrophy independently predict clinical worsening in definite multiple sclerosis: a 5-year, multicentre study.

OBJECTIVES: To evaluate the combined contribution of brain and cervical cord damage in predicting 5-year clinical worsening in a multicentre cohort of definite multiple sclerosis (MS) patients. METHODS: Baseline 3.0T brain and cervical cord T2-weighted and three-dimensional T1-weighted MRI was acquired in 367 patients with MS (326 relapse-onset and 41 progressive-onset) and 179 healthy controls. Expanded Disability Status Scale (EDSS) score was obtained at baseline and after a median follow-up of 5.1 years (IQR=4.8-5.2). At follow-up, patients were classified as clinically stable/worsened according to EDSS changes. Generalised linear mixed models identified predictors of clinical worsening, evolution to secondary progressive (SP) MS and reaching EDSS=3.0, 4.0 and 6.0 milestones at 5 years. RESULTS: At follow-up, 120/367 (33%) patients with MS worsened clinically; 36/256 (14%) patients with relapsing-remitting evolved to SPMS. Baseline predictors of EDSS worsening were progressive-onset versus relapse-onset MS (standardised beta (ß)=0.97), higher EDSS (ß=0.41), higher cord lesion number (ß=0.41), lower normalised cortical volume (ß=-0.15) and lower cord area (ß=-0.28) (C-index=0.81). Older age (ß=0.86), higher EDSS (ß=1.40) and cord lesion number (ß=0.87) independently predicted SPMS conversion (C-index=0.91). Predictors of reaching EDSS=3.0 after 5 years were higher baseline EDSS (ß=1.49), cord lesion number (ß=1.02) and lower normalised cortical volume (ß=-0.56) (C-index=0.88). Baseline age (ß=0.30), higher EDSS (ß=2.03), higher cord lesion number (ß=0.66) and lower cord area (ß=-0.41) predicted EDSS=4.0 (C-index=0.92). Finally, higher baseline EDSS (ß=1.87) and cord lesion number (ß=0.54) predicted EDSS=6.0 (C-index=0.91). CONCLUSIONS: Spinal cord damage and, to a lesser extent, cortical volume loss helped predicting worse 5-year clinical outcomes in MS.

Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes.

BACKGROUND: We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. METHODS: Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. RESULTS: Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. CONCLUSIONS: In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.

Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses.

PURPOSE: Volume measurement using MRI is important to assess brain atrophy in multiple sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis software introduce unwanted variability of volumes. To quantify theses effects, we compared within-scanner repeatability and between-scanner reproducibility of three different MR scanners for six brain segmentation methods. METHODS: Twenty-one people with MS underwent scanning and rescanning on three 3 T MR scanners (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) to obtain 3D T1-weighted images. FreeSurfer, FSL, SAMSEG, FastSurfer, CAT-12, and SynthSeg were used to quantify brain, white matter and (deep) gray matter volumes both from lesion-filled and non-lesion-filled 3D T1-weighted images. We used intra-class correlation coefficient (ICC) to quantify agreement; repeated-measures ANOVA to analyze systematic differences; and variance component analysis to quantify the standard error of measurement (SEM) and smallest detectable change (SDC). RESULTS: For all six software, both between-scanner agreement (ICCs ranging 0.4-1) and within-scanner agreement (ICC range: 0.6-1) were typically good, and good to excellent (ICC > 0.7) for large structures. No clear differences were found between filled and non-filled images. However, gray and white matter volumes did differ systematically between scanners for all software (p < 0.05). Variance component analysis yielded within-scanner SDC ranging from 1.02% (SAMSEG, whole-brain) to 14.55% (FreeSurfer, CSF); and between-scanner SDC ranging from 4.83% (SynthSeg, thalamus) to 29.25% (CAT12, thalamus). CONCLUSION: Volume measurements of brain, GM and WM showed high repeatability, and high reproducibility despite substantial differences between scanners. Smallest detectable change was high, especially between different scanners, which hampers the clinical implementation of atrophy measurements.

A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine.

Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R2*-mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R2* from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R2*-maps. In contrast, when using the U-Net as network architecture, a negative bias in R2* in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R2*. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R2* in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.

Exploring in vivo multiple sclerosis brain microstructural damage through T1w/T2w ratio: a multicentre study.

OBJECTIVES: To evaluate white matter and grey matter T1-weighted (w)/T2w ratio (T1w/T2w ratio) in healthy controls and patients with multiple sclerosis, and its association with clinical disability. METHODS: In this cross-sectional study, 270 healthy controls and 434 patients with multiple sclerosis were retrospectively selected from 7 European sites. T1w/T2w ratio was obtained from brain T2w and T1w scans after intensity calibration using eyes and temporal muscle. RESULTS: In healthy controls, T1w/T2w ratio increased until 50-60 years both in white and grey matter. Compared with healthy controls, T1w/T2w ratio was significantly lower in white matter lesions of all multiple sclerosis phenotypes, and in normal-appearing white matter and cortex of patients with relapsing-remitting and secondary progressive multiple sclerosis (p≤0.026), but it was significantly higher in the striatum and pallidum of patients with relapsing-remitting, secondary progressive and primary progressive multiple sclerosis (p≤0.042). In relapse-onset multiple sclerosis, T1w/T2w ratio was significantly lower in white matter lesions and normal-appearing white matter already at Expanded Disability Status Scale (EDSS) <3.0 and in the cortex only for EDSS ≥3.0 (p≤0.023). Conversely, T1w/T2w ratio was significantly higher in the striatum and pallidum for EDSS ≥4.0 (p≤0.005). In primary progressive multiple sclerosis, striatum and pallidum showed significantly higher T1w/T2w ratio beyond EDSS=6.0 (p≤0.001). In multiple sclerosis, longer disease duration, higher EDSS, higher brain lesional volume and lower normalised brain volume were associated with lower lesional and cortical T1w/T2w ratio and a higher T1w/T2w ratio in the striatum and pallidum (ß from -1.168 to 0.286, p≤0.040). CONCLUSIONS: T1w/T2w ratio may represent a clinically relevant marker sensitive to demyelination, neurodegeneration and iron accumulation occurring at the different multiple sclerosis phases.

Serum neurofilament as a predictor of 10-year grey matter atrophy and clinical disability in multiple sclerosis: a longitudinal study.

BACKGROUND: The predictive value of serum neurofilament light chain (sNfL) on long-term prognosis in multiple sclerosis (MS) is still unclear. OBJECTIVE: Investigate the relation between sNfL levels over a 2-year period in patients with relapsing-remitting MS, and clinical disability and grey matter (GM) atrophy after 10 years. METHODS: 85 patients, originally enrolled in a multicentre, randomised trial of ω-3 fatty acids, participated in a 10-year follow-up visit. sNfL levels were measured by Simoa quarterly until month 12, and then at month 24. The appearance of new gadolinium-enhancing (Gd+) lesions was assessed monthly between baseline and month 9, and then at months 12 and 24. At the 10-year follow-up visit, brain atrophy measures were obtained using FreeSurfer. RESULTS: Higher mean sNfL levels during early periods of active inflammation (Gd+ lesions present or recently present) predicted lower total (ß=-0.399, p=0.040) and deep (ß=-0.556, p=0.010) GM volume, lower mean cortical thickness (ß=-0.581, p=0.010) and higher T2 lesion count (ß=0.498, p=0.018). Of the clinical outcomes, higher inflammatory sNfL levels were associated with higher disability measured by the dominant hand Nine-Hole Peg Test (ß=0.593, p=0.004). Mean sNfL levels during periods of remission (no Gd+ lesions present or recently present) did not predict GM atrophy or disability progression. CONCLUSION: Higher sNfL levels during periods of active inflammation predicted more GM atrophy and specific aspects of clinical disability 10 years later. The findings suggest that subsequent long-term GM atrophy is mainly due to neuroaxonal degradation within new lesions.

The effect of gadolinium-based contrast-agents on automated brain atrophy measurements by FreeSurfer in patients with multiple sclerosis.

OBJECTIVE: To determine whether reliable brain atrophy measures can be obtained from post-contrast 3D T1-weighted images in patients with multiple sclerosis (MS) using FreeSurfer. METHODS: Twenty-two patients with MS were included, in which 3D T1-weighted MR images were obtained during the same scanner visit, with the same acquisition protocol, before and after administration of gadolinium-based contrast agents (GBCAs). Two FreeSurfer versions (v.6.0.1 and v.7.1.1.) were applied to calculate grey matter (GM) and white matter (WM) volumes and global and regional cortical thickness. The consistency between measures obtained in pre- and post-contrast images was assessed by intra-class correlation coefficient (ICC), the difference was investigated by paired t-tests, and the mean percentage increase or decrease was calculated for total WM and GM matter volume, total deep GM and thalamus volume, and mean cortical thickness. RESULTS: Good to excellent reliability was found between all investigated measures, with ICC ranging from 0.926 to 0.996, all p values < 0.001. GM volumes and cortical thickness measurements were significantly higher in post-contrast images by 3.1 to 17.4%, while total WM volume decreased significantly by 1.7% (all p values < 0.001). CONCLUSION: The consistency between values obtained from pre- and post-contrast images was excellent, suggesting it may be possible to extract reliable brain atrophy measurements from T1-weighted images acquired after administration of GBCAs, using FreeSurfer. However, absolute values were systematically different between pre- and post-contrast images, meaning that such images should not be compared directly. Potential systematic effects, possibly dependent on GBCA dose or the delay time after contrast injection, should be investigated. TRIAL REGISTRATION: Clinical trials.gov. identifier: NCT00360906. KEY POINTS: • The influence of gadolinium-based contrast agents (GBCAs) on atrophy measurements is still largely unknown and challenges the use of a considerable source of historical and prospective real-world data. • In 22 patients with multiple sclerosis, the consistency between brain atrophy measurements obtained from pre- and post-contrast images was excellent, suggesting it may be possible to extract reliable atrophy measurements in T1-weighted images acquired after administration of GBCAs, using FreeSurfer. • Absolute values were systematically different between pre- and post-contrast images, meaning that such images should not be compared directly, and measurements extracted from certain regions (e.g., the temporal pole) should be interpreted with caution.

Clinical applicability of quantitative atrophy measures on MRI in patients suspected of Alzheimer's disease.

OBJECTIVES: Neurodegeneration in suspected Alzheimer's disease can be determined using visual rating or quantitative volumetric assessments. We examined the feasibility of volumetric measurements of gray matter (GMV) and hippocampal volume (HCV) and compared their diagnostic performance with visual rating scales in academic and non-academic memory clinics. MATERIALS AND METHODS: We included 231 patients attending local memory clinics (LMC) in the Netherlands and 501 of the academic Amsterdam Dementia Cohort (ADC). MRI scans were acquired using local protocols, including a T1-weighted sequence. Quantification of GMV and HCV was performed using FSL and FreeSurfer. Medial temporal atrophy and global atrophy were assessed with visual rating scales. ROC curves were derived to determine which measure discriminated best between cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's dementia (AD). RESULTS: Patients attending LMC (age 70.9 ± 8.9 years; 47% females; 19% CN; 34% MCI; 47% AD) were older, had more cerebrovascular pathology, and had lower GMV and HCV compared to those of the ADC (age 64.9 ± 8.2 years; 42% females; 35% CN, 43% MCI, 22% AD). While visual ratings were feasible in > 95% of scans in both cohorts, quantification was achieved in 94-98% of ADC, but only 68-85% of LMC scans, depending on the software. Visual ratings and volumetric outcomes performed similarly in discriminating CN vs AD in both cohorts. CONCLUSION: In clinical settings, quantification of GM and hippocampal atrophy currently fails in up to one-third of scans, probably due to lack of standardized acquisition protocols. Diagnostic accuracy is similar for volumetric measures and visual rating scales, making the latter suited for clinical practice. In a real-life clinical setting, volumetric assessment of MRI scans in dementia patients may require acquisition protocol optimization and does not outperform visual rating scales. KEY POINTS: • In a real-life clinical setting, the diagnostic performance of visual rating scales is similar to that of automatic volumetric quantification and may be sufficient to distinguish Alzheimer's disease groups. • Volumetric assessment of gray matter and hippocampal volumes from MRI scans of patients attending non-academic memory clinics fails in up to 32% of cases. • Clinical MR acquisition protocols should be optimized to improve the output of quantitative software for segmentation of Alzheimer's disease-specific outcomes.

Evolution from a first clinical demyelinating event to multiple sclerosis in the REFLEX trial: Regional susceptibility in the conversion to multiple sclerosis at disease onset and its 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 ß-1a) versus placebo. This post hoc analysis evaluated the effect of sc IFN ß-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 ß-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 ß-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 ß-1a-treated patients (ratio: 0.95). Patients treated with sc IFN ß-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 ß-1a in an FCDE population.

Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis.

Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.

Longitudinal Assessment of Multiple Sclerosis with the Brain-Age Paradigm.

OBJECTIVE: During the natural course of multiple sclerosis (MS), the brain is exposed to aging as well as disease effects. Brain aging can be modeled statistically; the so-called "brain-age" paradigm. Here, we evaluated whether brain-predicted age difference (brain-PAD) was sensitive to the presence of MS, clinical progression, and future outcomes. METHODS: In a longitudinal, multicenter sample of 3,565 magnetic resonance imaging (MRI) scans, in 1,204 patients with MS and clinically isolated syndrome (CIS) and 150 healthy controls (mean follow-up time: patients 3.41 years, healthy controls 1.97 years), we measured "brain-predicted age" using T1-weighted MRI. We compared brain-PAD among patients with MS and patients with CIS and healthy controls, and between disease subtypes. Relationships between brain-PAD and Expanded Disability Status Scale (EDSS) were explored. RESULTS: Patients with MS had markedly higher brain-PAD than healthy controls (mean brain-PAD +10.3 years; 95% confidence interval [CI] = 8.5-12.1] versus 4.3 years; 95% CI = 2.1 to 6.4; p < 0.001). The highest brain-PADs were in secondary-progressive MS (+13.3 years; 95% CI = 11.3-15.3). Brain-PAD at study entry predicted time-to-disability progression (hazard ratio 1.02; 95% CI = 1.01-1.03; p < 0.001); although normalized brain volume was a stronger predictor. Greater annualized brain-PAD increases were associated with greater annualized EDSS score (r = 0.26; p < 0.001). INTERPRETATION: The brain-age paradigm is sensitive to MS-related atrophy and clinical progression. A higher brain-PAD at baseline was associated with more rapid disability progression and the rate of change in brain-PAD related to worsening disability. Potentially, "brain-age" could be used as a prognostic biomarker in early-stage MS, to track disease progression or stratify patients for clinical trial enrollment. ANN NEUROL 2020 ANN NEUROL 2020;88:93-105.

Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral.

OBJECTIVE: Spinal cord atrophy is a clinically relevant feature of multiple sclerosis (MS), but longitudinal assessments on magnetic resonance imaging using segmentation-based methods suffer from measurement variability, especially in multicenter studies. We compared the generalized boundary shift integral (GBSI), a registration-based method, with a standard segmentation-based method. METHODS: Baseline and 1-year spinal cord 3-dimensional T1-weighted images (1mm isotropic) were obtained from 282 patients (52 clinically isolated syndrome [CIS], 196 relapsing-remitting MS [RRMS], 34 progressive MS [PMS]), and 82 controls from 8 MAGNIMS (Magnetic Resonance Imaging in Multiple Sclerosis) sites on multimanufacturer and multi-field-strength scans. Spinal Cord Toolbox was used for C2-5 segmentation and cross-sectional area (CSA) calculation. After cord straightening and registration, GBSI measured atrophy based on the probabilistic boundary-shift region of interest. CSA and GBSI percentage annual volume change was calculated. RESULTS: GBSI provided similar rates of atrophy, but reduced measurement variability compared to CSA in all MS subtypes (CIS: -0.95 ± 2.11% vs -1.19 ± 3.67%; RRMS: -1.74 ± 2.57% vs -1.74 ± 4.02%; PMS: -2.29 ± 2.40% vs -1.29 ± 3.20%) and healthy controls (0.02 ± 2.39% vs -0.56 ± 3.77%). GBSI performed better than CSA in differentiating healthy controls from CIS (area under the curve [AUC] = 0.66 vs 0.53; p = 0.03), RRMS (AUC = 0.73 vs 0.59; p < 0.001), PMS (AUC = 0.77 vs 0.53; p < 0.001), and patients with disability progression from patients without progression (AUC = 0.59 vs 0.50; p = 0.04). Sample size to detect 60% treatment effect on spinal cord atrophy over 1 year was lower for GBSI than CSA (CIS: 106 vs 830; RRMS: 95 vs 335; PMS: 44 vs 215; power = 80%; alpha = 5%). INTERPRETATION: The registration-based method (GBSI) allowed better separation between MS patients and healthy controls and improved statistical power, when compared with a conventional segmentation-based method (CSA), although it is still far from perfect. ANN NEUROL 2019 ANN NEUROL 2019;86:704-713.

Gray matter T1-w/T2-w ratios are higher in Alzheimer's disease.

Myelin determines the conduction of neuronal signals along axonal connections in networks of the brain. Loss of myelin integrity in neuronal circuits might result in cognitive decline in Alzheimer's disease (AD). Recently, the ratio of T1-weighted by T2-weighted MRI has been used as a proxy for myelin content in gray matter of the cortex. With this approach, we investigated whether AD dementia patients show lower cortical myelin content (i.e., a lower T1-w/T2-w ratio value). We selected structural T1-w and T2-w MR images of 293 AD patients and 172 participants with normal cognition (NC). T1-w/T2-w ratios were computed for the whole brain and within 90 automated anatomical labeling atlas regions using SPM12, compared between groups and correlated with the neuronal injury marker tau in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE). In contrast to our hypothesis, AD patients showed higher whole brain T1-w/T2-w ratios than NC, and regionally in 31 anatomical areas (p < .0005; d = 0.21 to 0.48), predominantly in the inferior parietal lobule, angular gyrus, anterior cingulate, and precuneus. Regional higher T1-w/T2-w values were associated with higher CSF tau concentrations (p < .0005; r = .16 to .22) and worse MMSE scores (p < .0005; r = -.16 to -.21). These higher T1-w/T2-w values in AD seem to contradict previous pathological findings of demyelination and disconnectivity in AD. Future research should further investigate the biological processes reflected by increases in T1-w/T2-w values.

Deep gray matter volume loss drives disability worsening in multiple sclerosis.

OBJECTIVE: Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS. METHODS: We analyzed 3,604 brain high-resolution T1-weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing-remitting [RRMS], 128 secondary-progressive [SPMS], and 125 primary-progressive [PPMS]), over an average follow-up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow-up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time-to-EDSS progression. RESULTS: SPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time-to-EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow-up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (-1.45%), PPMS (-1.66%), and RRMS (-1.34%) than CIS (-0.88%) and HCs (-0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (-1.21%) was significantly faster than RRMS (-0.76%), CIS (-0.75%), and HCs (-0.51%). Similarly, the rate of parietal GM atrophy in SPMS (-1.24-%) was faster than CIS (-0.63%) and HCs (-0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001). INTERPRETATION: This large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210-222.

Gray matter networks and cognitive impairment in multiple sclerosis.

BACKGROUND: Coordinated patterns of gray matter morphology can be represented as networks, and network disruptions may explain cognitive dysfunction related to multiple sclerosis (MS). OBJECTIVE: To investigate whether single-subject gray matter network properties are related to impaired cognition in MS. METHODS: We studied 148 MS patients (99 female) and 33 healthy controls (HC, 21 female). Seven network parameters were computed and compared within MS between cognitively normal and impaired subjects, and associated with performance on neuropsychological tests in six cognitive domains with regression models. Analyses were controlled for age, gender, whole-brain gray matter volumes, and education level. RESULTS: Compared to MS subjects with normal cognition, MS subjects with cognitive impairment showed a more random network organization as indicated by lower lambda values (all p < 0.05). Worse average cognition and executive function were associated with lower lambda values. Impaired information processing speed, working memory, and attention were associated with lower clustering values. CONCLUSION: Our findings indicate that MS subjects with a more randomly organized gray matter network show worse cognitive functioning, suggesting that single-subject gray matter graphs may capture neurological dysfunction due to MS.

Progression of regional grey matter atrophy in multiple sclerosis.

See Stankoff and Louapre (doi:10.1093/brain/awy114) for a scientific commentary on this article.Grey matter atrophy is present from the earliest stages of multiple sclerosis, but its temporal ordering is poorly understood. We aimed to determine the sequence in which grey matter regions become atrophic in multiple sclerosis and its association with disability accumulation. In this longitudinal study, we included 1417 subjects: 253 with clinically isolated syndrome, 708 with relapsing-remitting multiple sclerosis, 128 with secondary-progressive multiple sclerosis, 125 with primary-progressive multiple sclerosis, and 203 healthy control subjects from seven European centres. Subjects underwent repeated MRI (total number of scans 3604); the mean follow-up for patients was 2.41 years (standard deviation = 1.97). Disability was scored using the Expanded Disability Status Scale. We calculated the volume of brain grey matter regions and brainstem using an unbiased within-subject template and used an established data-driven event-based model to determine the sequence of occurrence of atrophy and its uncertainty. We assigned each subject to a specific event-based model stage, based on the number of their atrophic regions. Linear mixed-effects models were used to explore associations between the rate of increase in event-based model stages, and T2 lesion load, disease-modifying treatments, comorbidity, disease duration and disability accumulation. The first regions to become atrophic in patients with clinically isolated syndrome and relapse-onset multiple sclerosis were the posterior cingulate cortex and precuneus, followed by the middle cingulate cortex, brainstem and thalamus. A similar sequence of atrophy was detected in primary-progressive multiple sclerosis with the involvement of the thalamus, cuneus, precuneus, and pallidum, followed by the brainstem and posterior cingulate cortex. The cerebellum, caudate and putamen showed early atrophy in relapse-onset multiple sclerosis and late atrophy in primary-progressive multiple sclerosis. Patients with secondary-progressive multiple sclerosis showed the highest event-based model stage (the highest number of atrophic regions, P < 0.001) at the study entry. All multiple sclerosis phenotypes, but clinically isolated syndrome, showed a faster rate of increase in the event-based model stage than healthy controls. T2 lesion load and disease duration in all patients were associated with increased event-based model stage, but no effects of disease-modifying treatments and comorbidity on event-based model stage were observed. The annualized rate of event-based model stage was associated with the disability accumulation in relapsing-remitting multiple sclerosis, independent of disease duration (P < 0.0001). The data-driven staging of atrophy progression in a large multiple sclerosis sample demonstrates that grey matter atrophy spreads to involve more regions over time. The sequence in which regions become atrophic is reasonably consistent across multiple sclerosis phenotypes. The spread of atrophy was associated with disease duration and with disability accumulation over time in relapsing-remitting multiple sclerosis.

Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study.

BACKGROUND AND PURPOSE: In vivoidentification of white matter lesions plays a key-role in evaluation of patients with multiple sclerosis (MS). Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations is lacking. In this work, five research-domain automated segmentation methods were evaluated using a multi-center MS dataset. METHODS: 70 MS patients (median EDSS of 2.0 [range 0.0-6.5]) were included from a six-center dataset of the MAGNIMS Study Group (www.magnims.eu) which included 2D FLAIR and 3D T1 images with manual lesion segmentation as a reference. Automated lesion segmentations were produced using five algorithms: Cascade; Lesion Segmentation Toolbox (LST) with both the Lesion growth algorithm (LGA) and the Lesion prediction algorithm (LPA); Lesion-Topology preserving Anatomical Segmentation (Lesion-TOADS); and k-Nearest Neighbor with Tissue Type Priors (kNN-TTP). Main software parameters were optimized using a training set (N = 18), and formal testing was performed on the remaining patients (N = 52). To evaluate volumetric agreement with the reference segmentations, intraclass correlation coefficient (ICC) as well as mean difference in lesion volumes between the automated and reference segmentations were calculated. The Similarity Index (SI), False Positive (FP) volumes and False Negative (FN) volumes were used to examine spatial agreement. All analyses were repeated using a leave-one-center-out design to exclude the center of interest from the training phase to evaluate the performance of the method on 'unseen' center. RESULTS: Compared to the reference mean lesion volume (4.85 ± 7.29 mL), the methods displayed a mean difference of 1.60 ± 4.83 (Cascade), 2.31 ± 7.66 (LGA), 0.44 ± 4.68 (LPA), 1.76 ± 4.17 (Lesion-TOADS) and -1.39 ± 4.10 mL (kNN-TTP). The ICCs were 0.755, 0.713, 0.851, 0.806 and 0.723, respectively. Spatial agreement with reference segmentations was higher for LPA (SI = 0.37 ± 0.23), Lesion-TOADS (SI = 0.35 ± 0.18) and kNN-TTP (SI = 0.44 ± 0.14) than for Cascade (SI = 0.26 ± 0.17) or LGA (SI = 0.31 ± 0.23). All methods showed highly similar results when used on data from a center not used in software parameter optimization. CONCLUSION: The performance of the methods in this multi-center MS dataset was moderate, but appeared to be robust even with new datasets from centers not included in training the automated methods.

Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant.

Grey matter atrophy is common in multiple sclerosis. However, in contrast with other neurodegenerative diseases, it is unclear whether grey matter atrophy in multiple sclerosis is a diffuse 'global' process or develops, instead, according to distinct anatomical patterns. Using source-based morphometry we searched for anatomical patterns of co-varying cortical thickness and assessed their relationships with white matter pathology, physical disability and cognitive functioning. Magnetic resonance imaging was performed at 3 T in 208 patients with long-standing multiple sclerosis (141 females; age = 53.7 ± 9.6 years; disease duration = 20.2 ± 7.1 years) and 60 age- and sex-matched healthy controls. Spatial independent component analysis was performed on cortical thickness maps derived from 3D T1-weighted images across all subjects to identify co-varying patterns. The loadings, which reflect the presence of each cortical thickness pattern in a subject, were compared between patients with multiple sclerosis and healthy controls with generalized linear models. Stepwise linear regression analyses were used to assess whether white matter pathology was associated with these loadings and to identify the cortical thickness patterns that predict measures of physical and cognitive dysfunction. Ten cortical thickness patterns were identified, of which six had significantly lower loadings in patients with multiple sclerosis than in controls: the largest loading differences corresponded to the pattern predominantly involving the bilateral temporal pole and entorhinal cortex, and the pattern involving the bilateral posterior cingulate cortex. In patients with multiple sclerosis, overall white matter lesion load was negatively associated with the loadings of these two patterns. The final model for physical dysfunction as measured with Expanded Disability Status Scale score (adjusted R(2) = 0.297; P < 0.001) included the predictors age, overall white matter lesion load, the loadings of two cortical thickness patterns (bilateral sensorimotor cortex and bilateral insula), and global cortical thickness. The final model predicting average cognition (adjusted R(2) = 0.469; P < 0.001) consisted of age, the loadings of two cortical thickness patterns (bilateral posterior cingulate cortex and bilateral temporal pole), overall white matter lesion load and normal-appearing white matter integrity. Although white matter pathology measures were part of the final clinical regression models, they explained limited incremental variance (to a maximum of 4%). Several cortical atrophy patterns relevant for multiple sclerosis were found. This suggests that cortical atrophy in multiple sclerosis occurs largely in a non-random manner and develops (at least partly) according to distinct anatomical patterns. In addition, these cortical atrophy patterns showed stronger associations with clinical (especially cognitive) dysfunction than global cortical atrophy.

Reproducible segmentation of white matter hyperintensities using a new statistical definition.

OBJECTIVES: We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. MATERIALS AND METHODS: T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer's disease) were used. The segmentation was performed using a proposed definition for WMH based on the one-tailed Kolmogorov-Smirnov test. RESULTS: The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85-0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83-0.94) that exceeded intra-rater similarity (Dice 0.75-0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed definition has better accuracy and reproducibility in the test dataset used. CONCLUSION: Overall, the presented definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility.