BACKGROUND: Few studies on multiple sclerosis (MS) have explored the variability of percentage brain volume change (PBVC) measurements obtained from different clinical MRIs. In a retrospective multicentre cohort study, we quantified the variability of annualised PBVC in clinical MRIs. METHODS: Clinical MRIs of relapse-onset MS patients were assessed by icobrain. Volumetric data were analysed on same-scanner and different-scanner MRI pairs if they passed quality control criteria. Alignment similarity between two images had to be comparable to same-scanner scan-rescan images. RESULTS: Of 6826 MRIs, 85 % had appropriate volumetric sequences and 4446 serial MRI pairs were analysed. 3334 (75 %) MRI pairs from 1207 patients met the inclusions. The PBVC of included MRI pairs showed variance of 0.78 % for same-scanner pairs and 0.80 % for different-scanner pairs. Further selection of included MRI pairs with the best variance resulted in 1885 (42 %) MRI pairs with PBVC variance of 0.34 %. Excluded MRI pairs with poor alignment similarity had variances of 2.97 % for same-scanner pairs and 20.79 % for different-scanner pairs. CONCLUSION: Icobrain should be utilised for PBVC determination only on selected MRIs with the best alignment similarity. Applying strict selection criteria for the included MRI pairs and longitudinal imaging on the same scanner remain mandatory to reduce PBVC variability.

• Keywords
• Brain, Icometrix, Real-world, Variability, Volumetry,
• MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging * MeSH
• Brain * diagnostic imaging   pathology   MeSH
• Multiple Sclerosis, Relapsing-Remitting diagnostic imaging   drug therapy   pathology   MeSH
• Retrospective Studies MeSH
• Multiple Sclerosis diagnostic imaging   pathology   MeSH
• Organ Size MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH

OBJECTIVES: This study aimed to investigate relationships between cholesterol profile, brain volumetric MRI, and clinical measures in a large observational cohort of multiple sclerosis (MS) patients. MATERIALS AND METHODS: We included 1.505 patients with 4.966 time points including complete lipid, clinical, and imaging data. The time among lipid, brain MRI and clinical measures was under 90 days. Cross-sectional statistical analysis at baseline was performed using an adjusted linear regression and analysis of longitudinal lipid and MRI measures data was performed using adjusted linear mixed models. RESULTS: We found associations between higher high-density lipoprotein cholesterol (HDL-C) and lower brain parenchymal fraction (BPF) at cross-sectional analysis at baseline (B = -0.43, CI 95%: -0.73, -0.12, p = 0.005), as well as in longitudinal analysis over follow-up (B = -0.32 ± 0.072, χ2 = 36.6; p = < 0.001). Higher HDL-C was also associated with higher T2-lesion volume in longitudinal analysis (B = 0.11 ± 0.023; χ2 = 23.04; p = < 0.001). We observed a weak negative association between low-density lipoprotein cholesterol (LDL-C) levels and BPF at baseline (B = -0.26, CI 95%: -0.4, -0.11, p = < 0.001) as well as in longitudinal analysis (B = -0.06 ± 0.03, χ2 = 4.46; p = 0.03). T2-LV did not show an association with LDL-C. We did not find any association between lipid measures and disability. The effect of lipid levels on MRI measures and disability was minimal (Cohen f2 < 0.02). CONCLUSIONS: Our results contradict the previously described exclusively positive effect of HDL-C on brain atrophy in patients with MS. Higher LDL-C was weakly associated with higher brain atrophy but not with higher lesion burden.

• Keywords
• Brain atrophy, Cholesterol, HDL, LDL, Lesion volume, Lipid, MRI, Multiple sclerosis,
• MeSH
• Cholesterol blood   MeSH
• Adult MeSH
• Cholesterol, HDL * blood   MeSH
• Cohort Studies MeSH
• Cholesterol, LDL blood   MeSH
• Middle Aged MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Magnetic Resonance Imaging * MeSH
• Brain * diagnostic imaging   pathology   MeSH
• Cross-Sectional Studies MeSH
• Multiple Sclerosis * diagnostic imaging   blood   pathology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Observational Study MeSH
• Names of Substances
• Cholesterol MeSH
• Cholesterol, HDL * MeSH
• Cholesterol, LDL MeSH

BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. METHODS: In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). RESULTS: We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). DISCUSSION: The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

• MeSH
• Deep Learning * MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Magnetic Resonance Imaging * MeSH
• Brain * diagnostic imaging   pathology   MeSH
• Neurodegenerative Diseases diagnostic imaging   MeSH
• Cross-Sectional Studies MeSH
• Retrospective Studies MeSH
• Multiple Sclerosis * diagnostic imaging   pathology   MeSH
• Aging * pathology   physiology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH

BACKGROUND: An association between lipid measures and cognitive decline in patients with multiple sclerosis (MS) has been suggested. OBJECTIVES: This study aimed to investigate relationships between lipid profile and cognitive performance in a large observational cohort of MS patients. MATERIALS AND METHODS: We included 211 patients with 316 available pairs of lipid and cognitive measures performed over follow-up. The time between lipid and cognitive measures did not exceed 90 days. Baseline data were analyzed by non-parametric Spearman rank correlation test. Repeated measures were analyzed using linear mixed models adjusted for sex, age, education level, disease-modifying therapy status, and depression. RESULTS: Baseline analyses showed a correlation between higher low-density lipoprotein cholesterol (LDL-C) and lower Categorical Verbal Learning Test (CVLT) (rho=-0.15; p = 0.04), lower Symbol Digit Modalities Test (SDMT) (rho=-0.16; p = 0.02) and lower Brief Visuospatial Memory Test-Revised (BVMT-R) scores (rho=-0.12; p = 0.04). Higher high-density lipoprotein cholesterol (HDL-C) was negatively correlated with lower SDMT scores (rho=-0.16; p = 0.02) and lower Paced Auditory Serial Addition Test-3 (PASAT-3) scores (rho=-0.24; p = 0.03). Mixed model analyses of repeated measures showed a negative association between higher LDL-C and lower CVLT (B=-0.02; p < 0.001, Cohen´s d = 0.08) and lower BVMT-R (B=-0.01; p = 0.03, Cohen´s d=-0.12). Also, the negative association between HDL-C and PASAT-3 was confirmed in the mixed model analysis (B=-0.18; p = 0.01, Cohen´s d = 0.07). Additional adjustments of the models for disability assessed by Expanded Disability Status Scale or Normalized Brain Volume did not change the results of the models substantially. CONCLUSIONS: Our results suggest a mild negative impact of dyslipidemia on cognitive performance in patients with MS. We propose that dyslipidemia contributes, at least in part, to cognitive decline in MS patients, independent of brain atrophy.

• Keywords
• Cognition, HDL, LDL, Multiple sclerosis,
• MeSH
• Adult MeSH
• Cholesterol, HDL blood   MeSH
• Cognition physiology   MeSH
• Cognitive Dysfunction * etiology   blood   physiopathology   MeSH
• Cholesterol, LDL * blood   MeSH
• Middle Aged MeSH
• Humans MeSH
• Neuropsychological Tests MeSH
• Multiple Sclerosis * blood   complications   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Observational Study MeSH
• Names of Substances
• Cholesterol, HDL MeSH
• Cholesterol, LDL * MeSH

BACKGROUND AND OBJECTIVES: In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes. METHODS: We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue. RESULTS AND CONCLUSIONS: Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.

• Keywords
• Enlarging lesions, Lesion subtyping, Multiple sclerosis, Quantitative MRI, Relaxometry,
• MeSH
• Adult MeSH
• Phenotype * MeSH
• Middle Aged MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Magnetic Resonance Imaging MeSH
• Brain diagnostic imaging   pathology   MeSH
• Multiparametric Magnetic Resonance Imaging MeSH
• Disease Progression MeSH
• Cross-Sectional Studies MeSH
• Multiple Sclerosis * diagnostic imaging   pathology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

The aim of this study was to describe the clinical and molecular genetic findings in seven individuals from three unrelated families with Blau syndrome. A complex ophthalmic and general health examination including diagnostic imaging was performed. The NOD2 mutational hot spot located in exon 4 was Sanger sequenced in all three probands. Two individuals also underwent autoinflammatory disorder gene panel screening, and in one subject, exome sequencing was performed. Blau syndrome presenting as uveitis, skin rush or arthritis was diagnosed in four cases from three families. In two individuals from one family, only camptodactyly was noted, while another member had camptodactyly in combination with non-active uveitis and angioid streaks. One proband developed two attacks of meningoencephalitis attributed to presumed neurosarcoidosis, which is a rare finding in Blau syndrome. The probands from families 1 and 2 carried pathogenic variants in NOD2 (NM_022162.3): c.1001G>A p.(Arg334Gln) and c.1000C>T p.(Arg334Trp), respectively. In family 3, two variants of unknown significance in a heterozygous state were found: c.1412G>T p.(Arg471Leu) in NOD2 and c.928C>T p.(Arg310*) in NLRC4 (NM_001199139.1). In conclusion, Blau syndrome is a phenotypically highly variable, and there is a need to raise awareness about all clinical manifestations, including neurosarcoidosis. Variants of unknown significance pose a significant challenge regarding their contribution to etiopathogenesis of autoinflammatory diseases.

• Keywords
• Blau syndrome, NOD2, autoinflammation, early onset sarcoidosis, neurosarcoidosis, uveitis,
• MeSH
• Arthritis * genetics   diagnosis   MeSH
• Hereditary Autoinflammatory Diseases MeSH
• Humans MeSH
• Lymphedema genetics   diagnosis   MeSH
• Mutation * MeSH
• Central Nervous System Diseases MeSH
• Arthropathy, Neurogenic genetics   diagnosis   MeSH
• Pedigree * MeSH
• Sarcoidosis * genetics   diagnosis   MeSH
• Exome Sequencing MeSH
• Nod2 Signaling Adaptor Protein * genetics   MeSH
• Synovitis * genetics   diagnosis   MeSH
• Uveitis * genetics   diagnosis   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Case Reports MeSH
• Names of Substances
• NOD2 protein, human MeSH Browser
• Nod2 Signaling Adaptor Protein * MeSH

Cerebellar atrophy is a characteristic sign of late-onset Tay-Sachs disease (LOTS). Other structural neuroimaging abnormalities are inconsistently reported. Our study aimed to perform a detailed whole-brain analysis and quantitatively characterize morphometric changes in LOTS patients. Fourteen patients (8 M/6F) with LOTS from three centers were included in this retrospective study. For morphometric brain analyses, we used deformation-based morphometry, voxel-based morphometry, surface-based morphometry, and spatially unbiased cerebellar atlas template. The quantitative whole-brain morphometric analysis confirmed the finding of profound pontocerebellar atrophy with most affected cerebellar lobules V and VI in LOTS patients. Additionally, the atrophy of structures mainly involved in motor control, including bilateral ventral and lateral thalamic nuclei, primary motor and sensory cortex, supplementary motor area, and white matter regions containing corticospinal tract, was present. The atrophy of the right amygdala, hippocampus, and regions of occipital, parietal and temporal white matter was also observed in LOTS patients in contrast with controls (p < 0.05, FWE corrected). Patients with dysarthria and those initially presenting with ataxia had more severe cerebellar atrophy. Our results show predominant impairment of cerebellar regions responsible for speech and hand motor function in LOTS patients. Widespread morphological changes of motor cortical and subcortical regions and tracts in white matter indicate abnormalities in central motor circuits likely coresponsible for impaired speech and motor function.

• Keywords
• GM2-gangliosidosis, MRI, brain atrophy, late-onset Tay-Sachs disease,
• MeSH
• Atrophy pathology   MeSH
• White Matter * diagnostic imaging   MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain pathology   MeSH
• Retrospective Studies MeSH
• Tay-Sachs Disease * pathology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

OBJECTIVES: Microstructural characterization of patients with multiple sclerosis (MS) has been shown to correlate better with disability compared to conventional radiological biomarkers. Quantitative MRI provides effective means to characterize microstructural brain tissue changes both in lesions and normal-appearing brain tissue. However, the impact of the location of microstructural alterations in terms of neuronal pathways has not been thoroughly explored so far. Here, we study the extent and the location of tissue changes probed using quantitative MRI along white matter (WM) tracts extracted from a connectivity atlas. METHODS: We quantified voxel-wise T1 tissue alterations compared to normative values in a cohort of 99 MS patients. For each WM tract, we extracted metrics reflecting tissue alterations both in lesions and normal-appearing WM and correlated these with cross-sectional disability and disability evolution after 2 years. RESULTS: In early MS patients, T1 alterations in normal-appearing WM correlated better with disability evolution compared to cross-sectional disability. Further, the presence of lesions in supratentorial tracts was more strongly associated with cross-sectional disability, while microstructural alterations in infratentorial pathways yielded higher correlations with disability evolution. In progressive patients, all major WM pathways contributed similarly to explaining disability, and correlations with disability evolution were generally poor. CONCLUSIONS: We showed that microstructural changes evaluated in specific WM pathways contribute to explaining future disability in early MS, hence highlighting the potential of tract-wise analyses in monitoring disease progression. Further, the proposed technique allows to estimate WM tract-specific microstructural characteristics in clinically compatible acquisition times, without the need for advanced diffusion imaging.

• Keywords
• Demyelinating diseases, Magnetic resonance imaging, Multiple sclerosis, Relaxometry, White matter,
• MeSH
• White Matter * diagnostic imaging   pathology   MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Brain diagnostic imaging   pathology   MeSH
• Cross-Sectional Studies MeSH
• Multiple Sclerosis * diagnostic imaging   pathology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: Although there is evidence that shows worse cognitive functioning in male patients with multiple sclerosis (MS), the role of brain pathology in this context is under-investigated. OBJECTIVE: To investigate sex differences in cognitive performance of MS patients, in the context of brain pathology and disease burden. METHODS: Brain MRI, neurological examination, neuropsychological assessment (Brief International Cognitive Assessment in MS-BICAMS, and Paced Auditory Verbal Learning Test-PASAT), and patient-reported outcome questionnaires were performed/administered in 1052 MS patients. RESULTS: Females had higher raw scores in the Symbol Digit Modalities Test (SDMT) (57.0 vs. 54.0; p < 0.001) and Categorical Verbal Learning Test (CVLT) (63.0 vs. 57.0; p < 0.001), but paradoxically, females evaluated their cognitive performance by MS Neuropsychological Questionnaire as being worse (16.6 vs 14.5, p = 0.004). Females had a trend for a weaker negative correlation between T2 lesion volume and SDMT ([Formula: see text] = - 0.37 in females vs. - 0.46 in men; interaction p = 0.038). On the other hand, women had a trend for a stronger correlation between Brain Parenchymal Fraction (BPF) and a visual memory test (Spearman's [Formula: see text] = 0.31 vs. 0.21; interaction p = 0.016). All these trends were not significant after correction for false discovery rate. CONCLUSIONS: Although, females consider their cognition as worse, males had at a group level slightly worse verbal memory and information processing speed. However, the sex differences in cognitive performance were smaller than the variability of scores within the same sex group. Brain MRI measures did not explain the sex differences in cognitive performance among MS patients.

• Keywords
• Brain atrophy, Cognition, Lesion volume, MRI, Multiple sclerosis, Sex,
• MeSH
• Cognition MeSH
• Cognitive Dysfunction * MeSH
• Cognition Disorders * diagnosis   MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain diagnostic imaging   MeSH
• Neuropsychological Tests MeSH
• Sex Characteristics MeSH
• Multiple Sclerosis * complications   diagnostic imaging   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

• Keywords
• EDSS progression in MS, brain network measures, graph theory, relapsing-remitting multiple sclerosis, structural covariance,
• MeSH
• Atrophy pathology   MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Young Adult MeSH
• Brain diagnostic imaging   pathology   MeSH
• Prognosis MeSH
• Disease Progression MeSH
• Multiple Sclerosis, Relapsing-Remitting * diagnostic imaging   pathology   MeSH
• Multiple Sclerosis * diagnostic imaging   pathology   MeSH
• Gray Matter diagnostic imaging   pathology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Research Support, Non-U.S. Gov't MeSH