Dynamics of choroid plexus volume is associated with the presence and development of fatigue in multiple sclerosis.

BACKGROUND: Immune-mediated processes are implicated in the pathogenesis of fatigue, a common symptom in multiple sclerosis (MS). The choroid plexus (CP) regulates central nervous system (CNS) immune homeostasis and undergoes volumetric modifications possibly contributing to MS-related fatigue. We explored the association between MS-related CP volume changes and fatigue dynamics. METHOD: Eighty-five patients with MS and 68 healthy controls (HC) underwent brain 3T MRI, neurological evaluation and Modified Fatigue Impact Scale (MFIS) at two timepoints (median follow-up=1.4 years). Normalised brain and regional grey matter (GM) volumes were obtained using FSL-SIENAx, FIRST, SIENA and tensor-based morphometry. CP volumes were quantified with in-house methods, and longitudinal changes were analysed using linear mixed models. RESULTS: At baseline, 25 (29%) patients with MS had fatigue (f-MS) (MFIS ≥38). Compared with HC, patients with MS had significantly higher brain T2-lesion volume, lower brain, deep GM, cortical volumes and higher CP volume (false discovery rate (FDR)-p ≤0.024). Compared with non-fatigued (nf-MS) patients, f-MS were older, more disabled (FDR-p ≤0.002) and showed numerically higher CP volume (FDR-p=0.076). At follow-up, 41 (68%) nf-MS remained non-fatigued (nf-FU-MS) and 19 (32%) developed fatigue (f-FU-MS). Patients with MS showed higher brain and deep GM atrophy rates versus HC (FDR-p ≤0.048), whereas clinical, lesional and brain volumetric changes were not significantly different among MS groups (FDR-p ≥0.287). CP volume significantly increased in all MS groups compared with HC (FDR-p ≤0.043), with greater enlargement in f-FU-MS versus nf-FU-MS (FDR-p=0.048). CONCLUSIONS: Larger CP and greater enlargement are associated with the presence and development of fatigue in MS, likely reflecting dynamic inflammatory states within the CNS, supporting the immunological contribution to MS-related fatigue.

Assessment of central vein sign and paramagnetic rim lesions in pediatric multiple sclerosis.

The evaluation of white matter lesions (WMLs) showing the central vein sign (CVS) and paramagnetic rim lesions (PRLs) has been suggested to enhance the diagnostic work-up of adult multiple sclerosis (MS). We aimed to evaluate the fulfillment of different CVS criteria and the added value of PRLs in 22 pediatric MS patients. Eleven patients (50%) fulfilled the 40%-rule threshold. Nineteen (86%) patients had ≥3 CVS+ WMLs or ≥1 PRL, whereas 17 (77%) had ≥6 CVS+ WMLs or ≥1 PRL. A simplified CVS-based approach, with the combined evaluation of ≥1 PRL in patients with ≥6 CVS+ WMLs, may improve MS diagnosis in pediatric patients.

Cerebrospinal Fluid-In Gradient of Cortical and Deep Gray Matter Damage in Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: A CSF-in gradient in cortical and thalamic gray matter (GM) damage has been found in multiple sclerosis (MS). We concomitantly explored the patterns of cortical, thalamic, and caudate microstructural abnormalities at progressive distances from CSF using a multiparametric MRI approach. METHODS: For this cross-sectional study, from 3T 3D T1-weighted scans, we sampled cortical layers at 25%-50%-75% depths from pial surface and thalamic and caudate bands at 2-3-4 voxels from the ventricular-GM interface. Using linear mixed models, we tested between-group comparisons of magnetization transfer ratio (MTR) and R2* layer-specific z-scores, CSF-in across-layer z-score changes, and their correlations with clinical (disease duration and disability) and structural (focal lesions, brain, and choroid plexus volume) MRI measures. RESULTS: We enrolled 52 patients with MS (33 relapsing-remitting [RRMS], 19 progressive [PMS], mean age: 46.4 years, median disease duration: 15.1 years, median: EDSS 2.0) and 70 controls (mean age 41.5 ± 12.8). Compared with controls, RRMS showed lower MTR values in the outer and middle cortical layers (false-discovery rate [FDR]-p ≤ 0.025) and lower R2* values in all 3 cortical layers (FDR-p ≤ 0.016). PMS had lower MTR values in the outer and middle cortical (FDR-p ≤ 0.016) and thalamic (FDR-p ≤ 0.048) layers, and in the outer caudate layer (FDR-p = 0.024). They showed lower R2* values in the outer cortical layer (FDR-p = 0.003) and in the outer thalamic layer (FDR-p = 0.046) and higher R2* values in all 3 caudate layers (FDR-p ≤ 0.031). Both RRMS and PMS had a gradient of damage, with lower values closer to the CSF, for cortical (FDR-p ≤ 0.002) and thalamic (FDR-p ≤ 0.042) MTR. PMS showed a gradient of damage for cortical R2* (FDR-p = 0.005), thalamic R2* (FDR-p = 0.004), and caudate MTR (FDR-p ≤ 0.013). Lower MTR and R2* of outer cortical, thalamic, and caudate layers and steeper gradient of damage toward the CSF were significantly associated with older age, higher T2-hyperintense white matter lesion volume, higher thalamic lesion volume, and lower brain volume (ß ≥ 0.08, all FDR-p ≤ 0.040). Lower MTR of outer caudate layer was associated with more severe disability (ß = -0.26, FDR-p = 0.040). No correlations with choroid plexus volume were found. DISCUSSION: CSF-in damage gradients are heterogeneous among different GM regions and through MS course, possibly reflecting different dynamics of demyelination and iron loss/accumulation.

Spatial correspondence among regional gene expressions and gray matter volume loss in multiple sclerosis.

In multiple sclerosis (MS), a non-random and clinically relevant pattern of gray matter (GM) volume loss has been described. Whether differences in regional gene expression might underlay distinctive pathological processes contributing to this regional variability has not been explored yet. Two hundred eighty-six MS patients and 172 healthy controls (HC) underwent a brain 3T MRI, a complete neurological evaluation and a neuropsychological assessment. Using Allen Human Brain Atlas, voxel-based morphometry and MENGA platform, we integrated brain transcriptome and neuroimaging data to explore the spatial cross-correlations between regional GM volume loss and expressions of 2710 genes involved in MS (p < 0.05, family-wise error-corrected). Enrichment analyses were performed to evaluate overrepresented molecular functions, biological processes and cellular components involving genes significantly associated with voxel-based morphometry-derived GM maps (p < 0.05, Bonferroni-corrected). A diffuse GM volume loss was found in MS patients compared to HC and it was spatially correlated with 74 genes involved in GABA neurotransmission and mitochondrial oxidoreductase activity mainly expressed in neurons and astrocytes. A more severe GM volume loss was spatially associated, in more disabled MS patients, with 44 genes involved in mitochondrial integrity of all resident cells of the central nervous system (CNS) and, in cognitively impaired MS patients, with 64 genes involved in mitochondrial protein heterodimerization and oxidoreductase activities expressed also in microglia and endothelial cells. Specific differences in the expressions of genes involved in synaptic GABA receptor activities and mitochondrial functions in resident CNS cells may influence regional susceptibility to MS-related excitatory/inhibitory imbalance and oxidative stress, and subsequently, to GM volume loss.

Chronic Active Lesions and Larger Choroid Plexus Explain Cognition and Fatigue in Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Chronic inflammation may contribute to cognitive dysfunction and fatigue in patients with multiple sclerosis (MS). Paramagnetic rim lesions (PRLs) and choroid plexus (CP) enlargement have been proposed as markers of chronic inflammation in MS being associated with a more severe disease course. However, their relation with cognitive impairment and fatigue has not been fully explored yet. Here, we investigated the contribution of PRL number and volume and CP enlargement to cognitive impairment and fatigue in patients with MS. METHODS: Brain 3T MRI, neurologic evaluation, and neuropsychological assessment, including the Brief Repeatable Battery of Neuropsychological Tests and Modified Fatigue Impact Scale, were obtained from 129 patients with MS and 73 age-matched and sex-matched healthy controls (HC). PRLs were identified on phase images of susceptibility-weighted imaging, whereas CP volume was quantified using a fully automatic method on brain three-dimensional T1-weighted and fluid-attenuated inversion recovery MRI sequences. Predictors of cognitive impairment and fatigue were identified using random forest. RESULTS: Thirty-six (27.9%) patients with MS were cognitively impaired, and 31/113 (27.4%) patients had fatigue. Fifty-nine (45.7%) patients with MS had ≥1 PRLs (median = 0, interquartile range = 0;2). Compared with HC, patients with MS showed significantly higher T2-hyperintense white matter lesion (WM) volume; lower normalized brain, thalamic, hippocampal, caudate, cortical, and WM volumes; and higher normalized CP volume (p from <0.001 to 0.040). The predictors of cognitive impairment (relative importance) (out-of-bag area under the curve [OOB-AUC] = 0.707) were normalized brain volume (100%), normalized caudate volume (89.1%), normalized CP volume (80.3%), normalized cortical volume (70.3%), number (67.3%) and volume (66.7%) of PRLs, and T2-hyperintense WM lesion volume (64.0%). Normalized CP volume was the only predictor of the presence of fatigue (OOB-AUC = 0.563). DISCUSSION: Chronic inflammation, with higher number and volume of PRLs and enlarged CP, may contribute to cognitive impairment in MS in addition to gray matter atrophy. The contribution of enlarged CP in explaining fatigue supports the relevance of immune-related processes in determining this manifestation independently of disease severity. PRLs and CP enlargement may contribute to the pathophysiology of cognitive impairment and fatigue in MS, and they may represent clinically relevant therapeutic targets to limit the impact of these clinical manifestations in MS.

A Fully Automatic Method to Segment Choroid Plexuses in Multiple Sclerosis Using Conventional MRI Sequences.

BACKGROUND: Choroid plexus (CP) volume has been recently proposed as a proxy for brain neuroinflammation in multiple sclerosis (MS). PURPOSE: To develop and validate a fast automatic method to segment CP using routinely acquired brain T1-weighted and FLAIR MRI. STUDY TYPE: Retrospective. POPULATION: Fifty-five MS patients (33 relapsing-remitting, 22 progressive; mean age = 46.8 ± 10.2 years; 31 women) and 60 healthy controls (HC; mean age = 36.1 ± 12.6 years, 33 women). FIELD STRENGTH/SEQUENCE: 3D T2-weighted FLAIR and 3D T1-weighted gradient echo sequences at 3.0 T. ASSESSMENT: Brain tissues were segmented on T1-weighted sequences and a Gaussian Mixture Model (GMM) was fitted to FLAIR image intensities obtained from the ventricle masks of the SIENAX. A second GMM was then applied on the thresholded and filtered ventricle mask. CP volumes were automatically determined and compared with those from manual segmentation by two raters (with 3 and 10 years' experience; reference standard). CP volumes from previously published automatic segmentation methods (freely available Freesurfer [FS] and FS-GMM) were also compared with reference standard. Expanded Disability Status Scale (EDSS) score was assessed within 3 days of MRI. Computational time was assessed for each automatic technique and manual segmentation. STATISTICAL TESTS: Comparisons of CP volumes with reference standard were evaluated with Bland Altman analysis. Dice similarity coefficients (DSC) were computed to assess automatic CP segmentations. Volume differences between MS and HC for each method were assessed with t-tests and correlations of CP volumes with EDSS were assessed with Pearson's correlation coefficients (R). A P value <0.05 was considered statistically significant. RESULTS: Compared to manual segmentation, the proposed method had the highest segmentation accuracy (mean DSC = 0.65 ± 0.06) compared to FS (mean DSC = 0.37 ± 0.08) and FS-GMM (0.58 ± 0.06). The percentage CP volume differences relative to manual segmentation were -0.1% ± 0.23, 4.6% ± 2.5, and -0.48% ± 2 for the proposed method, FS, and FS-GMM, respectively. The Pearson's correlations between automatically obtained CP volumes and the manually obtained volumes were 0.70, 0.54, and 0.56 for the proposed method, FS, and FS-GMM, respectively. A significant correlation between CP volume and EDSS was found for the proposed automatic pipeline (R = 0.2), for FS-GMM (R = 0.3) and for manual segmentation (R = 0.4). Computational time for the proposed method (32 ± 2 minutes) was similar to the manual segmentation (20 ± 5 minutes) but <25% of the FS (120 ± 15 minutes) and FS-GMM (125 ± 15 minutes) methods. DATA CONCLUSION: This study developed an accurate and easily implementable method for automatic CP segmentation in MS using T1-weighted and FLAIR MRI. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 4.

Monoaminergic network dysfunction and development of depression in multiple sclerosis: a longitudinal investigation.

BACKGROUND: Monoaminergic network dysfunction is thought to underpin depression in multiple sclerosis (MS) patients. However, longitudinal studies are lacking. OBJECTIVES: Here, we investigated the association between development of depressive symptoms in MS and changes of resting-state functional connectivity (RS FC) within monoaminergic networks. METHODS: Forty-nine MS patients without depression [Montgomery-Asberg Depression Scale (MADRS) ≤ 9] and 27 healthy controls underwent clinical and 3.0 T RS FC assessment at baseline and after a median follow-up of 1.6 years (interquartile range 1.0-2.1 years). Monoamine-related RS FC was derived by independent component analysis, constrained to PET atlases for dopamine, noradrenaline and serotonin transporters. Longitudinal changes of RS FC within monoaminergic networks and their correlations with MADRS scores were assessed. RESULTS: At baseline, MS patients showed decreased RS FC vs healthy controls in all PET-guided monoaminergic networks in frontal, cingulate and cerebellar cortices, and increased RS FC in parieto-occipital regions. Fourteen (29%) MS patients developed depressive symptoms (MADRS > 9) at follow-up (D-MS) and exhibited widespread RS FC decrease over time in the PET-guided dopamine network, mainly in orbitofrontal, occipital, anterior cingulate and precuneal cortices compared to patients who did not develop depressive symptoms. In D-MS, decreased RS FC over time was also observed in parahippocampal and occipital regions of the PET-guided noradrenaline network. Decreased RS FC over time in dopamine and noradrenaline PET-guided networks correlated with concomitant increased MADRS scores (r = range - 0.65/- 0.61, p < 0.001). CONCLUSIONS: The development of depressive symptoms in MS patients was associated with specific RS FC changes within the dopamine and noradrenaline networks.

Quantification of Thalamic Atrophy in MS: From the Multicenter Italian Neuroimaging Network Initiative Data Set to Clinical Application.

BACKGROUND AND PURPOSE: Thalamic atrophy occurs from the earliest phases of MS; however, this measure is not included in clinical practice. Our purpose was to obtain a reliable segmentation of the thalamus in MS by comparing existing automatic methods cross-sectionally and longitudinally. MATERIALS AND METHODS: MR images of 141 patients with relapsing-remitting MS (mean age, 38 years; range, 19-58 years; 95 women) and 69 healthy controls (mean age, 36 years; range, 22-69 years; 47 women) were retrieved from the Italian Neuroimaging Network Initiative repository: T1WI, T2WI, and DWI at baseline and after 1 year (136 patients, 31 healthy controls). Three segmentation software programs (FSL-FIRST, FSL-MIST, FreeSurfer) were compared. At baseline, agreement among pipelines, correlations with age, disease duration, clinical score, and T2-hyperintense lesion volume were evaluated. Effect sizes in differentiating patients and controls were assessed cross-sectionally and longitudinally. Variability of longitudinal changes in controls and sample sizes were assessed. False discovery rate-adjusted P < .05 was considered significant. RESULTS: At baseline, FSL-FIRST and FSL-MIST showed the highest agreement in the results of thalamic volume (R = 0.87, P < .001), with the highest effect size for FSL-MIST (Cohen d = 1.11); correlations with demographic and clinical variables were comparable for all software. Longitudinally, FSL-MIST showed the lowest variability in estimating thalamic volume changes for healthy controls (SD = 1.07%), the highest effect size (Cohen d = 0.44), and the smallest sample size at 80% power level (15 subjects per group). CONCLUSIONS: Multimodal segmentation by FSL-MIST increased the robustness of the results with better capability to detect small variations in thalamic volumes.

Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice.

MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.

Methods for Brain Atrophy MR Quantification in Multiple Sclerosis: Application to the Multicenter INNI Dataset.

BACKGROUND: Current therapeutic strategies in multiple sclerosis (MS) target neurodegeneration. However, the integration of atrophy measures into the clinical scenario is still an unmet need. PURPOSE: To compare methods for whole-brain and gray matter (GM) atrophy measurements using the Italian Neuroimaging Network Initiative (INNI) dataset. STUDY TYPE: Retrospective (data available from INNI). POPULATION: A total of 466 patients with relapsing-remitting MS (mean age = 37.3 ± 10 years, 323 women) and 279 healthy controls (HC; mean age = 38.2 ± 13 years, 164 women). FIELD STRENGTH/SEQUENCE: A 3.0-T, T1-weighted (spin echo and gradient echo without gadolinium injection) and T2-weighted spin echo scans at baseline and after 1 year (170 MS, 48 HC). ASSESSMENT: Structural Image Evaluation using Normalization of Atrophy (SIENA-X/XL; version 5.0.9), Statistical Parametric Mapping (SPM-v12); and Jim-v8 (Xinapse Systems, Colchester, UK) software were applied to all subjects. STATISTICAL TESTS: In MS and HC, we evaluated the intraclass correlation coefficient (ICC) among FSL-SIENA(XL), SPM-v12, and Jim-v8 for cross-sectional whole-brain and GM tissue volumes and their longitudinal changes, the effect size according to the Cohen's d at baseline and the sample size requirement for whole-brain and GM atrophy progression at different power levels (lowest = 0.7, 0.05 alpha level). False discovery rate (Benjamini-Hochberg procedure) correction was applied. A P value <0.05 was considered statistically significant. RESULTS: SPM-v12 and Jim-v8 showed significant agreement for cross-sectional whole-brain (ICC = 0.93 for HC and ICC = 0.84 for MS) and GM volumes (ICC = 0.66 for HC and ICC = 0.90) and longitudinal assessment of GM atrophy (ICC = 0.35 for HC and ICC = 0.59 for MS), while no significant agreement was found in the comparisons between whole-brain and GM volumes for SIENA-X/XL and both SPM-v12 (P = 0.19 and P = 0.29, respectively) and Jim-v8 (P = 0.21 and P = 0.32, respectively). SPM-v12 and Jim-v8 showed the highest effect size for cross-sectional GM atrophy (Cohen's d = -0.63 and -0.61). Jim-v8 and SIENA(XL) showed the smallest sample size requirements for whole-brain (58) and GM atrophy (152), at 0.7 power level. DATA CONCLUSION: The findings obtained in this study should be considered when selecting the appropriate brain atrophy pipeline for MS studies. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Neural stem cell transplantation in patients with progressive multiple sclerosis: an open-label, phase 1 study.

Innovative pro-regenerative treatment strategies for progressive multiple sclerosis (PMS), combining neuroprotection and immunomodulation, represent an unmet need. Neural precursor cells (NPCs) transplanted in animal models of multiple sclerosis have shown preclinical efficacy by promoting neuroprotection and remyelination by releasing molecules sustaining trophic support and neural plasticity. Here we present the results of STEMS, a prospective, therapeutic exploratory, non-randomized, open-label, single-dose-finding phase 1 clinical trial ( NCT03269071 , EudraCT 2016-002020-86), performed at San Raffaele Hospital in Milan, Italy, evaluating the feasibility, safety and tolerability of intrathecally transplanted human fetal NPCs (hfNPCs) in 12 patients with PMS (with evidence of disease progression, Expanded Disability Status Scale ≥6.5, age 18-55 years, disease duration 2-20 years, without any alternative approved therapy). The safety primary outcome was reached, with no severe adverse reactions related to hfNPCs at 2-year follow-up, clearly demonstrating that hfNPC therapy in PMS is feasible, safe and tolerable. Exploratory secondary analyses showed a lower rate of brain atrophy in patients receiving the highest dosage of hfNPCs and increased cerebrospinal fluid levels of anti-inflammatory and neuroprotective molecules. Although preliminary, these results support the rationale and value of future clinical studies with the highest dose of hfNPCs in a larger cohort of patients.

The insula modulates the effects of aerobic training on cardiovascular function and ambulation in multiple sclerosis.

BACKGROUND: Impairment of cardiovascular control is common in multiple sclerosis (MS), possibly due to damage of strategic brain regions such as the insula. Aerobic training (AT) targets cardiopulmonary system and may represent a neuroprotective strategy. PURPOSE: To investigate whether insular damage (T2-hyperintense lesions and volume) is associated with cardiovascular fitness (CF) and influences AT effects in MS. METHODS: Sixty-one MS patients were randomized to an AT intervention group (MS-AT) and a motor training control group (MS-C). At baseline and after training (24 sessions over 2-3 months), peak of oxygen consumption (VO2max), heart rate reserve (HRR), 6-min walk test (6MWT) and whole brain and insula MRI data were collected. Two healthy control (HC) groups were enrolled for CF and MRI data analysis. RESULTS: At baseline, MS patients vs HC showed impaired VO2max, HRR and 6MWT (p < 0.001) and widespread gray matter atrophy, including bilateral insula. In MS patients, left insula T2-lesion volume correlated with HRR (r = 0.27, p = 0.042). After training, MS-AT, especially those without insular T2-hyperintense lesions, showed 6MWT improvement (p < 0.05) and a stable insular volume, whereas MS-C showed left insular volume loss (p < 0.001). CONCLUSIONS: By increasing 6MWT performance, our results suggest that AT may improve walking capacity and submaximal measure of CF in MS patients. Such beneficial effect may be modulated by insula integrity.

Multicenter data harmonization for regional brain atrophy and application in multiple sclerosis.

BACKGROUND: In multiple sclerosis (MS), determination of regional brain atrophy is clinically relevant. However, analysis of large datasets is rare because of the increased variability in multicenter data. PURPOSE: To compare different methods to correct for center effects. To investigate regional gray matter (GM) volume in relapsing-remitting MS in a large multicenter dataset. METHODS: MRI scans of 466 MS patients and 279 healthy controls (HC) were retrieved from the Italian Neuroimaging Network Initiative repository. Voxel-based morphometry was performed. The center effect was accounted for with different methods: (a) no correction, (b) factor in the statistical model, (c) ComBat method and (d) subsampling procedure to match single-center distributions. By applying the best correction method, GM atrophy was assessed in MS patients vs HC and according to clinical disability, disease duration and T2 lesion volume. Results were assessed voxel-wise using general linear model. RESULTS: The average residuals for the harmonization methods were 5.03 (a), 4.42 (b), 4.26 (c) and 2.98 (d). The comparison between MS patients and HC identified thalami and other deep GM nuclei, the cerebellum and several cortical regions. At single-center analysis, the thalami were always involved, whereas different other regions were found in each center. Cerebellar atrophy correlated with clinical disability, while deep GM nuclei atrophy correlated with T2-lesion volume. CONCLUSION: Harmonization based on subsampling more effectively decreased the residuals of the statistical model applied. In comparison with findings from single-center analysis, the multicenter results were more robust, highlighting the importance of data repositories from multiple centers.

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set.

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS.

Exploring the Association of HLA Genetic Risk Burden on Thalamic and Hippocampal Atrophy in Multiple Sclerosis Patients.

Multiple sclerosis (MS) is a complex disease of the central nervous system for which human leukocyte antigen (HLA) alleles are major contributors to susceptibility. Several investigations have focused on the relationship between HLA and clinical parameters, while few studies have evaluated its correlation with brain magnetic resonance imaging (MRI) measures. We investigated the association between the HLA genetic burden (HLAGB), originating from the most updated HLA alleles associated with MS, and neuroimaging endophenotypes, with a specific focus on brain atrophy metrics. A monocentric Italian cohort of 334 MS patients with imputed HLA alleles and cross-sectional volumetric measures of white matter (WM), gray matter (GM), hippocampus, thalamus and T2-hyperintense lesions was investigated. Linear regression models with covariate adjustment were fitted for each metric. We detected no effect of HLAGB on WM and GM volumes. Interestingly, we found a marginal correlation between higher HLAGB and lower hippocampal volume (ß = -0.142, p = 0.063) and a nominal association between higher HLAGB and lower thalamic volume (ß = -0.299, p = 0.047). No association was found with T2 lesion volumes. The putative impact of higher HLAGB on hippocampus and thalamus suggests, if replicated in independent cohorts, a possible cumulative contribution of HLA risk loci on brain volumetric traits linked to clinical deficits in MS.

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.

Advanced diffusion-weighted imaging models better characterize white matter neurodegeneration and clinical outcomes in multiple sclerosis.

BACKGROUND: White matter (WM) atrophy is relevant in multiple sclerosis (MS), but the methods of analysis currently used are not specific for microstructural changes. The aims of this study were to assess the use of advanced diffusion-weighted imaging (DWI) techniques proposed as measures of baseline and longitudinal WM atrophy in MS and to analyze whether these measures helped explain MS clinical disability (including cognitive impairment) better than volumetric and diffusion tensor (DT)-derived measures. METHODS: 3DT1-weighted and DWI sequences were applied to 86 MS and 55 healthy controls (HC) at baseline and after one-year. Intra-cellular volume (vic) maps were computed from neurite orientation dispersion and density imaging model. Voxel-wise fiber-bundle cross-section (FCS) atrophy in MS compared to HC was estimated. Maps of fractional anisotropy and mean diffusivity were also obtained from DWI for a comparison with the proposed advanced DW-derived measures (vic and FCS). RESULTS: Both at baseline and after 1-year, only FCS measure showed a significant atrophy in relapsing-remitting (RR) MS compared to HC and in progressive MS compared to RRMS, mainly located in specific WM tracts (corticospinal tract, splenium of the corpus callosum, left optic radiation, bilateral cingulum, middle cerebellar peduncle and anterior commissure, p value < 0.05). Global baseline FCS and vic were the selected predictors of clinical (R-sq = 0.33, p = 0.007) and cognitive scores (R-sq = 0.29, p = 0.0014) in a linear regression model. CONCLUSION: Voxel-based FCS was able to detect WM tracts atrophy in MS clinical phenotypes with greater anatomical specificity compared to other measures (volumetric and DT-derived measures of WM damage). FCS and vic measured at baseline in the WM were the best predictors of clinical disability and cognitive impairment.

The association between cognition and motor performance is beyond structural damage in relapsing-remitting multiple sclerosis.

BACKGROUND: Previous studies demonstrated an association between motor and cognitive performance in multiple sclerosis (MS). However, disease-related brain damage might represent a common substrate to both phenomena, which was not considered before. OBJECTIVE: Aim of this study is to investigate whether the association between cognition and motor function is beyond structural damage in patients with MS. METHODS: Eighty-one healthy controls and 106 relapsing-remitting (RR) MS patients underwent a 3.0 T MRI with quantification of T2-lesion volumes, T1-lesion volumes and normalized brain volumes. A functional examination [Nine-Hole Peg Test (9-HPT), Timed 25-Foot Walk test (T25FW) and Expanded Disability Status Scale] and a neuropsychological evaluation (Brief Repeatable Battery of Neuropsychological Tests) were also administered. Association between demographic, clinical, cognitive, MRI and functional measures were analysed with univariate analyses and hierarchical linear regression. RESULTS: In RRMS patients, Spatial Recall Test and Symbol Digit Modalities Test were positively correlated with 9-HPT (p < 0.001) and T25FW (p ≤ 0.035); Paced Auditory Serial Addition Test (PASAT) correlated with 9-HPT (p ≤ 0.009). 9-HPT and T25FW were significantly associated with normalized brain volumes (p ≤ 0.016), T2- and T1-lesion volumes (p ≤ 0.009). Hierarchical regression models selected age and normalized deep gray matter volume as predictors of T25FW (adjusted-R2 = 0.109). Younger age, female sex, higher normalized gray matter volume and higher PASAT 2″ scores predicted higher 9-HPT scores (adjusted-R2 = 0.337). CONCLUSIONS: In RRMS patients, deficit in information processing speed and executive function may contribute to hand motor dysfunction beyond the effect of structural disease-related burden, supporting the integration of motor and cognitive assessment in clinical settings.

A Deep Learning Approach to Predicting Disease Progression in Multiple Sclerosis Using Magnetic Resonance Imaging.

OBJECTIVES: Magnetic resonance imaging (MRI) is an important tool for diagnosis and monitoring of disease course in multiple sclerosis (MS). However, its prognostic value for predicting disease worsening is still being debated. The aim of this study was to propose a deep learning algorithm to predict disease worsening at 2 years of follow-up on a multicenter cohort of MS patients collected from the Italian Neuroimaging Network Initiative using baseline MRI, and compare it with 2 expert physicians. MATERIALS AND METHODS: For 373 MS patients, baseline T2-weighted and T1-weighted brain MRI scans, as well as baseline and 2-year clinical and cognitive assessments, were collected from the Italian Neuroimaging Network Initiative repository. A deep learning architecture based on convolutional neural networks was implemented to predict: (1) clinical worsening (Expanded Disability Status Scale [EDSS]-based model), (2) cognitive deterioration (Symbol Digit Modalities Test [SDMT]-based model), or (3) both (EDSS + SDMT-based model). The method was tested on an independent data set and compared with the performance of 2 expert physicians. RESULTS: For the test set, the convolutional neural network model showed high predictive accuracy for clinical (83.3%) and cognitive (67.7%) worsening, although the highest accuracy was reached when training the algorithm using both EDSS and SDMT information (85.7%). Artificial intelligence classification performance exceeded that of 2 expert physicians (70% of accuracy for the human raters). CONCLUSIONS: We developed a robust and accurate model for predicting clinical and cognitive worsening of MS patients after 2 years, based on conventional T2-weighted and T1-weighted brain MRI scans obtained at baseline. This algorithm may be valuable for supporting physicians in their clinical practice for the earlier identification of MS patients at risk of disease worsening.