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Recently, brief mitoxantrone induction-chemotherapy followed by immune-modulation with glatiramer acetate (GA) was significantly more effective at reducing multiple sclerosis disease activity than with GA alone. \u003C\u002Fjats:p>\u003Cjats:p> Objective \u003C\u002Fjats:p>\u003Cjats:p> To examine whether the benefit of initial immune suppression with mitoxantrone before GA treatment is associated with more efficient immune modulation. \u003C\u002Fjats:p>\u003Cjats:p> Methods \u003C\u002Fjats:p>\u003Cjats:p> IgG1\u002FIgG4 GA-reactive antibody profiles, previously established as markers of GA-induced Th2 immune-deviation, were prospectively measured in vivo in patients treated with GA alone or with mitoxantrone induction therapy followed by GA. \u003C\u002Fjats:p>\u003Cjats:p> Results \u003C\u002Fjats:p>\u003Cjats:p> Significant and sustained increase in IgG4 antibodies (and the anticipated reversal of the IgG1\u002FIgG4 ratio) was seen in patients treated with GA alone. Combination therapy resulted in lesser IgG4 induction (and no reversal of IgG1\u002FIgG4 ratio). 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Background: Anti-aquaporin-4 antibodies are believed to have a central pathogenetic role in neuromyelitis optica (NMO). B-cell activating factor (BAFF) is one of the crucial factors that determines the fate and survival of B cells and may play a role in induction of antibody-mediated autoimmunity. \u003C\u002Fjats:p>\u003Cjats:p> Objectives: To evaluate the blood and cerebrospinal fluid (CSF) levels of BAFF in NMO and multiple sclerosis (MS) patients. \u003C\u002Fjats:p>\u003Cjats:p> Methods: Peripheral blood samples were collected from 21 definite NMO patients, 22 healthy controls and 45 MS patients and CSF from 8 NMO and 11 MS patients. BAFF levels were measured using an ELISA technique. \u003C\u002Fjats:p>\u003Cjats:p> Results: We found significantly higher levels of BAFF in the CSF of NMO patients compared with that in MS (215.6 ± 41 pg\u002Fml in NMO and 77.4 ± 11 pg\u002Fml in MS, p < 0.001). There were no differences in serum BAFF levels between NMO, MS and healthy donors. MS patients treated with interferon-beta (IFNβ) or glatiramer acetate (GA) had significantly higher serum BAFF levels, as compared with untreated patients (1227 ± 203 pg\u002Fml in untreated MS, 2253 ± 83.4 pg\u002Fml in GA-treated, p < 0.01, and 2106 ± 277.9 pg\u002Fml in interferon-treated, p < 0.05) \u003C\u002Fjats:p>\u003Cjats:p> Conclusion: The presence of increased BAFF, a soluble factor associated with B-cell activation in the proximity of the disease target organ (CSF) in NMO, and its increase in association with immunomodulating treatments, may help our understanding of the immunopathogenetic mechanisms involved in this disease and contribute to more successful and targeted therapeutic intervention. \u003C\u002Fjats:p>",{"EN":1067},"Preferential increase of B-cell activating factor in the cerebrospinal fluid of neuromyelitis optica in a white 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DMN functional connectivity was evaluated from rs-fMRI data via independent component analysis. T2 lesion load (LL) was computed by a semi-automatic method and global and local atrophy was estimated by SIENAX and SPM8 voxel-based morphometry analyses from 3D-T1 images. \u003C\u002Fjats:p>\u003Cjats:p> Results: When the whole group of RRMS patients was compared with HCs, DMN connectivity was significantly weaker in the anterior cingulate cortex, whereas it was significantly weaker in the core but stronger at the periphery of the posterior cingulate cortex. These findings were more evident in CP than CI patients. Observed DMN changes did not correlate with global atrophy or T2-LL, but were locally associated with regional grey matter loss. \u003C\u002Fjats:p>\u003Cjats:p> Conclusion: Relapsing–remitting multiple sclerosis patients show a consistent dysfunction of DMN at the level of the anterior node. 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Clinical outcome assessment in Multiple Sclerosis (MS) is challenging due to the diversity and fluctuating nature of MS symptoms. Traditional clinical scales such as the EDSS are inadequate in their assessment of key clinical dimensions of MS (e.g., cognitive function), and they have psychometric limitations as well. Based on analyses of pooled data from natural history studies and from placebo groups in clinical trials, the National MS Society's Clinical Outcomes Assessment Task Force recently proposed a new multidimensional clinical outcome measure, the MS Functional Composite (MSFC). The MSFC comprises quantitative functional measures of three key clinical dimensions of MS: leg function\u002Fambulation, arm\u002Fhand function, and cognitive function. Scores on component measures are converted to standard scores (z-scores), which are averaged to form a single MSFC score. Preliminary analyses confirm that: (1) the three clinical dimensions of the MSFC are relatively independent; (2) the MSFC is sensitive to clinical changes over 1- and 2-year intervals; and (3) the MSFC has acceptable criterion validity (i.e., predicts both concurrent and subsequent EDSS change). The advantages and potential limitations of incorporating quantitative functional outcome measures such as the MSFC into collaborative databases are discussed. \u003C\u002Fjats:p>",{"EN":1747},"The Multiple Sclerosis Functional Composite measure (MSFC): an integrated                 approach to MS clinical outcome assessment",{"VOID":1749},"10467383",{"VOID":1751},"10.1177\u002F135245859900500409","2024-10-15T21:26:48.053+00:00",[196],"https:\u002F\u002Fjournals.sagepub.com\u002Fdoi\u002F10.1177\u002F135245859900500409",[1756,1777,1796,1817],{"id":1757,"sortIndex":111,"researcher":24,"roles":1758,"affiliations":1759,"properties":1770},"6462cbe2-5ad0-43a5-8b20-ac3351ca25b3",[],[1760],{"id":1761,"sortIndex":25,"affiliation":1762,"properties":24},"f5ec21d1-6823-46e3-a1a8-6b44944cd4a3",{"id":1763,"createTime":1764,"updateTime":1764,"relativeEntities":1765,"slug":1766,"properties":1767,"entityType":53,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"28be5660-354a-43e0-b559-caf035547bb3","2024-10-15T21:26:48.087+00:00",[],"Center-for-Research-Methodology-and-Biometrics-AMC-Cancer-Research-Center-1600-Pierce-Street-Lakewood-Colorado-CO-80214-USA",{"title":1768},{"EN":1769},"Center for Research Methodology and Biometrics, AMC Cancer Research                         Center, 1600 Pierce Street, Lakewood, Colorado CO 80214 USA",{"openalex":1771,"orcid":1773,"title":1775},{"VOID":1772},"A5036008316",{"VOID":1774},"https:\u002F\u002Forcid.org\u002F0000-0002-8455-980X",{"EN":1776},"Gary Cutter",{"id":1778,"sortIndex":25,"researcher":24,"roles":1779,"affiliations":1780,"properties":1791},"82ff8aa6-0745-4a56-ad41-55c4098ae619",[],[1781],{"id":1782,"sortIndex":25,"affiliation":1783,"properties":24},"c0d7fa48-fdcf-4d7c-b294-87e421bc87c5",{"id":1784,"createTime":1785,"updateTime":1785,"relativeEntities":1786,"slug":1787,"properties":1788,"entityType":53,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"72b9e179-ccee-4827-ba64-6df0d11f6a96","2024-10-15T21:26:48.068+00:00",[],"Mellen-Center-U-10-Cleveland-Clinic-Foundation-9500-Euclid-Avenue-Cleveland-Ohio-OH-44195-5244-USA",{"title":1789},{"EN":1790},"Mellen Center (U-10), Cleveland Clinic Foundation, 9500 Euclid Avenue,                         Cleveland, Ohio OH 44195-5244 USA",{"openalex":1792,"title":1794},{"VOID":1793},"A5006689571",{"EN":1795},"Jill S. 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Vol II. Methodology for test and patient assessments and design of a computer-automated system",{},{"id":24,"text":1904,"url":24,"identifiers":1905},"10.1002\u002Fana.410400321",{"doi":1904},{"id":24,"text":1907,"url":24,"identifiers":1908},"10.1177\u002F135245859600200305",{"doi":1907},{"id":24,"text":1910,"url":24,"identifiers":1911},"10.1007\u002F978-1-4471-1271-6_4",{"doi":1910},{"id":24,"text":1913,"url":24,"identifiers":1914},"Goldsmith CH, 1993, J Rheumatol, 20, 575",{},{"id":24,"text":1916,"url":24,"identifiers":1917},"10.1002\u002Fana.410420318",{"doi":1916},{"id":24,"text":1919,"url":24,"identifiers":1920},"10.1212\u002FWNL.39.8.1018",{"doi":1919},{"id":24,"text":1922,"url":24,"identifiers":1923},"10.1002\u002Fana.410270603",{"doi":1922},{"id":24,"text":1925,"url":24,"identifiers":1926},"10.1002\u002Fana.410370108",{"doi":1925},{"id":24,"text":1928,"url":24,"identifiers":1929},"10.1002\u002Fana.410390304",{"doi":1928},{"id":24,"text":1931,"url":24,"identifiers":1932},"10.1093\u002Fbrain\u002F122.5.871",{"doi":1931},{"id":24,"text":1934,"url":24,"identifiers":1935},"Fischer JS, 1999, Administration and scoring manual for the MS Functional Composite measure (MSFC)",{},{"id":24,"text":1937,"url":24,"identifiers":1938},"10.1212\u002FWNL.49.5.1419",{"doi":1937},{"id":24,"text":1940,"url":24,"identifiers":1941},"Goodkin D, 1988, Arch Phys Med Rehabil, 69, 850",{},{"id":24,"text":1943,"url":24,"identifiers":1944},"10.2466\u002Fpms.1977.44.2.367",{"doi":1943},{"id":24,"text":1946,"url":24,"identifiers":1947},"10.1212\u002FWNL.39.2.161",{"doi":1946},{"id":24,"text":1949,"url":24,"identifiers":1950},"Fischer JS, Neurology, 52, A50",{},{"id":24,"text":1952,"url":24,"identifiers":1953},"10.1177\u002F135245859500100306",{"doi":1952},{"id":24,"text":1955,"url":24,"identifiers":1956},"Syndulko K, 1993, J Neuro Rehab, 7, 153",{},{"id":24,"text":1958,"url":24,"identifiers":1959},"Brooks BR, 1994, Motor neuron disease, 131",{},{"id":1961,"createTime":1962,"updateTime":1962,"relativeEntities":1963,"slug":1964,"properties":1965,"entityType":192,"verifyStatus":193,"verifyTime":1962,"verifyNote":194,"syncStatus":23,"languages":1979,"translateLanguages":24,"viewCount":25,"primaryUrl":1980,"fullTextUrl":24,"authors":1981,"publicationType":358,"publisherRelationship":2330,"citationCount":2368,"citationInfo":2369,"publishDate":2372,"publishYear":2373,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":2374,"isForceReanalyzing":578},"ab0531e8-681c-41c4-a82e-373654b89b94","2024-10-15T21:26:44.740+00:00",[],"Assessing-brain-connectivity-at-rest-is-clinically-relevant-in-early-multiple-sclerosis",{"mag":1966,"keywords":1968,"openalex":1969,"abstract":1971,"title":1973,"pm":1975,"doi":1977},{"VOID":1967},"2132603090",{},{"VOID":1970},"W2132603090",{"EN":1972},"\u003Cjats:sec>\u003Cjats:title>Objective:\u003C\u002Fjats:title>\u003Cjats:p> The present study aims to determine the clinical counterpart of brain resting-state networks reorganization recently evidenced in early multiple sclerosis. \u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>Methods:\u003C\u002Fjats:title>\u003Cjats:p> Thirteen patients with early relapsing–remitting multiple sclerosis and 14 matched healthy controls were included in a resting state functional MRI study performed at 3 T. Data were analyzed using group spatial Independent Component Analysis using concatenation approach (FSL 4.1.3) and double regression analyses (SPM5) to extract local and global levels of connectivity inside various resting state networks (RSNs). Differences in global levels of connectivity of each network between patients and controls were assessed using Mann–Whitney U-test. In patients, relationship between clinical data (Expanded Disability Status Scale and Multiple Sclerosis Functional Composite Score – MSFC) and global RSN connectivity were assessed using Spearman rank correlation. \u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>Results:\u003C\u002Fjats:title>\u003Cjats:p> Independent component analysis provided eight consistent neuronal networks involved in motor, sensory and cognitive processes. For seven RSNs, the global level of connectivity was significantly increased in patients compared with controls. No significant decrease in RSN connectivity was found in early multiple sclerosis patients. MSFC values were negatively correlated with increased RSN connectivity within the dorsal frontoparietal network ( r = −0.811, p = 0.001), the right ventral frontoparietal network ( r = − 0.587, p = 0.045) and the prefronto-insular network ( r = −0.615, p = 0.033). \u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>Conclusions:\u003C\u002Fjats:title>\u003Cjats:p> This study demonstrates that resting state networks reorganization is strongly associated with disability in early multiple sclerosis. 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Antibody titers against EBV antigens were measured in serum samples from 222 individuals who developed MS and 444 age, sex, and race\u002Fethnicity matched controls. Conditional logistic regression was used to estimate relative risks. \u003C\u002Fjats:p>\u003Cjats:p> Results: MS risk increased with increasing titers of anti-EBNA complex ( p < 10\u003Cjats:sup>−9\u003C\u002Fjats:sup>) and anti-EBNA-1 ( p = 5.8 × 10\u003Cjats:sup>−9\u003C\u002Fjats:sup>) titers. MS risk was 36-fold higher among individuals with anti-EBNA complex IgG titers ≥320 than among those with titers <20 (95% confidence interval [CI] 9.6–136), and 8-fold higher among those with anti-EBNA-1 ≥320 than among those with anti-EBNA-1 <20 (95% CI 2.6–23). These associations were consistent across gender and race\u002Fethnicity groups and independent from 25-hydroxyvitamin D levels. 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Cognitive impairment is common in multiple sclerosis (MS). In other populations, cognitive impairment is known to affect fitness-to-drive. Few studies have focused on fitness-to-drive in MS and no studies have solely focused on the influence of cognitive impairment. \u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>Objective:\u003C\u002Fjats:title>\u003Cjats:p> To assess fitness-to-drive in persons with MS with cognitive impairment and low physical disability. \u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>Methods:\u003C\u002Fjats:title>\u003Cjats:p> Persons with MS, aged 18–59 years with EDSS ⩽ 4.0, impaired processing speed, and impairment on at least one measure of memory or executive function, were recruited. Cognition was assessed using the Minimal Assessment of Cognitive Function battery. A formal on-road driving assessment was conducted. Chi-square analysis examined the association between the fitness-to-drive (pass\u002Ffail) and the neuropsychological test results (normal\u002Fimpaired). Bayesian statistics predicting failure of the on-road assessment were calculated. \u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>Results:\u003C\u002Fjats:title>\u003Cjats:p> Of 36 subjects, eight (22.2%) were unfit to drive. Only the BVMTR-IR, measuring visual-spatial memory, predicted on-road driving assessment failure ( X\u003Cjats:sup>2\u003C\u002Fjats:sup> ( df = 1, N = 36) = 3.956; p = 0.047) with a sensitivity of 100%, but low specificity (35.7%) due to false positives (18\u002F25). \u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>Conclusion:\u003C\u002Fjats:title>\u003Cjats:p> In persons with MS and impaired processing speed, impairment on the BVMTR-IR should lead clinicians to address fitness-to-drive. \u003C\u002Fjats:p>\u003C\u002Fjats:sec>",{"EN":2871},"On-road assessment of fitness-to-drive in persons with MS with cognitive impairment: A prospective study",{"VOID":2873},"28782411",{"VOID":2875},"10.1177\u002F1352458517723991",[196],"https:\u002F\u002Fjournals.sagepub.com\u002Fdoi\u002F10.1177\u002F1352458517723991",[2879,2900,2921,2942,2964,2985,3000,3019],{"id":2880,"sortIndex":25,"researcher":24,"roles":2881,"affiliations":2882,"properties":2893},"e0983c88-48cf-4309-ac35-2137bd289f98",[],[2883],{"id":2884,"sortIndex":25,"affiliation":2885,"properties":24},"21c8ed30-f0e7-4ac8-98ce-9c88554df098",{"id":2886,"createTime":2887,"updateTime":2887,"relativeEntities":2888,"slug":2889,"properties":2890,"entityType":53,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"5e1f3e9d-70d8-4d9c-b336-cda8e0cb5175","2024-10-01T19:48:14.182+00:00",[],"Department-of-Clinical-Neurological-Sciences-London-Health-Sciences-Center-Western-University-London-ON-Canada",{"title":2891},{"EN":2892},"Department of Clinical Neurological Sciences, London Health Sciences Center, Western University, London, ON, Canada",{"openalex":2894,"orcid":2896,"title":2898},{"VOID":2895},"A5037427363",{"VOID":2897},"https:\u002F\u002Forcid.org\u002F0000-0001-7522-6440",{"EN":2899},"Sarah A. 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The model that explained the highest variance of participants’ performance on a standardized road test was identified using multiple regression analysis. A discriminant equation containing the tests included in the best model was used to predict pass or fail performance on the test. \u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>Results:\u003C\u002Fjats:title>\u003Cjats:p> Performance on 12 cognitive and three visual tests were significantly associated with performance on the road test. Five of the tests together explained 59% of the variance and predicted the pass or fail outcome of the road test with 91% accuracy, 70% sensitivity, and 97% specificity. \u003C\u002Fjats:p>\u003C\u002Fjats:sec>\u003Cjats:sec>\u003Cjats:title>Conclusion:\u003C\u002Fjats:title>\u003Cjats:p> Participants’ on-road performance was more accurately predicted by the model identified in this study than using only performance on the SDSA test battery. The five psychometric\u002Foff-road tests should be used as a screening battery, after which a follow-up road test should be conducted to finally decide the fitness to drive of individuals with relapsing–remitting MS. 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