Characteristics of neurological and cognitive status in patients with multiple sclerosis in relation to the location and volumes of demyelination foci and the severity of brain atrophy

Mineev, K. K.; Прахова, Л. Н.; Ilves, A. G.; Kataeva, G. V.; Петров, А. М.; Reznikova, T. N.; Pozdnyakov, A. V.; Stolyarov, I. D.

doi:10.1007/s11055-008-9086-2

Cited by 14 publications

(10 citation statements)

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“…Twenty studies (7 cross-sectional and 13 longitudinal) evaluated the association between whole brain atrophy and disability; most included patients with mixed disease phenotype. Five of the cross-sectional studies reported a significant correlation between whole brain atrophy and EDSS ( p < 0.05 for all; see Table 1 for individual p -values) ( 37 , 43 , 54 , 56 , 58 ). In the sixth cross-sectional study, the level of brain atrophy was significantly greater in patients requiring ambulatory assistance compared with those not requiring assistance ( p = 0.001) ( 42 ).…”

Section: Resultsmentioning

confidence: 99%

The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis

2017

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IntroductionMultiple sclerosis (MS) is characterised by the accumulation of permanent neurological disability secondary to irreversible tissue loss (neurodegeneration) in the brain and spinal cord. MRI measures derived from T1-weighted image analysis (i.e., black holes and atrophy) are correlated with pathological measures of irreversible tissue loss. Quantifying the degree of neurodegeneration in vivo using MRI may offer a surrogate marker with which to predict disability progression and the effect of treatment. This review evaluates the literature examining the association between MRI measures of neurodegeneration derived from T1-weighted images and disability in MS patients.MethodsA systematic PubMed search was conducted in January 2017 to identify MRI studies in MS patients investigating the relationship between “black holes” and/or atrophy in the brain and spinal cord, and disability. Results were limited to human studies published in English in the previous 10 years.ResultsA large number of studies have evaluated the association between the previous MRI measures and disability. These vary considerably in terms of study design, duration of follow-up, size, and phenotype of the patient population. Most, although not all, have shown that there is a significant correlation between disability and black holes in the brain, as well as atrophy of the whole brain and grey matter. The results for brain white matter atrophy are less consistently positive, whereas studies evaluating spinal cord atrophy consistently showed a significant correlation with disability. Newer ways of measuring atrophy, thanks to the development of segmentation and voxel-wise methods, have allowed us to assess the involvement of strategic regions of the CNS (e.g., thalamus) and to map the regional distribution of damage. This has resulted in better correlations between MRI measures and disability and in the identification of the critical role played by some CNS structures for MS clinical manifestations.ConclusionThe evaluation of MRI measures of atrophy as predictive markers of disability in MS is a highly active area of research. At present, measurement of atrophy remains within the realm of clinical studies, but its utility in clinical practice has been recognized and barriers to its implementation are starting to be addressed.

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Section: Resultsmentioning

confidence: 99%

The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis

2017

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“…Thirty-nine studies reporting correlations between the PASAT and SDMT IPS measures and MRI assessments were identified for inclusion and analysis in this review [13, 17–54]. More studies evaluated the relationship between PASAT and atrophy ( n = 24) [13, 18–21, 23–25, 27, 29–34, 37, 39, 42, 44, 47–49, 53, 54] or T2LV ( n = 27) [13, 17–20, 24, 25, 27, 28, 30, 31, 33, 34, 36, 38–45, 47–50, 52] than SDMT and these MRI measures ( n = 18 for both atrophy [13, 18–25, 27, 29, 30, 33, 34, 39, 47, 48, 54] and T2LV [13, 17–20, 22, 24, 25, 27, 30, 33, 34, 39, 40, 45, 47, 50, 51]). Depiction of the full extracted data on the relationships between the individual MRI measures and each cognitive test are available in Supplementary Tables 1, 2, and 3 as an online appendix (see Supplementary Material available online at http://dx.doi.org/10.1155/2014/975803).…”

Section: Resultsmentioning

confidence: 99%

“…However, over half of studies (53.8%) [13, 18, 38, 42, 44, 47, 48] did not report correlations in RRMS patients, despite measuring T2LV and administering the PASAT test. Studies that evaluated MS patients with mixed disease courses found that correlations varied between T2LV and the PASAT test, but the relationship was strong in most studies (weak −0.23 to strong −0.58) reporting significant results.…”

Section: Resultsmentioning

confidence: 99%

Correlations between MRI and Information Processing Speed in MS: A Meta-Analysis

Rao

Martin

Huelin

et al. 2014

Multiple Sclerosis International

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Objectives. To examine relationships between conventional MRI measures and the paced auditory serial addition test (PASAT) and symbol digit modalities test (SDMT). Methods. A systematic literature review was conducted. Included studies had ≥30 multiple sclerosis (MS) patients, administered the SDMT or PASAT, and measured T2LV or brain atrophy. Meta-analysis of MRI/information processing speed (IPS) correlations, analysis of MRI/IPS significance tests to account for reporting bias, and binomial testing to detect trends when comparing correlation strengths of SDMT versus PASAT and T2LV versus atrophy were conducted. Results. The 39 studies identified frequently reported only significant correlations, suggesting reporting bias. Direct meta-analysis was only feasible for correlations between SDMT and T2LV (r = −0.45, P < 0.001) and atrophy in patients with mixed-MS subtypes (r = −0.54, P < 0.001). Familywise Holm-Bonferroni testing found that selective reporting was not the source of at least half of significant results reported. Binomial tests (P = 0.006) favored SDMT over PASAT in strength of MRI correlations. Conclusions. A moderate-to-strong correlation exists between impaired IPS and MRI in mixed MS populations. Correlations with MRI were stronger for SDMT than for PASAT. Neither heterogeneity among populations nor reporting bias appeared to be responsible for these findings.

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“…In addition to physical disability, cognitive impairment is also commonly present in MS [12] and cognitive decline cannot be sufficiently explained by white matter lesion measures [13]. Cross-sectional studies have shown a stronger relation between cognition and atrophy measures [14][15][16][17] and longitudinal studies indicated that atrophy may be able to predict cognitive impairment [18,19]. However, as with disability studies, most studies included relatively short FU periods and rather small sample sizes [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Predicting clinical progression in multiple sclerosis after 6 and 12 years

Dekker

Eijlers

Popescu

et al. 2019

Euro J of Neurology

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Background and purpose To predict disability and cognition in multiple sclerosis ( MS ) after 6 and 12 years, using early clinical and imaging measures. Methods A total of 115 patients with MS were selected and followed up after 2 and 6 years, with 79 patients also being followed up after 12 years. Disability was measured using the Expanded Disability Status Scale ( EDSS ); cognition was measured only at follow‐up using neuropsychological testing. Predictors of interest included EDSS score, baseline brain and lesion volumes and their changes over 2 years, baseline age, clinical phenotype, sex and educational level. Results Higher 6‐year EDSS score was predicted by early EDSS score and whole‐brain volume changes and baseline diagnosis of primary progressive MS (adjusted R 2 = 0.56). Predictors for 12‐year EDSS score included larger EDSS score changes and higher T1‐hypointense lesion volumes (adjusted R 2 = 0.38). Year 6 cognition was predicted by primary progressive MS phenotype, lower educational level, male sex and early whole‐brain atrophy (adjusted R 2 = 0.26); year 12 predictors included male sex, lower educational level and higher baseline T1‐hypointense lesion volumes (adjusted R 2 = 0.14). Conclusions Patients with early signs of neurodegeneration and a progressive disease onset were more prone to develop both disability progression and cognitive dysfunction. Male sex and lower educational level only affected cognitive dysfunction, which remains difficult to predict and probably needs more advanced imaging measures.

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Characteristics of neurological and cognitive status in patients with multiple sclerosis in relation to the location and volumes of demyelination foci and the severity of brain atrophy

Cited by 14 publications

References 9 publications

The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis

The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis

Correlations between MRI and Information Processing Speed in MS: A Meta-Analysis

Predicting clinical progression in multiple sclerosis after 6 and 12 years

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