Single MRI-Based Volumetric Assessment in Clinical Practice Is Associated With MS-Related Disability.

BACKGROUND: The added value of brain volume measurements in the clinical practice of multiple sclerosis (MS) has been questioned. PURPOSE: To investigate the contribution of volume measures obtained with magnetic resonance scans performed as part of regular care to predict measures of cognitive and physical MS disability in a real-world setting. STUDY TYPE: Retrospective. SUBJECTS: In all, 470 adults with diagnosed MS. FIELD STRENGTH/SEQUENCE: 3D fluid attenuation inversion recovery (FLAIR) and 3D T1 -weighted MR images at 3.0T MR. ASSESSMENT: Lesion and brain volume were measured by an automated method, MSmetrix, developed by icometrix. STATISTICAL TESTS: We used stepwise linear regression models to assess the added value of a single volumetric assessment in predicting Expanded Disability Status Scale (EDSS) and Symbol Digit Modalities Test (SDMT). Brain volumes categorized into quartiles were used as predictive variables in a time-to-event analysis and Cox proportional hazard regression with time to worsening from baseline as outcome measures. RESULTS: Brain and lesion volume in relapsing onset MS strongly contributed to the best models, with a substantial role for age in the EDSS model and a modest role for education in the SDMT model. Adding MR volumetric information increased the explained variance from 17% to 28% in the best model for EDSS and from 9% to 25% in the best model for SDMT. A significantly reduced hazard (P < 0.05) of SDMT worsening was found in the highest normalized brain volume quartiles (1375-1608 ml), compared with the lowest quartile (1201-1374 ml) in the total study population. DATA CONCLUSION: Our findings indicate that a single brain volumetric assessment contributes to the prediction of MS-related disability, with distinct patterns for EDSS as a measure of physical disability, and SDMT as a measure of cognitive disability. A threshold effect for the lowest brain volumes with regard to SDMT worsening over time was found. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1312-1321.

Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state.

Monitoring the level of consciousness in brain-injured patients with disorders of consciousness is crucial as it provides diagnostic and prognostic information. Behavioral assessment remains the gold standard for assessing consciousness but previous studies have shown a high rate of misdiagnosis. This study aimed to investigate the usefulness of electroencephalography (EEG) entropy measurements in differentiating unconscious (coma or vegetative) from minimally conscious patients. Left fronto-temporal EEG recordings (10-minute resting state epochs) were prospectively obtained in 56 patients and 16 age-matched healthy volunteers. Patients were assessed in the acute (≤1 month post-injury; n=29) or chronic (>1 month post-injury; n=27) stage. The etiology was traumatic in 23 patients. Automated online EEG entropy calculations (providing an arbitrary value ranging from 0 to 91) were compared with behavioral assessments (Coma Recovery Scale-Revised) and outcome. EEG entropy correlated with Coma Recovery Scale total scores (r=0.49). Mean EEG entropy values were higher in minimally conscious (73±19; mean and standard deviation) than in vegetative/unresponsive wakefulness syndrome patients (45±28). Receiver operating characteristic analysis revealed an entropy cut-off value of 52 differentiating acute unconscious from minimally conscious patients (sensitivity 89% and specificity 90%). In chronic patients, entropy measurements offered no reliable diagnostic information. EEG entropy measurements did not allow prediction of outcome. User-independent time-frequency balanced spectral EEG entropy measurements seem to constitute an interesting diagnostic - albeit not prognostic - tool for assessing neural network complexity in disorders of consciousness in the acute setting. Future studies are needed before using this tool in routine clinical practice, and these should seek to improve automated EEG quantification paradigms in order to reduce the remaining false negative and false positive findings.

Correlations of health status indicators with perceived neuropsychological impairment and cognitive processing speed in multiple sclerosis.

BACKGROUND: Comorbidity and health behaviours may explain heterogeneity regarding cognitive performance in multiple sclerosis. Patient-reported cognitive difficulties have impact but do not consistently correlate with objective cognitive performance. Our study aims to investigate whether health status indicators including comorbidities, body mass index, physical activity, smoking, sleeping behaviour and consumption patterns for fish, alcohol and caffeinated drinks are associated with measures of subjective and objective cognitive performance. METHODS: Survey data on self-reported cognitive performance, assessed with the MS Neuropsychological Screening Questionnaire (MSNQ), were related to the presence of arterial hypertension, diabetes mellitus, cardiovascular and chronic renal diseases, hypercholesterolemia, depression based on 2-question screening tool, health and consumption behaviors. We included the Symbol Digit Modalities Test when available within 6 months as an objective, performance-based metric of cognitive processing speed. We investigated the interrelation between all variables with a Spearman correlation matrix and corrected for multiple testing. Regression models were built and controlled for age, sex and phenotype. RESULTS: We used available data from 751 patients with definite MS, including 290 SDMT scores within a time window of 6 months, to study relations between variables. MSNQ and SDMT scores were not significantly correlated. Correlation patterns for subjective and objective performance differed. Age, disease duration and physical disability correlated with SDMT scores only. Regression analyses could be performed for MSNQ scores in 595/751 (79.2%) and for SDMT scores in 234/751 (31.2%) participants. After restricting variables to avoid collinearity and adjusting for the number of variables, regression models explained 15% of the variance for subjective and 14% of the variance for objective cognitive performance. A higher number of physical comorbidities, reporting depressive symptoms, sleeping 9 h or more and daily use of sleeping medication were associated with lower subjective cognitive performance, whereas increasing age was associated with reduced processing speed. These associations persisted after correction for multiple testing. CONCLUSION: Increasing age is associated with reduced cognitive processing speed whereas comorbidities and sleep behaviors contribute to subjective cognitive performance.