Conversion between the Montreal Cognitive Assessment and the Mini-Mental Status Examination.

BACKGROUND Early and accurate detection of cognitive changes using simple tools is essential for an appropriate referral to a more detailed neurocognitive assessment and for the implementation of therapeutic strategies. The Mini-Mental Status Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are two commonly used psychometric tests for cognitive screening. Both tests have different strengths and weaknesses. Preferences regarding test selection may therefore differ among clinicians. The aim of this retrospective observational cohort study was to define corresponding scores for the MMSE and the MoCA. METHODS We examined the relationship between the cognitive screening tests in 803 German-speaking Memory Clinic outpatients, encompassing a wide range of neurocognitive disorders. We produced a conversion table using the equipercentile equating method with log-linear smoothing. In addition, we conducted a systematic review of existing MMSE-MoCA conversions to create a table allowing for the conversion of MoCA scores into MMSE scores and vice versa using the weighted mean method. RESULTS The Memory Clinic sample showed that the prediction of MMSE to MoCA was overall less accurate compared to the conversion from MoCA to MMSE. The 19 studies included after thorough literature search showed that MoCA scores were consistently lower than MMSE scores. Eleven of 19 conversion studies had addressed the conversion of the MoCA to the MMSE, while two studies converted MMSE to MoCA scores. Another six studies applied bi-directional conversions. We provide an easy-to-use table covering the entire range of scores and taking into account all currently existing conversion formulas. CONCLUSION The comprehensive MMSE-MoCA conversion table enables a direct comparison of cognitive test scores at screening examinations and over the course of disease in patients with neurocognitive disorders

Consensus Paper: Radiological Biomarkers of Cerebellar Diseases

Hereditary and sporadic cerebellar ataxias represent a vast and still growing group of diseases whose diagnosis and differentiation cannot only rely on clinical evaluation. Brain imaging including magnetic resonance (MR) and nuclear medicine techniques allows for characterization of structural and functional abnormalities underlying symptomatic ataxias. These methods thus constitute a potential source of radiological biomarkers, which could be used to identify these diseases and differentiate subgroups of them, and to assess their severity and their evolution. Such biomarkers mainly comprise qualitative and quantitative data obtained from MR including proton spectroscopy, diffusion imaging, tractography, voxel-based morphometry, functional imaging during task execution or in a resting state, and from SPETC and PET with several radiotracers. In the current article, we aim to illustrate briefly some applications of these neuroimaging tools to evaluation of cerebellar disorders such as inherited cerebellar ataxia, fetal developmental malformations, and immune-mediated cerebellar diseases and of neurodegenerative or early-developing diseases, such as dementia and autism in which cerebellar involvement is an emerging feature. Although these radiological biomarkers appear promising and helpful to better understand ataxia-related anatomical and physiological impairments, to date, very few of them have turned out to be specific for a given ataxia with atrophy of the cerebellar system being the main and the most usual alteration being observed. Consequently, much remains to be done to establish sensitivity, specificity, and reproducibility of available MR and nuclear medicine features as diagnostic, progression and surrogate biomarkers in clinical routine

Enhanced diagnostic accuracy for neurocognitive disorders: a revised cut-off approach for the Montreal Cognitive Assessment

The Montreal Cognitive Assessment (MoCA) has good sensitivity for mild cognitive impairment, but specificity is low when the original cut-off (25/26) is used. We aim to revise the cut-off on the German MoCA for its use in clinical routine.; Data were analyzed from 496 Memory Clinic outpatients (447 individuals with a neurocognitive disorder; 49 with cognitive normal findings) and from 283 normal controls. Cut-offs were identified based on (a) Youden's index and (b) the 10th percentile of the control group.; A cut-off of 23/24 on the MoCA had better correct classification rates than the MMSE and the original MoCA cut-off. Compared to the original MoCA cut-off, the cut-off of 23/24 points had higher specificity (92% vs 63%), but lower sensitivity (65% vs 86%). Introducing two separate cut-offs increased diagnostic accuracies with 92% specificity (23/24 points) and 91% sensitivity (26/27 points). Scores between these two cut-offs require further examinations.; Using two separate cut-offs for the MoCA combined with scores in an indecisive area enhances the accuracy of cognitive screening. What do you want to do ? New mail Cop

The Montreal Cognitive Assessment: Normative Data from a German-Speaking Cohort and Comparison with International Normative Samples

The Montreal Cognitive Assessment (MoCA) is used to evaluate multiple cognitive domains in elderly individuals. However, it is influenced by demographic characteristics that have yet to be adequately considered.; The aim of our study was to investigate the effects of age, education, and sex on the MoCA total score and to provide demographically adjusted normative values for a German-speaking population.; Subjects were recruited from a registry of healthy volunteers. Cognitive health was defined using the Mini-Mental State Examination (score ≥27/30 points) and the Consortium to Establish a Registry for Alzheimer's Disease-Neuropsychological Assessment Battery (total score ≥85.9 points). Participants were assessed with the German version of the MoCA. Normative values were developed based on regression analysis. Covariates were chosen using the Predicted Residual Sums of Squares approach.; The final sample consisted of 283 participants (155 women, 128 men; mean (SD) age = 73.8 (5.2) years; education = 13.6 (2.9) years). Thirty-one percent of participants scored below the original cut-off (<26/30 points). The MoCA total score was best predicted by a regression model with age, education, and sex as covariates. Older age, lower education, and male sex were associated with a lower MoCA total score (p < 0.001).; We developed a formula to provide demographically adjusted standard scores for the MoCA in a German-speaking population. A comparison with other MoCA normative studies revealed considerable differences with respect to selection of volunteers and methods used to establish normative data