Harmonizing neuropsychological assessment for mild neurocognitive disorders in Europe.

INTRODUCTION: Harmonized neuropsychological assessment for neurocognitive disorders, an international priority for valid and reliable diagnostic procedures, has been achieved only in specific countries or research contexts. METHODS: To harmonize the assessment of mild cognitive impairment in Europe, a workshop (Geneva, May 2018) convened stakeholders, methodologists, academic, and non-academic clinicians and experts from European, US, and Australian harmonization initiatives. RESULTS: With formal presentations and thematic working-groups we defined a standard battery consistent with the U.S. Uniform DataSet, version 3, and homogeneous methodology to obtain consistent normative data across tests and languages. Adaptations consist of including two tests specific to typical Alzheimer's disease and behavioral variant frontotemporal dementia. The methodology for harmonized normative data includes consensus definition of cognitively normal controls, classification of confounding factors (age, sex, and education), and calculation of minimum sample sizes. DISCUSSION: This expert consensus allows harmonizing the diagnosis of neurocognitive disorders across European countries and possibly beyond.

The items in the Chinese version of the Montreal cognitive assessment basic discriminate among different severities of Alzheimer's disease.

BACKGROUND: To determine whether items of the Chinese version of the Montreal Cognitive Assessment Basic (MoCA-BC) could discriminate among cognitively normal controls (NC), and those with mild cognitive impairment (MCI), mild Alzheimer's disease (AD), and moderate-severe (AD), as well as their sensitivity and specificity. METHODS: MCI (n = 456), mild AD (n = 502) and moderate-severe AD (n = 102) patients were recruited from the memory clinic, Huashan Hospital, Shanghai, China. NC (n = 329) were recruited from health checkup outpatients. Five MoCA-BC item scores were collected in interviews. RESULTS: The MoCA-BC orientation test had high sensitivity and specificity for discrimination among MCI, mild AD and moderate-severe AD. The delayed recall memory test had high sensitivity and specificity for MCI screening. The verbal fluency test was efficient for detecting MCI and differentiating AD severity. CONCLUSIONS: Various items of the MoCA-BC can identify MCI patients early and identify the severity of dementia.

Aggregating validity indicators: The salience of domain specificity and the indeterminate range in multivariate models of performance validity assessment.

This study was designed to examine the "domain specificity" hypothesis in performance validity tests (PVTs) and the epistemological status of an "indeterminate range" when evaluating the credibility of a neuropsychological profile using a multivariate model of performance validity assessment. While previous research suggests that aggregating PVTs produces superior classification accuracy compared to individual instruments, the effect of the congruence between the criterion and predictor variable on signal detection and the issue of classifying borderline cases remain understudied. Data from a mixed clinical sample of 234 adults referred for cognitive evaluation (MAge = 46.6; MEducation = 13.5) were collected. Two validity composites were created: one based on five verbal PVTs (EI-5VER) and one based on five nonverbal PVTs (EI-5NV) and compared against several other PVTs. Overall, language-based tests of cognitive ability were more sensitive to elevations on the EI-5VER compared to visual-perceptual tests; whereas, the opposite was observed with the EI-5NV. However, the match between predictor and criterion variable had a more complex relationship with classification accuracy, suggesting the confluence of multiple factors (sensory modality, cognitive domain, testing paradigm). An "indeterminate range" of performance validity emerged that was distinctly different from both the Pass and the Fail group. Trichotomized criterion PVTs (Pass-Borderline-Fail) had a negative linear relationship with performance on tests of cognitive ability, providing further support for an "in-between" category separating the unequivocal Pass and unequivocal Fail classification range. The choice of criterion variable can influence classification accuracy in PVT research. Establishing a Borderline range between Pass and Fail more accurately reflected the distribution of scores on multiple PVTs. The traditional binary classification system imposes an artificial dichotomy on PVTs that was not fully supported by the data. Accepting "indeterminate" as a legitimate third outcome of performance validity assessment has the potential to improve the clinical utility of PVTs and defuse debates regarding "near-Passes" and "soft Fails."

Screening for cognitive dysfunction in systemic lupus erythematosus: the Montreal Cognitive Assessment Questionnaire and the Informant Questionnaire on Cognitive Decline in the Elderly.

BACKGROUND: Cognitive dysfunction (CD) is among the most common neuropsychiatric manifestations of systemic lupus erythematosus (SLE). Traditional neuropsychological testing and the Automated Neuropsychologic Assessment Metrics (ANAM) have been used to assess CD but neither is an ideal screening test. The Montreal Cognitive Assessment Questionnaire (MoCA) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) are brief and inexpensive tests. This study evaluated the MoCA and IQCODE as screening tools. METHODS: SLE patients fulfilling American College of Rheumatology (ACR) classification criteria were evaluated using the ANAM as the reference standard. The performance characteristics of the MoCA and IQCODE were assessed in comparison with normal controls (NCs) and rheumatoid arthritis (RA) patients. Four different definitions of CD were utilized. RESULTS: In total, 78 patients were evaluated. MoCA and ANAM scores were significantly correlated ( r = 0.51, p < 0.001). At the optimal cutoff, the sensitivity of the MoCA was ≥ 90% (depending on definition of CD) vs RA patients and ≥83% vs NCs. ANAM and IQCODE scores did not correlate ( p = 0.8152). IQCODE sensitivities were low for both RA patients and NCs regardless of definition and cutoff used. CONCLUSION: The MoCA appears to be a promising and practical screening tool for identification of patients with SLE at risk for CD.

Office-based assessment of cognitive impairment.

BACKGROUND: Dementia is an increasingly prevalent condition, currently affecting over 400,000 Australians, and this is expected to rise to over one million by 2056. Diagnosis of dementia is a clinical one, as there is no single well-defined blood test, imaging or cognitive function test that validly diagnoses dementia. There are also many causes of cognitive impairment other than dementia, which need to be identified or excluded. General practitioners (GPs) are on the frontline of presentation and will therefore play an increasingly important role in identification and management. OBJECTIVE: This article reviews what the GP can do in relation to office-based assessment for cognitive impairment. DISCUSSION: Dementia is an increasingly common condition, and it is important that GPs become familiar with its assessment and management in order to optimise access to care services, advance care planning and management of other conditions for their patients.

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different disease stages using brain structural information provided by magnetic resonance imaging (MRI) data. NEW METHOD: The neighborhood component analysis (NCA) is applied to select most powerful features for prediction. The ensemble decision tree classifier is built to predict which group the subject belongs to. The best features and model parameters are determined by cross validation of the training data. RESULTS: Our results show that 16 out of a total of 429 features were selected by NCA using 240 training subjects, including MMSE score and structural measures in memory-related regions. The boosting tree model with NCA features can achieve prediction accuracy of 56.25% on 160 test subjects. COMPARISON WITH EXISTING METHOD(S): Principal component analysis (PCA) and sequential feature selection (SFS) are used for feature selection, while support vector machine (SVM) is used for classification. The boosting tree model with NCA features outperforms all other combinations of feature selection and classification methods. CONCLUSIONS: The results suggest that NCA be a better feature selection strategy than PCA and SFS for the data used in this study. Ensemble tree classifier with boosting is more powerful than SVM to predict the subject group. However, more advanced feature selection and classification methods or additional measures besides structural MRI may be needed to improve the prediction performance.

Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People.

BACKGROUND: Various types of dementia and Mild Cognitive Impairment (MCI) are manifested as irregularities in human speech and language, which have proven to be strong predictors for the disease presence and progress ion. Therefore, automatic speech analytics provided by a mobile application may be a useful tool in providing additional indicators for assessment and detection of early stage dementia and MCI. METHOD: 165 participants (subjects with subjective cognitive impairment (SCI), MCI patients, Alzheimer's disease (AD) and mixed dementia (MD) patients) were recorded with a mobile application while performing several short vocal cognitive tasks during a regular consultation. These tasks included verbal fluency, picture description, counting down and a free speech task. The voice recordings were processed in two steps: in the first step, vocal markers were extracted using speech signal processing techniques; in the second, the vocal markers were tested to assess their 'power' to distinguish between SCI, MCI, AD and MD. The second step included training automatic classifiers for detecting MCI and AD, based on machine learning methods, and testing the detection accuracy. RESULTS: The fluency and free speech tasks obtain the highest accuracy rates of classifying AD vs. MD vs. MCI vs. SCI. Using the data, we demonstrated classification accuracy as follows: SCI vs. AD = 92% accuracy; SCI vs. MD = 92% accuracy; SCI vs. MCI = 86% accuracy and MCI vs. AD = 86%. CONCLUSIONS: Our results indicate the potential value of vocal analytics and the use of a mobile application for accurate automatic differentiation between SCI, MCI and AD. This tool can provide the clinician with meaningful information for assessment and monitoring of people with MCI and AD based on a non-invasive, simple and low-cost method.

The CERAD Neuropsychological Assessment Battery Is Sensitive to Alcohol-Related Cognitive Deficiencies in Elderly Patients: A Retrospective Matched Case-Control Study.

OBJECTIVES: Adverse effects of heavy drinking on cognition have frequently been reported. In the present study, we systematically examined for the first time whether clinical neuropsychological assessments may be sensitive to alcohol abuse in elderly patients with suspected minor neurocognitive disorder. METHODS: A total of 144 elderly with and without alcohol abuse (each group n=72; mean age 66.7 years) were selected from a patient pool of n=738 by applying propensity score matching (a statistical method allowing to match participants in experimental and control group by balancing various covariates to reduce selection bias). Accordingly, study groups were almost perfectly matched regarding age, education, gender, and Mini Mental State Examination score. Neuropsychological performance was measured using the CERAD (Consortium to Establish a Registry for Alzheimer's Disease). Classification analyses (i.e., decision tree and boosted trees models) were conducted to examine whether CERAD variables or total score contributed to group classification. RESULTS: Decision tree models disclosed that groups could be reliably classified based on the CERAD variables "Word List Discriminability" (tapping verbal recognition memory, 64% classification accuracy) and "Trail Making Test A" (measuring visuo-motor speed, 59% classification accuracy). Boosted tree analyses further indicated the sensitivity of "Word List Recall" (measuring free verbal recall) for discriminating elderly with versus without a history of alcohol abuse. CONCLUSIONS: This indicates that specific CERAD variables seem to be sensitive to alcohol-related cognitive dysfunctions in elderly patients with suspected minor neurocognitive disorder. (JINS, 2018, 24, 360-371).

Bridging cognitive screening tests in neurologic disorders: A crosswalk between the short Montreal Cognitive Assessment and Mini-Mental State Examination.

INTRODUCTION: To provide a crosswalk between the recently proposed short Montreal Cognitive Assessment (s-MoCA) and Mini-Mental State Examination (MMSE) within a clinical cohort. METHODS: A total of 791 participants, with and without neurologic conditions, received both the MMSE and the MoCA at the same visit. s-MoCA scores were calculated and equipercentile equating was used to create a crosswalk between the s-MoCA and MMSE. RESULTS: As expected, s-MoCA scores were highly correlated (Pearson r = 0.82, P < .001) with MMSE scores. s-MoCA scores correctly classified 85% of healthy older adults and 91% of individuals with neurologic conditions that impair cognition. In addition, we provide an easy to use table that enables the conversion of s-MoCA score to MMSE score. DISCUSSION: The s-MoCA is quick to administer, provides high sensitivity and specificity for cognitive impairment, and now can be compared directly with the MMSE.

An investigation of the efficiency of the mini-Kingston standardized cognitive assessment-revised in classifying patients according to DSM-5 major and mild neurocognitive disorders due to possible Alzheimer's disease.

BACKGROUND: The aim of this study was to examine the efficiency of the mini-Kingston standardized cognitive assessment-revised (mini-KSCAr) in classifying patients according to DSM-5 major and mild neurocognitive disorders (NCD) due to possible Alzheimer's disease (AD). METHODS: Files of 85 individuals who were tested on the Kingston standardized cognitive assessment-revised were reviewed and scores were calculated for the mini-KSCAr. Medical history, psychiatric and physical status, basic and instrumental activities of daily living, as well as scores on the Cambridge cognitive examination-revised (CAMCOG-R), and the clinical dementia rating (CDR) scale were used to establish DSM-5 diagnoses of major or mild neurocognitive disorders (NCD) due to possible AD or no cognitive decline. All participants were tested on the Mini-Mental State Examination (MMSE) and the clock drawing test (CDT), and a subset (N = 28) were also tested on three well-known memory tasks. RESULTS: Scores on the MMSE, CDT, and mini-KSCAr differed across groups, but only the mini-KSCAr significantly distinguished each group from the other two. The mini-KSCAr showed better sensitivity, specificity, and likelihood ratios (LRs) than did the MMSE and the CDT. A regression analysis revealed that the mini-KSCAr accounted for almost half of the variance in memory performance, whereas the MMSE and the CDT contributed nothing to this prediction once the mini-KSCAr was used. CONCLUSIONS: The mini-KSCAr is an efficient instrument for the diagnosis of DSM-5 major and mild NCD due to possible AD in a specialized psychogeriatric setting, and its utility is greater than that of the MMSE and the CDT.