Development of a computerized tool for the chinese version of the montreal cognitive assessment for screening mild cognitive impairment

Verbal and non-verbal recognition memory assessment: validation of a computerized version of the Recognition Memory Test

BACKGROUND

The use of computerized devices for neuropsychological assessment (CNADs) as an effective alternative to the traditional pencil-and-paper modality has recently increased exponentially, both in clinical practice and research, especially due to the pandemic. However, several authors underline that the computerized modality requires the same psychometric validity as "in-presence" tests. The current study aimed at building and validating a computerized version of the verbal and non-verbal recognition memory test (RMT) for words, unknown faces and buildings.

METHODS

Seventy-two healthy Italian participants, with medium-high education and ability to proficiently use computerized systems, were enrolled. The sample was subdivided into six groups, one for each age decade. Twelve neurological patients with mixed aetiology, age and educational level were also recruited. Both the computerized and the paper-and-pencil versions of the RMT were administered in two separate sessions.

RESULTS

In healthy participants, the computerized and the paper-and-pencil versions of the RMT showed statistical equivalence for words, unknown faces and buildings. In the neurological patients, no statistical difference was found between the performance at the two versions of the RMT. A moderate-to-good inter-rater reliability between the two versions was also found in both samples. Finally, the computerized version of the RMT was perceived as acceptable by both healthy participants and neurological patients at System Usability Scale (SUS).

CONCLUSION

The computerized version of the RMT can be used as a reliable alternative to the traditional version.

Linear brain measurement: a new screening method for cognitive impairment in elderly patients with cerebral small vessel disease

Background

The old adults have high incidence of cognitive impairment, especially in patients with cerebral small vessel disease (CSVD). Cognitive impairment is not easy to be detected in such populations. We aimed to develop clinical prediction models for different degrees of cognitive impairments in elderly CSVD patients based on conventional imaging and clinical data to determine the better indicators for assessing cognitive function in the CSVD elderly.

Methods

210 CSVD patients were screened out by the evaluation of Magnetic Resonance Imaging (MRI). Then, participants were divided into the following three groups according to the cognitive assessment results: control, mild cognitive impairment (MCI), and dementia groups. Clinical data were collected from all patients, including demographic data, biochemical indicators, carotid ultrasound, transcranial Doppler (TCD) indicators, and linear measurement parameters based on MRI.

Results

Our results showed that the brain atrophy and vascular lesions developed progressive worsening with increased degree of cognitive impairment. Crouse score and Interuncal distance/Bitemporal distance (IUD/BTD) were independent risk factors for MCI in CSVD patients, and independent risk factors for dementia in CSVD were Crouse Score, the pulsatility index of the middle cerebral artery (MCAPI), IUD/BTD, and Sylvian fissure ratio (SFR). Overall, the parameters with high performance were the IUD/BTD (OR 2.28; 95% CI 1.26-4.10) and SFR (OR 3.28; 95% CI 1.54-6.91), and the AUC (area under the curve) in distinguishing between CSVD older adults with MCI and with dementia was 0.675 and 0.724, respectively. Linear brain measurement parameters had larger observed effect than other indexes to identify cognitive impairments in CSVD patients.

Conclusion

This study shows that IUD/BTD and SFR are good predictors of cognitive impairments in CSVD elderly. Linear brain measurement showed a good predictive power for identifying MCI and dementia in elderly subjects with CSVD. Linear brain measurement could be a more suitable and novel method for screening cognitive impairment in aged CSVD patients in primary healthcare facilities, and worth further promotion among the rural population.

Optimal cut-off MoCA score for screening for mild cognitive impairment in elderly individuals in China: A systematic review and meta-analysis

AIM

To evaluate the optimal cut-off MoCA score for elderly individuals with MCI.

DESIGN

A systematic review and meta-analysis.

METHOD

Articles were retrieved from PubMed, Ovid, Embase, The Cochrane Library, PsycINFO, CBM, CNKI, WanFang and CQVIP and were assessed by using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Figures of the assessment were made by using Review Manager 5.3, and a meta-analysis of the data was conducted by using Bivariate Random-effects Meta-Analysis (BRMA) via Stata 14.0.

RESULTS

Seventeen articles were retrieved from the database, and when the cut-offs were 24/25 and 25/26, they represented the same diagnostic value; in addition, the AUC was 0.96, which demonstrated high predictive validity for mild cognitive impairment screening. However, the sensitivity was higher with 25/26 (se=0.95, sp=0.80), whereas the specificity was higher with 24/25 (se=0.92, sp=0.89).

Association between cognition and olfaction-specific parameters in patients with chronic rhinosinusitis

BACKGROUND

Patients with chronic rhinosinusitis (CRS) have reported significantly cognitive and olfactory dysfunction. This study aimed to explore the relationship between cognitive function and olfaction-specific parameters in patients with CRS.

METHODS

A cross-sectional survey method was used to investigate 98 participants, including 75 patients with CRS and 23 healthy controls. Cognitive function and psychophysical olfactory tests were performed. Olfactory cleft endoscopy scale and olfactory cleft computed tomography (CT) scores were obtained. Multivariate logistic regression was used to analyze the risk factors of Mild Cognitive Impairment (MCI) in patients with CRS.

RESULTS

There are significant differences in age, Montreal Cognitive Assessment (MoCA) scores, number of MCI, Lund-Mackay olfactory cleft (LM-OC) score, and blood eosinophil count between CRS with and without olfactory dysfunction groups (all P < 0.05). Total MoCA scores were positively correlated with thresholds-discrimination-identification (TDI) score (r = 0.541, P < 0.001), olfactory threshold (OT) (r = 0.440, P < 0.001), olfactory discrimination (OD) (r = 0.541, P < 0.001), and olfactory identification (OI) (r = 0.382, P = 0.001) scores. Furthermore, total MoCA scores were negatively correlated with LM-OC scores (r =  - 0.351, P = 0.002). After adjusting for patient demographics, only the OD score was an independent risk factor for MCI among patients with CRS (odds ratio = 0.792; P = 0.039). The OD scores less than 11.5 were the best predictor of MCI in patients with CRS.

CONCLUSION

Olfaction-specific clinical parameters were highly correlated with cognitive function in patients with CRS and the OD score was an independent risk factor for MCI in patients with CRS.

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients' cognitive function and early intervention is helpful to improve patient's quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer's disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM. Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM). Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index. Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers. Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.

Digital Cognitive Biomarker for Mild Cognitive Impairments and Dementia: A Systematic Review

The dementia population is increasing as the world’s population is growing older. The current systematic review aims to identify digital cognitive biomarkers from computerized tests for detecting dementia and its risk state of mild cognitive impairment (MCI), and to evaluate the diagnostic performance of digital cognitive biomarkers. A literature search was performed in three databases, and supplemented by a Google search for names of previously identified computerized tests. Computerized tests were categorized into five types, including memory tests, test batteries, other single/multiple cognitive tests, handwriting/drawing tests, and daily living tasks and serious games. Results showed that 78 studies were eligible. Around 90% of the included studies were rated as high quality based on the Newcastle–Ottawa Scale (NOS). Most of the digital cognitive biomarkers achieved comparable or even better diagnostic performance than traditional paper-and-pencil tests. Moderate to large group differences were consistently observed in cognitive outcomes related to memory and executive functions, as well as some novel outcomes measured by handwriting/drawing tests, daily living tasks, and serious games. These outcomes have the potential to be sensitive digital cognitive biomarkers for MCI and dementia. Therefore, digital cognitive biomarkers can be a sensitive and promising clinical tool for detecting MCI and dementia.

Do the dual-task "8-foot up and go" tests provide additional predictive value for early detection of cognitive decline in community-dwelling older women?

BACKGROUND

The 8-Foot Up and Go (8UG) test is a widely used mobility assessment. Some dual-task mobility assessments have been developed to help detect cognitive decline.

AIMS

This study developed a dual-task version of 8UG test to investigate the dual-task 8UG performance and to evaluate the ability of dual-task 8UG test in detecting cognitive decline.

METHODS

A total of 101 eligible community-dwelling women aged 60-74 years were grouped into the mild cognitive impairment group (MCI, n = 49) and the non-cognitive impairment group (NCI, n = 52). The 8UG tests under single-task (ST), manual dual-task (MT), and cognitive dual-task (CT) conditions were performed respectively. The dual-task cost (DTC) and the correct response rate (CRR) were calculated to quantify the dual-task interference.

RESULTS

Participants spent more time in performing the 8UG test under dual-task conditions. No differences were observed between NCI and MCI groups for 8UG parameters under ST and MT conditions (p > 0.05). When executing CT, significant differences were found in the number of correct answers and CRR (p < 0.05). CRR showed the strongest ability to predict MCI with a cut-off point of 0.50 (71.2% sensitivity and 61.2% specificity).

DISCUSSION

Both manual and cognitive dual-task were found to interfere with the 8UG performance. CRR with cutoff point of 0.50 could be a potential predictor of MCI in community-dwelling older women.

CONCLUSIONS

The CRR of the cognitive dual-task 8UG test could be recommended as a potential predictor for the early detection of MCI in community-dwelling older women.

Diagnostic performance of digital cognitive tests for the identification of MCI and dementia: A systematic review

BACKGROUND

The use of digital cognitive tests is getting common nowadays. Older adults or their family members may use online tests for self-screening of dementia. However, the diagnostic performance across different digital tests is still to clarify. The objective of this study was to evaluate the diagnostic performance of digital cognitive tests for MCI and dementia in older adults.

METHODS

Literature searches were systematically performed in the OVID databases. Validation studies that reported the diagnostic performance of a digital cognitive test for MCI or dementia were included. The main outcome was the diagnostic performance of the digital test for the detection of MCI or dementia.

RESULTS

A total of 56 studies with 46 digital cognitive tests were included in this study. Most of the digital cognitive tests were shown to have comparable diagnostic performances with the paper-and-pencil tests. Twenty-two digital cognitive tests showed a good diagnostic performance for dementia, with a sensitivity and a specificity over 0.80, such as the Computerized Visuo-Spatial Memory test and Self-Administered Tasks Uncovering Risk of Neurodegeneration. Eleven digital cognitive tests showed a good diagnostic performance for MCI such as the Brain Health Assessment. However, all the digital tests only had a few validation studies to verify their performance.

CONCLUSIONS

Digital cognitive tests showed good performances for MCI and dementia. The digital test can collect digital data that is far beyond the traditional ways of cognitive tests. Future research is suggested on these new forms of cognitive data for the early detection of MCI and dementia.

Clinical significance of the cognition-related pathogenic proteins in plasma neuronal-derived exosomes among normal cognitive adults over 45 years old with olfactory dysfunction

OBJECTIVES

Exosomal Phospho-Tau-181(P-T181-tau), Total tau (T-tau), and amyloid-β peptide 42 (Aβ42) have been proved the capacity for the amnestic mild cognitive impairment (MCI) and the diagnosis of Alzheimer's disease (AD). This study aimed to explore the cognitive function and the levels of P-T181-tau, T-tau, and Aβ42 in neuronal-derived exosomes (NDEs) extracted from plasma in normal cognitive adults over 45 years old with olfactory dysfunction.

METHODS

A cross-sectional survey of 29 participants aged over 45 was conducted. Plasma exosomes were isolated, precipitated, and enriched by immuno-absorption with anti- L1 cell adhesion molecule (L1CAM) antibody. NDEs were characterized by CD81, and extracted NDE protein (P-T181-tau, T-tau, and Aβ42) biomarkers were quantified by enzyme-linked immunosorbent assay (ELISAs). Olfactory performance was assessed by Sniffin' Sticks and cognitive performance was assessed by Montreal Cognitive Assessment (MoCA).

RESULTS

There was no significant difference between adults with olfactory dysfunction and without olfactory dysfunction regarding the cognitive function as measured by MoCA and all the participants showed normal cognition. Adults with olfactory dysfunction showed a higher concentration of P-T181-tau in plasma NDEs than did adults without olfactory dysfunction (P = 0.034). Both the levels of P-T181-tau (r = - 0.553, P = 0.003) and T-tau (r = - 0.417, P = 0.034) negatively correlated with the odor identification scores. In addition, the level of T-tau negatively correlated with MoCA scores (r = - 0.597, P = 0.002). The levels of P-T181-tau (r = - 0.464, P = 0.022) and T-tau (r = - 0.438, P = 0.032) negatively correlated with the delayed recall scores.

CONCLUSIONS

This study demonstrated that cognition-related pathogenic proteins including P-T181-tau in plasma NDEs were significantly increased in adults over 45 years old with olfactory dysfunction before the occurrence of cognitive impairment. The impaired odor identification and the delayed recall function were highly associated with the increased levels of P-T181-tau and T-tau in plasma NDEs.

Reliability and acceptability of using a social robot to carry out cognitive tests for community-dwelling older adults

AIM

To improve access to cognitive testing for older adults, the reliability and acceptability of a speech-based cognitive test administered by a social robot were investigated.

METHODS

The Japanese version of the Telephone Interview for Cognitive Status was administered by a social robot to participants recruited from retirement homes and adult daycare facilities. The robot's dialogue and gestures were preprogrammed, while the researcher controlled the timing of proceeding to the next question and scored participants' responses. We examined the internal consistency, alternate form reliability (experiment 1) and test-retest reliability (experiment 2) of the cognitive test. The acceptability of the cognitive test was also examined using a questionnaire in experiment 2.

RESULTS

A total of 66 individuals (mean age 81.2 ± 5.8 years) participated in experiment 1; the internal consistency (Cronbach's α) of the test was 0.691 and its alternate form reliability (measured by interclass correlation coefficient) was 0.728. A total of 40 of these individuals (mean age 82.0 ± 5.4 years) also participated in experiment 2, and the test-retest reliability was 0.818. According to the questionnaire responses, over half of the participants wanted (or very much wanted) to use the robot version of the test to measure the deterioration of their cognitive function.

CONCLUSIONS

A robot-administered cognitive test might have satisfactory reliability and acceptability to community-dwelling older adults if those aspects of the test implemented by the researcher can also be successfully automated. Geriatr Gerontol Int 2019; 19: 552-556.

Multimodal MRI for early diabetic mild cognitive impairment: study protocol of a prospective diagnostic trial

Background

Type 2 diabetes mellitus (T2DM) is a risk factor for dementia. Mild cognitive impairment (MCI), an intermediary state between normal cognition and dementia, often occurs during the prodromal diabetic stage, making early diagnosis and intervention of MCI very important. Latest neuroimaging techniques revealed some underlying microstructure alterations for diabetic MCI, from certain aspects. But there still lacks an integrated multimodal MRI system to detect early neuroimaging changes in diabetic MCI patients. Thus, we intended to conduct a diagnostic trial using multimodal MRI techniques to detect early diabetic MCI that is determined by the Montreal Cognitive Assessment (MoCA).

Methods

In this study, healthy controls, prodromal diabetes and diabetes subjects (53 subjects/group) aged 40-60 years will be recruited from the physical examination center of Tangdu Hospital. The neuroimaging and psychometric measurements will be repeated at a 0.5 year-interval for 2.5 years’ follow-up. The primary outcome measures are 1) Microstructural and functional alterations revealed with multimodal MRI scans including structure magnetic resonance imaging (sMRI), resting state functional magnetic resonance imaging (rs-fMRI), diffusion kurtosis imaging (DKI), and three-dimensional pseudo-continuous arterial spin labeling (3D-pCASL); 2) Cognition evaluation with MoCA. The second outcome measures are obesity, metabolic characteristics, lifestyle and quality of life.

Discussion

The study will provide evidence for the potential use of multimodal MRI techniques with psychometric evaluation in diagnosing MCI at prodromal diabetic stage so as to help decision making in early intervention and improve the prognosis of T2DM.

Trial registration

This study has been registered to ClinicalTrials.gov (NCT02420470) on April 2, 2015 and published on July 29, 2015.