Screening For Mild Cognitive Impairment Research Articles

Cognitive Screening for Mild Cognitive Impairment: Clinician Perspectives on Current Practices and Future Directions.

This study surveyed 51 specialist clinicians for their views on existing cognitive screening tests for mild cognitive impairment and their opinions about a hypothetical remote screener driven by artificial intelligence (AI). Responses revealed significant concerns regarding the sensitivity, specificity, and time taken to administer current tests, along with a general willingness to consider adopting telephone-based screening driven by AI. Findings highlight the need to design screeners that address the challenges of recognizing the earliest stages of cognitive decline and that prioritize not only accuracy but also stakeholder input.

Unearthing Subtle Cognitive Variations: A Digital Screening Tool for Detecting and Monitoring Mild Cognitive Impairment

Early diagnosis of mild cognitive impairment (MCI) is pivotal in mitigating the risk of cognitive impairment and the onset of dementia. However, prevailing clinical cognitive screening tests and biomarker assessment approaches often suffer from drawbacks such as high cost, invasiveness, time consumption, or subjectivity. In China, existing digital cognitive assessment tools face multiple challenges due to their distinctive cultural, language, and healthcare landscape. Therefore, we embarked on a study to develop a digital cognitive assessment tool, evaluate its efficacy in distinguishing between healthy individuals and MCI patients, and examine its acceptability among Chinese older adults. Through a series of symposiums, programming, and interviews with stakeholders, we iteratively designed the "BrainNursing" mobile application. The system consists of eleven single tasks and three dual tasks, each taking only 1–3 minutes. Subsequently, we conducted statistical comparisons of movement kinetics and physiological signals recorded during cognitive testing in 181 older adults to investigate which parameters could serve as effective digital biomarkers for MCI screening. Leveraging machine learning classifiers and a majority voting principle, we evaluated the classification performance of the BrainNursing system in detecting MCI, yielding an accuracy rate of 90.3%. Furthermore, our analysis of movement kinetics revealed that time, score, and sequence features are crucial in cognitive assessment. Contrasting with their healthy counterparts, MCI patients exhibited decreased heart rate variability, increased sympathetic nervous system activity, and weakened autonomic nervous system regulation in response to stimuli during cognitive testing. Finally, user experience feedback indicated that participants universally perceived BrainNursing as a convenient and user-friendly cognitive screening tool and provided valuable insights for further improvement.

A household survey of the prevalence of subjective cognitive decline and mild cognitive impairment among urban community-dwelling adults aged 30 to 65

While it is possible to detect cognitive decline before the age of 60, and there is a report indicating that certain cognitive abilities peak in one's 30s, the evidence regarding cognitive problems in populations younger than 65 years is scarce. This study aims to (1) determine the proportion of community-dwelling adults with different cognitive status, and (2) determine the prevalence of neuropsychiatric behaviors. A population-based survey was conducted in Chiang Mai, Thailand. Individuals aged 30 to 65 were recruited and assessed for demographic data, memory complaints, cognitive performance, and neuropsychiatric symptoms using self-reported questionnaires. In a total of 539 participants, 33.95% had mild cognitive impairment (MCI), 7.05% had subjective cognitive decline (SCD), and 52.50% had neuropsychiatric symptoms. The risk of MCI increased with age, and neuropsychiatric symptoms were significantly higher in those with MCI or SCD than in those without (p < 0.001). The most common complaints were sleep problems, anxiety, and irritability. Screening for MCI in adults aged < 65 years might be useful. However, further investigation on the appropriate age to screen and the program’s cost-effectiveness is suggested.

Dementia and mild cognitive impairment screening in an emergency homeless shelter.

Older adults represent the fastest growing segment of the homeless community. Little is known about the prevalence of dementia and mild cognitive impairment (MCI) in this population. Dementia and MCI screening using the Montreal Cognitive Assessment (MoCA) was incorporated into the standard senior evaluation for adult clients aged ≥ 55 in a large emergency homeless shelter. In a 6-week period, 104 of 112 (92.9%) assessments were positive for dementia or MCI using a standard cutoff of 26, and 81 (72.3%) were positive using a conservative cutoff of 23. There was no significant difference in MoCA scores based on sex or education level, and no significant correlation between age and MoCA score. Older adults experiencing homelessness may have a high likelihood of dementia or MCI. Routine MoCA screening in older adults experiencing homelessness is feasible and can help to identify services needed to successfully exit homelessness.

A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease

Background The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life. Objective This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model. Methods 416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n = 210) and the non-MCI (n = 206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure. Results Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model’s area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer–Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927). Conclusion The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies.

Investigating the relevance of the advanced activities of daily living tool for the screening and diagnosis of mild cognitive impairment and dementia to the Sub-Saharan African migrant population in Belgium: A qualitative focus group study

Introduction: Early diagnosis of dementia is partly based on the evaluation of the basic (b-), instrumental (i-) and advanced (a-) activities of daily living (ADL). Adapting the item content of assessment tools to maintain relevance, understanding, and equivalence of the items and scales across other groups is pertinent. However, there are limited assessment tools developed on or adapted for use on the growing older Sub-Saharan African (SSA) migrant population in Europe. Focusing on the item content, we evaluated the relevance of the advanced activities of daily living (a-ADL) tool to the older SSA migrant population in Belgium. Method: Two focus group discussions were organized with participants from the SSA community in Gent, Belgium. Relevant, irrelevant, unclear, and new items were identified. The data was analyzed through the inductive method while consensus agreements were used to arrive at refined themes. Results: Out of the 49 items on the tool, both groups unanimously agreed that 15 items were relevant and 23 irrelevant. Thirteen new items were identified, whereas 15 items were considered unclear. Conclusion: Participants agreed the tool was relevant but should be adapted. Verification and validation of these results in a larger SSA older migrant population in Belgium is strongly recommended.

Gesture imitation performance in community-dwelling older people: assessment of a gesture imitation task in the screening and diagnosis of mild cognitive impairment and dementia.

Gesture imitation, a simple tool for assessing visuospatial/visuoconstructive functions, is reportedly useful for screening and diagnosing dementia. However, gesture imitation performance in healthy older adults is largely unknown, as are the factors associated with lower performance. To address these unknowns, we examined the gesture imitation performance of a large number of community-dwelling older adults aged ≥65 years in Arao City, Kumamoto Prefecture (southern Japan). The examiner presented the participants with eight gesture patterns and considered it a success if they could imitate them within 10 s. The success rate of each gesture imitation was calculated for three diagnostic groups: cognitively normal (CN) (n = 1184), mild cognitive impairment (MCI) (n = 237), and dementia (n = 47). Next, we reorganised the original gesture imitation battery by combining six selected gestures with the following scoring method: if the participants successfully imitated the gestures, immediately or within 5 s, two points were assigned. If they succeeded within 5-10 s, one point was assigned. The sensitivity and specificity of the battery were investigated to detect the dementia and MCI groups. Factors associated with gesture imitation battery scores were examined. Except one complex gesture, the success rate of imitation in the CN group was high, approximately 90%. The sensitivity and specificity of the gesture imitation battery for discriminating between the dementia and CN groups and between the MCI and CN groups were 70%/88%, and 45%/75%, respectively. Ageing, male sex, and a diagnosis of dementia or MCI were associated with lower scores on the gesture imitation battery. Gesture imitation tasks alone may not be sufficient to detect MCI. However, by combining gestures with set time limits, gesture imitation tasks can be a low-burden and effective method for detecting dementia, even in community medicine, such as during health check-ups.

Psychometric Evaluation of a Tablet-Based Tool to Detect Mild Cognitive Impairment in Older Adults: Mixed Methods Study.

With the rapid aging of the global population, the prevalence of mild cognitive impairment (MCI) and dementia is anticipated to surge worldwide. MCI serves as an intermediary stage between normal aging and dementia, necessitating more sensitive and effective screening tools for early identification and intervention. The BrainFx SCREEN is a novel digital tool designed to assess cognitive impairment. This study evaluated its efficacy as a screening tool for MCI in primary care settings, particularly in the context of an aging population and the growing integration of digital health solutions. The primary objective was to assess the validity, reliability, and applicability of the BrainFx SCREEN (hereafter, the SCREEN) for MCI screening in a primary care context. We conducted an exploratory study comparing the SCREEN with an established screening tool, the Quick Mild Cognitive Impairment (Qmci) screen. A concurrent mixed methods, prospective study using a quasi-experimental design was conducted with 147 participants from 5 primary care Family Health Teams (FHTs; characterized by multidisciplinary practice and capitated funding) across southwestern Ontario, Canada. Participants included health care practitioners, patients, and FHT administrative executives. Individuals aged ≥55 years with no history of MCI or diagnosis of dementia rostered in a participating FHT were eligible to participate. Participants were screened using both the SCREEN and Qmci. The study also incorporated the Geriatric Anxiety Scale-10 to assess general anxiety levels at each cognitive screening. The SCREEN's scoring was compared against that of the Qmci and the clinical judgment of health care professionals. Statistical analyses included sensitivity, specificity, internal consistency, and test-retest reliability assessments. The study found that the SCREEN's longer administration time and complex scoring algorithm, which is proprietary and unavailable for independent analysis, presented challenges. Its internal consistency, indicated by a Cronbach α of 0.63, was below the acceptable threshold. The test-retest reliability also showed limitations, with moderate intraclass correlation coefficient (0.54) and inadequate κ (0.15) values. Sensitivity and specificity were consistent (63.25% and 74.07%, respectively) between cross-tabulation and discrepant analysis. In addition, the study faced limitations due to its demographic skew (96/147, 65.3% female, well-educated participants), the absence of a comprehensive gold standard for MCI diagnosis, and financial constraints limiting the inclusion of confirmatory neuropsychological testing. The SCREEN, in its current form, does not meet the necessary criteria for an optimal MCI screening tool in primary care settings, primarily due to its longer administration time and lower reliability. As the number of digital health technologies increases and evolves, further testing and refinement of tools such as the SCREEN are essential to ensure their efficacy and reliability in real-world clinical settings. This study advocates for continued research in this rapidly advancing field to better serve the aging population. RR2-10.2196/25520.

Text Dialogue Analysis for Primary Screening of Mild Cognitive Impairment: Development and Validation Study.

Artificial intelligence models tailored to diagnose cognitive impairment have shown excellent results. However, it is unclear whether large linguistic models can rival specialized models by text alone. In this study, we explored the performance of ChatGPT for primary screening of mild cognitive impairment (MCI) and standardized the design steps and components of the prompts. We gathered a total of 174 participants from the DementiaBank screening and classified 70% of them into the training set and 30% of them into the test set. Only text dialogues were kept. Sentences were cleaned using a macro code, followed by a manual check. The prompt consisted of 5 main parts, including character setting, scoring system setting, indicator setting, output setting, and explanatory information setting. Three dimensions of variables from published studies were included: vocabulary (ie, word frequency and word ratio, phrase frequency and phrase ratio, and lexical complexity), syntax and grammar (ie, syntactic complexity and grammatical components), and semantics (ie, semantic density and semantic coherence). We used R 4.3.0. for the analysis of variables and diagnostic indicators. Three additional indicators related to the severity of MCI were incorporated into the final prompt for the model. These indicators were effective in discriminating between MCI and cognitively normal participants: tip-of-the-tongue phenomenon (P<.001), difficulty with complex ideas (P<.001), and memory issues (P<.001). The final GPT-4 model achieved a sensitivity of 0.8636, a specificity of 0.9487, and an area under the curve of 0.9062 on the training set; on the test set, the sensitivity, specificity, and area under the curve reached 0.7727, 0.8333, and 0.8030, respectively. ChatGPT was effective in the primary screening of participants with possible MCI. Improved standardization of prompts by clinicians would also improve the performance of the model. It is important to note that ChatGPT is not a substitute for a clinician making a diagnosis.

Design and application of a game‐based WeChat mini‐program for screening cognitive impairments in Chinese older adults

AbstractBackgroundEarly screening for mild cognitive impairment (MCI) is the key method to prevent and delay dementia. Digital screening tools are more efficient than traditional paper‐and‐pencil cognitive assessments, and gamified versions would promote engagement and completion rates. We introduced here the design and development of the Game‐based Cognitive Assessment ‐ 3 Minute Version (G3) WeChat mini‐program for MCI screening in Chinese older adults.MethodG3 is a battery of three one‐minute digital games built within the WeChat platform. Developed based on the cognitive paradigm and classic neuropsychological tests such as Trail Making Test (TMT), the Silhouettes test, the Brief Visuospatial Memory Test‐Revised (BVMT‐R) test, and California Card Sorting Tests (CCST), the G3 mini‐games, “Number Ordering”, “Species Sorting”, and “Gold finding” would measure the computational and executive function, abstractive function and visual perception, memory, and attention, respectively. In each game, the difficulty will be automatically adjusted according to performance. G3 integrates users’ game scores, age, gender, and education levels, to generate a comprehensive assessment of cognitive function with results of the six cognitive domains (Figure 2).ResultAs of August 31, 2022, more than 11 million users have completed G3 tests, 85.38% of them are aged under 40, and 2.05% of them are aged over 60 (Figure 3). Although people in all age groups have desired to know their cognitive function, the number of users decreased as people aged. Among users aged 40‐70, more females than males participated in G3 games, indicating middle‐aged and older women paid more attention to cognitive function. Among users over 45 years old, those of lower education levels (primary school or no education), middle levels (middle or high school), and university degrees accounted for 2.93%, 32.4%, and 59.65% respectively. The completion rate of users aged over 45 is 92.28%, and those over 80 years exceeded 86%.ConclusionThe G3 was a battery of three novel 1‐minute game‐based digital cognitive tests. The process is simple, efficient, and interesting. It is a promising tool for self‐administering, advancing large‐scale community screening, and social education for MCI in Chinese older adults, warranting further clinical trials to validate its feasibility, sensitivity, and diagnostic value.

Design and application of a game‐based WeChat mini‐program for screening cognitive impairments in Chinese older adults

AbstractBackgroundEarly screening for mild cognitive impairment (MCI) is the key method to prevent and delay dementia. Digital screening tools are more efficient than traditional paper‐and‐pencil cognitive assessments, and gamified versions would promote engagement and completion rates. We introduced here the design and development of the Game‐based Cognitive Assessment ‐ 3 Minute Version (G3) WeChat mini‐program for MCI screening in Chinese older adults.MethodG3 is a battery of three one‐minute digital games built within the WeChat platform. Developed based on the cognitive paradigm and classic neuropsychological tests such as Trail Making Test (TMT), the Silhouettes test, the Brief Visuospatial Memory Test‐Revised (BVMT‐R) test, and California Card Sorting Tests (CCST), the G3 mini‐games, “Number Ordering”, “Species Sorting”, and “Gold finding” would measure the computational and executive function, abstractive function and visual perception, memory, and attention, respectively. In each game, the difficulty will be automatically adjusted according to performance. G3 integrates users’ game scores, age, gender, and education levels, to generate a comprehensive assessment of cognitive function with results of the six cognitive domains (Figure 2).ResultAs of August 31, 2022, more than 11 million users have completed G3 tests, 85.38% of them are aged under 40, and 2.05% of them are aged over 60 (Figure 3). Although people in all age groups have desired to know their cognitive function, the number of users decreased as people aged. Among users aged 40‐70, more females than males participated in G3 games, indicating middle‐aged and older women paid more attention to cognitive function. Among users over 45 years old, those of lower education levels (primary school or no education), middle levels (middle or high school), and university degrees accounted for 2.93%, 32.4%, and 59.65% respectively. The completion rate of users aged over 45 is 92.28%, and those over 80 years exceeded 86%.ConclusionThe G3 was a battery of three novel 1‐minute game‐based digital cognitive tests. The process is simple, efficient, and interesting. It is a promising tool for self‐administering, advancing large‐scale community screening, and social education for MCI in Chinese older adults, warranting further clinical trials to validate its feasibility, sensitivity, and diagnostic value.

Design and development of the intelligent voice recognition‐based cognitive assessment WeChat mini‐program

AbstractBackgroundEarly screening of mild cognitive impairment (MCI) is crucial for the elderly to access formal diagnosis and intervention of Alzheimer’s Disease (AD) and related dementias (ADRD) at early stages. Owing to the rapid development of artificial intelligence (AI) and voice recognition technology, the detection method using speech patterns as digital biomarkers collected by digital consumer devices can accommodate a huge demand for early and large‐scale detection. We here reported the design and development of the Shanghai Cognitive Screening (SCS) mini‐program, the first intelligent voice recognition‐based cognitive assessment WeChat mini‐program in China.MethodThe SCS mini‐program was developed based on the cognitive paradigm and classic pencil‐and‐paper neuropsychological assessments (e.g. Montreal cognitive assessment basic [MoCA‐B]). Through Automatic Speech Recognition (ASR), users speak to complete tasks including Naming, Immediate Recall, Delayed Recall, Symbol Digit Modalities Test (SDMT), Delayed RecallII, and Recognition (Figure 1). The acoustic, phonetics, and linguistic speech pattern variables would also be extracted and processed as digital biomarkers. The SCS intelligent prediction model enrolls speech pattern digital biomarkers, individual task scores, and time use variables with age, gender, and education level as linking variables, which enables to grade the user’s global cognitive function and individual cognitive domains (Figure 2).ResultAfter completing all tasks in the SCS, users can immediately read the comprehensive results which are divided into 4 categories corresponding to 4 grading intervals: “healthy” (cognitively normal), “low risk” (suspected SCD), “medium‐high risk” (suspected MCI), and “high risk” (suspected AD). We also use a radar chart with scores of cognitive domains (i.e., memory, attention, visual perception, language, and executive functions), a memory trend graph (i.e., immediate and delayed recalls), an attention persistence graph, and related advice in the automatically generated report (Figure 3).ConclusionThe SCS mini‐program is more efficient and economical compared to traditional paper‐based cognitive assessments. It is suitable for clinical cognitive assessment, remote self‐rating, and community screening for MCI and dementias among Chinese older adults and other ADRD high‐risk groups. Clinical trials are yet to be conducted to validate the accuracy and diagnostic value of the SCS WeChat mini‐program.

Early detection of mild cognitive impairment due to Alzheimer’s disease using combinations of an online cognitive assessment and blood biomarkers

AbstractBackgroundAccessible measurements to a large screening and early differentiation of mild cognitive impairment due to Alzheimer’s disease (MCI‐AD) from healthy elderly is increasing urgent with the prevalence of Alzheimer’s disease (AD) expanded.Methodan online cognitive assessment (Memtrax) and MoCA were performed, on 99 clinically diagnosed cognitively normal (CON) and 101 MCI‐AD participants. The plasma levels of phosphorylated tau (p‐tau)181 and neurofilament light (NfL) and amyloid beta (Aβ)42/40 were measured using Simoa assay. The relevance between Memtrax and blood biomarkers were conducted by Spearman correlation analysis. Discrimination models were built using assorted machine learning approaches combining Memtrax with blood biomarkers, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC).ResultThe AUC of Memtrax and MoCA in differentiating MCI‐AD from CON was similar. Memtrax has a strong correlation with phosphorylated tau (p‐tau)181 and neurofilament light (NfL), while less related to amyloid beta (Aβ)42/40. Combining Memtrax and blood biomarkers, the AUC of best model reach to 0.975 (95% CI: 0.950‐0.999).ConclusionThe combination of Memtrax and blood biomarkers is promising to the large screening and early detection in MCI‐AD.

Combining working memory and speech data in a digital app for early detection of cognitive impairment in primary care

AbstractBackgroundCognitive screening to detect mild cognitive impairment (MCI) and dementia in primary care settings has proven to be a challenging task. The ideal solution would be a brief, low‐burden assessment tool appropriate for use with individuals from diverse educational and cultural backgrounds that requires limited time and expertise from primary care providers. The purpose of this project was to develop a digital cognitive screening app derived from widely used cognitive and speech tasks using machine learning techniques.MethodParticipants were 104 old adults, 53 who were cognitively normal (CN) and 51 who were cognitively impaired (CI). All participants completed a working memory task (WM), followed by four speech‐language tasks, followed by a second administration of WM (WM2) to investigate the added utility of practice effects. Participants also completed the Quick Mild Cognitive Impairment (Qmci) screen. Bayesian adaptive regression trees were used to test 11 models. All models contained sex, age, education, and marital status. One model contained WM and the WM2‐WM difference. Four models contained 26 acoustic and 34 linguistic variables derived from each of the four speech tasks, and four more models were identical, except with the two WM variables added. For comparison, two models with Qmci were fitted: one with and one without the two WM variables.ResultThree models achieved cross‐validated classification accuracy of c≥0.90: WM by itself (estimated c = 0.91; Figure 1), WM and a personal narrative task (c = 0.94; Figure 2), and WM and a counting backwards task (c = 0.90). Models with picture description and procedural discourse tasks performed the worst. The QMCI‐only model yielded c = 0.91.ConclusionCombining WM, acoustic and linguistic variables involved in recounting personal memories, and the ability to benefit from prior exposure to a task discriminated CN from CI groups with a high degree of accuracy. This unique combination assesses key cognitive abilities known to be affected early in neurodegenerative diseases, executive functioning and memory, and includes both verbal and nonverbal tasks. Importantly, individuals with varying levels of cognitive functioning, including mild dementia, were able to use the app successfully, and the automated nature of the tool required minimal staff time or expertise.

Combining working memory and speech data in a digital app for early detection of cognitive impairment in primary care

AbstractBackgroundCognitive screening to detect mild cognitive impairment (MCI) and dementia in primary care settings has proven to be a challenging task. The ideal solution would be a brief, low‐burden assessment tool appropriate for use with individuals from diverse educational and cultural backgrounds that requires limited time and expertise from primary care providers. The purpose of this project was to develop a digital cognitive screening app derived from widely used cognitive and speech tasks using machine learning techniques.MethodParticipants were 104 old adults, 53 who were cognitively normal (CN) and 51 who were cognitively impaired (CI). All participants completed a working memory task (WM), followed by four speech‐language tasks, followed by a second administration of WM (WM2) to investigate the added utility of practice effects. Participants also completed the Quick Mild Cognitive Impairment (Qmci) screen. Bayesian adaptive regression trees were used to test 11 models. All models contained sex, age, education, and marital status. One model contained WM and the WM2‐WM difference. Four models contained 26 acoustic and 34 linguistic variables derived from each of the four speech tasks, and four more models were identical, except with the two WM variables added. For comparison, two models with Qmci were fitted: one with and one without the two WM variables.ResultThree models achieved cross‐validated classification accuracy of c = 0.90: WM by itself (estimated c = 0.91; Figure 1), WM and a personal narrative task (c = 0.94; Figure 2), and WM and a counting backwards task (c = 0.90). Models with picture description and procedural discourse tasks performed the worst. The QMCI‐only model yielded c = 0.91.ConclusionCombining WM, acoustic and linguistic variables involved in recounting personal memories, and the ability to benefit from prior exposure to a task discriminated CN from CI groups with a high degree of accuracy. This unique combination assesses key cognitive abilities known to be affected early in neurodegenerative diseases, executive functioning and memory, and includes both verbal and nonverbal tasks. Importantly, individuals with varying levels of cognitive functioning, including mild dementia, were able to use the app successfully, and the automated nature of the tool required minimal staff time or expertise.

The evaluation of translated neuropsychological assessments tools for the early detection of mild cognitive impairment (MCI) in low‐ and middle‐income countries (LMICs)

AbstractBackgroundThe incidence of mild cognitive impairment (MCI), particularly in low and middle‐income countries (LMICs) has been noticeably on the rise. With the high cost of dementia care, the need for tools that aid with the early detection of MCI is becoming of high importance, especially in LMICs. As a part of screening for MCI, many tests have been proposed evaluating for impairments in cognitive, behavioral, and functional domains, which are important for ruling out and evaluating for any reversible causes. While useful, most of these tests were developed in the west, limited works exist reporting the appropriateness and experience of using such tests for patients from LMICs.MethodIn this work, we aim to evaluate the use of translated digital assessment for detecting MCI in an Arabic‐speaking population in Palestine. For this purpose, we developed a digital test covering three cognitive, behavioral, and functional domains, where MMSE and the Neuropsychiatric Inventory ‐ Questionnaire (NPI‐Q) together with two functional assessment questionnaires were translated to Modern Standard Arabic (MSA) and presented in a digital interface. Upon the development and translation of the digital tests, field testing was conducted to check for use experience and appropriate patient comprehension of the questions to match their intended use.ResultFollowing the implementation of the digital assessment tools, quantitative and qualitative data was collected summarizing the user experience, where numerous limitations were noted. Despite being asked in their native language, several patients expressed the need for more clarifying questions. While all patients were Arabic speakers, some patients required the use of simpler terminology that matches their local dialects. Other patients reported the lack of relevance of certain questions, regarding the activities of daily living in the functional assessments. Overall, the patient’s experience and comprehension of questions varied with their age and literacy level.ConclusionDespite the high prevalence of dementia in LMICs, there are limited available neuropsychological assessment tools that are fit for the local cultural and linguistic. The findings suggest the need for the development of culturally and linguistically appropriate assessments to ensure appropriate patient comprehension, and avoid misdiagnosis of patients.

Screening, Assessment, and Pharmacologic Treatment of Mild Cognitive Impairment and Early Alzheimer's Disease: The Role for Monoclonal Antibodies.

Screening, Assessment, and Pharmacologic Treatment of Mild Cognitive Impairment and Early Alzheimer's Disease: The Role for Monoclonal Antibodies.

Retracted: Feasibility and Validity of Cambridge Neuropsychological Test Automated Battery in Mild Cognitive Impairment Screening for Patients with Atrial Fibrillation.

[This retracts the article DOI: 10.1155/2022/1527292.].

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study.

With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a "virtual kiosk test" for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment. We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features. A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test-developed by our group-where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls. In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=-3.44; P=.004), and a greater number of errors (t49=-3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score. Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.

Prefrontal event-related potential markers in association with mild cognitive impairment.

Alzheimer's disease (AD) is among the leading contributors of dementia globally with approximately 60-70% of its cases. Current research is focused on the mild cognitive impairment (MCI), which is associated with cognitive decline but does not disrupt routine activities. Event-related potential (ERP) research is essential in screening patients with MCI. Low-density channel electroencephalography (EEG) is frequently used due to its convenience, portability, and affordability, making it suitable for resource-constrained environments. Despite extensive research on neural biomarkers for cognitive impairment, there is a considerable gap in understanding the effects on early stages of cognitive processes, particularly when combining physiological and cognitive markers using portable devices. The present study aimed to examine cognitive shortfalls and behavioral changes in patients with MCI using prefrontal selective attention ERP recorded from a prefrontal two-channel EEG device. We assessed cognitive decline using the Mini-Mental State Examination (MMSE) and the Seoul Neuropsychological Screening Battery (SNSB). We administered auditory selective attention tasks to 598 elderly participants, including those with MCI (160) and cognitively normal (CN) individuals (407). We conducted statistical analyses such as independent t-tests, Pearson's correlations, and univariate and multiple logistic regression analyses to assess group differences and associations between neuropsychological tests, ERP measures, behavioral measures, and MCI prevalence. Our findings revealed that patients with MCI demonstrated slower information-processing abilities, and exhibited poorer task execution, characterized by reduced accuracy, increased errors, and higher variability in response time, compared to CN adults. Multiple logistic regression analyses confirmed the association between some ERP and behavioral measures with MCI prevalence, independent of demographic and neuropsychological factors. A relationship was observed between neuropsychological scores, ERP, and behavioral measures. The slower information processing abilities, and poor task execution in the MCI group compared to the CN individuals suggests flawed neurological changes and reduced attentional maintenance during cognitive processing, respectively. Hence, the utilization of portable EEG devices to capture prefrontal selective attention ERPs, in combination with behavioral assessments, holds promise for the identification of mild cognitive deficits and neural alterations in individuals with MCI. This approach could potentially augment the traditional neuropsychological tests during clinical screening for MCI.