Screening For Mild Cognitive Impairment Research Articles

Plasma Aβ42:Aβ40 ratio as a biomarker for cognitive impairment in haemodialysis patients: a multicentre study.

Mild cognitive impairment (MCI) and dementia are more prevalent in patients undergoing haemodialysis (HD). Although the cerebrospinal fluid amyloid beta (Aβ) and tau (τ) have proven to be valid biomarkers for the diagnosis of Alzheimer's disease (AD) in the general population, the roles of plasma Aβ and τ for the diagnosis of cognitive impairment in HD patients remain unknown. We conducted a cross-sectional study including patients receiving HD in three hospitals in Shanghai. All patients completed the Montreal Cognitive Assessment-Basic (MoCA-B). To validate the effectiveness of the MoCA-B score for screening MCI, a subset group underwent neuropsychological batteries. Serum proteomes were compared in HD patients with normal cognitive function and dementia. Plasma Aβ42, Aβ40 and total τ were measured using a single molecule array. A total of 311 HD patients were enrolled (mean age 63years, 55% male). The best cut-off score of MoCA-B for differentiating MCI and normal cognition was 24, with an area under the curve of 0.94. Serum proteomics revealed that neurodegenerative pathways related to AD were enriched in HD patients with dementia. The plasma Aβ42:Aβ40 ratio was significantly reduced in patients with MCI and dementia and was independently associated with cognitive function after adjusting for age, sex and education levels. We validated the MoCA-B as an optimal cognitive function screening instrument for MCI in HD patients. The plasma Aβ42:Aβ40 ratio was a potential biomarker in distinguishing normal cognition, MCI and dementia in HD populations.

Reading and lexical–semantic retrieval tasks outperforms single task speech analysis in the screening of mild cognitive impairment and Alzheimer's disease

Age-related cognitive impairment have increased dramatically in recent years, which has risen the interes in developing screening tools for mild cognitive impairment and Alzheimer's disease. Speech analysis allows to exploit the behavioral consequences of cognitive deficits on the patient's vocal performance so that it is possible to identify pathologies affecting speech production such as dementia. Previous studies have further shown that the speech task used determines how the speech parameters are altered. We aim to combine the impairments in several speech production tasks in order to improve the accuracy of screening through speech analysis. The sample consists of 72 participants divided into three equal groups of healthy older adults, people with mild cognitive impairment, or Alzheimer's disease, matched by age and education. A complete neuropsychological assessment and two voice recordings were performed. The tasks required the participants to read a text, and complete a sentence with semantic information. A stepwise linear discriminant analysis was performed to select speech parameters with discriminative power. The discriminative functions obtained an accuracy of 83.3% in simultaneous classifications of several levels of cognitive impairment. It would therefore be a promising screening tool for dementia.

A Design and Development Approach for Mild Cognitive Impairment Screening

A Design and Development Approach for Mild Cognitive Impairment Screening

Eye movement dysfunction for screening mild cognitive impairment

AbstractBackgroundEarly screening of cognitive impairment is crucial to delay cognitive deterioration and daily living disability in the elderly. However, the complexity and high cost of existing strategies appear to be great limits to the early screening on a large scale. Eye movement test shows a huge advantage in the early identification of mild cognitive impairment (MCI) because of quick, non‐invasive and cost‐effective features.MethodThe study involved a total of 151 participants: 90 MCI patients and 50 cognitive normal controls (CN) . All participants underwent eye movement signals under four visual tasks, including direct gaze towards target, smooth pursuit tasks, saccading toward a jumping target and looking away a jumping target. Five features were extracted from eye movement signals by linear and nonlinear analysis, including saccade number in resting state, saccade number in smooth persuit tasks, saccade lantency, antisaccade lantency, antisaccade error rate. Discriminant features selection was performed using the logistic regression (LR) analysis. Receiver operator characteristic (ROC) analyses and the area under the curve (AUC) were employed to investigate classification ability.ResultAfter the penalization of LR analysis, there were two top features that exhibited significant differences between MCI and CN, which included saccade number in smooth pursuit tasks and antisaccade error rate (p<0.05). ROC curves showed a favorable diagnostic accuracy of the two features in the discrimination between controls and MCI (AUC: 0.823, P<0.01).ConclusionThese results suggest that eye movement test has the potential to screen patients with MCI from the elderly with cognitive normality. Therefore, eye movement test is a powerful tool for broad screening in community‐dwelling older adults.

Comparison of DBFS with MoCA and MMSE tools for MCI screening.

Mild cognitive impairment (MCI) has been associated with many diseases. The MCI could be a marker for the early diagnosis of certain diseases. Early detection of MCI could be beneficial for restoration of cognitive reserves. One hundred and five subjects were included in the study, underwent the Digital Brain Function Screen (DBFS) test as well as the Montreal Cognitive Assessment (MoCA) test and 73 subjects took the Mini-Mental State Examination (MMSE) test. DBFS test and retest was taken by 16 subjects. The test scores of DBFS tool showed significant positive correlation with MoCA and MMSE test scores. In conclusion, the DBFS tool could be an effective digital tool which can overcome the disadvantages of traditional tools of screening MCI like MoCA and MMSE.

Exercise program to reduce the risk of cognitive decline and physical frailty in older adults: study protocol for an open label double-arm clinical trial.

It is a big problem that many older adults are physically inactive. Well-known benefits of physical exercise include a decrease in the risk of cognitive decline and physical frailty. Therefore, this study aims to examine whether our proposed exercise program can prevent cognitive decline and improve physical function in the elderly. This study will include nondemented older adults (n = 103) without regular exercise habits. The trial will include a physical exercise training program (once a week) and nutritional lectures (once a month) over 5 months and follow-up for ≥1 year. The primary endpoint is the program's efficacy in preventing cognitive decline, as assessed by changes in the memory performance index of the mild cognitive impairment (MCI) screen; the secondary endpoints are the incidence of MCI and dementia, physical testing, and frailty proportion. In the exploratory phase of the study, we will elucidate the underlying diseases causing MCI in community-dwelling older adults by neuroimaging. This double-arm trial that aims to assess the impact of physical exercise on nondemented older adults' cognitive and physical function. Furthermore, our newly developed exercise program will be easy for older adults to undertake.Clinical Trial Registration: https://clinicaltrials.gov/, identifier [jRCT 1040220140].

Correlation of Body Mass Index and age with Mild Cognitive Impairment (MCI) in elderly of Guwahati City, Assam

Objective: To determine the correlation between BMI and age with MCI and after stratifying for BMI and socio-demographic characteristics.
 Methods: This cross-sectional study was conducted among 576 elderly (≥60 years) in Guwahati city using a multi-stage sampling technique. Hindi Mini-Mental State Examination tool was used for screening for dementia and MCI. Data were collected using a pre-designed and pre-tested schedule and SPSS was used for analysis.
 Results: HMMSE scores were not significantly correlated with BMI (r =0.07). However, when stratified, a significantly negative correlation of BMI with HMMSE scores was seen for illiterates (r =-0.21), primary school education (r =-0.48) and unskilled workers (r =-0.49). There was a significant negative correlation between age and cognition for elderly belonging to OBC, (r =-0.21), Lower middle (III), (r =-0.39), Upper Lower (r =-0.17), Lower (V), (r =-0.26), Graduate, Post Graduate,(r = -0.23), Middle School, (r =-0.36), Illiterate, (r =-0.34), Clerical, (r =-0.60), Semi Professional, (r =-0.62), skilled worker (r=-0.68), Unemployed, (r =-0.15) and Obese, (r =-0.30).
 Conclusion: Various factors like age, category, socioeconomic status, and Body Mass Index (BMI) were found to be predictors of cognition among the elderly.Further studies are needed to evaluate the correlations between age, BMI, and cognitive decline.

Risk factors for mild cognitive impairment among older adults in a hospital in southern Nigeria.

About 63% of people living with mild cognitive impairment (MCI) and dementia live in low- and middle-income countries (LMICs). Emerging evidence suggests that early risk factors for the development of MCI and dementia can be modified by public health and preventive intervention approaches. This study aimed to assess the prevalence of MCI in older adult patients and its relationship with some risk factors. The study was conducted among older adults at the Geriatric Clinic of the Family Medicine Department of a hospital in southern Nigeria. A cross-sectional study was carried out involving 160 subjects aged 65 years and above over a period of 3 months. Socio-demographic and clinical data were obtained using an interviewer-administered questionnaire. Subjects were accessed for impaired cognition using the 10-word delay recall test scale. Data were analysed using SPSS version 23. There were 64 males and 96 females; male to female ratio was 1:1.5. Majority of the study population were in age range of 65-74 years. The overall prevalence of MCI was 59.4%. Respondents with tertiary education were 82% less likely to have MCI on logistic regression analysis (OR = 0.18, 95% CI = 0.465-0.719). Mild cognitive impairment was prevalent among older adults in this study and was found to be significantly associated with low level of education.Contribution: It is therefore recommended that screening for MCI and known risk factors should be prioritized at geriatric clinics.

Category Switching Test: A Brief Amyloid-β-Sensitive Assessment Tool for Mild Cognitive Impairment.

The Category Switching Test (CaST) is a verbal fluency test with active semantic category switching. This study aimed to explore the association between CaST performance and brain amyloid-β (Aβ) burden in patients with mild cognitive impairment (MCI) and the neurofunctional mechanisms. A total of 112 participants with MCI underwent Florbetapir positron emission tomography, resting-state functional magnetic resonance imaging, and a neuropsychological test battery. The high Aβ burden group had worse CaST performance than the low-burden group. CaST score and left middle temporal gyrus fractional amplitude of low-frequency fluctuations (fALFF) related inversely to the global Florbetapir standardized uptake value rate. Functional connectivity between the left middle temporal gyrus and frontal lobe decreased widely and correlated with CaST score in the high Aβ burden group. Thus, CaST score and left middle temporal gyrus fALFF were valuable in discriminating high Aβ burden. CaST might be useful in screening for MCI with high Aβ burden.

Comparison of the Six Item Cognitive Impairment Test (6CIT) to Commonly-Used Short Cognitive Screening Instruments in a Memory Clinic Population.

Short cognitive screening instruments (CSI) are required to identify cognitive impairment in busy outpatient clinics. While the Six Item Cognitive Impairment Test (6CIT) is commonly used, its accuracy in those with mild cognitive impairment (MCI) and subjective cognitive decline (SCD) and against more widely-used CSIs is less well established. To examine the diagnostic accuracy of the 6CIT against the Montreal Cognitive Assessment (MoCA) and Quick Mild Cognitive Impairment (Qmci) screen across the cognitive spectrum in a memory clinic population. In total, 142 paired assessments were available (21 with SCD, 32 MCI, and 89 with dementia). Consecutive patients underwent a comprehensive assessment and were screened using the 6CIT, Qmci, and MoCA. Accuracy was determined from the area under receiver operating characteristic curves (AUC). The median age of patients was 76 (±11) years; 68% were female. The median 6CIT score was 10/28 (±14). The 6CIT was strongly, negatively, and statistically significantly correlated with the Qmci (r = -0.84) and MoCA (r = -0.86). The 6CIT had good accuracy for separating cognitive impairment (MCI or dementia) from SCD, (AUC:0.88; 0.82-0.94), similar to the MoCA (AUC:0.92; 0.87-0.97, p = 0.308), but statistically lower than the Qmci (AUC:0.96; 0.94-0.99, p = 0.01). The 6CIT was faster to administer, median time 2.05 minutes versus 4.38 and 9.5 for the Qmci and MoCA, respectively. While the Qmci was more accurate than the 6CIT, the shorter administration time of the 6CIT, suggests it may be useful when assessing or monitoring cognitive impairment in busy memory clinics, though larger samples are required to evaluate.

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening.

Advances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients' behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment. We collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers. The experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands. It was found that patients' classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

Can the fNIRS-derived neural biomarker better discriminate mild cognitive impairment than a neuropsychological screening test?

Early detection of mild cognitive impairment (MCI), a pre-clinical stage of Alzheimer's disease (AD), has been highlighted as it could be beneficial to prevent progression to AD. Although prior studies on MCI screening have been conducted, the optimized detection way remain unclear yet. Recently, the potential of biomarker for MCI has gained a lot of attention due to a relatively low discriminant power of clinical screening tools. This study evaluated biomarkers for screening MCI by performing a verbal digit span task (VDST) using functional near-infrared spectroscopy (fNIRS) to measure signals from the prefrontal cortex (PFC) from a group of 84 healthy controls and 52 subjects with MCI. The concentration changes of oxy-hemoglobin (HbO) were explored during the task in subject groups. Findings revealed that significant reductions in HbO concentration were observed in the PFC in the MCI group. Specially, the mean of HbO (mHbO) in the left PFC showed the highest discriminant power for MCI, which was higher than that of the Korean version of montreal cognitive assessment (MoCA-K) widely used as a screening tool for MCI. Furthermore, the mHbO in the PFC during the VDST was identified to be significantly correlated to the MoCA-K scores. These findings shed new light on the feasibility and superiority of fNIRS-derived neural biomarker for screening MCI.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models.

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques. To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia. We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia. In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases. The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

TRANSLATION, CULTURALADAPTATION AND VALIDATION OF GUJARATI VERSION OF MONTREAL COGNITIVE ASSESSMENT (MOCA) IN OLDER ADULTS

Context: Mild cognitive impairment (MCI) is a term used to describe a level of decline in cognition which is seen as an intermediate stage between normal ageing and dementia in older adults. The Montreal Cognitive Assessment (MoCA) is a useful screening tool validated for MCI. It has been translated in various languages other than English, including various Indian languages such as Hindi, Malayalam, etc. Aims: For the cognitive screening of Gujarati speaking older adults, the Gujarati version of MoCA is required. The present study, therefore, aimed to develop Gujarati version of the MoCA and investigate its validity and reliability for screening MCI in gujarati speaking older adults. Settings and Design: It is a cross sectional study design. The scale was translated in Gujarati as per suggested guidelines and its psychometric properties were evaluated. Qualitative and quantitative validation through expert review was done and content validity Index (CVI) was calculated. Study participants were 30 older adults. Two estimators of reliability i.e., internal consistency reliability and test retest reliability were evaluated. Statistical analysis and results: The content validity was established through qualitative reviews and with I-CVI of each item on scale more than or equal to 0.78 and SCVI/Ave = 0.93. The scale has good internal consistency, Cronbach's alpha (α) =0.74 and high test-retest reliability, intra class correlation coefcient (ICC) =0.87. Conclusions:The Gujarati translated version of MoCAis a valid and reliable tool for detecting mild cognitive impairment in older adults.

Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts.

Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed. Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations. Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as on the unrelated validation cohort. The algorithms achieved a performance of AUC 0.73 and AUC 0.77 in the respective training cohorts and AUC 0.81 in the unseen validation cohorts. The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.

An Effective Test (EOmciSS) for Screening Older Adults With Mild Cognitive Impairment in a Community Setting: Development and Validation Study

Early detection of mild cognitive impairment (MCI) symptoms is an important step to its diagnosis and intervention. We developed a new screening test called "Efficient Online MCI Screening System" (EOmciSS) for use in community-dwelling older adults. It is a self-paced cognitive test to be completed within 10 minutes on tablets or smartphones in homes or care centers for older adults. This study aims to test the validity of EOmciSS for identifying community-dwelling older adults with MCI risks. Participants (N=827) completed EOmciSS and other screening tests for MCI. The psychometric properties tested were "subscale item difficulty," "discriminative index," "internal consistency," and "construct validity." We also tested between-group discrimination using the cross-validation method in an MCI group and a normal cognitive function (NCF) group. A total of 3 accuracy factors and 1 reaction time factor explained the structure of the 20 item factors. The difficulty level of accuracy factors (ie, "trail making," "clock drawing," "cube copying," "delayed recall") was 0.63-0.99, whereas that of the reaction time factor was 0.77-0.95. The discriminative index of the medium-to-high-difficulty item factors was 0.39-0.97. The internal consistency (Cronbach α) ranged from .41 (for few item factors) to .96. The training data set contained 9 item factors (CC-Acc1, P<.001; CD-Acc1, P=.07; CD-Acc2, P=.06; CD-Acc3, P<.001; TM-Acc4, P=.07; DR-Acc1, P=.03; RS, P=.06; DR-RT1, P=.02; and DR-RT2, P=.05) that were significant predictors for an MCI classification versus NCF classification. Depressive symptoms were identified as significant factors (P<.001) influencing the performance of participants, and were an integral part of our test system. Age (P=.15), number of years of education (P=.18), and proficiency in using an electronic device (P=.39) did not significantly influence the scores nor classification of participants. Application of the MCI/NCF cutoff score (7.90 out of 9.67) to the validation data set yielded an area under the curve of 0.912 (P<.001; 95% CI 0.868-0.955). The sensitivity was 84.9%, specificity was 85.1%, and the Youden index was 0.70. EOmciSS was valid and reliable for identifying older adults with significant risks of MCI. Our results indicate that EOmciSS has higher sensitivity and specificity than those of the Computer-Administered Neuropsychological Screen for Mild Cognitive Impairment and the Computerized Cognitive Screen. The user interface, online operation, and self-paced format allowed the test system to be operated by older adults or their caregivers in different settings (eg, home or care centers for older adults). Depressive symptoms should be an integral part in future MCI screening systems because they influence the test performance and, hence, MCI risk. Chinese Clinical Trial Registry ChiCTR2000039411; http://www.chictr.org.cn/showprojen.aspx?proj=62903.

Screening of Mild Cognitive Impairment Through Conversations With Humanoid Robots: Exploratory Pilot Study.

The rising number of patients with dementia has become a serious social problem worldwide. To help detect dementia at an early stage, many studies have been conducted to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they focus on cognitive function or conversational speech during the examinations. In contrast, conversational humanoid robots are expected to be used in the care of older people to help reduce the work of care and monitoring through interaction. This study focuses on early detection of mild cognitive impairment (MCI) through conversations between patients and humanoid robots without a specific examination, such as neuropsychological examination. This was an exploratory study involving patients with MCI and cognitively normal (CN) older people. We collected the conversation data during neuropsychological examination (Mini-Mental State Examination [MMSE]) and everyday conversation between a humanoid robot and 94 participants (n=47, 50%, patients with MCI and n=47, 50%, CN older people). We extracted 17 types of prosodic and acoustic features, such as the duration of response time and jitter, from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into CN people and patients with MCI. Furthermore, we conducted an automatic classification experiment using a support vector machine (SVM) to verify whether it is possible to automatically classify these 2 groups by the features identified in the statistical significance test. We obtained significant differences in 5 (29%) of 17 types of features obtained from the MMSE conversational speech. The duration of response time, the duration of silent periods, and the proportion of silent periods showed a significant difference (P<.001) and met the reference value r=0.1 (small) of the effect size. Additionally, filler periods (P<.01) and the proportion of fillers (P=.02) showed a significant difference; however, these did not meet the reference value of the effect size. In contrast, we obtained significant differences in 16 (94%) of 17 types of features obtained from the everyday conversations with the humanoid robot. The duration of response time, the duration of speech periods, jitter (local, relative average perturbation [rap], 5-point period perturbation quotient [ppq5], difference of difference of periods [ddp]), shimmer (local, amplitude perturbation quotient [apq]3, apq5, apq11, average absolute differences between the amplitudes of consecutive periods [dda]), and F0cov (coefficient of variation of the fundamental frequency) showed a significant difference (P<.001). In addition, the duration of response time, the duration of silent periods, the filler period, and the proportion of fillers showed significant differences (P<.05). However, only jitter (local) met the reference value r=0.1 (small) of the effect size. In the automatic classification experiment for the classification of participants into CN and MCI groups, the results showed 66.0% accuracy in the MMSE conversational speech and 68.1% accuracy in everyday conversations with the humanoid robot. This study shows the possibility of early and simple screening for patients with MCI using prosodic and acoustic features from everyday conversations with a humanoid robot with the same level of accuracy as the MMSE.

The Chinese version of informant AD8 for mild cognitive impairment and dementia screening in community-dwelling older adults.

Conducting routine mild cognitive impairment (MCI) and dementia screening for older adults in the community is important, which not only can improve our understanding of these diseases but also can increase early detection and treatment. To analyze the reliability and validity of the informant AD8 and explore the cut-off values for screening MCI and dementia in the community-dwelling older adults, this study adopted a multi-stage cluster sampling method to recruit 327 participants aged 60 and over in communities. The informant AD8 and Clinical Dementia Rating (CDR) scales were used to evaluate cognitive function of the subjects, and the receiver operating characteristic curves (ROC) was conducted to test the screening efficacy. Among the 327 participants, 33.0% of them met the criteria of MCI, and 3.4% of them met the criteria of dementia. The area under the receiver operating characteristic curve (AUC) of the informant AD8 for screening dementia was 0.974, with a screening cut-off of three, sensitivity of 90.9% and specificity of 89.0%. But it has a poor discriminability in MCI screening [AUC = 0.645, 95% confidence interval (CI): 0.578-0.711]. This study suggests that the informant AD8 is an ideal and useful tool for dementia screening in community-dwelling older adults. However, it is less capable to distinguish older adults with MCI from those with normal cognitive function.

Validity and reliability of the persian version of the modified telephone interview for cognitive status among community-dwelling older adults in Iran.

The validity and reliability of the Persian version of the Modified Telephone Interview for Cognitive Status (P-TICS-M) in older adults living in the Iranian community for a comprehensive screening of mild cognitive impairment and dementia was investigated. In the first phase, translation, re-translation, and word-taking were performed by using the face validity and content validity. In the second phase, a stratified convenient sampling with 150 participants aged ≥60 years was conducted based on cognitive status using the global deterioration scale in 2018. The external and internal reliability of the P-TICS-M using the interclass correlation coefficient and Cronbach's alpha coefficient of total items of this tool were estimated. The mean age of the participants was 68.6 (standard deviation±7.4) years. According to global deterioration scale, 87 (58.0%) had normal cognition, 40 (26.7%) had mild cognitive impairment, and 23 (15.3%) had dementia. The Spearman's correlation coefficient between P-TICS-M scores and Mini-Mental State Examination scale was 0.764. In exploratory factor analysis, seven domains were detected, which were compatible with those defined by the tool developer. The Cronbach's alpha of the P-TICS-M was 0.920. The absolute agreement between test-retest score was >0.90. The sensitivity of 92.2, 94.8, and 100%, and also the specificity of 79.4, 88.2, and 89.8% were calculated for detecting subjects with dementia, respectively. Furthermore, a mild cognitive impairment cutoff of >28 was determined. The development and validation of a P-TICS-M tool can be useful in identifying older adult people with cognitive impairment. Demographic characteristics (level of education, age) can also affect the cutoff point of this tool.

A new early warning method for mild cognitive impairment due to Alzheimer's disease based on dynamic evaluation of the "spatial executive process".

Mild cognitive impairment (MCI) due to Alzheimer's disease (AD), as an early stage of AD, is an important point for early warning of AD. Neuropathological studies have shown that AD pathology in pre-dementia patients involves the hippocampus and caudate nucleus, which are responsible for controlling cognitive mechanisms such as the spatial executive process (SEP). The aim of this study is to design a new method for early warning of MCI due to AD by dynamically evaluating SEP. We designed fingertip interaction handwriting digital evaluation paradigms and analyzed the dynamic trajectory of fingertip interaction and image data during "clock drawing" and "repetitive writing" tasks. Extracted fingertip interaction digital biomarkers were used to assess participants' SEP disorders, ultimately enabling intelligent diagnosis of MCI due to AD. A cross-sectional study demonstrated the predictive performance of this new method. We enrolled 30 normal cognitive (NC) elderly and 30 MCI due to AD patients, and clinical research results showed that there may be neurobehavioral differences between the two groups in digital biomarkers captured during SEP. The early warning performance for MCI due to AD of this new method (areas under the curve (AUC) = 0.880) is better than that of the Minimum Mental State Examination (MMSE) neuropsychological scale (AUC = 0.856) assessed by physicians. Patients with MCI due to AD may have SEP disorders, and this new method based on dynamic evaluation of SEP will provide a novel human-computer interaction and intelligent early warning method for home and community screening of MCI due to AD.