Identification Of Mild Cognitive Impairment Research Articles

Exploring inertial sensor-based balance biomarkers for early detection of mild cognitive impairment.

Dementia is characterized by a progressive loss of cognitive abilities, and diagnosing its early stages Mild Cognitive Impairment (MCI), is difficult since it is a transitory state that is different from total cognitive collapse. Recent clinical research studies have identified that balance impairments can be a significant indicator for predicting dementia in older adults. Accordingly, the current research focuses on finding innovative postural balance-based digital biomarkers by using wearable inertial sensors and pre-screening of MCI in home settings using machine learning techniques. For this research, sixty subjects (30 cognitively normal and 30 MCI) with waist-mounted inertial sensor performed balance tasks in four different standing postures: eyes-open, eyes-closed, right-leg-lift, and left-leg-lift. The significant balance biomarkers for MCI identification are discovered by our research, demonstrating specific characteristics in each of these four states. A robust feature selection approach is ensured by the multi-step methodology that combines the strengths of Filter techniques, Wrapper methods, and SHAP (Shapley Additive exPlanations) technique. The proposed balance biomarkers have the potential to detect MCI (with 75.8% accuracy), as evidenced by the results of machine learning algorithms for classification. This work adds to the growing body of literature targeted at enhancing understanding and proactive management of cognitive loss in older populations and lays the groundwork for future research efforts aimed at refining digital biomarkers, validating findings, and exploring longitudinal perspectives.

Predictive models for Alzheimer's disease diagnosis and MCI identification: The use of cognitive scores and artificial intelligence algorithms

The paper presents a comprehensive study on predictive models for Alzheimer's disease (AD) and Mild cognitive impairment (MCI) diagnosis, implementing a combination of cognitive scores and artificial intelligence algorithms. The research includes detailed analyses of clinical and demographic variables such as age, education, and various cognitive and functional scores, investigating their distributions and correlations with AD and MCI. The study utilizes several machine-learning classifiers, comparing their performance through metrics like accuracy, precision, and area under the ROC curve (AUC). Key findings include the influence of gender on AD prevalence, the potential protective effect of education, and the significance of functional decline and cognitive performance scores in the models. The results demonstrate the effectiveness of ensemble methods and the robustness of the models across different data subsets, highlighting the potential of artificial intelligence in enhancing diagnostic accuracy for Alzheimer's disease and Mild cognitive impairment.

Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach.

Mild cognitive impairment plays a crucial role in predicting the early progression of Alzheimer's disease, and it can be used as an important indicator of the disease progression. Currently, numerous studies have focused on utilizing the functional brain network as a novel biomarker for mild cognitive impairment diagnosis. In this context, we employed a graph convolutional neural network to automatically extract functional brain network features, eliminating the need for manual feature extraction, to improve the mild cognitive impairment diagnosis performance. However, previous graph convolutional neural network approaches have primarily concentrated on single modes of brain connectivity, leading to a failure to leverage the potential complementary information offered by diverse connectivity patterns and limiting their efficacy. To address this limitation, we introduce a novel method called the graph convolutional neural network with multimodel connectivity, which integrates multimode connectivity for the identification of mild cognitive impairment using fMRI data and evaluates the graph convolutional neural network with multimodel connectivity approach through a mild cognitive impairment diagnostic task on the Alzheimer's Disease Neuroimaging Initiative dataset. Overall, our experimental results show the superiority of the proposed graph convolutional neural network with multimodel connectivity approach, achieving an accuracy rate of 92.2% and an area under the Receiver Operating Characteristic (ROC) curve of 0.988.

Mild Cognitive Impairment detection based on EEG and HRV data

Brain volume decrease is usually connected to neurodegeneration and aging. In this environment, an important percentage of elderly persons suffer from mild cognitive impairment (MCI), a kind of dementia that can lead to Alzheimer's disease (AD). Since the symptoms of cognitive impairment are scarcely discernible, developing a safe and effective method for early MCI detection has emerged as an important challenge. According to this regard, numerous cognitive training tests can be targeted to help aging people retain a good quality of life, especially in the case of fragility disorders. A Consortium to Establish a Registry for Alzheimer's Disease (CERAD) task was initially created to detect the early stages of AD. This task specifically targets various tests related to specific cognitive domains. However, it has since developed into a popular diagnostic tool for many kinds of dementia, such as MCI. Several low-cost equipment, such as electroencephalography (EEG) and heart rate variability (HRV), may be useful for predicting MCI. On the other side, various machine learning (ML) models can be employed to extract/analyze relevant features from biomedical and physiological signals, especially in the context of anomaly detection and classification. To this regard, we developed a new method based on ML models to categorize MCI and healthy control (HC) patients during the CERAD task using EEG and HRV multimodal data. Our dataset includes 15 subjects who were randomly assigned to training and testing groups of 7 HC and 8 MCI, respectively. Our raw EEG and HRV data are analyzed to extract time, frequency, and non-linear features. A scaling step is employed to reduce the significant disparity between features. For the classification task, five ML models are evaluated, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and gradient boosting (GB). To enhance accuracy, a hybrid ML model with a voting system is developed, combining the top ML models with the highest accuracy rates. A comparison step is performed between the use of ML and hybrid ML models. The experimental findings demonstrated the efficacy of our proposed technique, which included a hybrid ML model. An average accuracy of 93.86%, a sensitivity of 93.87%, and a specificity of 93.53% are achieved. The obtained results allow one to conclude that the first CERAD test plays a prominent role as a novel biomarker with an ultra-short duration for early MCI identification through the combination of EEG and HRV signals.

MACHINE-LEARNING-BASED INTERPRETABLE PREDICTION FOR MILD COGNITIVE IMPAIRMENT: FROM ADNI DATABASE

Abstract Objective An accurate prediction model was still lacking for mild cognitive impairment (MCI). Therefore, this study aimed to predict the 3- and 5-year MCI risk utilizing interpretable machine learning, and to identify possible biomarkers for intervention. Methods A total of 444 participants were obtained from the ADNI database. Sociodemographic, neuropsychological, ApoE gene, and structural MRI imaging information was used as predictors. Data was randomly divided into training (70%) and test sets (30%). Six machine learning methods were constructed to predict the risk of MCI. Model performance was assessed by area under the ROC curve (AUC). SHapley Additive exPlanations (SHAP) was used to select important predictors. We also constructed a simplified MCI prediction model for clinical practice. Results The mean age (standard deviation) of study population was 73.7 (6.1) years old. A total of 40, and 60 participants progressed into MCI within 3, and 5 years, respectively. Logistic regression (AUC: 0.97) and support vector machine (AUC: 0.93) are the optimal model for 3- and 5-year prediction. The simplified models based on top three neuropsychological test scores also showed high prediction accuracy (AUC > 0.90). APOE gene, intracranial volume, and Clinical Dementia Rating Scale Sum of Boxes (CDRSB) score were top three predictors for 3-year prediction, while CDRSB score, modified Preclinical Alzheimer’s Cognitive Composite Test with Follow-up Test scale (mPACCtrailsB) score, and Functional Activity Questionnaire (FAQ) score were top three predictors for 5-year prediction. Conclusion Interpretable machine learning showed promise in early identification of MCI, and would be valuable for targeted prevention.

A new perspective on cognitive rehabilitation: layout design methods for wandering behavior in dementia patients

The global prevalence of dementia is on the rise, and existing medical models can no longer fulfill the pervasive need for intervention. Dementia is incurable, but the identification of Mild Cognitive Impairment (MCI) provides a window of opportunity for early intervention. The environmental therapy theory has been widely adopted, however, the implementation pathways and empirical evidence for cognitive behavioral interventions are still limited. The role of interior layout design as one of the environmental elements in guiding human behavioural patterns has been demonstrated by numerous scholars and extended to be explored in relation to perceptual and cognitive behavioural dimensions. There has been a great deal of research on Wandering Behavior In Patients With Cognitive Disorders in the medical field, but only sporadic reports in the field of design. Employing an “environment-behavior-neuroscience” research approach, this paper constructs the relationship between wandering behavior, cognitive function, and environmental factors, and explores the possibility of environmental intervention to improve cognitive functions. The study shows that: 1) The wandering patterns of dementia patients can be guided through landmarks; 2) There is a direct relationship between the depth of spatial communication and the layout, and optimizing the spatial layout can foster communication and theoretically have a restorative effect on cognitive function; 3) Centripetal layouts are more effective in behavior guidance, while multicore circulation path layouts can accommodate the wandering needs of dementia patients. As dementia is incurable, early intervention is the most effective approach, making this research highly valuable.

FNIRS-based graph frequency analysis to identify mild cognitive impairment in Parkinson’s disease

Background:Early identification of mild cognitive impairment (MCI) is essential for its treatment and the prevention of dementia in Parkinson’s disease (PD). Existing approaches are mostly based on neuropsychological assessments, while brain activation and connection have not been well considered. New Method:This paper presents a neuroimaging-based graph frequency analysis method and the generated features to quantify the brain functional neurodegeneration and distinguish between PD-MCI patients and healthy controls. The Stroop color-word experiment was conducted with 20 PD-MCI patients and 34 healthy controls, and the brain activation was recorded with functional near-infrared spectroscopy (fNIRS). Then, the functional brain network was constructed based on Pearson’s correlation coefficient calculation between every two fNIRS channels. Next, the functional brain network was represented as a graph and decomposed in the graph frequency domain through the graph Fourier transform (GFT) to obtain the eigenvector matrix. Total variation and weighted zero crossings of eigenvectors were defined and integrated to quantify functional interaction between brain regions and the spatial variability of the brain network in specific graph frequency ranges, respectively. After that, the features were employed in training a support vector machine (SVM) classifier. Results:The presented method achieved a classification accuracy of 0.833 and an F1 score of 0.877, significantly outperforming existing methods and features. Comparison with Existing Methods:Our method provided improved classification performance in the identification of PD-MCI. Conclusion:The results suggest that the presented graph frequency analysis method well identify PD-MCI patients and the generated features promise functional brain biomarkers for PD-MCI diagnosis.

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

Efficacy of the visual cognitive assessment test for mild cognitive impairment/mild dementia diagnosis: a meta-analysis.

Mild cognitive impairment (MCI) is an intermediate stage between normal ageing and dementia. The early identification of MCI is important for timely intervention. The visual cognitive assessment test (VCAT) is a brief language-neutral screening tool for detecting MCI/mild dementia. This meta-analysis evaluated the diagnostic efficacy of the VCAT for MCI/mild dementia. Medline, Embase, Google Scholar, and Cochrane Library were searched from their inception until August 2023 to identify studies using VCAT to diagnose MCI/mild dementia. The primary outcome was to assess the diagnostic accuracy of the VCAT for detecting MCI/mild dementia through area under the receiver operating characteristic curve (AU-ROC) analysis. The secondary outcome was to explore the correlation between VCAT scores and MCI/mild dementia presence by comparing scores among patients with and without MCI/mild dementia. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated. Five studies with 1,446 older adults (mean age 64-68.3 years) were included. The percentage of participants with MCI/mild dementia versus controls ranged from 16.5% to 87% across studies. All studies were conducted in Asian populations, mostly Chinese, in Singapore and Malaysia. The pooled sensitivity was 80% [95% confidence interval (CI) 68%-88%] and the specificity was 75% (95% CI 68%-80%). The AU-ROCC was 0.77 (95% CI 0.73-0.81). Patients with MCI/mild dementia had lower VCAT scores than the controls (mean difference -6.85 points, p < 0.00001). VCAT demonstrated acceptable diagnostic accuracy in distinguishing MCI/mild dementia in cognitively normal older adults. As a language-neutral and culturally unbiased tool, the VCAT shows promise in detecting MCI/mild dementia. Further studies in non-Asian populations are required. https://www.crd.york.ac.uk/prospero/, identifier: CRD42023453453.

Mild Cognitive Impairment Subtype Performance in Comparison to Healthy Older Controls on the NIH Toolbox and Cogstate.

Early detection is necessary for the treatment of dementia. Computerized testing has become more widely used in clinical trials; however, it is unclear how sensitive these measures are to early signs of neurodegeneration. We investigated the use of the NIH Toolbox-Cognition (NIHTB-CB) and Cogstate-Brief computerized neuropsychological batteries in the identification of mild cognitive impairment (MCI) versus healthy older adults [healthy control (HC)] and amnestic (aMCI) versus nonamnestic MCI (naMCI). Exploratory analyses include investigating potential racial differences. Two hundred six older adults were diagnosed as aMCI (n = 58), naMCI (n = 15), or cognitively healthy (HC; n = 133). The NIH Toolbox-CB subtests of Flanker, Picture Sequence Memory, and Picture Vocabulary significantly differentiated MCI from HC. Further, subtests from both computerized batteries differentiated patients with aMCI from those with naMCI. Although the main effect of race differences was noted on tests and in diagnostic groups was significant, there were no significant race-by-test interactions. Computer-based subtests vary in their ability to help distinguish MCI subtypes, though these tests provide less expensive and easier-to-administer clinical screeners to help identify patients early who may qualify for more comprehensive evaluations. Further work is needed, however, to refine computerized tests to achieve better precision in distinguishing impairment subtypes.

Rule-Based Identification of Individuals with Mild Cognitive Impairment or Alzheimer's Disease Using Clinical Notes from the United States Veterans Affairs Healthcare System.

Early identification of individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) is a clinical and research imperative. Use of diagnostic codes for MCI and AD identification has limitations. We used clinical notes to supplement diagnostic codes in the Veterans Affairs Healthcare System (VAHS) electronic health records (EHR) to identify and establish cohorts of Veterans recorded with MCI or AD. Targeted keyword searches for MCI ("Mild cognitive impairment;" "MCI") and AD ("Alz*") were used to extract clinical notes from the VAHS EHR from fiscal year (FY) 2010 through FY 2019. Iterative steps of inclusion and exclusion were applied until searches achieved a positive predictive value ≥ 80%. MCI and AD cohorts were identified via clinical notes and/or diagnostic codes (i.e., including Veterans recorded by "Notes Only," "Notes + Code," or "Codes Only"). A total of 2,134,661 clinical notes from 339,007 Veterans met the iterative search criteria for MCI due to any cause and 4,231,933 notes from 572,063 Veterans met the iterative search criteria for AD. Over the 10-year study period, the number of clinical notes recording AD was generally stable, whereas the number for MCI more than doubled. More Veterans were identified for the MCI or AD cohorts via clinical notes than by diagnostic codes, particularly in the AD cohort. Among Veterans identified by having "Notes + Code" for MCI, the number first recorded by a code was lower than the number first recorded by a note until FY 2015 and then gradually became comparable after FY 2015. Among Veterans identified by having "Notes + Code" for AD, the number first recorded by a note was more than double the number first recorded by a code AD in each of the FYs. Clinical note-based identification captured more Veterans recorded with MCI and AD than diagnostic code-based identification.

DAUF: A disease-related attentional UNet framework for progressive and stable mild cognitive impairment identification

Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) plays a significant role in early Alzheimer's disease (AD) diagnosis, which can effectively boost the life quality of patients. Recently, convolutional neural network (CNN)- based methods using structural magnetic resonance imaging (sMRI) images have shown effective for AD identification. However, these CNN-based methods fail to effectively explore the feature extraction of disease-related multi-scale tissues, such as ventricles, hippocampi and cerebral cortex. To address this issue, we propose an end-to-end disease-related attentional UNet framework (DAUF) for identifying pMCI and sMCI, by embedding a devised dual disease-related attention module (D2AM) and a novel tree-structured feature fusion classifier (TFFC). Specifically, D2AM leverages the complementarity between feature maps and attention maps and the complementary features from the encoder and decoder, so as to highlight discriminative semantic and detailed features. Additionally, TFFC is a powerfully joint multi-scale feature fusion and classification head, by employing the homogeneity among multi-scale features, so that the discriminative features of the multi-scale tissues are adequately fused for enhancing classification performance. Finally, extensive experiments demonstrate the superior performance of DAUF, with the effectiveness of D2AM and TFFC on identifying pMCI and sMCI subjects.

Mild cognitive impairment identification based on motor and cognitive dual-task pooled indices.

This study investigates the possibility of adopting motor and cognitive dual-task (MCDT) approaches to identify subjects with mild cognitive impairment (MCI) and subjective cognitive impairment (SCI). The upper and lower motor performances of 44 older adults were assessed using the SensHand and SensFoot wearable system during three MCDTs: forefinger tapping (FTAP), toe-tapping heel pin (TTHP), and walking 10 m (GAIT). We developed five pooled indices (PIs) based on these MCDTs, and we included them, along with demographic data (age) and clinical scores (Frontal Assessment Battery (FAB) scores), in five logistic regression models. Models which consider cognitively normal adult (CNA) vs MCI subjects have accuracies that range from 67% to 78%. The addition of clinical scores stabilised the accuracies, which ranged from 85% to 89%. For models which consider CNA vs SCI vs MCI subjects, there are great benefits to considering all three regressors (age, FAB score, and PIs); the overall accuracies of the three-class models range between 50% and 59% when just PIs and age are considered, whereas the overall accuracy increases by 18% when all three regressors are utilised. Logistic regression models that consider MCDT PIs and age have been effective in distinguishing between CNA and MCI subjects. The inclusion of clinical scores increased the models' accuracy. Particularly high performances in distinguishing among CNA, SCI, and MCI subjects were obtained by the TTHP PI. This study suggests that a broader framework for MCDTs, which should encompass a greater selection of motor tasks, could provide clinicians with new appropriate tools.

Quantification of identifying cognitive impairment using olfactory-stimulated functional near-infrared spectroscopy with machine learning: a post hoc analysis of a diagnostic trial and validation of an external additional trial

BackgroundWe aimed to quantify the identification of mild cognitive impairment and/or Alzheimer’s disease using olfactory-stimulated functional near-infrared spectroscopy using machine learning through a post hoc analysis of a previous diagnostic trial and an external additional trial.MethodsWe conducted two independent, patient-level, single-group, diagnostic interventional trials (original and additional trials) involving elderly volunteers (aged > 60 years) with suspected declining cognitive function. All volunteers were assessed by measuring the oxygenation difference in the orbitofrontal cortex using an open-label olfactory-stimulated functional near-infrared spectroscopy approach, medical interview, amyloid positron emission tomography, brain magnetic resonance imaging, Mini-Mental State Examination, and Seoul Neuropsychological Screening Battery.ResultsIn total, 97 (original trial) and 36 (additional trial) elderly volunteers with suspected decline in cognitive function met the eligibility criteria. The statistical model reported classification accuracies of 87.3% in patients with mild cognitive impairment and Alzheimer’s disease in internal validation (original trial) but 63.9% in external validation (additional trial). The machine learning algorithm achieved 92.5% accuracy with the internal validation data and 82.5% accuracy with the external validation data. For the diagnosis of mild cognitive impairment, machine learning performed better than statistical methods with internal (86.0% versus 85.2%) and external validation data (85.4% versus 68.8%).InterpretationIn two independent trials, machine learning models using olfactory-stimulated oxygenation differences in the orbitofrontal cortex were superior in diagnosing mild cognitive impairment and Alzheimer’s disease compared to classic statistical models. Our results suggest that the machine learning algorithm is stable across different patient groups and increases generalization and reproducibility.Trial registrationClinical Research Information Service (CRiS) of Republic of Korea; CRIS numbers, KCT0006197 and KCT0007589.Graphical

Role of comprehensive geriatric assessment in screening for mild cognitive disorders.

The role of comprehensive geriatric assessment (CGA) in screening for mild cognitive disorders was not known. To evaluate the role of CGA in screening for mild cognitive disorders. A total of 100 elderly people who underwent health examinations in our hospital and community between January 2020 and December 2021 were included for analysis. Using Petersen as the diagnostic gold standard, healthy individuals were included in the control group and patients with mild cognitive impairment were assigned to the study group. The correlation between the cognitive function of the patients and their baseline clinical profiles was analyzed. Patients' Montreal Cognitive Assessment (MoCA) and CGA screening results were compared, and the sensitivity and specificity were calculated to assess the screening role of CGA. CGA assessment yielded higher diagnostic accuracy than MoCA. The results of the multivariate regression analysis showed no correlation of gender, age, body mass index and literacy with cognitive function. Patients with mild cognitive impairment obtained significantly lower MoCA scores than healthy individuals (P < 0.05). In the CGA scale, patients with mild cognitive impairment showed significantly lower Mini-mental State Examination, Miniature Nutritional Assessment and Berg Balance Scale scores, and higher Activity of Daily Living, Instrumental Activities of Daily Living Scale and Frailty Screening Inventory scores than healthy individuals (P < 0.05), whereas the other assessment scales showed no significant differences (P > 0.05). The CGA provides higher diagnostic sensitivity and specificity than the MoCA (P < 0.05). CGA allows accurate identification of mild cognitive impairment with high sensitivity and specificity, facilitating timely and effective intervention, and is thus recommended for clinical use.

Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification.

This work presents a novel approach to estimate brain functional connectivity networks via generative learning. Due to the complexity and variability of rs-fMRI signal, we consider it as a random variable, and utilize variational autoencoder networks to encode it as a confidence distribution in the latent space rather than as a fixed vector, so as to establish the relationship between them. First, the mean time series of each brain region of interest is mapped into a multivariate Gaussian distribution. The correlation between two brain regions is measured by the Jensen-Shannon divergence that describes the statistical similarity between two probability distributions, and then the adjacency matrix is created to indicate the functional connectivity strength of pairwise brain regions. Meanwhile, our findings show that the adjacency matrices obtained at VAE latent spaces of different dimensionalities have good complementarity for MCI identification in precision and recall, and the classification performance can be further boosted by an efficient cascade of classifiers. This proposal constructs brain functional networks from a statistical modeling standpoint, improving the statistical ability of population data and the generalization ability of observation data variability. We evaluate the proposed framework over the task of identifying subjects with MCI from normal controls, and the experimental results on the public dataset show that our method significantly outperforms both the baseline and current state-of-the-art methods.

Identification of Mild cognitive impairment based on quadruple GCN model constructed with multiple features from higher-order brain connectivity

Mild cognitive impairment (MCI) is the early stage of Alzheimer's disease, which is associated with abnormal brain proteins, the recognition of MCI being a challenging task. Recent studies have shown that the performance of MCI identification can be improved by combining protein features captured in Positron Emission Computed Tomography(PET). Nevertheless, there are still great challenges in extracting effective features from the vast amount of information. Most brain networks only considered the unilateral features of nodes or edges, ignored the interactions between them. In response to this problem, our study proposed to combine the quadruple Siamese network and GCN with self-attention pooling(QS-SAGCN) for MCI identification. In detail, we constructed the multiple protein features network(MPN) and higher-order MPN(MPHN) by PET images to promote the MCI identification. Furthermore, a pooling operation with self-attention mechanism was incorporated into GCN(SAGCN), which considered the node characteristics and topology in the graph network to facilitate the acquisition of robust biomarkers, simultaneously. Additionally we combined quadruple Siamese network with SAGCN as classification framework to improve the identification accuracy. Our proposed MCI identification method was evaluated on 230 subjects (including 117 MCI subjects, 113 normal control subjects) with both 18F-AV-1451 PET and 18F-AV-45 PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results showed that 1) QS-SAGCN enhanced the ability of feature identification, laying the foundation for obtaining more effective biomarkers for MCI patients; 2) The MCI identification accuracy (93.5%) was obtained by combining QS-SAGCN and higher-order network, indicating that the framework had advantages in mental disorders recognition. Finally, through comparison, the accuracy of our proposed MCI recognition method was superior to some of the existing state-of-the-art methods. Overall, the MCI identification method in this study was effective and promising to assist in the diagnosis of MCI in future clinical practice.

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&lt;0.05). ROC curves showed a favorable diagnostic accuracy of the two features in the discrimination between controls and MCI (AUC: 0.823, P&lt;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.

P.004 Comparison of the Montreal Cognitive Assessment (MoCA) and Rowland Universal Dementia Assessment Scale (RUDAS) for identification of mild cognitive impairment and dementia

Background: There are many cognitive tests that detect mild cognitive impairment (MCI) and dementia such as the Montreal Cognitive Assessment (MoCA) and Rowland Universal Dementia Assessment Scale (RUDAS). The comparative performance of these screening tests for identifying MCI and dementia is unknown. Methods: The MoCA and RUDAS were administered during baseline visits for patients in the Calgary Neurosciences Program. Those that enrolled in the Prospective Registry of Persons with Memory Symptoms (PROMPT) had their scores related to their final clinical diagnosis. Cut-off scores of 26 for the MoCA and 22 for the RUDAS were used to indicate a positive result. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of both cognitive scores were compared. Results: The sensitivity, specificity, PPV, NPV, and accuracy of the MoCA (n = 125) was 89.3%, 72.7%, 93.9%, 59.3%, and 86.4%, respectively. The sensitivity, specificity, PPV, NPV, and accuracy of the RUDAS (n = 125) was 47.6%, 100%, 100%, 29.0%, 56.8%, respectively. Conclusions: In patients with cognitive complaints presenting to a specialist clinic, the MoCA was more sensitive and accurate than the RUDAS for a final clinician diagnosis of mild cognitive impairment or dementia when using the standard cut-offs.

Validation of a new cognitive screening tool, the Brain Health Test‐7, for identification of mild cognitive impairment and early dementia

AbstractBackgroundThe Brain Health Test‐7 (BHT‐7) was developed to identify patients with mild cognitive impairment (MCI) and early dementia. Here we report the validity of the BHT‐7 versus the Mini‐Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in subjects of different psychiatry or neurology clinics.MethodPatients with the chief complaint of memory decline were recruited in this study from the outpatient clinic of psychiatry or neurology in 3 different kinds of hospitals between November 2017 to December 2020. All patients underwent the evaluation of the BHT‐7, MMSE, MoCA, and CDR by trained research assistants or board‐certified psychologists. The final clinical diagnosis (normal, MCI, dementia) was made by consensus meeting, taking into account all available data, including clinical history and symptoms, brain image, cognitive tests, and CDR. Demographic data (age, gender, and education level) and the scores of the MMSE, MoCA, and BHT‐7 between groups were compared. Logistic regression was adopted for analysis of optimal cutoff values, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the ROC curve.ResultWe enrolled a total of 1090 subjects (normal 402, MCI 317, dementia 371); of them, 705 (64.7%) were female. There was a statistically significant difference in age, years of education, and 3 cognitive test scores among the 3 groups of subjects. Compared with the MMSE and MoCA, the BHT‐7 performed slightly better in differentiating MCI and dementia. In sub‐group analysis (divided based on 6 years of education), the BHT‐7 still performed slightly better than MMSE and MoCA. The testing time for the BHT‐7 was about 5‐7 minutes, shorter than that of the MMSE and MoCA. For BHT‐7, the cutoff point was 17 between normal and MCI, and 14 between normal and dementia. These cutoff points of BHT‐7 were consistent through 3 different settings (medical center’s psychiatry and neurology clinics, and outpatient clinic of a psychiatry center). In contrast, the cutoff points were inconsistent for MMSE and MoCA in different settings.ConclusionThe results support that BHT‐7 may be a useful cognitive screening tool for MCI or early dementia in various clinic settings.