Identification Of Mild Cognitive Impairment Research Articles

On the Clinimetrics of the Montreal Cognitive Assessment: Cutoff Analysis in Patients with Mild Cognitive Impairment due to Alzheimer's Disease.

In the era of disease-modifying therapies, empowering the clinical neuropsychologist's toolkit for timely identification of mild cognitive impairment (MCI) is crucial. Here we examine the clinimetric properties of the Montreal Cognitive Assessment (MoCA) for the early diagnosis of MCI due to Alzheimer's disease (MCI-AD). Data from 48 patients with MCI-AD and 47 healthy controls were retrospectively analyzed. Raw MoCA scores were corrected according to the conventional Nasreddine's 1-point correction and demographic adjustments derived from three normative studies. Optimal cutoffs were determined while previously established cutoffs were diagnostically reevaluated. The original Nasreddine's cutoff of 26 and normative cutoffs (non-parametric outer tolerance limit on the 5th percentile of demographically-adjusted score distributions) were overly imbalanced in terms of Sensitivity (Se) and Specificity (Sp). The optimal cutoff for Nasreddine's adjustment showed adequate clinimetric properties (≤23.50, Se = 0.75, Sp = 0.70). However, the optimal cutoff for Santangelo's adjustment (≤22.85, Se = 0.65, Sp = 0.87) proved to be the most effective for both screening and diagnostic purposes according to Larner's metrics. The results of post-probability analyses revealed that an individual testing positive using Santangelo's adjustment combined with a cutoff of 22.85 would have 84% post-test probability of receiving a diagnosis of MCI-AD (LR+ = 5.06). We found a common (mal)practice of bypassing the applicability of normative cutoffs in diagnosis-oriented clinical practice. In this study, we identified optimal cutoffs for MoCA to be allocated in secondary care settings for supporting MCI-AD diagnosis. Methodological and psychometric issues are discussed.

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification.

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson's correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain's operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI's K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson's correlation (K-PC) has some advantage over deep learning.

Cortical and subcortical gray matter abnormalities in mild cognitive impairment

Gray matter changes are thought to be closely related to cognitive decline in mild cognitive impairment (MCI) patients. The study aimed to explore cortical and subcortical structural alterations in MCI and their association with cognitive assessment. 24 MCI patients and 22 normal controls (NCs) were included. Voxel-based morphometry (VBM), vertex-based shape analysis and surface-based morphometry (SBM) analysis were applied to explore subcortical nuclei volume, shape and cortical morphology. Correlations between structural changes and cognition were explored using spearman correlation analysis. Support vector machine (SVM) classification evaluated MCI identification accuracy. MCI patients showed significant atrophy in the left thalamus, left hippocampus, left amygdala, right pallidum, right hippocampus, along with inward deformation in the left amygdala. SBM analysis revealed that MCI group exhibited shallower sulci depth in the left hemisphere and increased cortical gyrification index (GI) in the right frontal gyrus. Correlation analysis showed the positive correlation between right hippocampus volume and episodic memory, while negative correlation between the altered GI and memory performance in MCI group. SVM analysis demonstrated superior performance of sulci depth and GI derived from SBM in MCI identification. When combined with cortical and subcortical metrics, SVM achieved a peak accuracy of 89 % in distinguishing MCI from NC. The study reveals significant gray matter structural changes in MCI, suggesting their potential role in underlying functional differences and neural mechanisms behind memory impairment in MCI.

Outcomes of Patients With Mild Cognitive Impairment With Lewy Bodies or Alzheimer Disease at 3 and 5 Years After Diagnosis.

Retrospective studies indicate that dementia with Lewy bodies (DLB) may be preceded by a mild cognitive impairment (MCI) prodrome. Research criteria for the prospective identification of MCI with Lewy bodies (MCI-LB) have been developed. We aimed to assess the prognosis of a prospectively identified MCI-LB cohort at 2 key milestones, 3- and 5 years after diagnosis, to examine classification stability over time and rates of adverse outcomes (dementia or death). This was a retrospective examination of data from 2 longitudinal observational cohort studies where participants with MCI were prospectively recruited from North East England and differentially classified as MCI due to Alzheimer disease (MCI-AD), possible MCI-LB, or probable MCI-LB. Adverse outcomes (DLB/other dementia or death) and stability of disease-specific classifications were examined in each group. Of 152 participants with baseline MCI (54 MCI-AD, 29 possible MCI-LB, and 69 probable MCI-LB), 126 were followed for up to 3 years (mean age 75.3 years; 40% female). We found that prospective probable MCI-LB classifications were both sensitive (91%) and specific (94%) to classifications either remaining as probable MCI-LB or progressing to DLB (in some cases autopsy confirmed) for 3 or more years after. Classifications were at least as stable as those in MCI-AD. In this cohort with disease-specific MCI classifications, rates of progression to dementia were high: 55% of MCI-LB had developed DLB within 3 years. Dementia occurred in 47% of MCI-AD over the same duration (odds ratio 1.68, 95% CI 0.66-4.26, p = 0.278). Premature death was a common competing risk, occurring in 9% of MCI-AD and 11% of MCI-LB within 3 years. These findings support that prospectively identified probable MCI-LB is a prodromal presentation of DLB and that disease-specific classifications of MCI may reliably identify different prodromal dementias.

Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases.

Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer's disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10-20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer's disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer's Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59-0.91) and Alzheimer's disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer's subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.

A simple self-supervised learning framework with patch-based data augmentation in diagnosis of Alzheimer’s disease

Alzheimer’s disease (AD) stands as a prominent age-related disorder with significant global impact. Utilizing computer-aided diagnosis aids in the timely identification of mild cognitive impairment, facilitating early intervention and management. Self-supervised learning models have attracted much attention due to their advantages of no manual labeling, so they are very suitable for solving the problems of difficulty in data acquisition and high cost of manual labeling in medical image processing. However, the existing self-supervised algorithms applied to medical images often have poor diagnostic effects and consume large resources, as a consequence, the model’s ability to learn meaningful representations from medical images is hampered, leading to suboptimal performance. Subsequently, diverse patches extracted from the same brain are amalgamated to create contrast views, and attention weights are then employed to enhance the fitting and generalization capacity of the model. Experimental results on the ADNI dataset with 1365 subjects show that PD-SIM has been improved in the diagnosis of different diseases(such as the classification ACC of AD and CN reached 0.797, and the classification ACC of early cognitive impairment reached 0.7036), and also alleviates the problem of large consumption of computer resources, downstream tasks require only 52ms per image. The proposed method performs well in atrophic structure identification and AD diagnosis. Therefore, PD-SIM has a wide range of application prospects and is of great practical significance within the realm of medical image analysis. The data and code can be found at https://github.com/Z1Ting/PD-SIM.

Brain check-up: a structured approach diagnosing mild cognitive impairment in the primary care setting

The reason-related identification of mild cognitive impairment (MCI) in primary care is helpful to treat reversible causes or decelerate progression to dementia by optimal management of existing risk factors. In this process general practitioners are in akey position. The present feasibility study investigated the practicability of adiagnostic algorithm (brain check-up), comprising neuropsychological examinations, differential diagnoses and follow-up measures. By means of astandardized questionnaire, the utilization and practicability of the brain check-up was surveyed in n = 37 medical practices of general practitioners and internists in Germany. The brain check-up was performed by n = 37physicians in 389 patients (66%). The main barriers to implementation included patients' fear of facing the results, the professionals' lack of time, and costs. Overall, 73% of the participants agreed that the brain check-up was practical in everyday treatment. Long waiting times for an appointment with aneurological/psychiatric specialist were perceived as abarrier for optimal care. The structured algorithm is convenient in physician's everyday practice and can contribute to identify patients with MCI more easily. Therefore, it appears to be ahelpful tool in primary care. To achieve sustainability in everyday use, identified barriers need to be addressed during the implementation phase.

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.

Economic Impact of Progression from Mild Cognitive Impairment to Alzheimer Disease in the United States

BackgroundLimited evidence exists on the economic burden of individuals who progress from mild cognitive impairment (MCI) to Alzheimer disease and related dementia disorders (ADRD).ObjectivesTo assess the all-cause health care resource utilization and costs for individuals who develop ADRD following an MCI diagnosis compared to those with stable MCI.DesignThis was a retrospective cohort study from January 01, 2014, to December 31, 2019.SettingThe Merative MarketScan Commercial and Medicare Databases were used.ParticipantsIndividuals were included if they: (1) were aged 50 years or older; (2) had ≥1 claim with an MCI diagnosis based on the International Classification of Diseases, Ninth Revision (ICD-9) code of 331.83 or the Tenth Revision (ICD-10) code of G31.84; and had continuous enrollment. Individuals were excluded if they had a diagnosis of Parkinson’s disease or ADRD or prescription of ADRD medication.MeasurementsOutcomes included all-cause utilization and costs per patient per year in the first 12 months following MCI diagnosis, in total and by care setting: inpatient admissions, emergency department (ED) visits, outpatient visits, and pharmacy claims.ResultsOut of the total of 5185 included individuals, 1962 (37.8%) progressed to ADRD (MCI-to-ADRD subgroup) and 3223 (62.2%) did not (Stable MCI subgroup). Adjusted all-cause utilization was higher for all care settings in the MCI-to-ADRD subgroup compared with the Stable MCI subgroup. Adjusted all-cause mean total costs ($34599 vs $24541; mean ratio [MR], 1.41 [95% CI, 1.31–1.51]; P<.001), inpatient costs ($47463 vs $38004; MR, 1.25 [95% CI, 1.08–1.44]; P=.002), ED costs ($4875 vs $3863; MR, 1.26 [95% CI, 1.11–1.43]; P<.001), and outpatient costs ($16652 vs $13015; MR, 1.28 [95% CI, 1.20–1.37]; P<.001) were all significantly higher for the MCI-to-ADRD subgroup compared with the Stable MCI subgroup.ConclusionsIndividuals who progressed from MCI to ADRD had significantly higher health care costs than individuals with stable MCI. Early identification of MCI and delaying its progression is important to improve patient and economic outcomes.

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.

A Novel Method to Identify Mild Cognitive Impairment Using Dynamic Spatio-Temporal Graph Neural Network.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the identification of mild cognitive impairment (MCI) research, MCI patients are relatively at a higher risk of progression to Alzheimer's disease (AD). However, almost machine learning and deep learning methods are rarely analyzed from the perspective of spatial structure and temporal dimension. In order to make full use of rs-fMRI data, this study constructed a dynamic spatiotemporal graph neural network model, which mainly includes three modules: temporal block, spatial block, and graph pooling block. Our proposed model can extract the BOLD signal of the subject's fMRI data and the spatial structure of functional connections between different brain regions, and improve the decision-making results of the model. In the study of AD, MCI and NC, the classification accuracy reached 83.78% outperforming previously reported, which manifested that our model could effectively learn spatiotemporal, and dynamic spatio-temporal method plays an important role in identifying different groups of subjects. In summary, this paper proposed an end-to-end dynamic spatio-temporal graph neural network model, which uses the information of the temporal dimension and spatial structure in rs-fMRI data, and achieves the improvement of the three classification performance among AD, MCI and NC.

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 &amp;gt; 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.