Normal Cognition Research Articles

Identification of Alzheimer's disease using Convolutional Neural Network

Alzheimer's disease (AD) as a brain disease has caused a progressive, devastating effect on the memory and general mental and physical coordination of victims. The impact on victims is irreversible, and the cause has yet to be identified. The treatment at full-blown can be difficult, but it could be properly managed in the early phase. Hence, there is a need for an efficient and effective early diagnosis. Machine learning techniques have proved to be successful in image classification. It was on this premise that this paper adopted a machine learning approach. The approach used a convolutional neural network with transfer learning to classify structural Magnetic Resonance Images (sMRI) into a multi-classification of 3 classes. The classes were Normal Cognitive (NC), Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). K-fold cross validation was employed to validate the test set. The sMRI subjects included 97 NC, 57 MCI, and 24 AD patients. The proposed method achieved an overall accuracy of 94% on classification based on the multiclass classification.

Higher serum Lp-PLA2 is associated with cognitive impairment in Parkinson's disease patients.

To explore the association between lipoprotein-associated phospholipase A2 (Lp-PLA2) and the risk of cognitive impairment in Parkinson's disease (PD-CI). A case-control study involving 100 hospitalized PD patients and 60 healthy controls was carried out. Serum Lp-PLA2 level was detected by automatic biochemical analyzer. Based on whether Parkinson's patients have cognitive impairment, PD patients were subdivided to analyze the clinical value of Lp-PLA2. Relationship between Lp-PLA2 and PD-CI risk was analyzed by logistic regression. Diagnostic value of Lp-PLA2 in PD-CI patients was investigated using receiver's operator characteristic curves. The levels of serum Lp-PLA2 activity in Parkinson's disease with normal cognition (PD-NC) and PD-CI patients were significantly higher than those in healthy controls (HCs), respectively. Furthermore, compared to the PD-NC group, the serum Lp-PLA2 activity level was significantly higher in PD-CI patients. Multivariable logistic regression analysis indicated that higher Lp-PLA2 level was an independent risk factor for PD patients with cognitive impairment. Moreover, the area under the efficacy curve of Lp-PLA2 for predicting PD-CI is 0.659. Our study shows that higher levels of Lp-PLA2 activity in PD patients are associated with the risk of developing cognitive impairment. Therefore, given the wide availability, safety, and convenience of monitoring serum Lp-PLA2 activity, it may serve as an early biomarker for cognitive impairment in PD patients.

A novel automatic detection and classification algorithm for elderly cognitive impairment using CSVM

Alzheimer’s disease (AD) is a degenerative disease with insidious onset and chronic nervous system, which can be roughly classified into three stages: preclinical AD, mild cognitive impairment (MCI), and clinical AD. Given that timely treatment of MCI can delay the course of AD, identifying what stage of the disease is at the time of early diagnosis is critical in clinical practice. Based on the survey data of elderly residents in Changsha community, this paper constructs the cognitive impairment data set and explores the application of the emerging classification algorithm Convolutional Support Vector Machine(CSVM) in identifying elderly cognitive impairment, a small data classification problem. CSVM is an improved Support Vector Machine classification(SVC) algorithm, which uses the convolution filtering idea of the Convolutional Neural Networks(CNN) algorithm to preprocess the nonlinearly separable sample set to improve the classification ability of SVM. In addition, the optimal convolution filter (solution) is found by the Simplified Swarm Optimization(SSO) algorithm and orthogonal array experimental design. A variable update amplitude method is also proposed to optimize the SSO update strategy further. Aiming at the problem of an unbalanced sample set, a stratified sampling method is used to divide and cross-validate the sample data, and a variety of evaluation indicators are used to evaluate the performance of the model. Experimental results show that the data set processed by a specific convolution filter can significantly improve the classification performance of linear SVM. The accurate differentiation of normal cognition(NC), MCI, and AD helps intervene in adjuvant treatment for potential patients timely.

An investigation into the potential association between nutrition and Alzheimer's disease.

Malnutrition is the most common nutritional issue in Alzheimer's disease (AD) patients, but there is still a lack of a comprehensive evaluation of the nutritional status in AD patients. This study aimed to determine the potential association of various nutritional indices with AD at different stages. Subjects, including individuals with normal cognition (NC) and patients diagnosed with AD, were consecutively enrolled in this cross-sectional study. Demographics, body composition, dietary patterns, nutritional assessment scales and nutrition-related laboratory variables were collected. Binary logistics regression analyses and receiver operating characteristic (ROC) curves were used to indicate the association between nutrition-related variables and AD at different stages. Totals of 266 subjects, including 73 subjects with NC, 72 subjects with mild cognitive impairment due to AD (AD-MCI) and 121 subjects with dementia due to AD (AD-D) were included. There was no significant difference in dietary patterns, including Mediterranean diet and Mediterranean-DASH diet intervention for neurodegenerative delay (MIND) diet between the three groups. Lower BMI value, smaller hip and calf circumferences, lower Mini Nutritional Assessment (MNA) and Geriatric Nutritional Risk Index (GNRI) scores, and lower levels of total protein, albumin, globulin, and apolipoprotein A1 were associated with AD (all p < 0.05). Total protein and albumin levels had the greatest ability to distinguish AD from non-AD (AUC 0.80, 95% CI 0.74-0.84, p < 0.001), increased by combining calf circumference, MNA score and albumin level (AUC 0.83, 95% CI 0.77-0.88, p < 0.001). Albumin level had the greatest ability to distinguish NC from AD-MCI (AUC 0.75, 95% CI 0.67-0.82, p < 0.001), and MNA score greatest ability to distinguish AD-MCI from AD-D (AUC 0.72, 95% CI 0.65-0.78, p < 0.001). Nutritional status of AD patients is significantly compromised compared with normal controls, and tends to be worsened with AD progresses. Early identification and intervention of individuals with nutritional risk or malnutrition may be significantly beneficial for reducing the risk, development, and progression of AD.

Bile acid profile associated with CSF and PET biomarkers in Alzheimer’s disease

BackgroundRecent studies have shown that gut microbiota can affect the development of Alzheimer’s disease (AD) through various mechanisms. Bile acids (BAs), which are the final byproducts of cholesterol metabolism created through both the human body and gut microbiome, appear to be influenced by gut microbiota and may impact AD pathological characteristics such as the accumulation of tau and amyloid-β. We aimed to investigate the associations between various serum BAs and CSF biomarkers (including Aβ, total tau, and p-tau). Additionally, we sought to examine the longitudinal changes in brain Aβ and tau through PET imaging in relation to BAs profile.MethodsThe data of 828 subjects including 491 diagnosed with mild cognitive impairment (MCI), 119 patients diagnosed with AD, and 267 cognitively normal (CN) participants were obtained from ADNI. The baseline and longitudinal [18F] florbetapir and [18F] flortaucipir PET standard uptake value ratios (SUVR) measures were obtained to assess the accumulation of tau and Aβ. Moreover, baseline levels of serum BAs and CSF Aβ1–42, tau, and p-tau were used.ResultsAfter FDR correction we observed that five BAs level and relevant calculated ratios were associated with CSF p-tau and tau, three with CSF Aβ1–42. Furthermore, three BAs level and relevant calculated ratios were associated with the tau-PET rate of change, and two with the Aβ rate of change.ConclusionThe findings from our study suggest a correlation between altered profiles of BAs and CSF and imaging biomarkers associated with AD. These results provide supporting evidence for the link between the gut microbiome and the pathological features of AD.

Diffusion MRI relates to plasma Aβ42/40 in PET negative participants without dementia.

Magnetic resonance imaging (MRI) biomarkers are needed for indexing early biological stages of Alzheimer's disease (AD), such as plasma amyloid-β (Aβ42/40) positivity in Aβ positron emission tomography (PET) negative individuals. Diffusion free-water (FW) MRI was acquired in individuals with normal cognition (NC) and mild cognitive impairment (MCI) with Aβ plasma-/PET- (NC=22, MCI=60), plasma+/PET- (NC=5, MCI=20), and plasma+/PET+ (AD dementia=21) biomarker status. Gray and white matter FW and fractional anisotropy (FAt) were compared cross-sectionally and the relationships between imaging, plasma and PET biomarkers were assessed. Plasma+/PET- demonstrated increased FW (24 regions) and decreased FAt (66 regions) compared to plasma-/PET-. FW (16 regions) and FAt (51 regions) were increased in plasma+/PET+ compared to plasma+/PET-. Composite brain FW correlated with plasma Aβ42/40 and p-tau181. FW imaging changes distinguish plasma Aβ42/40 positive and negative groups, independent of group differences in cognitive status, Aβ PET status, and other plasma biomarkers (i.e., t-tau, p-tau181, glial fibrillary acidic protein, neurofilament light). Plasma Aβ42/40 positivity is associated with brain microstructure decline. Plasma+/PET- demonstrated increased FW in 24 total GM and WM regions. Plasma+/PET- demonstrated decreased FAt in 66 total GM and WM regions. Whole-brain FW correlated with plasma Aβ42/40 and p-tau181 measures. Plasma+/PET- demonstrated decreased cortical volume and thickness.

Increased extra-neurite conductivity of brain in patients with Alzheimer's disease: A pilot study

The objectives of this study were to investigate how the extra-neurite conductivity (EC) and intra-neurite conductivity (IC) were reflected in Alzheimer's disease (AD) patients compared with old cognitively normal (CN) people and patients with amnestic mild cognitive impairment (MCI) and to evaluate the association between those conductivity values and cognitive decline. To do this, high-frequency conductivity (HFC) at the Larmor frequency was obtained using MRI-based electrical property tomography (MREPT) and was decomposed into EC and IC using information of multi-shell multi-gradient direction diffusion tensor images. This prospective single-center study included 20 patients with mild or moderate AD, 25 patients with amnestic MCI, and 21 old CN participants. After decomposing EC and IC from HFC for all participants, we performed voxel-based and regions-of-interest analyses to compare conductivity between the three participant groups and to evaluate the association with either age or the Mini-Mental State Examination (MMSE) scores. We found increased EC in AD compared to CN and MCI. EC was significantly negatively associated with MMSE scores in the insula, and middle temporal gyrus. EC might be used as an imaging biomarker for helping to monitor cognitive function.

Machine Learning Predicts Conversion from Normal Aging to Mild Cognitive Impairment Using Medical History, APOE Genotype, and Neuropsychological Assessment.

Identifying individuals at risk for mild cognitive impairment (MCI) is of urgent clinical need. This study aimed to determine whether machine learning approaches could harness longitudinal neuropsychology measures, medical data, and APOEɛ4 genotype to identify individuals at risk of MCI 1 to 2 years prior to diagnosis. Data from 676 individuals who participated in the 'APOE in the Predisposition to, Protection from and Prevention of Alzheimer's Disease' longitudinal study (N = 66 who converted to MCI) were utilized in supervised machine learning algorithms to predict conversion to MCI. A random forest algorithm predicted conversion 1-2 years prior to diagnosis with 97% accuracy (p = 0.0026). The global minima (each individual's lowest score) of memory measures from the 'Rey Auditory Verbal Learning Test' and the 'Selective Reminding Test' were the strongest predictors. This study demonstrates the feasibility of using machine learning to identify individuals likely to convert from normal cognition to MCI.

Does the Cognitive Change Index Predict Future Cognitive and Clinical Decline? Longitudinal Analysis in a Demographically Diverse Cohort.

The Cognitive Change Index (CCI) is a widely-used measure of self-perceived cognitive ability and change. Unfortunately, it is unclear if the CCI predicts future cognitive and clinical decline. We evaluated baseline CCI to predict transition from normal cognition to cognitive impairment in nondemented older adults and in predementia groups including, subjective cognitive decline, motoric cognitive risk syndrome, and mild cognitive impairment. Different versions of the CCI were assessed to uncover any differential risk sensitivity. We also examined the effect of ethnicity/race on CCI. Einstein Aging Study participants (N = 322, Mage = 77.57±4.96, % female=67.1, Meducation = 15.06±3.54, % non-Hispanic white = 46.3) completed an expanded 40-item CCI version (CCI-40) and neuropsychological evaluation (including Clinical Dementia Rating Scale [CDR], Montreal Cognitive Assessment, and Craft Story) at baseline and annual follow-up (Mfollow-up=3.4 years). CCI-40 includes the original 20 items (CCI-20) and the first 12 memory items (CCI-12). Linear mixed effects models (LME) and generalized LME assessed the association of CCI total scores at baseline with rate of decline in neuropsychological tests and CDR. In the overall sample and across predementia groups, the CCI was associated with rate of change in log odds on CDR, with higher CCI at baseline predicting faster increase in the odds of being impaired on CDR. The predictive validity of the CCI broadly held across versions (CCI-12, 20, 40) and ethnic/racial groups (non-Hispanic black and white). Self-perception of cognitive change on the CCI is a useful marker of dementia risk in demographically/clinically diverse nondemented samples. All CCI versions successfully predicted decline.

Abstract A030: The Montreal Cognitive Assessment (MoCA) administered virtually or via Duo APP in primary brain tumor patients: a preliminary analysis

Abstract Background: Primary brain tumor (PBT) patients experience cognitive dysfunction (CD) because of both their tumor and its treatment. Evaluating CD in PBT populations may inform patient outcomes and clinical management and allow exploration of biologic underpinnings associated with its occurrence. The Montreal Cognitive Assessment (MoCA) is a brief assessment of objective measures of cognition that evaluates 8 specific cognitive domains. Our group has previously reported the feasibility of MoCA, and this study includes using the newly developed MoCA Duo APP to evaluate and report the cognitive function of patients with PBT. Associations between total MoCA scores and clinical and demographic characteristics were explored. Methods: This cohort consisted of adult PBT patients (n=172) enrolled in the NCI-NOB Natural History Study (NCT02851706: PI T. Armstrong). Assessments were performed between February 2020 and July 2023 by MoCA certified assessors and administered in the clinic or via telehealth. MoCA scores range from 0 to 30 (normal cognition ≥ 26). Healthcare providers collected demographic and clinical characteristics, with Karnofsky performance status (KPS) scores categorized as poor (≤ 80) or good (≥ 90). Descriptive statistics, independent t-tests, one-way ANOVAs, and Pearson’s correlation were conducted using IBM SPSS Statistics software. Results: The majority of patients were male (58%), white (81%), had ≥12 years of education (87%), had high-grade (3/4) tumors (65%), and a good KPS score (68%). The mean MoCA score was 25 (median: 26; range: 6-30) with a mean completion time of 10.7 minutes (median: 10; range: 6.1-41.1). Forty-two percent (n=73) were classified as abnormal. The median scores were lowest in the visuospatial/executive (4/5; range: 0-5), abstraction (2/3; range: 0-3) and delayed recall (4/5; range: 0-5) domains. A univariate analysis identified lower MoCA scores in patients with high-grade tumors (p&amp;lt;0.001), poor KPS scores (p&amp;lt;0.001), who underwent two or more surgeries (p=0.004) or treatments (p=0.038), had recurrence (p=0.002), had progression on current imaging (p=0.017), and had current anticonvulsant (p=0.014) and corticosteroid use (p=0.036). Age at diagnosis (r=-0.157, p=0.021) and at visit (r=-0.193, p=0.005) had a weak negative correlation with MoCA scores. Conclusions: Our results indicate that patient age, tumor progression, treatment, and concomitant medications are associated with MoCA scores. Further analysis will evaluate tumor methylation status and blood-based biomarkers associated with occurrence to develop prediction models that can be evaluated in larger cohorts. Future investigations should compare MoCA scores to other forms of cognitive assessment, including patient-reported outcomes, incorporating multiple timepoints to monitor CD in patients throughout their treatment and survivorship care. Citation Format: McKenzie C. Kauss, Elizabeth Vera, Kimberly Reinhart, Hope Miller, Jaime Garcia, Morgan Johnson, Madhura V. Managoli, Maeve Pascoe, Kaitlynn Slattery, Alex R. Wollet, Mark R. Gilbert, Alvina Acquaye-Mallory, Terri S. Armstrong, Vivian A. Guedes. The Montreal Cognitive Assessment (MoCA) administered virtually or via Duo APP in primary brain tumor patients: a preliminary analysis [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A030.

An effective diagnosis of schizophrenia using kernel ridge regression-based optimized RVFL classifier

Schizophrenia (SCZ) is a severe mental and debilitating neuropsychiatric disorder that disrupts a person’s thought processes, emotions, and behavior. Due to misdiagnosis, self-denial, and social stigma, many SCZ cases go untreated. Magnetic resonance imaging (MRI) is an excellent noninvasive tool for soft tissue contrast imaging because it provides crucial data on tissue structure size, position, and shape. The Resnet50 network is a deep residual learning framework used for feature extraction. Random-vector functional link network (RVFL) is an example of a single-hidden-layer feedforward network in which input features and hidden layer features are fed to the output layer. In this paper, we introduced a kernel ridge regression-based random vector functional link (KRR-RVFL) classifier which focuses on addressing the linearity issues in RVFL by designating the kernel function in the input layer for the precise diagnosis of SCZ. The genetic algorithm (GA) seeks to minimize the loss function by optimizing the weights and biases of the KRR-RVFL network. The classification performance is investigated on the SCZ and cognitive normal (CN) subjects, collected from the available open neuro platform, including 99 participants. The results of the suggested network show superior performance to the recent state-of-the-art networks in terms of accuracy 93.66%, sensitivity 92.22%, specificity 95.17%, precision 95.33%, F-measure 93.74%, and G-mean 93.68%. The performance metrics demonstrated the applicability of this framework for assisting clinicians in the automatic, precise evaluation of SCZ.

Neurovascular coupling impairment as a mechanism for cognitive deficits in COVID-19.

Components that comprise our brain parenchymal and cerebrovascular structures provide a homeostatic environment for proper neuronal function to ensure normal cognition. Cerebral insults (e.g. ischaemia, microbleeds and infection) alter cellular structures and physiologic processes within the neurovascular unit and contribute to cognitive dysfunction. COVID-19 has posed significant complications during acute and convalescent stages in multiple organ systems, including the brain. Cognitive impairment is a prevalent complication in COVID-19 patients, irrespective of severity of acute SARS-CoV-2 infection. Moreover, overwhelming evidence from in vitro, preclinical and clinical studies has reported SARS-CoV-2-induced pathologies in components of the neurovascular unit that are associated with cognitive impairment. Neurovascular unit disruption alters the neurovascular coupling response, a critical mechanism that regulates cerebromicrovascular blood flow to meet the energetic demands of locally active neurons. Normal cognitive processing is achieved through the neurovascular coupling response and involves the coordinated action of brain parenchymal cells (i.e. neurons and glia) and cerebrovascular cell types (i.e. endothelia, smooth muscle cells and pericytes). However, current work on COVID-19-induced cognitive impairment has yet to investigate disruption of neurovascular coupling as a causal factor. Hence, in this review, we aim to describe SARS-CoV-2's effects on the neurovascular unit and how they can impact neurovascular coupling and contribute to cognitive decline in acute and convalescent stages of the disease. Additionally, we explore potential therapeutic interventions to mitigate COVID-19-induced cognitive impairment. Given the great impact of cognitive impairment associated with COVID-19 on both individuals and public health, the necessity for a coordinated effort from fundamental scientific research to clinical application becomes imperative. This integrated endeavour is crucial for mitigating the cognitive deficits induced by COVID-19 and its subsequent burden in this especially vulnerable population.

Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

INTRODUCTION: Medical images still need to be examined by medical personnel, which is a prolonged and vulnerable progression. The dataset used included 4 classes of 6400 training and test MRI images each and was collected from Kaggle such as cognitively normal (CN), Mild Cognitive Impairment stage (MCI), moderate cognitive impairment (Moderate MCI), and Severe stage of cognitive impairment (AD).&#x0D; OBJECTIVES: There was a glaring underrepresentation of the Alzheimer Disease (AD) class. The accuracy and effectiveness of diagnoses can be improved with the use of neural network models.&#x0D; METHODS: In order to establish which CNN-based algorithm performed the multi-class categorization of the AD patient's brain MRI images most accurately. Thus, examine the effectiveness of the popular CNN-based algorithms like Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, and Faster R-CNN.&#x0D; RESULTS: On the confusion matrix, R-CNN performed the best.&#x0D; CONCLUSION: R-CNN is quick and offers a high precision of 98.67% with a low erroneous measure of 0.0133, as shown in the research.

A joint autoencoder and classifier deep neural network for AD and MCI classification

AbstractIn this article, we present a new approach to distinguish progressive mild cognitively impaired (pMCI) subjects, who eventually develop Alzheimer's disease (AD) from stable MCI (sMCI) subjects whose situation does not deteriorate into AD. The proposed approach combines the discriminating capabilities of classifiers and representation learning capacities of autoencoders into a unified architecture, and is hence termed as joint autoencoder and classifier deep neural network (JACDNN). JACDNN employs a single classifier and multiple autoencoders that are trained together to perform pattern classification. The classifier in JACDNN is trained using standard approaches to distinguish between subject from different classes using the binary cross entropy loss. The autoencoders in JACDNN, regularizes individual layers in the network used for classification to learn representations useful for reconstructing a given input. The performance of JACDNN has been evaluated on several machine learning problems pertaining to dementia, namely AD versus cognitively normal (CN) subjects, AD versus sMCI, CN versus pMCI, and pMCI versus sMCI. These problems are targeted using two datasets. The first dataset consist of gray matter (GM) features of subjects and the second dataset consist of combination of GM and white matter (WM) features. It is observed that better classification results are obtained when the classifier is built on GM and WM as compared with GM features alone. Performance comparison of JACDNN with other existing approaches has been conducted for these problems. The results clearly indicate that JACDNN performs better than other existing approaches for these problems.

Current Practices and Attitudes of Cardiac Nurses Regarding Cognitive Screening in Patients With Acute Coronary Syndrome

Cognitive impairment (CI) is common in patients with acute coronary syndrome (ACS) but is often undetected and may affect recovery and secondary prevention uptake. Nurses play a crucial role providing care for patients with ACS and promoting secondary prevention. This study aimed to explore current nursing practices and barriers regarding CI screening in patients with ACS. Cardiac nurses were recruited from three metropolitan teaching hospitals and two professional associations in Australia and undertook a 38-question purpose-built survey. A total of 95 nurses participated (mean age 38±13 years; 78% [n=74] female): 69 were registered nurses, and 48% had received CI training. Only 16% of nurses in our sample reported that they regularly screen for CI, and 23% reported that they never screen; however, 59% believed screening should be part of everyday practice. Nurses mostly screened when ward policy required admission/daily cognitive screening (34%) or when they suspected cognitive problems or decline (39%). Nurses in acute settings (vs non-acute) were nine times more likely to screen when adjusting for confounders. The typically used screening instruments assessed delirium/confusion and dementia but not milder CI. Common barriers to screening included communication difficulties, patients too unstable/unwell, time constraints requiring clinical care prioritisation, and being unaware of patients' normal cognition status. Screening practices for CI in the context of ACS were found to be suboptimal, with only 16% of nurses in our sample reporting regularly screening. The most used methods focus on screening for delirium. Given current practice, many CI cases will be missed, especially mild CI, which will negatively affect secondary prevention efforts. Further research is required to identify appropriate methods to implement routine screening within the nursing clinical workflow and establish a suitable screening tool.

Naturalistic assessment of everyday multitasking in Parkinson’s disease with and without mild cognitive impairment

Objective: Multitasking is an essential part of everyday functioning often not formally assessed by traditional neuropsychological tests. Although individuals with Parkinson’s disease (PD) experience both motor and cognitive difficulties, previous research has demonstrated more pronounced functional difficulties with the presence of mild cognitive impairment (PD-MCI). The current study compared individuals with PD-MCI, PD with normal cognition (PD-NC), and healthy controls on a naturalistic task of multitasking, the Day Out Task (DOT). Method: Participants were 38 healthy older adults (HOA), 23 individuals with PD-NC, and 15 individuals with PD-MCI. Participants completed a battery of neuropsychological tasks and the DOT. Informants also completed a self-reported questionnaire of participants’ everyday executive functioning. Results: Compared to PD-NC and HOA, participants with PD-MCI were less accurate and efficient and took longer to complete the DOT. After controlling for motor performance, only DOT accuracy remained worse, with poorer accuracy resulted from more subtasks being left incomplete or being completed inaccurately by the PD-MCI group. DOT sequencing was a significant predictor of informant reported everyday dysexecutive symptoms. Conclusions: The findings highlight that individuals with PD-MCI are likely to experience difficulties completing complex everyday tasks due to both motor and cognitive impairments. Clinicians may therefore recommend strategies to support efficiency and accuracy in complex tasks of everyday functioning in treatment considerations.

Dual-task turn velocity - a novel digital biomarker for mild cognitive impairment and dementia.

Disorders associated with cognitive impairment impose a significant burden on both families and society. Previous studies have indicated that gait characteristics under dual-task as reliable markers of early cognitive impairment. Therefore, digital gait detection has great potential for future cognitive screening. However, research on digital biomarkers based on smart devices to identify cognitive impairment remains limited. The aim of this study is to explore digital gait biomarkers by utilizing intelligent wearable devices for discriminating mild cognitive impairment and dementia. This study included 122 subjects (age: 74.7 ± 7.7 years) diagnosed with normal cognition (NC, n = 38), mild cognitive impairment (MCI, n = 42), or dementia (n = 42). All subjects underwent comprehensive neuropsychological assessments and cranial Magnetic Resonance Imaging (MRI). Gait parameters were collected using validated wearable devices in both single-task and dual-task (DT). We analyzed the ability of gait variables to predict MCI and dementia, and examined the correlations between specific DT-gait parameters and sub-cognitive functions as well as hippocampal atrophy. Our results demonstrated that dual-task could significantly improve the ability to predict cognitive impairment based on gait parameters such as gait speed (GS) and stride length (SL). Additionally, we discovered that turn velocity (TV and DT-TV) can be a valuable novel digital marker for predicting MCI and dementia, for identifying MCI (DT-TV: AUC = 0.801, sensitivity 0.738, specificity 0.842), and dementia (DT-TV: AUC = 0.923, sensitivity 0.857, specificity 0.842). The correlation analysis and linear regression analysis revealed a robust association between DT-TV and memory function, as well as the hippocampus atrophy. This study presents a novel finding that DT-TV could accurately identify varying degrees of cognitive impairment. DT-TV is strongly correlated with memory function and hippocampus shrinkage, suggests that it can accurately reflect changes in cognitive function. Therefore, DT-TV could serve as a novel and effective digital biomarker for discriminating cognitive impairment.

Postural Control Characteristics in Alzheimer's Disease, Dementia With Lewy Bodies, and Vascular Dementia.

Dementia often results in postural control impairment, which could signify central nervous system dysfunction. However, no studies have compared postural control characteristics among various types of dementia. This study aimed to compare static postural control in patients with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and vascular dementia (VaD). Cross-sectional relationship between the clinical diagnoses (AD, DLB, VaD, or normal cognition [NC]) of outpatients at a memory clinic and their upright postural control characteristics were examined. In the postural control test, participants were instructed to maintain a static upright standing on a stabilometer for 60 seconds under the eyes-open and eyes-closed conditions. Forty postural control parameters, including distance, position, and velocity in the anterior-posterior and medio-lateral directions, derived from the trajectory of the center of mass sway, were calculated. The characteristics of each type of dementia were compared to those of NC, and the differences among the 3 types of dementia were evaluated using linear regression models. The study included 1789 participants (1206 with AD, 111 with DLB, 49 with VaD, and 423 with NC). Patients with AD exhibited distinct postural control characteristics, particularly in some distance and velocity parameters, only in the eyes-closed condition. Those with DLB exhibited features in the mean position in the anterior-posterior direction. In patients with VaD, significant differences were observed in most parameters, except the power spectrum. Patients with AD, DLB, and VaD display disease-specific postural control characteristics when compared to cognitively normal individuals.

Prevalence of mild behavioural impairment and its association with cognitive and functional impairment in normal cognition, mild cognitive impairment, and mild Alzheimer's dementia.

Mild behavioural impairment (MBI) is an emergent and persistent neuropsychiatric symptom (NPS) in subjects aged 50 and older who are at risk for cognitive decline. We examined the prevalence of MBI across the spectrum from cognitively normal (CN), mild cognitive impairment (MCI), to dementia, and further investigated the association between the MBI domain and cognitive and functional impairment. MBI was assessed in 2337 elderly patients in the Alzheimer's Disease Neuroimaging Initiative database (mean age, 73.04 years; 52.8% male). Among the subjects, 868 (37.1%) had normal cognition, 1066 (45.6%) had MCI, and 403 (17.2%) had mild Alzheimer's dementia (AD). MBI was evaluated in accordance with the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment diagnostic criteria for MBI, utilising the Neuropsychiatric Inventory. We compared the prevalence of the MBI domain with CN using multinominal logistic regression analysis and further quantified the magnitude of the association between MCI/AD and the MBI domains by calculating the population attributable risk (PAR). We assessed the association between the MBI domains and cognitive and functional impairment using simultaneous linear regression analysis. The most common MBI domains in each diagnostic group were affective dysregulation followed by impulse dyscontrol, decreased motivation, social inappropriateness, and abnormal perception or thought content. The PARs for MBI domains in subjects with MCI or AD were respectively: 16.60% and 24.34% for affective dysregulation; 3.72% and 18.06% for impulse dyscontrol; 4.78% and 14.13% for decreased motivation, 1.91% and 2.29% for social inappropriateness; and 0.68% and 3.85% for abnormal perception or thought content. All MBI domains except for social inappropriateness were significantly associated with a higher 11-item Alzheimer's Disease Assessment Scale-Cognitive Subscale total score. All MBI domains were significantly associated with a higher Functional Activities Questionnaire total score. Our findings show that MBI is highly prevalent across subjects with CN, MCI, and AD and is associated with cognitive and functional decline. MBI could be a crucial clinical phenotype relevant to the risk of cognitive and functional impairment, and provides a useful dimension pertinent to diagnostic approaches.

Improvements in performance based measures of functional capacity and cognition after computerized functional skills training in older people with mild cognitive impairment and healthy comparators

With no pharmacological treatments for Mild Cognitive impairment (MCI), computerized training strategies have been attempted. A computerized skills training intervention, FUNSAT, previously produced training-related gains in cognition in MCI and in comparators with normal cognition (NC). A new remotely delivered version of FUNSAT was administered to a new sample of participants with NC and MCI. Outcomes measures included cognition (BAC) and functional capacity (VRFCAT) to examine training transfer. Participants with MCI (n = 92) and NC (n = 72) trained for up to 12 weeks on FUNSAT. Half the MCI participants started with 3 weeks of computerized cognitive training (CCT). Baseline, post-training, and 30-day follow-up scores on cognition and functional capacity were compared. Participants improved on both cognition (d = 0.80) and functional capacity (d = 0.64), with no differences in training gains across MCI and NC, although treatment with CCT in MCI was associated with similar gains with fewer FUNSAT training sessions. This is the first treatment study in MCI to demonstrate transfer to untrained measures of functional capacity. NC improved in cognition and functional capacity with skills training alone. These findings have implications for other conditions, such as schizophrenia, where functional capacity is a treatment target.