Mild Cognitive Impairment Participants Research Articles

The Effects of a Nonpharmacological Intervention Practice for Older Adults with Mild Cognitive Impairment and Their Family Caregivers in China.

Mild cognitive impairment (MCI) marks a critical phase in the progression to dementia. In our study, social workers utilized the Multicomponent Nonpharmacological Intervention Approach (MCNIA) to aid MCI participants (N = 52) and their caregivers, dividing into intervention and control groups. The intervention group underwent an additional regimen of non-pharmacological therapies besides pharmacological treatment. Our findings highlighted that: 1) MCNIA significantly enhanced cognitive and daily living abilities in the intervention group; 2) Caregivers experienced reduced burdens and improved social support; 3) Correlation analyses involving biomarkers indicated that MCNIA was particularly effective in alleviating depression in those with slightly more severe cognitive impairment.

Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients.

Plasma biomarkers of Alzheimer's disease (AD) pathology, neurodegeneration, and neuroinflammation are ideally suited for secondary prevention programs in self-sufficient persons at-risk of dementia. Plasma biomarkers have been shown to be highly correlated with traditional imaging biomarkers. However, their comparative predictive value versus traditional AD biomarkers is still unclear in cognitively unimpaired (CU) subjects and with mild cognitive impairment (MCI). Plasma (Aβ42/40, p-tau181, p-tau231, NfL, and GFAP) and neuroimaging (hippocampal volume, centiloid of amyloid-PET, and tau-SUVR of tau-PET) biomarkers were assessed at baseline in 218 non-demented subjects (CU = 140; MCI = 78) from the Geneva Memory Center. Global cognition (MMSE) was evaluated at baseline and at follow-ups up to 5.7 years. We used linear mixed-effects models and Cox proportional-hazards regression to assess the association between biomarkers and cognitive decline. Lastly, sample size calculations using the linear mixed-effects models were performed on subjects positive for amyloid-PET combined with tau-PET and plasma biomarker positivity. Cognitive decline was significantly predicted in MCI by baseline plasma NfL (β=-0.55), GFAP (β=-0.36), hippocampal volume (β = 0.44), centiloid (β=-0.38), and tau-SUVR (β=-0.66) (all p < 0.05). Subgroup analysis with amyloid-positive MCI participants also showed that only NfL and GFAP were the only significant predictors of cognitive decline among plasma biomarkers. Overall, NfL and tau-SUVR showed the highest prognostic values (hazard ratios of 7.3 and 5.9). Lastly, we demonstrated that adding NfL to the inclusion criteria could reduce the sample sizes of future AD clinical trials by up to one-fourth in subjects with amyloid-PET positivity or by half in subjects with amyloid-PET and tau-PET positivity. Plasma NfL and GFAP predict cognitive decline in a similar manner to traditional imaging techniques in amyloid-positive MCI patients. Hence, even though they are non-specific biomarkers of AD, both can be implemented in memory clinic workups as important prognostic biomarkers. Likewise, future clinical trials might employ plasma biomarkers as additional inclusion criteria to stratify patients at higher risk of cognitive decline to reduce sample sizes and enhance effectiveness.

Characterizing Neighborhood Vulnerabilities in Mild Cognitive Impairment using the Environmental Justice Index

Characterizing Neighborhood Vulnerabilities in Mild Cognitive Impairment using the Environmental Justice Index

Effect of Metformin on Plasma and Cerebrospinal Fluid Biomarkers in Non-Diabetic Older Adults with Mild Cognitive Impairment Related to Alzheimer's Disease.

Alzheimer's disease (AD) is a complicated condition involving multiple metabolic and immunologic pathophysiological processes that can occur with the hallmark pathologies of amyloid-β, tau, and neurodegeneration. Metformin, an anti-diabetes drug, targets several of these disease processes in in vitro and animal studies. However, the effects of metformin on human cerebrospinal fluid (CSF) and plasma proteins as potential biomarkers of treatment remain unexplored. Using proteomics data from a metformin clinical trial, identify the impact of metformin on plasma and CSF proteins. We analyzed plasma and CSF proteomics data collected previously (ClinicalTrials.gov identifier: NCT01965756, conducted between 2013 and 2015), and conduced bioinformatics analyses to compare the plasma and CSF protein levels after 8 weeks of metformin or placebo use to their baseline levels in 20 non-diabetic patients with mild cognitive impairment (MCI) and positive AD biomarkers participants. 50 proteins were significantly (unadjusted p < 0.05) altered in plasma and 26 in CSF after 8 weeks of metformin use, with 7 proteins in common (AZU1, CASP-3, CCL11, CCL20, IL32, PRTN3, and REG1A). The correlation between changes in plasma and CSF levels of these 7 proteins after metformin use relative to baseline levels was high (r = 0.98). The proteins also demonstrated temporal stability. Our pilot study is the first to investigate the effect of metformin on plasma and CSF proteins in non-diabetic patients with MCI and positive AD biomarkers and identifies several candidate plasma biomarkers for future clinical trials after confirmatory studies.

An examination of semantic performance in mild cognitive impairment progressors and nonprogressors.

Mild cognitive impairment (MCI) is a risk factor for developing Alzheimer's disease (AD), and about half of older people with MCI will progress to AD within the next 5 years. The aim of the present study was to compare the semantic performance of MCI progressors (MCI-p) and nonprogressors (MCI-np). The hypothesis was that MCI-p would present with poorer semantic performance relative to MCI-np at baseline, indicating that semantic deficits may increase the risk of future decline toward AD. Fifty-six MCI participants (aged 65-89) from the Consortium for Early Identification of Alzheimer's Disease-Quebec study were analyzed, with 18 progressing and 38 remaining stable over 2 years. Analysis of covariance assessed their initial semantic and nonsemantic cognitive performance, and mixed analyses of variance gauged longitudinal patterns of cognitive decline at the 2-year follow-up. In the semantic domain, MCI-p performed significantly worse than MCI-np at baseline on two semantic tests (category fluency and object decision). In other cognitive domains, MCI-p performed worse than MCI-np on a test of executive functions (cognitive flexibility) but showed similar performance on a test of episodic memory. There were no significant differences between groups in the rates of progression on semantic tests over the 2-year period, but a steeper decline was observed in MCI-p at follow-up on tests of global cognition, episodic memory, and processing speed. This suggest that MCI patients who present with semantic memory impairment in addition to episodic memory impairment are at greater risk of future progression to AD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Short-Term Practice Effects on Cognitive Tests Across the Late Life Cognitive Spectrum and How They Compare to Biomarkers of Alzheimer's Disease.

Practice effects on cognitive testing in mild cognitive impairment (MCI) and Alzheimer's disease (AD) remain understudied, especially with how they compare to biomarkers of AD. The current study sought to add to this growing literature. Cognitively intact older adults (n = 68), those with amnestic MCI (n = 52), and those with mild AD (n = 45) completed a brief battery of cognitive tests at baseline and again after one week, and they also completed a baseline amyloid PET scan, a baseline MRI, and a baseline blood draw to obtain APOE ɛ4 status. The intact participants showed significantly larger baseline cognitive scores and practice effects than the other two groups on overall composite measures. Those with MCI showed significantly larger baseline scores and practice effects than AD participants on the composite. For amyloid deposition, the intact participants had significantly less tracer uptake, whereas MCI and AD participants were comparable. For total hippocampal volumes, all three groups were significantly different in the expected direction (intact > MCI > AD). For APOE ɛ4, the intact had significantly fewer copies of ɛ4 than MCI and AD. The effect sizes of the baseline cognitive scores and practice effects were comparable, and they were significantly larger than effect sizes of biomarkers in 7 of the 9 comparisons. Baseline cognition and short-term practice effects appear to be sensitive markers in late life cognitive disorders, as they separated groups better than commonly-used biomarkers in AD. Further development of baseline cognition and short-term practice effects as tools for clinical diagnosis, prognostic indication, and enrichment of clinical trials seems warranted.

Examining a Preclinical Alzheimer's Cognitive Composite for Telehealth Administration for Reliability Between In-Person and Remote Cognitive Testing with Neuroimaging Biomarkers.

The preclinical Alzheimer's cognitive composite (PACC) was developed for in-person administration to capture subtle cognitive decline. At the outset of the COVID-19 pandemic, cognitive testing was increasingly performed remotely by telephone or video administration. It is desirable to have a harmonized composite measurement derived from both in-person and remote assessments for identifying cognitive changes and to examine its relationship with common neuroimaging biomarkers. We defined a telehealth compatible PACC (tPACC) and examined its relationship with neuroimaging biomarkers related to neurodegeneration, brain function and perfusion, white matter integrity, and amyloid-β. We examined 648 participants' neuroimaging and in-person and remote cognitive testing data from the Wake Forest Alzheimer's Disease Research Center's Clinical Core cohort (observational study) to calculate a modified PACC (PACC5-RAVLT) score and tPACC scores (in-person and remote). We performed Spearman/intraclass correlation coefficient (ICC) analyses for reliability of tPACC scores and linear regression models to evaluate associations between tPACC and neuroimaging. Bland-Altman plots for agreement were constructed across cognitively normal and impaired (mild cognitive impairment and dementia) participants. There was a significant positive relationship between tPACCin-person and PACC5-RAVLT (Overall group: r2 = 0.94, N = 648), and tPACCin-person and tPACCremote (validation subgroup: ICC = 0.82, n = 53). Overall, tPACC showed significant associations with brain thickness/volume, gray matter perfusion, white matter free water, and amyloid-β deposition. There is a good agreement between tPACCand PACC5-RAVLTfor cognitively normal and impaired individuals. The tPACC is associated with common neuroimaging markers of Alzheimer's disease.

Effects of Lexical Priming on Priming Type and Presence of Distractors in Persons with Mild Cognitive Impairment

Objectives: The purpose of the study is to examine if the lexical priming effect is valid in persons with mild cognitive impairment (MCI). In addition, this study attempted to examine the correlation between working memory and sentence production ability for MCI and healthy adults (HA).Methods: Fifteen individuals with MCI and sixteen HA described action pictures in sentences following lexical (agent or theme) primes. Priming effect was analyzed in terms of priming type and presence of distractor.Results: 1) Persons with MCI did show significantly lower proportion of passive sentences compared to HA. Overall, the proportion of passives were significantly higher in the theme primed condition versus the agent primed condition. 2) The difference in the presence of distractor stimuli in the agent primed condition was significantly larger than in the theme primed condition. 3) It was observed that the better the working memory ability of MCI participants, the higher the production of active sentences when compared with passive sentences in the theme primed condition.Conclusion: Persons with MCI used word-driven information as a strategy of sentence production like HA did. In addition, MCI participants showed difficulty in production of complex structures such as passive sentences. In previous research, most of the studies examined the priming effect through structural priming tasks for HA and aphasia patients. The results of this study also showed the effect of lexical priming in persons with MCI, and the finding of lower production of passive sentences in persons with MCI compared to HA emphasized the linguistic deficits that may appear in individuals with MCI.

Associations of Frailty with Neuropsychiatric Symptoms of Alzheimer's Disease: A Longitudinal Study.

Frailty is a vulnerability state increasing the risk of many adverse health outcomes, but little is known about the effects of frailty on neuropsychiatric health. To explore the associations between frailty and the risk of neuropsychiatric symptoms (NPSs) in Alzheimer's disease (AD), especially in its different clinical stages. We included 2,155 individuals assessed using modified frailty index-11 (mFI-11), Neuropsychiatric Inventory (NPI) and Neuropsychiatric Inventory Questionnaire (NPI-Q) in the Alzheimer's Disease Neuroimaging Initiative (ADNI). The relationships between frailty and NPSs were explored with logistic regression models and Cox proportional hazard regression models. Causal mediation analyses were conducted to explore the mediation factors between frailty and NPSs. Among mild cognitive impairment (MCI) participants, frailty was cross-sectionally associated with an increased risk of apathy, and longitudinally associated with increased risk of depression and apathy. Among AD participants, frailty was cross-sectionally associated with increased risk of depression and anxiety, and longitudinally associated with an increased risk of apathy. Among participants with cognitive progression, frailty was associated with increased risk of depression and apathy. In MCI participants, the influence of frailty on NPSs was partially mediated by hippocampus volume, whole brain volume, and monocytes, with mediating proportions ranging from 8.40% to 9.29%. Frailty was associated with NPSs such as depression, anxiety, and apathy among MCI, AD, and cognitive progression participants. Atrophy of the hippocampus and whole brain, as well as peripheral immunity may be involved in the potential mechanisms underlying the above associations.

Feasibility and usefulness of cognitive monitoring using a new home-based cognitive test in mild cognitive impairment: a prospective single arm study

BackgroundThe risk of dementia is increased in subjects with mild cognitive impairment (MCI). Despite the plethora of in-person cognitive tests, those that can be administered over the phone are lacking. We hypothesized that a home-based cognitive test (HCT) using phone calls would be feasible and useful in non-demented elderly. We aimed to assess feasibility and validity of a new HCT as an optional cognitive monitoring tool without visiting hospitals.MethodsOur study was conducted in a prospective design during 24 weeks. We developed a new HCT consisting of 20 questions (score range 0–30). Participants with MCI (n = 38) were consecutively enrolled and underwent regular HCTs during 24 weeks. Associations between HCT scores and in-person cognitive scores and Alzheimer’s disease (AD) biomarkers were evaluated. In addition, HCT scores in MCI participants were cross-sectionally compared with age-matched cognitively normal (n = 30) and mild AD dementia (n = 17) participants for discriminative ability of the HCT.ResultsHCT had good intra-class reliability (test-retest Cronbach’s alpha 0.839). HCT scores were correlated with the Mini-Mental State Examination (MMSE), verbal memory delayed recall, and Stroop test scores but not associated with AD biomarkers. HCT scores significantly differed among cognitively normal, MCI, and mild dementia participants, indicating its discriminative ability. Finally, 32 MCI participants completed follow-up evaluations, and 8 progressed to dementia. Baseline HCT scores in dementia progressors were lower than those in non-progressors (p = 0.001).ConclusionThe feasibility and usefulness of the HCT were demonstrated in elderly subjects with MCI. HCT could be an alternative option to monitor cognitive decline in early stages without dementia.

A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease.

This study introduces a novel brain connectome matrix, track-weighted PET connectivity(twPC) matrix, which combines positron emission tomography (PET) and diffusion magnetic resonance imaging data to compute a PET-weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre-tracking streamline point-to-point connectivity calculated from diffusion MRI (dMRI). Using tau-PET, dMRI and T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity (SC) and twPC matrices were computed and analysed using the network-based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment (MCI), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC. The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD, and networks with increased twPC were identified in early MCI, late MCI and AD. The altered network topologies were mostly different between twPC and SC, although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti-correlation with disease progression. twPC provides a new means for analysing subject-specific PET and MRI-derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. PRACTITIONER POINTS: New method is proposed to compute patient-specific PET connectome guided by MRI fibre-tracking. Track-weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.

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.

Predicting progression of cognitive decline to dementia using dyadic patterns of subjective reporting: evidence from the CompAS longitudinal study.

To analyze the validity of self and informant reports, depressive symptomatology, and some sociodemographic variables to predict the risk of cognitive decline at different follow-up times. A total of 337 participants over 50 years of age included in the CompAS and classified as Cognitively Unimpaired (CU), Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI) groups were assessed at baseline and three follow-ups. A short version of the QAM was administered to assess the severity of subjective cognitive complaints (SCCs), and the GDS-15 was used to evaluate the depressive symptoms. At each follow-up assessment, participants were reclassified according to the stability, regression or progression of their conditions. Logistic regression analysis was used to predict which CU, SCD and MCI participants would remain stable, regress or progress at a 3rd follow-up by using self- and informant-reported complaints, depressive symptomatology, age and education at baseline and 2nd follow-ups as the predictive variables. Overall, self-reported complaints predicted progression between the asymptomatic and presymptomatic stages. As the objective deterioration increased, i.e., when SCD progressed to MCI or dementia, the SCCs reported by informants proved the best predictors of progression. Depressive symptomatology was also a predictor of progression from CU to SCD and from SCD to MCI. A late increase in self-reported complaints make valid estimates to predict subjective decline at asymptomatic stages. However, an early increase in complaints reported by informants was more accurate in predicting objective decline from asymptomatic stages. Both, early and late decrease in self-reported complaints successfully predict dementia from prodromic stage. Only late decrease in self-reported complaints predict reversion from prodromic and pre-symptomatic stages.

A screening method for mild cognitive impairment in elderly individuals combining bioimpedance and MMSE.

We investigated a screening method for mild cognitive impairment (MCI) that combined bioimpedance features and the Korean Mini-Mental State Examination (K-MMSE) score. Data were collected from 539 subjects aged 60 years or older at the Gwangju Alzheimer's & Related Dementias (GARD) Cohort Research Center, A total of 470 participants were used for the analysis, including 318 normal controls and 152 MCI participants. We measured bioimpedance, K-MMSE, and the Seoul Neuropsychological Screening Battery (SNSB-II). We developed a multiple linear regression model to predict MCI by combining bioimpedance variables and K-MMSE total score and compared the model's accuracy with SNSB-II domain scores by the area under the receiver operating characteristic curve (AUROC). We additionally compared the model performance with several machine learning models such as extreme gradient boosting, random forest, support vector machine, and elastic net. To test the model performances, the dataset was divided into a training set (70%) and a test set (30%). The AUROC values of SNSB-II scores were 0.803 in both sexes, 0.840 for males, and 0.770 for females. In the combined model, the AUROC values were 0.790 (0.773) for males (and females), which were significantly higher than those from the model including MMSE scores alone (0.723 for males and 0.622 for females) or bioimpedance variables alone (0.640 for males and 0.615 for females). Furthermore, the accuracies of the combined model were comparable to those of machine learning models. The bioimpedance-MMSE combined model effectively distinguished the MCI participants and suggests a technique for rapid and improved screening of the elderly population at risk of cognitive impairment.

Prevalence of orofacial pain in individuals with mild cognitive impairment or dementia: A systematic review and meta-analysis.

This systematic review investigated the prevalence of orofacial pain in patients with mild cognitive impairment (MCI) or dementia. The search was conducted in five databases (Medline (Ovid), Embase (Ovid), CINAHL, Scopus and LILACS), in three grey literature sources and in included articles' reference lists. Three independent reviewers performed study selection, quality appraisal and data extraction. The risk of bias was assessed with the National Institutes of Health tool. Prevalence was calculated using the random-effects model. Subgroup analysis and meta-regression were used to explore the heterogeneity of results. The database and grey literature search led to 12 246 results, from which nine studies were included; a further four were selected through citation searching. The total sample comprised 6115 patients with dementia and 84 with MCI. All studies had high risk of bias. The overall estimated pooled prevalence of orofacial pain among dementia participants was 19.0% (95% CI, 11.0%-27.0%; I2 , 97.1%, P < .001). Only one study included MCI participants, among which the prevalence of orofacial pain was 20.5%. Subgroup analysis demonstrated that the different sources of diagnosis might explain the heterogeneity. A higher prevalence of orofacial pain was observed in dementia participants aged over 80 years or living in nursing homes. Meta-regression analysis showed a nonlinear relationship between age and the prevalence of orofacial pain. The pooled data from the primary studies revealed that 2 out of 10 patients with dementia have orofacial pain. Further research is needed to clarify the magnitude in individuals with MCI.

Choroid plexus enlargement in mild cognitive impairment on MRI: a large cohort study.

Previous studies have shown possible choroid plexus (CP) dysfunction in Alzheimer's disease (AD) and highlighted CP enlargement on magnetic resonance imaging (MRI) as a predictive factor of AD. However, few studies have assessed the relationship between CP volume (CPV) and mild cognitive impairment (MCI). In this large elderly population study, we investigated the changes in CPV in patients with MCI using MRI above 65 years. This cross-sectional study included 2144 participants (median age, 69 years; 60.9% females) who underwent 3T MRI; they were grouped as 218 MCI participants and 1904 cognitively healthy controls. The total intracranial volume (ICV), total brain volume (TBV), CPV, hippocampal volume (HV), and lateral ventricle volume (LVV) were calculated. CPV/ICV was a significant independent predictor of MCI (p < 0.01) after adjusting for potential confounders (age, sex, hypertension, hyperlipidemia, diabetes, and education level). The CPV/ICV ratio was also a significant independent predictor of MCI after adjusting for the TBV/ICV ratio (p = 0.022) or HV/ICV ratio (p = 0.017), in addition to potential confounders. The CPV was significantly correlated with the LVV (r = 0.97, p < 0.01). We identified a relationship between CPV and MCI, which could not be explained by the degree of brain atrophy. Our results support CP dysfunction in MCI. Choroid plexus volume measurement may serve as a valuable imaging biomarker for diagnosing and monitoring mild cognitive impairment. The enlargement of the choroid plexus, independent of brain atrophy, suggests its potential role in mild cognitive impairment pathology. • The study examines choroid plexus volume in relation to cognitive decline in elderly. • Enlarged choroid plexus volume independently indicates mild cognitive impairment presence. • Choroid plexus volume could be a specific biomarker for early mild cognitive impairment diagnosis.

Brain activation during scene encoding fMRI in the Alzheimer’s disease continuum: Association with amyloid and tau burden in PE

Background and Hypothesis:This project assessed brain activation during a scene encoding task in 4 groups: older adults who were cognitively normal (CN), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia due to Alzheimer’s disease (AD). Associations between scene encoding related brain activation and tau, amyloid, and other biomarkers were analyzed. Our hypothesis was that higher levels of cerebral tau and amyloid would be associated with reduced scene encoding activation. In addition, we hypothesized that scene encoding activation would be significantly different between cognitively normal and cognitively impaired groups.&#x0D; Methods:234 individuals from the Indiana Memory and Aging Study (79 CN, 67 SCD, 70 MCI, and 18 AD) completed structural and functional MRI, clinical/cognitive assessment and biomarkers; 155 underwent amyloid ([18F]florbetapir/[18F]florbetaben) PET, while 111 also underwent [18F]flortaucipir PET. For the fMRI scene encoding task, participants were asked to view and remember a set of images. A one-way ANOVA test was used to analyze scene encoding related activation differences among the 4 groups. Regression was used to identify associations between scene encoding activation and tau and amyloid deposition.&#x0D; Results:Significant differences in activation were observed between the MCI and CN groups, including less activation in widespread regions during the task and reduced deactivation in the default mode network (DMN) in MCI participants relative to CN. Significant associations between higher amyloid and tau deposition and altered scene encoding activation were also observed.&#x0D; Conclusion and Potential Impact:Cognitive decline is associated with activation changes during scene encoding, as well as reduced deactivation in the DMN, especially in the posterior cingulate region. Higher cerebral amyloid and tau deposition predicted decreased scene encoding related activation. These findings are consistent with models linking cognitive status, functional brain activation during episodic encoding, and pathophysiological processes in the AD continuum.

Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series

BackgroundIndividuals in the early stages of Alzheimer’s Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records carry valuable insights into cerebral cortex brain activities to analyze neuronal degeneration. To enhance the precision of dementia diagnosis, automatic and intelligent methods are required for the analysis and processing of EEG signals. New methodsThis paper aims to address the challenges associated with MCI diagnosis by leveraging EEG signals and deep learning techniques. The analysis in this study focuses on processing the information embedded within the sequence of raw EEG time series data. EEG recordings are collected from 10 Healthy Controls (HC) and 10 MCI participants using 19 electrodes during a 30 min eyes-closed session. EEG time series are transformed into 2 separate formats of input tensors and applied to deep neural network architectures. Convolutional Neural Network (CNN) and ResNet from scratch are performed with 2D time series with different segment lengths. Furthermore, EEGNet and DeepConvNet architectures are utilized for 1D time series. ResultsResNet demonstrates superior effectiveness in detecting MCI when compared to CNN architecture. Complete discrimination is achieved using EEGNet and DeepConvNet for noisy segments. Comparison with existing methodsResNet has yielded a 3 % higher accuracy rate compared to CNN. None of the architectures in the literature have achieved 100 % accuracy except proposed EEGNet and DeepConvnet. ConclusionDeep learning architectures hold great promise in enhancing the accuracy of early MCI detection.

Investigating the relationship between sleep disturbances and white matter hyperintensities in older adults on the Alzheimer's disease spectrum.

While studies report that sleep disturbance can have negative effects on brain vasculature, its impact on cerebrovascular diseases such as white matter hyperintensities (WMHs) in beta-amyloid-positive older adults remains unexplored. Sleep disturbance, WMH burden, and cognition in normal controls (NCs), and individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD), were examined at baseline and longitudinally. A total of 912 amyloid-positive participants were included (198 NC, 504 MCI, and 210 AD). Individuals with AD reported more sleep disturbances than NC and MCI participants. Those with sleep disturbances had more WMHs than those without sleep disturbances in the AD group. Mediation analysis revealed an effect of regional WMH burden on the relationship between sleep disturbance and future cognition. These results suggest that WMH burden and sleep disturbance increase from aging to AD. Sleep disturbance decreases cognition through increases in WMH burden. Improved sleep could mitigate the impact of WMH accumulation and cognitive decline.

Neurophysiological markers in community-dwelling older adults with mild cognitive impairment: an EEG study

BackgroundNeurodegeneration and structural changes in the brain due to amyloid deposition have been observed even in individuals with mild cognitive impairment (MCI). EEG measurement is considered an effective tool because it is noninvasive, has few restrictions on the measurement environment, and is simple and easy to use. In this study, we investigated the neurophysiological characteristics of community-dwelling older adults with MCI using EEG.MethodsDemographic characteristics, cognitive function, physical function, resting-state MRI and electroencephalogram (rs-EEG), event-related potentials (ERPs) during Simon tasks, and task proportion of correct responses and reaction times (RTs) were obtained from 402 healthy controls (HC) and 47 MCI participants. We introduced exact low-resolution brain electromagnetic tomography-independent component analysis (eLORETA-ICA) to assess the rs-EEG network in community-dwelling older adults with MCI.ResultsA lower proportion of correct responses to the Simon task and slower RTs were observed in the MCI group (p < 0.01). Despite no difference in brain volume between the HC and MCI groups, significant decreases in dorsal attention network (DAN) activity (p < 0.05) and N2 amplitude of ERP (p < 0.001) were observed in the MCI group. Moreover, DAN activity demonstrated a correlation with education (Rs = 0.32, p = 0.027), global cognitive function (Rs = 0.32, p = 0.030), and processing speed (Rs = 0.37, p = 0.010) in the MCI group. The discrimination accuracy for MCI with the addition of the eLORETA-ICA network ranged from 0.7817 to 0.7929, and the area under the curve ranged from 0.8492 to 0.8495.ConclusionsThe eLORETA-ICA approach of rs-EEG using noninvasive and relatively inexpensive EEG demonstrates specific changes in elders with MCI. It may provide a simple and valid assessment method with few restrictions on the measurement environment and may be useful for early detection of MCI in community-dwelling older adults.