Decline In Cognitive Abilities Research Articles

Sevoflurane-induced cognitive effect on α7-nicotine receptor and M1 acetylcholine receptor expression in the hippocampus of aged rats.

Sevoflurane treatment increases the incidence of postoperative cognitive dysfunction (POCD), and patients with POCD show a decline in cognitive abilities compared to preoperative levels. This study aimed to investigate whether the activation of α7 nicotinic acetylcholine receptor (α7nAChR) and the expression of M1 acetylcholine receptor (mAChR M1) in the hippocampus affects the cognitive function of aged rats. Forty-eight Sprague-Dawley (SD) rats of 1-week- and 12-months-old were divided into eight groups: four groups for α7nAChR and four groups for mAChR M1, respectively. All SD rats received 1.0-02% sevoflurane for α7nAChR and 1.0-02% sevoflurane for mAChR M1 for 2-6 h, respectively. The Y-maze test was used to assess the ability to learn and memory after receiving sevoflurane for 7 days at the same moment portion. RT-PCR was used to determine the expression of α7nAChR and mAChR M1 in the hippocampus of rats. The α7nAChR mitigated the formation of sevoflurane-induced memory impairment by modulating the translocation of NR2B from the intracellular reservoir to the cell surface reservoir within the hippocampus. Next, sevoflurane-induced decline of cognitive function and significantly decreased mAChR M1 expression at mRNA levels. α7nAChR regulates the trafficking of NR2B in the hippocampus of rats via the Src-family tyrosine kinase (SFK) pathway. This regulation is associated with cognitive deficits induced by sevoflurane in hippocampal development. Sevoflurane affects the cognitive function of rats by suppressing the mAChR M1 expression at mRNA levels in the hippocampus.

Improvement of both human and animal memory by synergy between fructooligosaccharide and L‐theanine function establishing a safe and effective food supplement

AbstractAging is classically associated with a decline of cognitive abilities, especially in relation to memory. While the development of potential treatments for neurodegenerative diseases has been in sharp focus, mild cognitive impairment (MCI), a form of age‐related memory loss, in the absence of severe functional impairment, a condition experienced by many healthy adults, has received relatively little attention. Advances in this space would make significant contributions to the goal of healthy aging and may also help promote cognitive performance across the wider population. The individual action of either fructooligosaccharide (FOS) or L‐theanine, both natural plant‐derived molecules, has been tentatively linked with improvements in cognition, but our understanding remains far from complete. We therefore determined the effect of different dose combinations of FOS and L‐theanine (termed MT‐01/GBL‐Memory1) in mice against FOS and L‐theanine monotherapy. FOS and L‐theanine were found to synergistically enhance murine memory in our animal tests at a dose of 100 mg/kg (coefficient of drug interaction (CDI) < 1). In a subsequent human trial, we demonstrated that MT‐01 improved the memory of healthy adults after 1 month of consumption. Our results suggest that a combination of FOS and L‐theanine synergistically enhances murine memory within a specific dose range. We show that this plant natural product regimen also improves human memory in a population of healthy adults. MT‐01 therefore represents a novel, safe, and effective dietary supplement to promote human memory and cognition.

Dementia U-Care: Comprehensive Cognitive Screening Application for Seniors

The prevalence of cognitive impairment increases with age, particularly impacting seniors as it advances to severe dementia. These conditions pose significant challenges for afflicted individuals and their caregivers, manifesting as profound impacts on daily life and imposing considerable emotional and financial burdens on families. Mild cognitive impairment (MCI) denotes an intermediate stage between normal cognitive function and dementia, signifying a decline in cognitive abilities while maintaining normal daily life activities. Identifying MCI early in seniors within the community is pivotal to preventing further cognitive decline.In response to the challenge of traditional cognitive function assessments, which require trained healthcare professionals and take 20-30 minutes per case, we introduce "Dementia U-Care," an innovative app designed to assist community health workers in screening, monitoring, and collecting cognitive data. Accessible on mobile devices, it allows seniors to respond through drawing and writing, simplifying data collection compared to paper forms. Dementia U-Care streamlines preliminary assessments, empowering professionals and reducing fatigue and errors. This tool enables prompt screening, minimizing test-related stress, with an average testing time of 13.06 minutes, ranging from 9 to 20 minutes. The evaluation indicates high satisfaction with Dementia U-Care, with a mean score of 9.37±1.12. Users are generally pleased with its quality and user experience, demonstrating its effectiveness in meeting their needs and providing a positive experience.

Influence of Sex on Cognitive and Motor Dual-Task Performance Among Young Adults: A Cross-Sectional Study.

To investigate the sex-related differences in single-task performance through motor torque, cognitive tasks and walking speed, and the combined dual-task costs (DTCs) considering both motor and cognitive performance in young adults. Sixty-seven non-athletic subjects 37 females and 30 males were enrolled. The study measured their knee extension muscle torque using an isokinetic strength dynamometer and their walking speed using the one step app. these assessments were performed both with and without a cognitive task, and the DTCs were calculated. The females exhibited significantly larger motor performance dual task effect through (torque-DTC, speed-DTC) compared with males while exhibiting smaller cognitive dual task effect with muscle torque and speed. Deterioration in motor performance during muscle force production and speed during dual tasks was large in females compared to males, whereas males experience a decline in cognitive ability when performing dual tasks compared with females.

AI-based tool for early detection of Alzheimer's disease

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease. Employing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our model undergoes training enriched by advanced image preprocessing techniques, achieving outstanding accuracy (testing 98.17 %, validation 97.52 %, training 99.62 %). The strategic use of transfer learning fortifies our competitive edge, incorporating the hippocampus approach and, notably, a progressive data augmentation technique. This innovative augmentation strategy gradually introduces augmentation factors during training, significantly elevating accuracy and enhancing the model's generalization ability. The study emphasizes practical application with a user-friendly website, empowering radiologists to predict class probabilities, track disease progression, and visualize patient images in both 2D and 3D formats, contributing significantly to the advancement of early AD detection.

Early Detection of Alzheimers Disease through Deep Learning in MRI Scans

Abstract: Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory impairment, leading to dementia and irreversible decline in cognitive abilities. This disease causes the person to suffer from memory loss, unusual behaviour, and language problems. The initial symptoms, such as episodic memory impairment and the navigational problem of the patient, are typical variants. In the proposed system we are using The Deep Learning Models as Modified CNN along with MobileNet, and VGG16 algorithms and classify the images as either Mild Demented or Moderate Demented or Non-Demented or Very Mild Demented. In this study we are using Augmented ADNI dataset which consists of 40000 images. This can be utilized in the future to classify the types of different classifications easily which is easy to find out the infections in initial stages and can be cured in the initial stages only.

Interactive effects of hearing aid use and cognitive function in patients with hearing loss.

There has been a significant increase in scientific investigations of the hearing-dementia association among the research on potentially modifiable risk factors for cognitive impairment. We tested two clinical questions. Analysis 1: does persistent hearing aid (HA) use decrease the decline in cognitive function caused by ageing? Analysis 2: does cognitive function at the time of HA fitting predict future persistent HA use? This case-control study performed at two referral centres reported data obtained over a 4.5-year period. We recruited a group of patients with cognitive decline, aged 65 or older with or without hearing loss. The intervention consisted of the use of HAs. The primary outcome measures were adherence to continuous HA use and cognitive function measured using the Japanese version of the Mini-Mental State Examination Test and the Reading Cognitive Test Kyoto. Eighteen HA users and 18 controls were included in the first analysis. HA use was associated with a deceleration of cognitive decline 12 months later. In the second analysis, 11 participants with good adherence to HA use were compared with 12 participants who showed poor adherence to HA use. Among the variables employed in this study, cognitive function measured using the Reading Cognitive Test Kyoto was significantly lower in participants with poor adherence to HA. HA use in cognitively impaired individuals with hearing loss can slow age-related cognitive decline. Cognitively impaired people with hearing loss who fail to commit to HA use tend to have lower cognitive measurement scores before HA fitting. HA use is generally more challenging as people age and their cognitive abilities decline. Therefore, it is desirable that HAs be used when hearing loss and dementia are in their early stages.

Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study.

Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia. In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined. Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions. The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.

PEGylated Chitosan Biodegradable Nanoparticles Delivery of Salvia officinalis and Melissa officinalis for Enhanced Brain Targeting

Background: Alzheimer's disease (AD) is a progressive neurodegenerative condition characterized by the gradual decline of cognitive abilities, primarily caused by impairments in the cholinergic system. AD is diagnosed based on the presence of specific pathological features, in-cluding senile plaques, neurofibrillary tangles, and the loss of neurons and synapses. Despite on-going efforts, the etiology of AD remains unclear, and there is a significant lack of effective treatments to meet the medical needs of affected individuals. The complex nature of AD, involv-ing multiple factors, presents challenges in the development of potential therapies. Numerous ob-stacles hinder the achievement of optimal pharmacological concentration of promising molecules for AD treatment. These obstacles include the presence of the blood-brain barrier (BBB), which restricts the entry of therapeutic agents into the brain, as well as issues related to poor bioavaila-bility and unfavorable pharmacokinetic profiles. Unfortunately, many therapeutically promising compounds have failed to overcome these hurdles and demonstrate efficacy in treating AD. Methods: The PEGylated chitosan nanoconjugate was developed and evaluated for delivery of anti-Alzheimer natural extract of Salvia officinalis and Melissa officinalis to the brain. The nano-conjugates (S-PCN and M-PCN) were developed by ionic gelation technique. Result: The nanoconjugates (S-PCN and M-PCN) were evaluated for various optical and in-vitro parameters. MTT assay on UCSD229i-SAD1-1 human astrocytoma cells indicated IC50 values of 0.42, 0.49, 0.67, and 0.75 μM for S-PCN, M-PCN formulations, and free Salvia officinalis and Melissa officinalis extracts, respectively. The In vitro assessments using cell lines have confirmed the improved uptake and distribution of nanoconjugates compared to free extracts. These findings were validated through confocal microscopy and apoptosis assays, revealing a substantial in-crease in the accumulation of nanoconjugates within the brain. The targeting potential OF M- PCN over S-PCN was found to be 2-fold significant. Conclusion: Based on the findings, it can be inferred that biodegradable PEGylated chitosan nanoconjugates hold promise as effective nano-targeting agents for delivering anti-Alzheimer drugs to the brain. The incorporation of PEGylated chitosan nanoparticles in this approach demonstrates enhanced delivery capabilities, ultimately leading to improved therapeutic out-comes.

Gene expression patterns of CRYM and SIGLEC10 in Alzheimer's disease: potential early diagnostic indicators.

Alzheimer's disease (AD) is a neurological condition that may lead to dementia as well as a slow and steady decline in cognitive ability. Finding early signs that may be used in the diagnosis of ADis still a difficult aim to achieve in the field of medical practice. The purpose of this research was to investigate to determineany differences in the gene expression patterns of crystallin mu (CRYM) and sialic acid-binding immunoglobulin-like lectin 10 (SIGLEC10) in whole blood samples obtained from fifty individuals who were diagnosed with ADand fifty individuals as a control group. When compared with controls, it was discovered that the expression of the CRYM gene was substantially decreased in AD patients, but the expression of the SIGLEC10 gene was significantly higher. A positive correlation between CRYM and SIGLEC10 was noticed solely in patients with AD. Furthermore, assessing the diagnostic value of these genes, CRYM and SIGLEC10 transcript levels displayed an area under the curve (AUC) of 0.74 and 0.81, respectively. These results suggest that alterations in CRYM and SIGLEC10 expression may be implicated in AD pathology and that these genes expression levels can potentially serve as biomarkers for early detection and diagnosis of AD. Nevertheless, further validation of these findings requires the inclusion of more extensive and heterogeneous cohorts. The findings derived from this study possess the capability to offer a significant contribution towards the progression of innovative diagnostic and therapeutic strategies for AD.

Novel Dual-Acting Hybrids Targeting Type-2 Cannabinoid Receptors and Cholinesterase Activity Show Neuroprotective Effects In Vitro and Amelioration of Cognitive Impairment In Vivo.

Alzheimer's disease (AD) is a neurodegenerative form of dementia characterized by the loss of synapses and a progressive decline in cognitive abilities. Among current treatments for AD, acetylcholinesterase (AChE) inhibitors have efficacy limited to symptom relief, with significant side effects and poor compliance. Pharmacological agents that modulate the activity of type-2 cannabinoid receptors (CB2R) of the endocannabinoid system by activating or blocking them have also been shown to be effective against neuroinflammation. Herein, we describe the design, synthesis, and pharmacological effects in vitro and in vivo of dual-acting compounds that inhibit AChE and butyrylcholinesterase (BChE) and target CB2R. Within the investigated series, compound 4g proved to be the most promising. It achieved IC50 values in the low micromolar to submicromolar range against both human cholinesterase isoforms while antagonizing CB2R with Ki of 31 nM. Interestingly, 4g showed neuroprotective effects on the SH-SY5Y cell line thanks to its ability to prevent oxidative stress-induced cell toxicity and reverse scopolamine-induced amnesia in the Y-maze forced alternation test in vivo.

Real world data analysis of frontotemporal dementia: Implications for future clinical research

Frontotemporal dementia (FTD) is a progressive decline of cognitive abilities associated with other neuropsychiatric comorbidities. A real-world data (RWD) analysis of a large electronic healthcare records (EHR) database identified the comorbidities of FTD. Deidentified EHRs in the TriNetX Network database from >155,000,000 individuals in the United States established an FTD Cohort (ICD-10 Code G31.0) of adult patients who visited a healthcare provider in 2022. The non-FTD cohort were age-matched individuals who had not received a diagnosis of ICD-10 Code G31.0, and who had visited a healthcare provider in 2022. The median age of both cohorts was 73 years. A comparative analysis was performed between the FTD and non-FTD cohorts. There were 6660 individuals (aged ≥18) with FTD and 11,810,060 individuals (aged ≥63) without a diagnosis of FTD, with healthcare visits in 2022. There were 25 ICD-10 Codes for disorders that were present in >10% of FTD patients, with a Relative Risk (RR) of ≥2.0 compared the non-FTD cohort. Multiple neuropsychiatric disorders had RRs ≥ 2.0, with minimal evidence for significant involvement of other organ systems. These data document that FTD, as known previously, is associated with multiple neuropsychiatric comorbidities. There was minimal evidence of comorbid involvement of other organ systems. These data provide a baseline of general FTD symptoms for the rapidly evolving analysis of genetic subvariants of FTD. These data also provide insights into the clinical management of FTD, as well as recommendations for specific endpoints in clinical trials.

Semantic Clustering during Verbal Episodic Memory Encoding and Retrieval in Older Adults: One Cognitive Mechanism of Superaging.

Normal aging is commonly accompanied by a decline in cognitive abilities, including memory, yet some individuals maintain these abilities as they get older. We hypothesize that semantic clustering, as an effective strategy for improving performance on episodic recall tasks, may contribute to the maintenance of youthful memory in older adults. We investigated the dynamics of spontaneous production and utilization of the semantic clustering strategy in two independent samples of older adults who completed a list learning paradigm (N1 = 40 and N2 = 29, respectively). Specifically, we predicted and observed that older adults who spontaneously used a semantic clustering strategy throughout the encoding process learned more words by the culmination of the encoding trials (Sample 1, R2= 0.53, p < 0.001; Sample 2, R2= 0.51, p < 0.001), and that those who utilized this strategy during retrieval recalled more words, when compared to older adults who did not produce or utilize a semantic clustering strategy during both a short (Sample 1, R2 = 0.81, p < 0.001; Sample 2, R2 = 0.70, p < 0.001) and long delay retrieval (Sample 1, R2 = 0.83, p < 0.001; Sample 2, R2 = 0.77, p < 0.001). We further predicted and observed that older adults who maintained a youthful level of delayed free recall (i.e., "Superagers") produced (Sample 1, F(1, 38) = 17.81, p < 0.0001; Sample 2, F(1, 27) = 14.45, p < 0.0001) and utilized (Sample 1, F(1, 39) = 25.84, p < 0.0001; Sample 2, F(1, 27) = 12.97, p < 0.01) more semantic clustering than did older individuals with normal memory for their age. These results suggest one cognitive mechanism through which Superagers maintain youthful memory function and raise the possibility that older adults may be able to train themselves to use strategies to promote better memory.

Comparing the performance of statistical, machine learning, and deep learning algorithms to predict time-to-event: A simulation study for conversion to mild cognitive impairment.

Mild Cognitive Impairment (MCI) is a condition characterized by a decline in cognitive abilities, specifically in memory, language, and attention, that is beyond what is expected due to normal aging. Detection of MCI is crucial for providing appropriate interventions and slowing down the progression of dementia. There are several automated predictive algorithms for prediction using time-to-event data, but it is not clear which is best to predict the time to conversion to MCI. There is also confusion if algorithms with fewer training weights are less accurate. We compared three algorithms, from smaller to large numbers of training weights: a statistical predictive model (Cox proportional hazards model, CoxPH), a machine learning model (Random Survival Forest, RSF), and a deep learning model (DeepSurv). To compare the algorithms under different scenarios, we created a simulated dataset based on the Alzheimer NACC dataset. We found that the CoxPH model was among the best-performing models, in all simulated scenarios. In a larger sample size (n = 6,000), the deep learning algorithm (DeepSurv) exhibited comparable accuracy (73.1%) to the CoxPH model (73%). In the past, ignoring heterogeneity in the CoxPH model led to the conclusion that deep learning methods are superior. We found that when using the CoxPH model with heterogeneity, its accuracy is comparable to that of DeepSurv and RSF. Furthermore, when unobserved heterogeneity is present, such as missing features in the training, all three models showed a similar drop in accuracy. This simulation study suggests that in some applications an algorithm with a smaller number of training weights is not disadvantaged in terms of accuracy. Since algorithms with fewer weights are inherently easier to explain, this study can help artificial intelligence research develop a principled approach to comparing statistical, machine learning, and deep learning algorithms for time-to-event predictions.

Why Dementia is Associated with Poor Morbidity and Novel Treatments: An In-Depth Discussion about Alzheimer’s Disease

One of the most common forms of dementia around the world is Alzheimer’s. By 2022, it is estimated one out of every three seniors diagnosed with Alzheimer’s will eventually die due to complications from the disease [1]. There are several biological and physical markers of Alzheimer’s that may indicate that one has the disease, with symptoms all relating to a decline in cognitive function. The general development of Alzheimer’s is ascribed to two factors, genetic and environmental. Along with the growth of an aging population and the absence of a definitive cure, the search for novel treatments has gained significant momentum. Some promising treatments include: immunotherapies targeting amyloid-beta plaques, such as the monoclonal antibody ‘donanemab’; precision medicine for personalized therapies, namely deep brain stimulation (DBS); and TRIM11 as a biological target for the slowing of the decline in cognitive abilities. Despite progression in the development of novel treatments, the pathophysiological mechanism of the disease is yet to be understood, accounting for various trial-and-error type experiments. Continued research and a comprehensive approach are vital for the development of effective therapies for Alzheimer’s disease.

Detectability and accuracy of computational measurements of in-silico and physical representations of enlarged perivascular spaces from magnetic resonance images

BackgroundMagnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. But the limits of validity of their quantification have not been established. New methodWe use a purpose-built digital reference object to construct an in-silico phantom for addressing this need, and validate it using a physical phantom. We use cylinders of different sizes as models for PVS. We also evaluate the influence of ‘PVS’ orientation, and different sets of parameters of the two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, in the measurements’ accuracy. ResultsPVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter relies on a careful tuning of several parameters. Alpha= 0.5, beta= 0.5 and c= 500 yielded the best results. RORPO does not have these requirements and allows detecting smaller cylinders in their entirety more consistently in the absence of noise and confounding artefacts. The Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1 mm diameter and 2 mm length. ‘PVS’ orientation did not affect measurements in data with isotropic voxels. Comparison with existent methodsDoes not apply. ConclusionsThe in-silico and physical phantoms presented are useful for establishing the validity of quantification methods of tubular small structures.

Evidence of distinct subjective cognitive decline profiles in a community sample

AbstractBackgroundSubjective Cognitive Decline (SCD) refers to a self‐perceived decline in cognitive ability despite normal performance on objective tests. SCD has been conceptualized as a marker of preclinical Alzheimer’s disease (AD) but research has uncovered considerable heterogeneity in symptoms and outcomes. Therefore, it is likely that SCD encompasses distinct subtypes, similarly to Mild Cognitive Impairment (MCI). Here we sought to identify and characterize subgroups based on patterns of subjective cognitive symptoms.MethodsCommunity‐dwelling participants aged ≥50 years (n = 483), located within the United States, were recruited via a crowdsourcing website, and completed a survey consisting of the following: SCD Questionnaire‐MyCog, PROMIS depression scale, and demographic questions. The SCD‐Q includes 24 items assessing self‐perceived recent decline on memory, language, and executive functioning tasks with a yes or no format. Two‐step cluster analysis using AIC was performed on all SCD‐Q items.ResultsThe analysis yielded a 3‐cluster solution with fair cohesion and separation (silhouette measure = 0.46). Cluster 1 (No Subjective Decline) included 36% of participants reporting no decline except for sporadic endorsement (≤2%) on a few memory tasks. Cluster 2 (Mild Subjective Decline) consisted of 42% of participants who frequently (≤50%) endorsed decline on select memory and language tasks, and less frequently on executive functioning tasks (≤25%). Cluster 3 (Moderate Subjective Decline) included 22% of participants who almost unanimously (≥80%) reported decline on a wide range of memory and language tasks, and frequently (≥60%) on executive functioning tasks. Clusters differed on total SCD‐Q scores (1&lt;2&lt;3, M = 0.1, 3.5, 13.7), percent below the SCD cutoff of 7/24 (1 and 2&lt;3, 0%, 8.4%, 100%), depression score (1&lt;2&lt;3, M = 10.9, 13.0, 16.7), gender (percent women 1&lt;2 and 3, 58.7%, 71.0%, 704%) and AD family history (percent with history 1&lt;2 and 3, 20%, 29%, 34.9%) but not on age.ConclusionWe identified three distinct subgroups in our community sample, defined by escalating subjective symptom burden, growing involvement of language and executive functioning domains and increasing prevalence of AD risk factors (female gender/AD family history). All Moderate‐Subjective‐Decline subgroup members but none/very few of the others, scored above the published SCD cut‐off. Longitudinal outcomes are needed to establish subgroup validity.

Estimating the prevalence of mild cognitive impairment from clinical notes in U.S. Veterans

AbstractBackgroundIndividuals with mild cognitive impairment (MCI), a noticeable decline in cognitive abilities, are at increased risk of developing Alzheimer’s disease (AD) or other dementia. Risk factors for dementia, such as obesity, stroke, depression are highly prevalent among U.S. veterans We report on the trends in the prevalence of MCI using clinical notes over the past 5 years in the Veteran Affairs, the largest integrated healthcare system in the U.S.MethodPrevalent cases of MCI were identified from VA clinical notes for all Veterans aged 50 years and older in VA care from fiscal years (FYs) 2015‐19. VA clinical notes are maintained in searchable text integration utility files (TIU) available in the VA Corporate Data Warehouse (CDW). Pharmacy clinical notes were excluded. “Mild cognitive impairment” keyword was searched in clinical notes to identify patients with MCI. We validated the keyword “mild cognitive impairment” in 199 clinical notes. The positive predictive value for this validation was approximately 91%. For each FY, age and sex adjusted rates directly standardized to the 2010 U.S. Census general population were estimated.ResultA total of 308,930 prevalent cases of MCI were identified from VA clinical notes from FYs 2015‐19. Standardized prevalence rates for MCI increased steeply, with prevalence of 0.9% in FY 2015, increasing to 1.31% in FY 2019. Standardized age adjusted MCI prevalence rates also increased sharply for male (0.77% in FY 2015 to 1.10% in FY 2019) and female veterans (1.01% in FY 2015 to 1.50% in FY 2019) with rates higher for females than males for each FY.ConclusionWe observed recent trends of increasing prevalence rates for MCI among veterans identified from VA clinical notes. Considering the increased risk of dementia among individuals with MCI, our study finding of increasing prevalence of MCI among veterans highlights the critical need for effective interventions and treatment to target veterans with MCI.

Alzheimer disease biomarkers are associated with decline in subjective memory, attention, and spatial navigation ability in clinically normal adults.

Subtle changes in memory, attention, and spatial navigation abilities have been associated with preclinical Alzheimer disease (AD). The current study examined whether baseline AD biomarkers are associated with self- and informant-reported decline in memory, attention, and spatial navigation. Clinically normal (Clinical Dementia Rating Scale (CDR®) = 0) adults aged 56-93 (N = 320) and their informants completed the memory, divided attention, and visuospatial abilities (which assesses spatial navigation) subsections of the Everyday Cognition Scale (ECog) annually for an average of 4 years. Biomarker data was collected within (±) 2 years of baseline (i.e., cerebrospinal fluid (CSF) p-tau181/Aβ42 ratio and hippocampal volume). Clinical progression was defined as CDR > 0 at time of final available ECog. Self- and informant-reported memory, attention, and spatial navigation significantly declined over time (ps < .001). Baseline AD biomarkers were significantly associated with self- and informant-reported decline in cognitive ability (ps < .030), with the exception of p-tau181/Aβ42 ratio and self-reported attention (p = .364). Clinical progression did not significantly moderate the relationship between AD biomarkers and decline in self- or informant-reported cognitive ability (ps > .062). Post-hoc analyses indicated that biomarker burden was also associated with self- and informant-reported decline in total ECog (ps < .002), and again clinical progression did not significantly moderate these relationships (ps > .299). AD biomarkers at baseline may indicate risk of decline in self- and informant-reported change in memory, attention, and spatial navigation ability. As such, subjectively reported decline in these domains may have clinical utility in tracking the subtle cognitive changes associated with the earliest stages of AD.

Artificial intelligence modelling human mental fatigue: A comprehensive survey

Mental fatigue refers to the decline in cognitive abilities that can occur as a result of prolonged mental exertion. Neuroscientists have been studying mental fatigue for a while. They clearly understand some underlying mechanisms of mental fatigue, such as brain chemistry and neural activity changes. However, defining mental fatigue is still an open research question. Despite this, neuroscience and cognitive psychology has made significant progress in understanding the causes and consequences of mental fatigue. In contrast, computer scientists presumably have a limited understanding of mental fatigue. This lack of understanding leads to inadequate models of mental fatigue in computer science. However, the ever evolving field of artificial intelligence (AI) shows a great potential to answer the open challenges in mental fatigue modelling. For instance, AI with fuzzy rules, machine learning or deep learning algorithms, as well as the methods of model explanation can be a valuable tool for creating accurate models of mental fatigue. Artificial intelligence models can learn from large amounts of data and accurately predict mental fatigue. However, modelling efforts are often more focused on acquiring parameters than studying and validating them within the model. Models developed in this way suffer problems with reliability making it challenging to understand the underlying causes of mental fatigue. Therefore, it is essential balance between these parameters’ correct acquisition, validation and interaction to create more accurate models of mental fatigue. In our survey, we have observed that an unspecified modelling impact the model at four scale : experimental design, data acquisition and processing, choice of indicators or parameters and reasoning. We provide some useful guidelines through criticisms for modelling, which would be closer to reality.