MMSE Subscale Scores as Useful Predictors of AD Conversion in Mild Cognitive Impairment

Diagnosis of Alzheimer's disease using transfer learning with multi-modal 3D Inception-v4

Background

Deep learning (DL) technologies are playing increasingly important roles in computer-aided diagnosis in medicine. In this study, we sought to address issues related to the diagnosis of Alzheimer's disease (AD) based on multi-modal features, and introduced a multi-modal three-dimensional Inception-v4 model that employs transfer learning for AD diagnosis based on magnetic resonance imaging (MRI) and clinical score data.

Methods

The multi-modal three-dimensional (3D) Inception-v4 model was first pre-trained using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Subsequently, independent validation data were used to fine-tune the model with pre-trained weight parameters. The model was quantitatively evaluated using the mean values obtained from five-fold cross-validation. Further, control experiments were conducted to verify the performance of the model patients with AD, and in the study of disease progression.

Results

In the AD diagnosis task, when a single image marker was used, the average accuracy (ACC) and area under the curve (AUC) were 62.21% and 71.87%, respectively. When transfer learning was not employed, the average ACC and AUC were 75.74% and 83.13%, respectively. Conversely, the combined approach proposed in this study achieved an average ACC of 87.84%, and an average AUC of 90.80% [with an average precision (PRE) of 87.21%, an average recall (REC) of 82.52%, and an average F1 of 83.58%].

Conclusions

In comparison with existing methods, the performance of the proposed method was superior in terms of diagnostic accuracy. Specifically, the method showed an enhanced ability to accurately distinguish among various stages of AD. Our findings show that multi-modal feature fusion and transfer learning can be valuable resources in the treatment of patients with AD, and in the study of disease progression.

An Investigation of the Correlation Between Retinal Nerve Fiber Layer Thickness with Blood Biochemical Indices and Cognitive Dysfunction in Patients with Type 2 Diabetes Mellitus

Objective

The study aimed to explore the correlation between retinal nerve fiber layer thickness (RNFLT) with blood biochemical indicators and cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM) and the possible mechanism, thereby providing more theoretical basis for the occurrence and prevention of diabetes related complications.

Methods

Eighty T2DM patients treated in our hospital from March 2022 to September 2022 were selected as the study subjects, and the clinical data of the patients were retrospectively analyzed. All patients underwent fundus fluorescein angiography (FFA) to analyze the changes in retinal blood vessels. Patients who met the inclusion criteria were divided as the diabetic retinopathy (DR) group (n=46) and simple diabetes group (n=34). The RNFLT, blood biochemical indexes and changes in cognitive functions of the patients were detected. The correlation between RNFLT with blood biochemical indexes and cognitive dysfunction was analyzed.

Results

Compared with the simple diabetes group, patients in the DR group had much lower mean, nasal, inferior and superior thicknesses (P<0.01). There existed no significant difference in blood pressure, body mass index (BMI), blood lipids (triglycerides, cholesterol, low-density lipoprotein, high-density lipoprotein) between the two groups (P>0.05). Compared with the simple diabetes group, patients in the DR group had much higher fasting blood glucose (FBG), hemoglobin A1c (HbA1c), fasting insulin (FINS), insulin resistance (HOMA-IR) index, apolipoprotein B (ApoB)/apolipoprotein A1 (ApoA1) (P<0.001). Besides, the DR group had sharply lower scores on the Mini-Mental State Examination (MMSE) scale and higher levels of the Trail Making Test-A (TMT-A) and TMT-B (P<0.001). Spearman correlation analysis confirmed that the mean RNFLT was negatively correlated with the levels of FBG, HbA1c, HOMA-IR index, TMT-A and TMT-B (P<0.05), positively correlated with the score of mini-mental state examination (MMSE) (P<0.05), and was no significant correlation with FINS and ApoB/ApoA1 (P>0.05).

Conclusion

DR patients had significantly reduced RNFLT, elevated levels of blood glucose related indicators, and cognitive dysfunction. There existed a correlation between RNFLT and FBG, HbA1c, HOMA-IR index, TMT-A, TMT-B and MMSE.

Alterations in driving ability and their relationship with morphometric magnetic resonance imaging indicators in patients with amnestic mild cognitive impairment and Alzheimer's disease

BACKGROUND

Drivers with dementia are at a higher risk of motor vehicle accidents. The characteristics of driving behaviour of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) have not been fully elucidated. We investigated driving ability and its relationship with cognitive function and magnetic resonance imaging (MRI) morphometry indicators.

METHODS

The driving abilities of 19 patients with AD and 11 with amnestic MCI (aMCI) were evaluated using a driving simulator. The association between each driving ability parameter and the Mini-Mental State Examination (MMSE) score or voxel-based specific regional analysis system for AD (VSRAD) was assessed.

RESULTS

Patients with AD made a significantly higher number of operational errors than those with aMCI in attention allocation in the complex task test (P = 0.0008). The number of operational errors in attention allocation in the complex task test significantly and negatively correlated with MMSE scores in all participants (r = -0.4354, P = 0.0162). The decision time in the selective reaction test significantly and positively correlated with the severity and extent of medial temporal structural atrophy (r = 0.4807, P = 0.0372; r = 0.4862, P = 0.0348; respectively).

CONCLUSION

An increase in the operational errors for attention allocation in the complex task test could be a potential indicator of progression from aMCI to AD. Atrophy of the medial temporal structures could be a potential predictor of impaired judgement in driving performance in aMCI and AD. A driving simulator could be useful for evaluating the driving abilities of individuals with aMCI and AD.

Associations of modifiable and non-modifiable risk factors with cognitive functions - a prospective, population-based, 17 years follow-up study of 3,229 individuals

BACKGROUND

Although several cardiovascular, demographic, genetic and lifestyle factors have been associated with cognitive function, little is known about what type of cognitive impairment they are associated with. The aim was to examine the associations between different risk factors and future memory and attention/executive functions, and their interaction with APOE genotype.

METHODS

Participants from a large, prospective, population-based, Swedish study were included (n = 3,229). Linear regression models were used to examine baseline hypertension, body mass index (BMI), long-term glucose levels (HbA1c), different lipid levels, physical activity, alcohol consumption, smoking, education, APOE genotype, age and sex. All models were adjusted for follow-up time and basic demographics, and, in a second step, all significant predictors were included to examine independent effects. Follow-up outcomes were memory and attention/executive functions.

RESULTS

The mean age at baseline was 56.1 (SD 5.7) years and 59.7% were women. The mean follow-up time was 17.4 (range 14.3-20.8) years. When examining independent effects, APOE ε4 genotype(p < 0.01), and higher HbA1c(p < 0.001), were associated with future low memory function. Higher BMI (p < 0.05), and HbA1c(p < 0.05), lower high-density lipoprotein cholesterol (HDL-C)(p < 0.05)and stroke(p < 0.001) were associated with future low attention/executive function. The strongest factors associated with both better memory and attention/executive functions were higher education and alcohol consumption. Further, significant interaction effects between predictors and APOE genotype were found. For memory function, the protective effects of education were greater among ɛ4-carriers(p < 0.05). For attention/executive function, the protective effects of alcohol were greater among ɛ2 or ɛ4-carriers(p < 0.05). Also, attention/executive function was lower among ɛ4-carriers with higher BMI(p < 0.05) and ɛ2-carriers with higher HbA1c-levels(p < 0.05).

CONCLUSIONS

Targeting cardiovascular risk factors in mid-life could have greater effect on future attention/executive functions rather than memory, whereas targeting diabetes could be beneficial for multiple cognitive domains. In addition, effects of different risk factors may vary depending on the APOE genotype. The varied cognitive profiles suggest that different mechanisms and brain regions are affected by the individual risk factors. Having detailed knowledge about the specific cognitive effects of different risk factors might be beneficial in preventive health counseling.

Sex differences in luteinizing hormone aggravates Aβ deposition in APP/PS1 and Aβ1-42-induced mouse models of Alzheimer's disease

Alzheimer's disease (AD) exhibits a higher incidence rate among older women, and dysregulation of the hypothalamic-pituitary-gonadal (HPG) axis during aging is associated with cognitive impairments and the development of dementia. luteinizing hormone (LH) has an important role in CNS function, such as mediating neuronal pregnenolone production, and modulating neuronal plasticity and cognition. The sex differences in LH and its impact on Aβ deposition in AD individuals remain unclear, with no reported specific mechanisms. Here, we show through data mining that LH-related pathways are significantly enriched in female AD patients. Additionally, LH levels are elevated in female AD patients and exhibit a negative correlation with cognitive levels but a positive correlation with AD pathology levels, and females exhibit a greater extent of AD pathology, such as Aβ deposition. In vivo, we observed that the exogenous injection of LH exacerbated behavioral impairments induced by Aβ1-42 in mice. LH injection resulted in worsened neuronal damage and increased Aβ deposition. In SH-SY5Y cells, co-administration of LH with Aβ further exacerbated Aβ-induced neuronal damage. Furthermore, LH can dose-dependently decrease the levels of NEP and LHR proteins while increasing the expression of GFAP and IBA1 in vivo and in vitro. Taken together, these results indicate that LH can exacerbate cognitive impairment and neuronal damage in mice by increasing Aβ deposition. The potential mechanism may involve the reduction of NEP and LHR expression, along with the exacerbation of Aβ-induced inflammation.

MAPPING ALZHEIMER'S DISEASE PSEUDO-PROGRESSION WITH MULTIMODAL BIOMARKER TRAJECTORY EMBEDDINGS

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by progressive cognitive degeneration and motor impairment, affecting millions worldwide. Mapping the progression of AD is crucial for early detection of loss of brain function, timely intervention, and development of effective treatments. However, accurate measurements of disease progression are still challenging at present. This study presents a novel approach to understanding the heterogeneous pathways of AD through longitudinal biomarker data from medical imaging and other modalities. We propose an analytical pipeline adopting two popular machine learning methods from the single-cell transcriptomics domain, PHATE and Slingshot, to project multimodal biomarker trajectories to a low-dimensional space. These embeddings serve as our pseudotime estimates. We applied this pipeline to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to align longitudinal data across individuals at various disease stages. Our approach mirrors the technique used to cluster single-cell data into cell types based on developmental timelines. Our pseudotime estimates revealed distinct patterns of disease evolution and biomarker changes over time, providing a deeper understanding of the temporal dynamics of AD. The results show the potential of the approach in the clinical domain of neurodegenerative diseases, enabling more precise disease modeling and early diagnosis.

Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis

In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset. Furthermore, the local data relationships are learned according to the samples' transformed distances to exploit the local data structure adaptively. For regression, in contrast to previous works that overlook the correlations among cognitive scores, we learn a latent score space to capture the correlations and employ the latent space to design a regression model with l2,1 -norm regularization, facilitating the feature selection in regression task. Moreover, the missing cognitive scores can be recovered in the latent space for increasing the number of available training samples. Meanwhile, to capture the correlations between the two tasks and describe the local relationships between samples, we construct an adaptive shared graph to guide the subspace learning in classification and the latent cognitive score learning in regression simultaneously. An efficient iterative optimization algorithm is proposed to solve the optimization problem. Extensive experiments on three datasets validate the discriminability of the features selected by SMJFS.

Personality and cognitive errors in the Healthy Aging in Neighborhoods of Diversity across the Life Span study

This study examines the association between personality and cognitive errors in the Healthy Aging in Neighborhoods of Diversity across the Life Span study, a sample diverse across race (Black, White) and SES (above, below 125% of the federal poverty line). Participants (N=1,062) completed a comprehensive personality questionnaire and were administered a brief mental status screener of cognitive errors. Higher neuroticism was associated with more cognitive errors, whereas higher openness and conscientiousness were associated with fewer errors. These associations were independent of age, sex, race, poverty status, and education and were generally not moderated by these factors. These findings support the associations between personality and cognition across race and SES.

An Early Detection and Classification of Alzheimer's Disease Framework Based on ResNet-50

OBJECTIVE

The objective of this study is to develop a more effective early detection system for Alzheimer's disease (AD) using a Deep Residual Network (ResNet) model by addressing the issue of convolutional layers in conventional Convolutional Neural Networks (CNN) and applying image preprocessing techniques.

METHODS

The proposed method involves using Contrast Limited Adaptive Histogram Equalizer (CLAHE) and Boosted Anisotropic Diffusion Filters (BADF) for equalization and noise removal and K-means clustering for segmentation. A ResNet-50 model with shortcut links between three residual layers is proposed to extract features more efficiently. ResNet-50 is preferred over other ResNet types due to its intermediate depth, striking a balance between computational efficiency and improved performance, making it a widely adopted and effective architecture for various computer vision tasks. While other ResNet variations may offer higher depths, they are more prone to overfitting and computational complexity, which can hinder their practical application. The proposed method is evaluated on a dataset of MRI scans of AD patients.

RESULTS

The proposed method achieved high accuracy and minimum losses of 95% and 0.12, respectively. While some models showed better accuracy, they were prone to overfitting. In contrast, the suggested framework, based on the ResNet-50 model, demonstrated superior performance in terms of various performance metrics, providing a robust and reliable approach to Alzheimer's disease categorization.

CONCLUSION

The proposed ResNet-50 model with shortcut links between three residual layers, combined with image preprocessing techniques, provides an effective early detection system for AD. The study demonstrates the potential of deep learning and image processing techniques in developing accurate and efficient diagnostic tools for AD. The proposed method improves the existing approaches to AD classification and provides a promising framework for future research in this area.

Effect of Immersive Virtual Reality Reminiscence versus Traditional Reminiscence Therapy on Cognitive Function and Psychological Well-being among Older Adults in Assisted Living Facilities: A randomized controlled trial

BACKGROUND

Virtual reality (VR) reminiscence is an innovative strategy that integrates technology into the care of older adults. Limited research was conducted to compare the role of VR reminiscence and traditional RT in improving older adults' cognitive and psychological well-being.

AIM

Investigate the effect of virtual reality reminiscence versus traditional reminiscence therapy on cognitive function and psychological well-being among older adults in assisted living facilities.

METHODS

A randomized controlled trial research design was followed. Sixty older adults were recruited and randomly assigned to three equal groups (20 older adults for each group).

RESULTS

Post interventions, a significant increase in the mean scores of cognitive function and psychological well-being was evident among the VR and RT groups with statistically significant differences (P <0.05) compared with pre-intervention and the control group.

CONCLUSION

Application of VR reminiscence or traditional RT is efficacious in improving cognitive function and psychological well-being among institutionalized older adults.

Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning

Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

Ultra-Early Screening of Cognitive Decline Due to Alzheimer's Pathology

Alzheimer's pathology can be assessed and defined via Aβ and tau biomarkers. The preclinical period of Alzheimer's disease is long and lasts several decades. Although effective therapies to block pathological processes of Alzheimer's disease are still lacking, downward trends in the incidence and prevalence of dementia have occurred in developed countries. Accumulating findings support that education, cognitive training, physical exercise/activities, and a healthy lifestyle can protect cognitive function and promote healthy aging. Many studies focus on detecting mild cognitive impairment (MCI) and take a variety of interventions in this stage to protect cognitive function. However, when Alzheimer's pathology advances to the stage of MCI, interventions may not be successful in blocking the development of the pathological process. MCI individuals reverting to normal cognitive function exhibited a high probability to progress to dementia. Therefore, it is necessary to take effective measures before the MCI stage. Compared with MCI, an earlier stage, transitional cognitive decline, may be a better time window in which effective interventions are adopted for at-risk individuals. Detecting this stage in large populations relies on rapid screening of cognitive function; given that many cognitive tests focus on MCI detection, new tools need to be developed.

Risk of cognitive impairment from exposure to incense smoke

Incense is aromatic biotic material that releases fragrant smoke when burned. We aim to investigate the cognition risks from incense smoke. We obtained data from Taiwan Biobank in community. Cognition function was assessed by mini-mental state examination (MMSE). There were 978 participants in our study, including incense exposure (N = 131) and without incense exposure (N = 847). MMSE scores and registration sub-scores were lowered in incense exposure group than the other group. Incense exposure is one of the independent risk factors for cognitive decline in MMSE and registration sub-scores after adjusting confounding factors We concluded the risk of cognitive impairment, with predominant in registration in healthy individuals with incense exposure in community.

Predicting conversion of patients with Mild Cognitive Impairment to Alzheimer's disease using bedside cognitive assessments

INTRODUCTION

Patients diagnosed with Mild Cognitive Impairment (MCI) often go on to develop dementia, however many do not. Although cognitive tests are widely used in the clinic, there is limited research on their potential to help predict which patients may progress to Alzheimer's disease (AD) from those that do not.

METHODS

MCI patients (n = 325) from the longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI-2) dataset were tracked across a 5 year period. Upon initial diagnosis, all patients underwent a series of cognitive tests including the Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog 13). Twenty-five percent (n = 83) of those initially diagnosed with MCI subsequently developed AD within 5 years.

RESULTS

We showed that those individuals that progressed to AD had significantly lower scores upon baseline testing on the MMSE and MoCA, and higher scores on the ADAS-13, compared to those that did not convert. However, not all tests were equivalent. We showed that the ADAS-13 offers the best predictability of conversion (Adjusted Odds ratio (AOR) = 3.91). This predictability was higher than that offered by the two primary biomarker Amyloid-beta (Aβ, AOR = 1.99) and phospho-tau (Ptau, AOR = 1.72). Further analysis on the ADAS-13 showed that MCI patients that subsequently converted to AD performed particularly poorly on delayed-recall (AOR = 1.93), word recognition (AOR = 1.66), word finding difficulty (AOR = 1.55) and orientation (1.38) test items.

CONCLUSIONS

Cognitive testing using the ADAS-13 may offer a simpler, less invasive, more clinically relevant and a more effective method of determining those that are in danger of converting from MCI to AD.

Cognitive dysfunction and the 25-item National Eye Institute Visual Function Questionnaire

Introduction

Visual function and cognitive impairment are interrelated; however, little is known about the impact of modifying treatable vision impairment on the development of cognitive dysfunction. This study examines the relationship between cognition and self-reported visual function using the National Eye Institute's Visual Function Questionnaire (NEI VFQ).

Methods

Participants completed the NEI VFQ 25-Item questionnaire as well as the Mini-Mental State Examination (MMSE). Additionally, all participants were assigned a consensus clinical diagnosis based on established criteria. We used a general linear model and analysis of variance approach to compare means between multiple groups.

Results

A significant association between overall composite score on the NEI VFQ and total MMSE score was revealed (P = 0.04). On average, for every 1-point increase in MMSE score, the overall composite score increased by 0.40 units (95% confidence interval: 0.03-0.77).

Discussion

Reduced visual function should raise concerns about cognitive decline and prompt additional assessment.

Relationship between cognitive domains and hearing ability in memory clinic patients: How did the relationship change after 6 months of introducing a hearing aid?

OBJECTIVE

We aimed to evaluate the relationship between hearing ability and cognitive domains and determine how the relationship changes after 6 months of introducing a hearing aid.

METHODS

We conducted a 6-month hearing aid lending study between September 2014 and March 2019, including 59 older participants who visited the Memory Clinic at the National Center for Geriatrics and Gerontology. The hearing level was assessed using pure tone audiometry. Speech intelligibility was measured using the monosyllabic word discrimination score. We assessed the relationship between hearing ability and cognitive domains using the Mini-Mental State Examination (MMSE) total score and four subscale scores (orientation, memory, attention, and language). Differences in the cognitive function between baseline (pre-) and 6 months later (post-) after introducing a hearing aid were also assessed.

RESULTS

The pre-orientation score was significantly associated with the pure-tone average (p = 0.013), and the pre-language score was significantly associated with speech intelligibility (p = 0.006) after adjusting for confounders. None of the MMSE subscale scores were significantly different between pre- and post-scores, however, an expectation of improvement with continuous hearing aid use was implied in the attention domain.

CONCLUSION

We found a significant association between hearing ability and cognitive domains in individuals whose cognitive functions were not considered healthy. The presence of a potential relationship between cognitive domains, hearing ability, and auditory compensation is suggested.

Prevalence of Comorbid Dementia in Late-life Depression and Bipolar Disorder: A Retrospective Inpatient Study

Background:

Dementia in patients with late-life mood disorders is clinically important.

Objective:

We aimed to investigate the prevalence of dementia in patients with late-life major depressive disorder (MDD) or bipolar disorder (BD) and to clarify the clinical characteristics associated with the diagnosis of dementia.

Methods:

The prevalence of dementia at hospital discharge and the clinical characteristics at hospitalization who are diagnosed with MDD or BD over 65 years of age, from the medical records of 684 patients who had been admitted from 2015 to 2020 were investigated.

Results:

A total of 66 patients with MDD (n = 50) and BD (n = 16) were analyzed. The prevalence of dementia was significantly higher in MDD than in BD (24.0% versus 0%; p = 0.026). The mean age at onset of MDD was significantly older in the MDD with dementia group than in the MDD without (76.9±6.3 years versus 62.2±14.0 years; p < 0.001). The rate of first depressive episode at this admission was significantly higher in the MDD with dementia group (91.7% versus 30.3%; p < 0.001). The diagnosis of dementia was significantly associated with lower scores for “insomnia early” (p = 0.019) and higher scores for “insight” (p = 0.049) on the 17-item Hamilton Depression Rating (HAMD-17) subscales and lower scores for “recall” (p = 0.003) on the MMSE subscales.

Conclusion:

The older age of first onset of depression, “insomnia early”, “insight” and “recall” may be useful indicators for a diagnosis of dementia in late-life depression.

Comparison of two cognitive screening measures in a longitudinal sample of youth at-risk for psychosis

BACKGROUND

Validated screening tools are needed to detect subtle cognitive impairment in individuals at-risk for developing psychosis. Here, the utility of the Mini-Mental Status Examination (MMSE) and Penn Computerized Neurocognitive Battery (CNB) were evaluated for detecting cognitive impairment in individuals with psychosis spectrum (PS) symptoms.

METHODS

Participants (n = 229; 54 % female) completed the MMSE and CNB at baseline and two-year follow-up. PS (n = 91) and typically developing (TD; n = 138) participants were enrolled at baseline based on the presence or absence of PS symptoms. After two years, 65 participants remained PS, 104 participants remained TD, 23 participants had Emergent (EP) subthreshold PS symptoms, and 37 participants were experiencing Other Psychopathology (OP).

RESULTS

Generally, those with PS had lower scores than TD on both the MMSE (p < 0.0001) and CNB (p < 0.0001). Additionally, OP participants performed lower on the MMSE than TD (p = 0.02). Receiver operating characteristic (ROC) analyses indicated similar area under the curve (AUCs) for the two instruments (0.67); the MMSE showed higher specificity (0.71 vs. 0.62), while the CNB showed higher sensitivity (0.66 vs 0.52). Use of the MMSE and CNB in combination provided the highest diagnostic classification.

CONCLUSION

The MMSE and CNB can be used to screen for cognitive impairment in PS. The MMSE is better at ruling out PS-related cognitive impairment while the CNB is better at ruling in PS-related cognitive impairment. Overall, our results indicate that both tests are useful in screening for cognitive impairment, particularly in combination, in a PS population.

Analysis of the Clock-Reading Ability in Patients with Cognitive Impairment: Comparison of Analog Clocks and Digital Clocks

Background: Time disorientation is one of the main symptoms observed in patients with dementia; however, their clock-reading ability has not been fully reported. Objective: This study aimed to investigate the clock-reading ability of both digital and analog clocks in patients with dementia. We newly devised the clock-reading test (CRT) and the number-reading test (NRT) to assess cognitive factors that may affect clock-reading ability. Furthermore, the discriminating power of the CRT was calculated. Methods: 104 participants were categorized based on their Mini-Mental State Examination (MMSE) scores as follows: subjective cognitive decline ∼ mild cognitive impairment (SCD∼MCI, N = 43), early Alzheimer’s disease (AD) (N = 26), and middle-to-late AD (N = 35). Their cognitive abilities were evaluated using the clock-drawing test (CDT), CRT, and NRT. Results: Cognitive decline leads to impairment of clock-reading ability which is more pronounced in the analog clocks than digital ones. This deficit in clock-reading is attributed to a loss of semantic memory regarding clocks at all stages. Additionally, visuospatial dysfunction and reduced ability of number recognition may lead to deficit in clock-reading in the advanced stage of AD. The discriminating power of the CRT (analog) (AUC = 0.853) was high enough to detect cognitive decline. Conclusion: Digital clocks are more readable by patients with dementia. Since reading clocks is closely associated with daily life, the CRT has proved to be a useful tool. A decline of analog clock-reading may be an early detector for the onset of dementia in elderly patients.

Staging Disease Severity Using the Alzheimer's Disease Composite Score (ADCOMS): A Retrospective Data Analysis

INTRODUCTION

The Alzheimer's disease (AD) composite score (ADCOMS) has been shown to be a more sensitive measure of cognitive change in early AD (i.e., mild cognitive impairment [MCI] and mild AD) than commonly used measures. This study derived ADCOMS value ranges associated with different disease severity stages across the predementia and AD continuum.

METHODS

Data from patients enrolled in the Alzheimer's Disease Neuroimaging Initiative were assessed at baseline and month 24. Data were randomly split into derivation and validation samples. Receiver-operating characteristic (ROC) curves of ADCOMS values were generated in the derivation sample to assess the sensitivity and specificity of ADCOMS cutoff values compared with existing disease severity cutoff scores using the Clinical Dementia Rating (CDR) global, CDR Sum of Boxes, Alzheimer's Disease Assessment Scale-Cognitive Subscale, and Mini-Mental State Examination. Optimal ADCOMS cutoff values for each disease stage were compared between the derivation and the validation samples using a χ2 test. The diagnostic accuracy of the derived ADCOMS cutoff values was then assessed. The analyses were repeated for the subset with positive amyloid β confirmation (Aβ +).

RESULTS

The following ADCOMS value ranges for the total population and Aβ + population were identified: < 0.29 indicative of normal cognition, 0.29 to < 0.45 indicative of MCI, 0.45-0.77 indicative of mild AD, and > 0.77 indicative of at least moderate AD. The reliability of these ADCOMS value ranges was supported by diagnostic accuracy tests and tests indicating no significant difference in the ROC curves between the derivation and validation samples.

CONCLUSION

ADCOMS values ranges can be used to assess the severity of cognitive decline. The derived severity threshold score ranges for ADCOMS will enable its use as an endpoint in clinical trials assessing disease progression and clinical outcomes of disease-modifying therapies in persons with MCI or early AD, including patients with Aβ + confirmation.

REM sleep behavioral disorder may be an independent risk factor for orthostatic hypotension in Parkinson's disease

PURPOSE

The purpose of this study was to evaluate the association between clinically possible rapid eye movement (REM) sleep behavioral disorder (pRBD) and orthostatic hypotension (OH) in PD patients, as well as to explore the mechanisms underlying the association.

METHODS

PD patients (n = 116) were assigned to a group with OH (PD-OH) or without OH (PD-NOH). General demographic and clinical data were collected. A series of scales were used to assess the clinical symptoms in the two groups.

RESULTS

A total of 27 patients (23.3%) had OH. The PD-OH group showed significantly higher H-Y staging score and significantly higher frequencies of pRBD, anxiety, depression, and cognitive impairment than the PD-NOH group. Binary logistic regression analysis identified the following factors as independently associated with PD-OH: H-Y staging [odds ratio (OR) 2.565, 95% confidence interval (CI) 1.160-5.673; P = 0.020], RBD (OR 7.680, 95% CI 1.944-30.346; P = 0.004), UPDRS II (OR 1.021, 95% CI 0.980-1.063; P = 0.020), depression (OR 7.601, 95% CI 1.492-38.718; P = 0.015), and cognitive impairment (OR 0.824, 95% CI 0.696-0.976; P = 0.025).

CONCLUSIONS

Our results suggest that pRBD is an independent risk factor for OH in patients with PD. We speculate that there may be a close relationship between RBD and OH, which requires attention. Early diagnosis of RBD may help predict the appearance of OH in PD patients.

The relationship between cognitive function and competence in inhaler technique in older adults with airway disease

Cognitive impairment has high prevalence in older adults with airway diseases, and may influence their competence in inhaler use, thereby negatively affecting patient prognosis. We aimed to evaluate the relationship between cognitive function and competence in inhaler technique. We enrolled 108 inhaler naïve older adults (≥60 years) with airway disease in this prospective observational study and performed the Korean version of the Mini-Mental State Examination (K-MMSE). After explaining the inhaler technique, we scored its competence. While the total K-MMSE score was unrelated to the inhaler score, the orientation for place (estimates=0.62, p = 0.009), registration (estimates=0.988, p = 0.037), and recall (estimates=0.161, p = 0.048) were positively associated with the score. Low K-MMSE scores were associated with lower odds ratio for the competence of the "exhale" step (adjusted odds ratio= 0.23, p = 0.018). Thus, a K-MMSE-mediated evaluation of cognitive function in older adults with airway disease can be a useful tool to predict inhaler competence.

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer's Disease

The early diagnosis of Alzheimer's disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer's patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer's disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

Air Pollution Is Associated with Poor Cognitive Function in Taiwanese Adults

The issue of air pollution is gaining increasing attention worldwide, and mounting evidence has shown an association between air pollution and cognitive decline. The aim of this study was to investigate the relationships between air pollutants and cognitive impairment using the Mini-Mental State Exam (MMSE) and its sub-domains. In this study, we used data from the Taiwan Biobank combined with detailed daily data on air pollution. Cognitive function was assessed using the MMSE and its five subgroups of cognitive functioning. After multivariable linear regression analysis, a high level of particulate matter with an aerodynamic diameter of ≤2.5 μm (PM_2.5), low ozone (O₃), high carbon monoxide (CO), high sulfur dioxide (SO₂), high nitric oxide (NO), high nitrogen dioxide (NO₂), and high nitrogen oxide (NO_x) were significantly associated with low total MMSE scores. Further, high SO₂ and low O₃ were significantly associated with low MMSE G1 scores. Low O₃, high CO, high SO₂, high NO₂, and high NO_x were significantly associated with low MMSE G4 scores, and high PM_2.5, high particulate matter with an aerodynamic diameter of ≤10 μm (PM₁₀), high SO₂, high NO₂, and high NO_x were significantly associated with low MMSE G5 scores. Our results showed that exposure to different air pollutants may lead to general cognitive decline and impairment of specific domains of cognitive functioning, and O₃ may be a protective factor. These findings may be helpful in the development of policies regarding the regulation of air pollution.