Design requirements for a digital storytelling application for people with mild cognitive impairment (MCI).

Background: The current digital storytelling applications present advantages for individuals with Mild Cognitive Impairment (MCI); however, there exists a notable oversight regarding their potential to facilitate group-based storytelling activities with this population. This study endeavors to identify design requirements for a more inclusive and accessible digital storytelling tool for people with MCI. Method: The methodological framework encompasses distinct stages, commencing with focus groups and interviews (Stage 1), followed by prototyping workshops (Stage 2) and qualitative prototype testing (Stage 3). The comprehensive three-stage research involved participants residing in Beijing, China, including 43 people with MCI aged 65-95 years (M = 79.09, SD = 8.99), with a mean Montreal Cognitive Assessment score of 21.91 (range = 18-26, SD = 2.40). Additionally, 17 care partners and 10 occupational or clinical therapists actively participated. Result: The culmination of the three-stage research process has yielded 12 discernible key design requirements. Preferred storytelling themes center around narratives designed to elicit positive emotions. The narrative material generation process involves a systematic approach, unlocking memories through carefully formulated questions. In memory retrieval, users are provided with hints, bolstering confidence and perpetuating a semblance of face-to-face interaction. The focus in story sharing lies in transcending mere narration and extending it to a wider audience. Conclusion: This case study centers on crafting a digital storytelling application to enhance social connections for people with MCI. It delves into crucial design requirements addressing memory challenges, emphasizing individual preparation and group sharing. The developed digital storytelling application demonstrates potential to offer valuable memory support and foster personal and collective connections. Future research will focus on formal testing to evaluate these outcomes.

The Impact of Medication Regimen Adjustment Ratio on Adherence and Glycemic Control in Patients with Type 2 Diabetes and Mild Cognitive Impairment.

BACKGROUND: An antidiabetic medication regimen is crucial for maintaining glycemic control. Type 2 diabetes mellitus (T2DM) and cognitive dysfunction have a bidirectional relationship. This study aims to explore the impact that adjusting antidiabetic medication regimens has on medication adherence, glycemic control, and cognitive function in patients with T2DM and mild cognitive impairment (MCI). METHODS: This is an observational cross-sectional analysis that includes 364 consecutive inpatients with T2DM. Clinical data were collected, medication adherence was assessed using the Medication Adherence Report Scale (MARS-5), and cognitive status was evaluated using the Chinese version of the Montreal Cognitive Assessment (MoCA) and Mini-mental State Examination (MMSE). These data were obtained both during hospitalization and at a three-month follow-up. Multivariable logistic regression analysis was applied to determine the association between changes in medication regimens and medication adherence, glycemic control, and cognitive function. RESULTS: Baseline medication adherence was high across all three different cognitive status groups, with no significant difference in MARS-5 scores. At the 3-month follow-up, the group with a high adjustment ratio of antidiabetic medication regimens showed an increase in their hemoglobin A1c (HbA1c) level compared to the baseline, while the group with a low adjustment ratio showed a decrease in this level. In addition, the MoCA, MMSE, and MARS-5 scores of the high-adjustment group were significantly lower than those of the low-adjustment group. CONCLUSIONS: A high ratio of medication adjustment was significantly associated with worse medication adherence and glycemic control in T2DM patients with MCI. Patients with a low ratio of medication adjustment had good adherence and better glycemic control. Clinicians should take cognitive status into account when adjusting antidiabetic regimens for T2DM patients and may need to provide additional guidance to patients with cognitive impairment to improve adherence and glycemic outcomes.

Behavioral Alterations of Spatial Cognition and Role of the Apolipoprotein E-ε4 in Patients with MCI Due to Alzheimer's Disease: Results from the BDSC-MCI Project.

Background: Beyond memory deterioration, spatial disorientation may occur along the continuum of normal aging-dementia of Alzheimer's type. The present study aims at detecting behavioral disorders of spatial cognition in prodromal Alzheimer's disease (AD) and verifying the association between Apolipoprotein E-ε4 (ApoE-ε4) genotype and gait patterns during a real-world naturalistic task. Methods: A sample of 58 elderly participants, of which 20 patients with mild cognitive impairment with CFS biomarker evidence of AD, 23 individuals with subjective cognitive decline (SCD), and 15 healthy controls (HCs), was tested by a modified version of the Detour Navigation Test (DNT-mv). Generalized linear models were run to explore the association between group belonging and wrong turns (WTs)/moments of hesitation (MsH) as behavioral disorientation scores of the DNT-mv as well as the effect of ApoE-ε4 genotype on time and walking speed registered by a smartphone app providing GPS tracking of body movement around urban environments. Results: Patients with MCI due to AD reported more WTs than individuals with SCD and HCs. Further, the ApoE-ε4 genotype determined a lower capacity in spatial information processing, influencing gait during naturalistic spatial navigation tasks. Conclusions: Behavior alterations of spatial cognition can be detected ecologically in prodromal AD. The use of technological solutions supporting gait analysis may help in corroborating the experimental observation.

A prediction model of dementia conversion for mild cognitive impairment by combining plasma pTau181 and structural imaging features.

AIMS: The early stages of Alzheimer's disease (AD) are no longer insurmountable. Therefore, identifying at-risk individuals is of great importance for precise treatment. We developed a model to predict cognitive deterioration in patients with mild cognitive impairment (MCI). METHODS: Based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we constructed models in a derivation cohort of 761 participants with MCI (138 of whom developed dementia at the 36th month) and verified them in a validation cohort of 353 cognitively normal controls (54 developed MCI and 19 developed dementia at the 36th month). In addition, 1303 participants with available AD cerebrospinal fluid core biomarkers were selected to clarify the ability of the model to predict AD core features. We assessed 32 parameters as candidate predictors, including clinical information, blood biomarkers, and structural imaging features, and used multivariable logistic regression analysis to develop our prediction model. RESULTS: Six independent variables of MCI deterioration were identified: apolipoprotein E ε4 allele status, lower Mini-Mental State Examination scores, higher levels of plasma pTau181, smaller volumes of the left hippocampus and right amygdala, and a thinner right inferior temporal cortex. We established an easy-to-use risk heat map and risk score based on these risk factors. The area under the curve (AUC) for both internal and external validations was close to 0.850. Furthermore, the AUC was above 0.800 in identifying participants with high brain amyloid-ß loads. Calibration plots demonstrated good agreement between the predicted probability and actual observations in the internal and external validations. CONCLUSION: We developed and validated an accurate prediction model for dementia conversion in patients with MCI. Simultaneously, the model predicts AD-specific pathological changes. We hope that this model will contribute to more precise clinical treatment and better healthcare resource allocation.

Feasibility and Clinical Effectiveness of Computer-Based Cognitive Rehabilitation in Illiterate and Low-Educated Individuals with Mild Cognitive Impairment: Preliminary Data.

OBJECTIVE: We investigated the feasibility of the RehaCom cognitive rehabilitation software in illiterate and low-educated individuals with mild cognitive impairment (MCI) and its clinical effectiveness in improving cognitive functions. METHODS: Twenty illiterate or low-educated individuals with MCI were randomly assigned to an intervention (IG; n = 10) and control group (CG; n = 10). The IG participated in the cognitive enhancement program for 6 weeks, twice a week and a duration of 50-60 min for each session, while the CG did not receive any kind of intervention. RESULTS: The two groups were demographically matched. The IG successfully completed all sessions of the cognitive enhancement program. A within-subject comparison between baseline and post-intervention assessment of cognitive functions indicated that the IG improved significantly on all administered neuropsychological tests, in contrast to the CG, whose performance remained stable between baseline and final assessment. A between-group comparison found statistically significant differences between the IG and CG groups on the Hindi Mental State Examination, Mini-Mental State Examination, and on delayed memory and recognition tasks, in favor of the IG. CONCLUSIONS: The findings of the present study support the feasibility of applying computerized cognitive enhancement programs to illiterate and low-educated individuals. Moreover, these programs appear to contribute positively to improving the cognitive functions of this population group. In order to generalize and confirm similar findings in a broader population of illiterate and low-educated individuals, future studies should include larger samples, possibly with longer duration of treatment and control groups that will receive non-targeted interventions as placebo interventions.

Optimizing individualized therapy decision-making in multiple myeloma (MM): integration and impact of the Revised Myeloma Comorbidity Index in the MM-tumor board.

Multiple Myeloma (MM) is a hematological disease predominantly affecting elderly patients. The complexity of current treatment necessitates individualized approaches. Therein, functional assessment (FA) tools, such as the Revised Comorbidity Index (R-MCI) at our University- and Comprehensive Cancer Center Freiburg, play a crucial role. This study aimed to determine (a) the implementation of the R-MCI in our MM-tumor board (MM-TB), (b) its impact on treatment guidance at baseline and (c) potential changes during follow-up. This exploratory study investigated R-MCI coverage and distribution in a cohort of patients with multiple TB presentations. Among them, a follow-up patient cohort undergoing subsequent MM-therapy was analyzed to determine treatment adjustments and changes in patients' condition measured by R-MCI alterations. During our 3-year assessment period, 565 patients were presented in our MM-TB, totaling 1256 TB-presentations. In the multiple TB presentation cohort, the median number of TB presentations was 3 (range: 2-12). R-MCI scores within the MM-TB were available in 94%, whereas in 6%, the R-MCI had not been integrated. Among these, potential failure to identify the need for treatment modifications was determined. In the follow-up cohort, patient characteristics were typical for referral/university centers. Dose reductions were performed in 55% and were more prevalent among patients with ≥ 4 vs. lesser TB presentations. Most patients (55%) showed a fitness stabilization or improvement via follow-up R-MCI. R-MCI integration in MM-TB exceeded > 90%, indicating its successful integration for treatment support. Our results underscore its value in guiding therapy decisions, providing a comprehensive assessment beyond age considerations.

A multimodal cross-transformer-based model to predict mild cognitive impairment using speech, language and vision.

Mild Cognitive Impairment (MCI) is an early stage of memory loss or other cognitive ability loss in individuals who maintain the ability to independently perform most activities of daily living. It is considered a transitional stage between normal cognitive stage and more severe cognitive declines like dementia or Alzheimer's. Based on the reports from the National Institute of Aging (NIA), people with MCI are at a greater risk of developing dementia, thus it is of great importance to detect MCI at the earliest possible to mitigate the transformation of MCI to Alzheimer's and dementia. Recent studies have harnessed Artificial Intelligence (AI) to develop automated methods to predict and detect MCI. The majority of the existing research is based on unimodal data (e.g., only speech or prosody), but recent studies have shown that multimodality leads to a more accurate prediction of MCI. However, effectively exploiting different modalities is still a big challenge due to the lack of efficient fusion methods. This study proposes a robust fusion architecture utilizing an embedding-level fusion via a co-attention mechanism to leverage multimodal data for MCI prediction. This approach addresses the limitations of early and late fusion methods, which often fail to preserve inter-modal relationships. Our embedding-level fusion aims to capture complementary information across modalities, enhancing predictive accuracy. We used the I-CONECT dataset, where a large number of semi-structured conversations via internet/webcam between participants aged 75+ years old and interviewers were recorded. We introduce a multimodal speech-language-vision Deep Learning-based method to differentiate MCI from Normal Cognition (NC). Our proposed architecture includes co-attention blocks to fuse three different modalities at the embedding level to find the potential interactions between speech (audio), language (transcribed speech), and vision (facial videos) within the cross-Transformer layer. Experimental results demonstrate that our fusion method achieves an average AUC of 85.3% in detecting MCI from NC, significantly outperforming unimodal (60.9%) and bimodal (76.3%) baseline models. This superior performance highlights the effectiveness of our model in capturing and utilizing the complementary information from multiple modalities, offering a more accurate and reliable approach for MCI prediction.

Prediction and validation of mild cognitive impairment in occupational dust exposure population based on machine learning.

OBJECTIVE: Workers exposed to dust for extended periods may experience varying degrees of cognitive impairment. However, limited research exists on the associated risk factors. This study aims to identify key variables using machine learning algorithms (ML) and develop a model to predict the occurrence of mild cognitive impairment in miners. METHODS: A total of 1938 miners were included in the study. Univariate analysis and multivariable logistic regression were employed to identify independent risk factors for cognitive impairment among miners. The dataset was randomly divided into a training set and a validation set in an 8:2 ratio of 1550 and 388 individuals, respectively. An additional group of 351 miners was collected as a test set for cognitive impairment assessment. Seven machine learning algorithms, including XGBoost, Logistic Regression, Random Forest, Complement Naive Bayes, Multi-layer Perceptron, Support Vector Machine, and K-Nearest Neighbors, were used to establish a predictive model for mild cognitive impairment in the dust-exposed population, based on baseline characteristics of the workers. The predictive performance of the models was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), and the XGBoost model was further explained using the Shapley Additive exPlanations (SHAP) package. Cognitive function assessments using rank sum tests were conducted to compare differences in cognitive domains between the mild cognitive impairment group and the normal group. RESULTS: Univariate and multivariable logistic regression analyses revealed that education level, Age, Work years, SSRS (Self-Rating Scale for Stress), and HAMA (Hamilton Anxiety Rating Scale) were independent risk factors for cognitive impairment among dust-exposed workers. Comparative analysis of the performance of the seven machine learning algorithms demonstrated that XGBoost (training set: AUC=0.959, validation set: AUC=0.795) and Logistic Regression (training set: AUC=0.818, validation set: AUC=0.816) models exhibited superior predictive performance. Results from the test set showed that the AUC of the XGBoost model (AUC=0.913) outperformed the Logistic Regression model (AUC=0.778). Miners with mild cognitive impairment exhibited significant impairments (p<0.05) in visual-spatial abilities, attention, abstract thinking, and memory areas. CONCLUSION: Machine learning algorithms can predict the risk of cognitive impairment in this population, with the XGBoost algorithm showing the best performance. The developed model can guide the implementation of appropriate preventive measures for dust-exposed workers.

Dynamics of Cognitive Impairment in MCI Patients over a Three-Year Period: The Informative Role of Blood Biomarkers, Neuroimaging, and Genetic Factors.

Given the high growth rates of cognitive decline among the elderly population and the lack of effective etiological treatments, early diagnosis of cognitive impairment progression is an imperative task for modern science and medicine. It is of particular interest to identify predictors of an unfavorable subsequent course of cognitive disorders, specifically, rapid progression. Our study assessed the informative role of various risk factors on the dynamics of cognitive impairment among mild cognitive impairment (MCI) patients. The study included patients with MCI (N = 338) who underwent neuropsychological assessment, magnetic resonance imaging (MRI) examination, blood sampling for general and biochemical analysis, APOE genotyping, and polygenic risk score (PRS) evaluation. The APOE ε4/ε4 genotype was found to be associated with a diminished overall cognitive scores initial assessment and negative cognitive dynamics. No associations were found between cognitive changes and the PRS. The progression of cognitive impairment was associated with the width of the third ventricle and hematological parameters, specifically, hematocrit and erythrocyte levels. The absence of significant associations between the dynamics of cognitive decline and PRS over three years can be attributed to the provided suitable medical care for the prevention of cognitive impairment. Adding other risk factors and their inclusion in panels assessing the risk of progression of cognitive impairment should be considered.

Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study.

BACKGROUND: About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied. OBJECTIVE: This study aims to establish associations between cognitive abilities and various attributes of speech and natural language production. To date, the majority of research has been cross-sectional, relying mostly on data from structured interactions and restricted to textual versus acoustic analyses. METHODS: In a sample of 71 older (mean age 83.3, SD 7.0 years) community-dwelling adults who completed qualitative interviews and cognitive testing, we investigated the performance of both acoustic and psycholinguistic features associated with cognitive deficits contemporaneously and at a 1-2 years follow up (mean follow-up time 512.3, SD 84.5 days). RESULTS: Combined acoustic and psycholinguistic features achieved high performance (F1-scores 0.73-0.86) and sensitivity (up to 0.90) in estimating cognitive deficits across multiple domains. Performance remained high when acoustic and psycholinguistic features were used to predict follow-up cognitive performance. The psycholinguistic features that were most successful at classifying high cognitive impairment reflected vocabulary richness, the quantity of speech produced, and the fragmentation of speech, whereas the analogous top-ranked acoustic features reflected breathing and nonverbal vocalizations such as giggles or laughter. CONCLUSIONS: These results suggest that both acoustic and psycholinguistic features extracted from qualitative interviews may be reliable markers of cognitive deficits in late life.

Association of Physical Activity and Dietary Diversity with Cognitive Function in the Elderly with Type 2 Diabetes Mellitus: Findings from a Cross-Sectional Study.

AIMS: Mild cognitive impairment (MCI) is a common complication of type 2 diabetes mellitus (T2DM). Changes in lifestyle and dietary patterns play a crucial role in preventing both diabetes and cognitive impairment. METHODS: A cross-sectional study was conducted on 899 aging participants. The Dietary Diversity Score (DDS) was used to evaluate dietary diversity. The physical activity (PA) levels were divided based on metabolic equivalents and weekly activity time. Individual PA levels were further re-scored and combined with DDS scores to obtain each participant's total score. RESULTS: Regardless of T2DM status, individuals with MCI had lower DDS and plant-derived DDS compared to non-MCI individuals. Non-MCI subjects had higher total PA and DDS scores than MCI subjects. There were differences in the correlation between DDS or PA scores and blood glucose and MoCA scores among different groups. The subjects with high DDS levels showed a significantly decreased risk of MCI and T2DM + MCI. Those with a total PA and DDS score in Q4 showed a significantly decreased risk of MCI and T2DM + MCI compared to Q1. CONCLUSIONS: A diversified diet improved blood glucose levels and cognitive function. Elderly individuals with diverse diets and adequate PA had a reduced risk of developing T2DM and MCI.

Mild cognitive impairment prediction based on multi-stream convolutional neural networks.

BACKGROUND: Mild cognitive impairment (MCI) is the transition stage between the cognitive decline expected in normal aging and more severe cognitive decline such as dementia. The early diagnosis of MCI plays an important role in human healthcare. Current methods of MCI detection include cognitive tests to screen for executive function impairments, possibly followed by neuroimaging tests. However, these methods are expensive and time-consuming. Several studies have demonstrated that MCI and dementia can be detected by machine learning technologies from different modality data. This study proposes a multi-stream convolutional neural network (MCNN) model to predict MCI from face videos. RESULTS: The total effective data are 48 facial videos from 45 participants, including 35 videos from normal cognitive participants and 13 videos from MCI participants. The videos are divided into several segments. Then, the MCNN captures the latent facial spatial features and facial dynamic features of each segment and classifies the segment as MCI or normal. Finally, the aggregation stage produces the final detection results of the input video. We evaluate 27 MCNN model combinations including three ResNet architectures, three optimizers, and three activation functions. The experimental results showed that the ResNet-50 backbone with Swish activation function and Ranger optimizer produces the best results with an F1-score of 89% at the segment level. However, the ResNet-18 backbone with Swish and Ranger achieves the F1-score of 100% at the participant level. CONCLUSIONS: This study presents an efficient new method for predicting MCI from facial videos. Studies have shown that MCI can be detected from facial videos, and facial data can be used as a biomarker for MCI. This approach is very promising for developing accurate models for screening MCI through facial data. It demonstrates that automated, non-invasive, and inexpensive MCI screening methods are feasible and do not require highly subjective paper-and-pencil questionnaires. Evaluation of 27 model combinations also found that ResNet-50 with Swish is more stable for different optimizers. Such results provide directions for hyperparameter tuning to further improve MCI predictions.

Correlation between Vegetable and Fruit Intake and Cognitive Function in Older Adults: A Cross-Sectional Study in Chongqing, China.

OBJECTIVE: To explore the correlation between different types of vegetable and fruit intake and cognitive function among the older adults in Chongqing, China, and to provide a scientific basis for developing efficient lifestyle interventions for the prevention of Mild Cognitive Impairment (MCI). METHOD: Approximately 728 older adults in urban and rural areas of Chongqing were surveyed using face-to-face questionnaires. Cognitive function was assessed with the Montreal Cognitive Assessment-Basic (MoCA-B) scale, and the vegetable and fruit intake groups were investigated with the Simple Food Frequency Counting Survey Scale. Binary logistic regression was used to explore the effect of the vegetable and fruit intake group on cognitive function. Subgroup analysis was used to demonstrate the robustness of the results. RESULT: Of the 728 participants in the study, 36.40% were likely to have MCI, which is higher than the national average for this condition. After adjusting for confounders, compared to the Q1 group, fruit and root vegetable intake was a protective factor for MCI, showing a dose-response relationship (p < 0.05). Only lower intake (Q2) of total vegetables, medium intake (Q2, Q3) of solanaceous vegetables, and medium-high intake (Q2, Q4) of fungi and algae was protective against MCI, whereas the leafy vegetables showed no relation to MCI. Apart from this, participants who were older, female, unmarried, non-smoking, and engaged in physical labor, and who had an average monthly income of less than 3000 RMB were more likely to suffer from cognitive impairment. CONCLUSION: This suggested that the fruit-intake groups and some vegetable-intake groups showed a protective effect on cognitive function, and might behave differently depending on their different intake and demographic characteristics. A sensible, healthy diet can help prevent MCI.

The Dual Task Ball Balancing Test and Its Association With Cognitive Function: Algorithm Development and Validation.

BACKGROUND: Dual task paradigms are thought to offer a quantitative means to assess cognitive reserve and the brain's capacity to allocate resources in the face of competing cognitive demands. The most common dual task paradigms examine the interplay between gait or balance control and cognitive function. However, gait and balance tasks can be physically challenging for older adults and may pose a risk of falls. OBJECTIVE: We introduce a novel, digital dual-task assessment that combines a motor-control task (the "ball balancing" test), which challenges an individual to maintain a virtual ball within a designated zone, with a concurrent cognitive task (the backward digit span task [BDST]). METHODS: The task was administered on a touchscreen tablet, performance was measured using the inertial sensors embedded in the tablet, conducted under both single- and dual-task conditions. The clinical use of the task was evaluated on a sample of 375 older adult participants (n=210 female; aged 73.0, SD 6.5 years). RESULTS: All older adults, including those with mild cognitive impairment (MCI) and Alzheimer disease-related dementia (ADRD), and those with poor balance and gait problems due to diabetes, osteoarthritis, peripheral neuropathy, and other causes, were able to complete the task comfortably and safely while seated. As expected, task performance significantly decreased under dual task conditions compared to single task conditions. We show that performance was significantly associated with cognitive impairment; significant differences were found among healthy participants, those with MCI, and those with ADRD. Task results were significantly associated with functional impairment, independent of diagnosis, degree of cognitive impairment (as indicated by the Mini Mental State Examination [MMSE] score), and age. Finally, we found that cognitive status could be classified with >70% accuracy using a range of classifier models trained on 3 different cognitive function outcome variables (consensus clinical judgment, Rey Auditory Verbal Learning Test [RAVLT], and MMSE). CONCLUSIONS: Our results suggest that the dual task ball balancing test could be used as a digital cognitive assessment of cognitive reserve. The portability, simplicity, and intuitiveness of the task suggest that it may be suitable for unsupervised home assessment of cognitive function.

[Clinical and neuropsychological features and changes to regional cerebral blood flow in |patients with Parkinson's disease dementia].

This study aimed to clarify associations of clinical and neuropsychological features and change in regional cerebral blood flow (rCBF) on 123I-IMP-SPECT in patients with Parkinson's disease (PD) who developed dementia. Sixty-one PD patients (mean age, 65.9 ± 8.6 years; mean disease duration, 11.0 ± 11.0 years) were recruited and followed-up for two years. Clinical and neuropsychological characteristics, and rCBF from SPECT were compared between PD patients who developed dementia (PDD+) and those who remained undemented (PDD-). Thirty-eight PD patients (62.3%) were diagnosed with PD-MCI at baseline. During follow-up, 22 PD patients (36%) developed dementia (PDD+). Univariate logistic regression models showed that Hoehn and Yahr scale 4 (odds ratio [OR] 5.85; 95% confidence interval [CI] 1.35-30.75]), visual hallucination (OR 5.95; 95%CI 1.67-25.4]), and PD-MCI (OR 6.47; 95%CI 1.57-39.63]) represented a significant risk factor for PDD+. Among neuropsychological parameters, WAIS (Wechsler Adult Intelligence Scale)-III block design (OR 6.55; 95%CI 1.66-29.84), letter number sequencing (OR 7.01; 95%CI 1.65-36.64), digit-symbol coding (OR 3.90; 95%CI 1.13-14.2), Wechsler Memory Scale, revised (WMS-R) visual paired associates II (delayed recall) (OR 4.68; 95%CI 1.36-17.36), Logical memory I (immediate recall) (OR 8.30; 95%CI 1.37-90.89), Logical memory II (delayed recall) (OR 6.61; 95%CI 1.35-44.33), Visual reproduction I (immediate recall) (OR 7.67; 95%CI 2.11-31.40), and Visual reproduction II (delayed recall) (OR 5.64; 95%CI 1.62-21.47) were significant risk factors. Decreased rCBF assessed using the general linear model (two-sample t-test) by SPM8 was observed in the left precuneus (0, -66, 16), right cuneus (6, -76, 30), and left angular gyrus (-46, -74, 32) in PDD+ compared with PDD- patients. Collectively, we have here shown that clinical and neuropsychological characteristics as well as changes to rCBF in PD patients who converted to PDD+. These features should be carefully monitored to detect the development of dementia in PD patients.

Methods for assessment of rey auditory verbal learning test performance in memory clinic patients and healthy adults - at the cross-roads of learning theory and clinical utility.

Background: Knowledge is still lacking regarding the preferred method for evaluation of learning in the Rey Auditory Verbal Learning Test (RAVLT). Validity of different methods was examined by the effect size in differentiating diagnostic stages in memory clinic patients versus healthy adults and the strength of association between RAVLT performance and brain atrophy. Method: The study included individuals with dementia (n = 247), Mild Cognitive Impairment (MCI, n = 709), Subjective Cognitive Impairment (SCI, n = 175) and cognitively unimpaired adults serving as healthy controls (HC, n = 102). All patients went through a comprehensive clinical examination and neuropsychological assessment of cognition including episodic memory gauged with RAVLT and brain imaging of medial temporal atrophy, cortical atrophy, and white matter hyperintensity. Results: The standard method for evaluation of learning in RAVLT (summed score over five trials) together with the late learning method (mean of trials 4 and 5) were the two most powerful methods according to group differentiation (discriminant validity). Both methods also showed considerable association with medial temporal atrophy (construct validity). The initial RAVLT performance represented by results on trial 1 and the constant in regression analysis with the power function provided information regarding attention that was important for the separation of SCI and HC. Conclusions: The most favorable clinical utility was indicated by discriminant and construct validity by total learning (standard method) including both attention- and learning-related parts and late learning of RAVLT performance, while theoretical understanding of mental processes involved in RAVLT performance was provided by the distinction between initial versus the subsequent learning performance.

EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.

In recent years, electroencephalography (EEG) has been investigated for identifying brain disorders. This technique involves placing multiple electrodes (channels) on the scalp to measure the brain's activities. This study focuses on accurately detecting mild cognitive impairment (MCI) from the recorded EEG signals. To achieve this, this study first introduced discrete wavelet transform (DWT)-based approaches to generate reliable biomarkers for MCI. These approaches decompose each channel's signal using DWT into a set of distinct frequency band signals, then extract features using a non-linear measure such as band power, energy, or entropy. Various machine learning approaches then classify the generated features. We investigated these methods on EEGs recorded using 19 channels from 29 MCI patients and 32 healthy subjects. In the second step, the study explored the possibility of decreasing the number of EEG channels while preserving, or even enhancing, classification accuracy. We employed multi-objective optimization techniques, such as the non-dominated sorting genetic algorithm (NSGA) and particle swarm optimization (PSO), to achieve this. The results show that the generated DWT-based features resulted in high full-channel classification accuracy scores. Furthermore, selecting fewer channels carefully leads to better accuracy scores. For instance, with a DWT-based approach, the full-channel accuracy achieved was 99.84%. With only four channels selected by NSGA-II, NSGA-III, or PSO, the accuracy increased to 99.97%. Furthermore, NSGA-II selects five channels, achieving an accuracy of 100%. The results show that the suggested DWT-based approaches are promising to detect MCI, and picking the most useful EEG channels makes the accuracy even higher. The use of a small number of electrodes paves the way for EEG-based diagnosis in clinical practice.

"Let's talk about sex, inflammaging, and cognition, baby": A meta-analysis and meta-regression of 106 case-control studies on mild cognitive impairment and Alzheimer's disease.

Background: Chronic inflammation is recognised as an important component of Alzheimer's disease (AD), yet its relationship with cognitive decline, sex-differences, and age is not well understood. This study investigated the relationship between inflammatory markers, cognition, sex, and age in individuals with mild cognitive impairment (MCI) and AD. Methods: A systematic review was performed to identify case-control studies which measured cognitive function and inflammatory markers in serum, plasma, and cerebrospinal fluid in individuals with MCI or AD compared with healthy control (HC) participants. Meta-analysis was performed with Hedges' g calculated in a random effects model. Meta-regression was conducted using age, sex, and mini-mental status exam (MMSE) values. Results: A total of 106 studies without a high risk of bias were included in the meta-analysis including 18,145 individuals: 5625 AD participants, 3907 MCI participants, and 8613 HC participants. Combined serum and plasma meta-analysis found that IL1ß, IL6, IL8, IL18, CRP, and hsCRP were significantly raised in individuals with AD compared to HC. In CSF, YKL40, and MCP-1 were raised in AD compared to HC. YKL40 was also raised in MCI compared to HC. Meta-regression analysis highlighted several novel findings: MMSE was negatively correlated with IL6 and positively correlated with IL1α in AD, while in MCI studies, MMSE was negatively correlated with IL8 and TNFα. Meta-regression also revealed sex-specific differences in levels of IL1α, IL4, IL6, IL18, hsCRP, MCP-1, and YKL-40 across AD and MCI studies, and age was found to account for heterogeneity of CRP, MCP-1, and IL4 in MCI and AD. Conclusion: Elevated levels of IL6 and YKL40 may reflect microglial inflammatory activity in both MCI and AD. Systemic inflammation may interact with the central nervous system, as poor cognitive function in individuals with AD and MCI was associated with higher levels of serum and plasma proinflammatory cytokines IL6 and TNFα. Moreover, variations of systemic inflammation between males and females may be modulated by sex-specific hormonal changes, such as declining oestrogen levels in females throughout the menopause transition. Longitudinal studies sampling a range of biospecimen types are needed to elucidate the nuances of the relationship between inflammation and cognition in individuals with MCI and AD, and understand how systemic and central inflammation differentially impact cognitive function.

Early Alzheimer's Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment.

Alzheimer's disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer's disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer's disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer's disease detection and highlights future research directions.

Effects of a dual intervention (motor and virtual reality-based cognitive) on cognition in patients with mild cognitive impairment: a single-blind, randomized controlled trial.

BACKGROUND: The increase in cases of mild cognitive impairment (MCI) underlines the urgency of finding effective methods to slow its progression. Given the limited effectiveness of current pharmacological options to prevent or treat the early stages of this deterioration, non-pharmacological alternatives are especially relevant. OBJECTIVE: To assess the effectiveness of a cognitive-motor intervention based on immersive virtual reality (VR) that simulates an activity of daily living (ADL) on cognitive functions and its impact on depression and the ability to perform such activities in patients with MCI. METHODS: Thirty-four older adults (men, women) with MCI were randomized to the experimental group (n = 17; 75.41 ± 5.76) or control (n = 17; 77.35 ± 6.75) group. Both groups received motor training, through aerobic, balance and resistance activities in group. Subsequently, the experimental group received cognitive training based on VR, while the control group received traditional cognitive training. Cognitive functions, depression, and the ability to perform activities of daily living (ADLs) were assessed using the Spanish versions of the Montreal Cognitive Assessment (MoCA-S), the Short Geriatric Depression Scale (SGDS-S), and the of Instrumental Activities of Daily Living (IADL-S) before and after 6-week intervention (a total of twelve 40-minutes sessions). RESULTS: Between groups comparison did not reveal significant differences in either cognitive function or geriatric depression. The intragroup effect of cognitive function and geriatric depression was significant in both groups (p < 0.001), with large effect sizes. There was no statistically significant improvement in any of the groups when evaluating their performance in ADLs (control, p = 0.28; experimental, p = 0.46) as expected. The completion rate in the experimental group was higher (82.35%) compared to the control group (70.59%). Likewise, participants in the experimental group reached a higher level of difficulty in the application and needed less time to complete the task at each level. CONCLUSIONS: The application of a dual intervention, through motor training prior to a cognitive task based on Immersive VR was shown to be a beneficial non-pharmacological strategy to improve cognitive functions and reduce depression in patients with MCI. Similarly, the control group benefited from such dual intervention with statistically significant improvements. TRIAL REGISTRATION: ClinicalTrials.gov NCT06313931; https://clinicaltrials.gov/study/NCT06313931 .