Internal Consistency Over Time of Subjective Cognitive Decline: Drawing Preclinical Alzheimer's Disease Trajectories

Predictive Models for the Transition from Mild Neurocognitive Disorder to Major Neurocognitive Disorder: Insights from Clinical, Demographic, and Neuropsychological Data

Neurocognitive disorders (NCDs) are progressive conditions that severely impact cognitive function and daily living. Understanding the transition from mild to major NCD is crucial for personalized early intervention and effective management. Predictive models incorporating demographic variables, clinical data, and scores on neuropsychological and emotional tests can significantly enhance early detection and intervention strategies in primary healthcare settings. We aimed to develop and validate predictive models for the progression from mild NCD to major NCD using demographic, clinical, and neuropsychological data from 132 participants over a two-year period. Generalized Estimating Equations were employed for data analysis. Our final model achieved an accuracy of 83.7%. A higher body mass index and alcohol drinking increased the risk of progression from mild NCD to major NCD, while female sex, higher praxis abilities, and a higher score on the Geriatric Depression Scale reduced the risk. Here, we show that integrating multiple factors-ones that can be easily examined in clinical settings-into predictive models can improve early diagnosis of major NCD. This approach could facilitate timely interventions, potentially mitigating the progression of cognitive decline and improving patient outcomes in primary healthcare settings. Further research should focus on validating these models across diverse populations and exploring their implementation in various clinical contexts.

A rat model established by simulating genetic-environmental interactions recapitulates human Alzheimer's disease pathology

BACKGROUND

In humans, Alzheimer's disease (AD) is typically sporadic in nature, and its pathology is usually influenced by extensive factors. The study established a rat model based on the genetic-environmental interaction.

METHODS

A rat model was established by transduction of an adeno-associated virus combined with acrolein treatment. Rats were assigned to the normal control (NC), acrolein group, AAV (-) group, AAV-APP group, and AAV-APP/acrolein group. The success of model construction was verified in multiple ways, including by assessing cognitive function, examining microstructural changes in the brain in vivo, and performing immunohistochemistry. The contribution of genetic (APP mutation) and environmental (acrolein) factors to AD-like phenotypes in the model was explored by factorial analysis.

RESULTS

1) The AAV-APP/acrolein group showed a decline in cognitive function, as indicated by a reduced gray matter volume in key cognition-related brain areas, lower FA values in the hippocampus and internal olfactory cortex, and Aβ deposition in the cortex and hippocampus. 2) The AAV-APP group also showed a decline in cognitive function, although the group exhibited atypical brain atrophy in the gray matter and insignificant Aβ deposition. 3) The acrolein group did not show any significant changes in Aβ levels, gray matter volume, or cognitive function. 4) The genetic factor (APP mutation) explained 39.74% of the AD-like phenotypes in the model factors, and the environmental factor (acrolein exposure) explained 33.3%.

CONCLUSIONS

The genetic-environmental interaction rat model exhibited a phenotype that resembled the features of human AD and will be useful for research on AD.

Examining the Academic Trends in Neuropsychological Tests for Executive Functions Using Virtual Reality: Systematic Literature Review

Background

In neuropsychology, fully immersive virtual reality (VR) has been spotlighted as a promising tool. It is considered that VR not only overcomes the existing limitation of neuropsychological tests but is also appropriate for treating executive functions (EFs) within activities of daily living (ADL) due to its high ecological validity. While fully immersive VR offers new possibilities of neuropsychological tests, there are few studies that overview the intellectual landscape and academic trends in the research related to mainly targeted EFs with fully immersive VR.

Objective

The objective of this study is to get an overview of the research trends that use VR in neuropsychological tests and to analyze the research trends using fully immersive VR neuropsychological tests with experimental articles.

Methods

This review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles were searched in three web databases using keywords related to VR, EFs, and cognitive abilities. The study was conducted in two steps, keyword analysis and in-depth systematic review. In the web database search from 2000 to 2019, 1167 articles were initially collected, of which 234 articles in the eligibility phase were used to conduct keyword analysis and a total of 47 articles were included for systematic review.

Results

In keyword analysis, the number of articles focused on dementia including the keywords “MCI,” “SCD,” and “dementia” were highlighted over the period, rather than other symptoms. In addition, we identified that the use of behavioral and physiological data in virtual environments (VEs) has dramatically increased in recent studies. In the systematic review, we focused on the purpose of study, assessment, treatment, and validation of usability and structure. We found that treatment studies and uncategorized studies including presence and cybersickness issues have emerged in the recent period. In addition, the target symptoms and range of participants were diversified.

Conclusions

There has been a continuously increasing interest in dealing with neuropsychology by using fully immersive VR. Target cognitive abilities have been diversified, as well as target symptoms. Moreover, the concept of embodied cognition was transplanted in this research area.

Inference comprehension from reading in individuals with mild cognitive impairment

Inference comprehension is a complex ability that recruits distinct cognitive domains, such as language, memory, attention, and executive functions. Therefore, it might be sensitive to identify early deficits in subjects with MCI. To compare the performance of subjects with mild cognitive impairment (MCI) in an inference reading comprehension task, and to analyze the correlations between inferential comprehension and other cognitive functions. We studied 100 individuals aged 60 and over, divided into MCI (50) [aMCI (35), naMCI (15)], and cognitively healthy individuals [controls (50)]. The Implicit Management Test (IMT) was used to assess inference in reading comprehension in five categories: explicit, logical, distractor, pragmatic, and "others". MCI group performed worse than controls in logical, pragmatic, distractor, and "others" questions (p < 0.01). The aMCI and naMCI subgroups presented a similar performance in all types of questions (p > 0.05). We observed significant correlations between the total IMT score and the TMT-A in the naMCI group (r = - 0.562, p = 0.036), and the Rey-Osterrieth Complex Figure and RAVLT tasks in the aMCI group (r = 0.474, p = 0.010 and r = 0.593, p = 0.0001, respectively). The MCI group as a whole performed worse than controls on the logical, pragmatic, other and distractor questions, and consequently on the total score. There were no differences in explicit questions, which impose lower inferential demands. The aMCI group suffered a significant impact from memory on inference comprehension, and difficulties in executive functions impacted naMCI performance. The IMT was useful to differentiate MCI patients from cognitively healthy individuals, but not MCI subgroups among themselves.

Demographic characteristics and neuropsychological assessments of subjective cognitive decline (SCD) (plus)

BACKGROUND

Since SCD (plus) was standardized, little is known about its demographic characteristics and its outcomes of neuropsychological assessments, including the SCD questionnaire 9 (SCD-Q9).

OBJECTIVE

To characterize SCD (plus) by comparing the neuropsychological features among its subgroups and with normal controls (NC). Also, to explore its demographics and to understand the relation of the chief complaints and the scores of SCD-Q9.

METHODS

Multistage stratified cluster random sampling was conducted to select participants. As a result, 84 NC and 517 SCD (plus) were included. SCD (plus) was further classified into several subgroups (SCD-C: concerned cognitive decline; SCD-F: complaints about SCD within the past five years; SCD-P: feeling performance being not as good as their peers; SCD+: presented> 3 of SCD (plus) features; SCD-: presented ≤ 3 of SCD (plus) features (see the diagnostic criteria for the details)) and between-group comparisons of neuropsychological scores were conducted. Point-biserial correlation and binary logistic regression analyses were performed to investigate the demographic characteristics of its subgroups. Finally, Spearman correlation was used to better understand the relation of SCD (plus) to SCD-Q9.

RESULTS

(1) Scores of AVLT-LR (AVLT-LR: Auditory Verbal Learning Test-Long Delayed Recall) and MoCA-B (MoCA: Montreal Cognitive Assessment-Basic) were lower in the SCD-P group than those in the NC group, and the SCD+ group scored lower in the MoCA-B and CDT(CDT: Clock Drawing Test) than the SCD- group. (2) Females were more concerned than male participants. Individuals with lower education level felt that their cognitive performance were worse than their peers. Also, younger people might express concerns more than the more elderly. People who had complaints of SCD-P might be more likely to report SCD-C, but less likely to report SCD-F. (3) Positive correlations were found between the chief complaints of SCD (plus) and some items of SCD-Q9.

CONCLUSIONS

SCD (plus) may be related to demographic factors. Individuals with SCD (plus) already exhibited cognitive impairment, which can be detected by SCD-Q9.

Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods

Alzheimer's Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.

Functional cognitive disorders: a systematic review

Cognitive symptoms are common, and yet many who seek help for cognitive symptoms neither have, nor go on to develop, dementia. A proportion of these people are likely to have functional cognitive disorders, a subtype of functional neurological disorders, in which cognitive symptoms are present, associated with distress or disability, but caused by functional alterations rather than degenerative brain disease or another structural lesion. In this Review, we have systematically examined the prevalence and clinical associations of functional cognitive disorders, and related phenotypes, within the wider cognitive disorder literature. Around a quarter of patients presenting to memory clinics received diagnoses that might indicate the presence of functional cognitive disorders, which were associated with affective symptoms, negative self-evaluation, negative illness perceptions, non-progressive symptom trajectories, and linguistic and behavioural differences during clinical interactions. Those with functional cognitive disorder phenotypes are at risk of iatrogenic harm because of misdiagnosis or inaccurate prediction of future decline. Further research is imperative to improve diagnosis and identify effective treatments for functional cognitive disorders, and better understanding these phenotypes will also improve the specificity of diagnoses of prodromal degenerative brain disease.