Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models

Background

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.

Objective

To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia.

Materials and methods

We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.

Results

In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases.

Conclusion

The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

Cognitive and behavioral abnormalities in individuals with Alzheimer’s disease, mild cognitive impairment, and subjective memory complaints

In this study, we investigated the ability of commonly used neuropsychological tests to detect cognitive and functional decline across the Alzheimer’s disease (AD) continuum. Moreover, as preclinical AD is a key area of investigation, we focused on the ability of neuropsychological tests to distinguish the early stages of the disease, such as individuals with Subjective Memory Complaints (SMC). This study included 595 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset who were cognitively normal (CN), SMC, mild cognitive impairment (MCI; early or late stage), or AD. Our cognitive measures included the Rey Auditory Verbal Learning Test (RAVLT), the Everyday Cognition Questionnaire (ECog), the Functional Abilities Questionnaire (FAQ), the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog), the Montreal Cognitive Assessment scale (MoCA), and the Trail Making test (TMT-B). Overall, our results indicated that the ADAS-13, RAVLT (learning), FAQ, ECog, and MoCA were all predictive of the AD progression continuum. However, TMT-B and the RAVLT (immediate and forgetting) were not significant predictors of the AD continuum. Indeed, contrary to our expectations ECog self-report (partner and patient) were the two strongest predictors in the model to detect the progression from CN to AD. Accordingly, we suggest using the ECog (both versions), RAVLT (learning), ADAS-13, and the MoCA to screen all stages of the AD continuum. In conclusion, we infer that these tests could help clinicians effectively detect the early stages of the disease (e.g., SMC) and distinguish the different stages of AD.

Association of depression with cognitive frailty: A systematic review and meta-analysis

BACKGROUND AND AIM

The relationship between cognitive frailty and depression is unclear and quantitative analyses are lacking. We conducted a systematic review and meta-analysis to investigate the relationship between cognitive frailty and depression.

METHODS

We systematically searched Embase, PubMed, Medline (Ovid), Web of Science, and APA PsycInfo (American Psychological Association PsycInfo) databases until April 2022. Meta-analysis was performed using the Stata software. The prevalence between cognitive frailty and depression them was estimated by extracting the proportion of cognitive frailty and depression in the total number of patients. We extracted odds ratios (ORs) and 95 % confidence intervals (CI) to estimate the relationship between cognitive frailty and depression.

RESULTS

A meta-analysis of 15 studies revealed that cognitive frailty in older adults was associated with a higher risk of depression (OR = 2.06, 95 % CI = 1.72-2.48, p = 0.001). Eight studies involved the prevalence of cognitive frailty and depression, with an overall prevalence of depression of 46 % (95 % CI, 30 % -62 %; p < 0.0001) in cognitively frail patients.

LIMITATION

Differences in definitions and assessment methods for cognitive frailty across studies.

CONCLUSION

The prevalence of cognitive frailty combined with depression in the elderly is high wherein both are mutually affected. More prospective studies are needed to investigate the relationship between cognitive frailty and depression and to propose targeted treatment options and preventive measures to improve the quality of life of the elderly population.

Prevalence of Alzheimer's disease in rural and urban areas in Cuba and factors influencing on its occurrence: epidemiological cross-sectional protocol

INTRODUCTION

According to the World Alzheimer's Report 2019, around 50 million people suffer from dementia, worldwide. Observational analysis revealed the existence of particular factors associated with the onset and progression of Alzheimer's disease (AD). There are no international homogeneous principles for the early detection and evaluation of memory impairment and possible AD. This work aimed at (1) determining the prevalence of possible AD in the elderly residing in urban and rural regions in Cuba and (2) identifying the main factors that could significantly influence on its occurrence.

METHODS AND ANALYSIS

The study includes four neuropsychological tests (Clock Drawing Test, Mini-Mental Status Examination, Short Portable Mental Status Questionnaire, Cognitive and Non-Cognitive Alzheimer's Disease Assessment Scale) and two scales (Clinical Dementia Rating and Global Deterioration Scale). Moreover, the protocol includes a survey with demographic and socioeconomic information, educational level, occupation, health, neuropsychological status of subjects, familial pathological history, comorbidities and lifestyles. The study will comprise a total of 1092 subjects aged ≥60, of both genders, and from every ethnic group settled in rural and urban areas.

PRIMARY OUTCOMES

prevalence of possible AD.

SECONDARY OUTCOMES

correlation among risk and protective factors and AD, and comparison of the performance of neuropsychological tests and scales.

ETHICS AND DISSEMINATION

This research met the ethical codes of the Declaration of Helsinki. The Scientific Research Council of the Promoting Research Institute and the Ethics Committee of the Health Authorities approved the protocol. The proper written informed consent is also incorporated. The results of the survey will be published in scientific papers and shared with the Health Authorities of each municipality.

Cultural adaptation of Alzheimer's disease assessment scale-cognitive subscale for use in India and validation of the Tamil version for South Indian population

OBJECTIVE

Currently no standardized tools are available in the Indian languages to assess changes in cognition. Our objectives are to culturally adapt the Alzheimer's disease Assessment Scale-Cognitive Subscale (ADAS-Cog) for use in India and to validate the Tamil version in an urban Tamil-speaking older adult population.

METHODS

Two panels of key stakeholders and a series of qualitative interviews informed the cultural and linguistic adaptation of the ADAS-Cog-Tamil. Issues related to levels of literacy were considered during the adaptation. Validation of the ADAS-Cog-Tamil was completed with 107 participants - 54 cases with a confirmed diagnosis of mild-moderate dementia, and 53 age, gender and education matched controls. Concurrent validity was examined with the Vellore Screening Instrument for Dementia (VSID) in Tamil. Internal consistency using Cronbach's alpha, sensitivity and specificity data using the Area under the Receiver Operating Characteristics (AUROC) curve values were computed. Inter-rater reliability was established in a subsample.

RESULTS

The ADAS-Cog-Tamil shows good internal consistency (α = 0.91), inter-rater reliability and concurrent validity (with VSID-Patient version: r = -0.84 and with VSID-Caregiver version: r = -0.79). A cut-off score of 13, has a specificity of 89% and sensitivity of 90% for the diagnosis of dementia.

CONCLUSION

ADAS-Cog-Tamil, derived from a rigorous, replicable linguistic and cultural adaptation process involving service users and experts, shows good psychometric properties despite the limitations of the study. It shows potential for use in clinical settings with urban Tamil speaking populations. The English version of the tool derived from the cultural adaptation process could be used for further linguistic adaptation across South Asia.

Altered Domain Functional Network Connectivity Strength and Randomness in Schizophrenia

Functional connectivity is one of the most widely used tools for investigating brain changes due to schizophrenia. Previous studies have identified abnormal functional connectivity in schizophrenia patients at the resting state brain network level. This study tests the existence of functional connectivity effects at whole brain and domain levels. Domain level refers to the integration of data from several brain networks grouped by their functional relationship. Data integration provides more consistent and accurate information compared to an individual brain network. This work considers two domain level measures: functional connectivity strength and randomness. The first measure is simply an average of connectivities within the domain. The second measure assesses the unpredictability and lack of pattern of functional connectivity within the domain. Domains with less random connectivity have higher chance of exhibiting a biologically meaningful connectivity pattern. Consistent with prior observations, individuals with schizophrenia showed aberrant domain connectivity strength between subcortical, cerebellar, and sensorial brain areas. Compared to healthy volunteers, functional connectivity between cognitive and default mode domains showed less randomness, while connectivity between default mode-sensorial areas showed more randomness in schizophrenia patients. These differences in connectivity patterns suggest deleterious rewiring trade-offs among important brain networks.

Cardiac rehabilitation and its effects on cognition in patients with coronary artery disease and heart failure

INTRODUCTION

Cardiac rehabilitation program is an evidence-based intervention and established model of exercise delivery following myocardial infarction and heart failure. Although it forms an important part of recovery and helps to prevent future events and complications, there has been little focus on its potential cognitive benefits. Areas covered: Coronary artery disease and heart failure are common heart problems associated with significant morbidity and mortality, and cognitive decline is commonly seen in affected individuals. Cognitive impairment may influence patient self-management by reducing medication adherence, rendering patients unable to make lifestyle modifications and causing missed healthcare visits. Cognitive assessment in cardiac rehabilitation as an outcome measure has the potential to improve clinical, functional and behavioral domains as well as help to reduce gaps in the quality of care in these patients. Expert commentary: Limited evidence at present has shown that cardiac rehabilitation and exercise has potential in preventing cognitive decline. Cardiac prehabilitation, a rehabilitation-like program delivered before cardiac surgery, may also play a role in preventing postoperative cognitive dysfunction, but needs future research studies to support it.

Validation study of the Alzheimer's disease assessment scale-cognitive subscale (ADAS-Cog) for the Portuguese patients with mild cognitive impairment and Alzheimer's disease

OBJECTIVE

The Alzheimer's disease assessment scale-Cognitive Subscale (ADAS-Cog) is a battery to assess cognitive performance in Alzheimer's disease (AD) and was developed according to the core characteristics of cognitive decline in AD: memory, language, praxis, constructive ability, and orientation. The aim of this study was to explore the diagnostic accuracy and discriminative capacity of the ADAS-Cog for Mild Cognitive Impairment (MCI) and AD, using cut-off points for the Portuguese population.

METHOD

The European Portuguese version of the ADAS-Cog was administrated to 650 participants, divided into a control group (n = 210), an MCI group (n = 240), and an AD group (n = 200). The clinical groups fulfilled standard international diagnostic criteria. Controls were healthy cognitive participants actively integrated in the community. The neuropsychological assessment protocol included the ADAS-Cog, the Mini Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Adults and Older Adults Functional Assessment Inventory (IAFAI).

RESULTS

The ADAS-Cog revealed good psychometric indicators, and the total scores were significantly different between the three groups (p < .001: Control < MCI < AD). The optimal cut-off points established were: MCI > 9 points (AUC = .835; sensitivity = 58% and specificity = 91%) and AD > 12 points (AUC = .996; sensitivity = 94% and specificity = 98%).

CONCLUSIONS

Our findings confirmed the capacity of the ADAS-Cog total score to identify cognitive impairment in AD patients, with poor sensitivity for MCI, in a Portuguese cohort.