Low Functional network integrity in cognitively unimpaired and MCI subjects with depressive symptoms: results from a multi-center fMRI study.

Evidence suggests that depressive symptomatology is a consequence of network dysfunction rather than lesion pathology. We studied whole-brain functional connectivity using a Minimum Spanning Tree as a graph-theoretical approach. Furthermore, we examined functional connectivity in the Default Mode Network, the Frontolimbic Network (FLN), the Salience Network, and the Cognitive Control Network. All 183 elderly subjects underwent a comprehensive neuropsychological evaluation and a 3 Tesla brain MRI scan. To assess the potential presence of depressive symptoms, the 13-item version of the Beck Depression Inventory (BDI) or the Geriatric Depression Scale (GDS) was utilized. Participants were assigned into three groups based on their cognitive status: amnestic mild cognitive impairment (MCI), non-amnestic MCI, and healthy controls. Regarding affective symptoms, subjects were categorized into depressed and non-depressed groups. An increased mean eccentricity and network diameter were found in patients with depressive symptoms relative to non-depressed ones, and both measures showed correlations with depressive symptom severity. In patients with depressive symptoms, a functional hypoconnectivity was detected between the Anterior Cingulate Cortex (ACC) and the right amygdala in the FLN, which impairment correlated with depressive symptom severity. While no structural difference was found in subjects with depressive symptoms, the volume of the hippocampus and the thickness of the precuneus and the entorhinal cortex were decreased in subjects with MCI, especially in amnestic MCI. The increase in eccentricity and diameter indicates a more path-like functional network configuration that may lead to an impaired functional integration in depression, a possible cause of depressive symptomatology in the elderly.

Lifestyle and behavioural changes in older adults during the Covid-19 pandemic are associated with subjective cognitive complaints.

Subjective cognitive complaints (SCC) is a self-reported experience of persistently impaired cognitive functions which could be the earliest red flag of neurocognitive disorders. The COVID-19 pandemic and related restriction measures changed the lifestyle and behaviour of older adults. The aim of this study was to assess the relation of these changes and SCC status in Hungary. This cross-sectional study analysed the data of 359 elderly Hungarians who filled out the WW-FINGERS-SARS-CoV2 survey. A quarter of the respondents (n:88) reported SCC in connection with the pandemic. We compared sociodemographic features, health status, lifestyle, and social life parameters between subjects with reported SCC and without. To eliminate the potential interrelation across group differences, stepwise logistic regression was applied. Participants with SCC showed the following characteristics, compared to individuals without: (1) they were older; (2) they were more likely to be women; (3) they had a higher number of chronic disorders; (4) showed more prominent impairment in physical mobility; (5) had worse sleep quality; (6) spent less time with family; and (7) used internet more frequently during the pandemic (all p's < 0.001). Logistic regression highlighted that only two parameters were related to SCC status independently, the physical mobility (ability to walk 500 m without difficulties; OR = 1.186; p < 0.001; 95%CI = 1.101, 1.270) and changes in time spent with grandchildren (OR = 1.04; p = 0.015; 95%CI = 1.008, 1.073). Our study draws attention to the importance of physical mobility and quality time with family as key factors in the cognitive well-being of elderly people.

Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment.

Mild cognitive impairment (MCI) is a potential therapeutic window in the prevention of dementia; however, automated detection of early cognitive deterioration is an unresolved issue. The aim of our study was to compare various classification approaches to differentiate MCI patients from healthy controls, based on rs-fMRI data, using machine learning (ML) algorithms. Own dataset (from two centers) and ADNI database were used during the analysis. Three fMRI parameters were applied in five feature selection algorithms: local correlation, intrinsic connectivity, and fractional amplitude of low frequency fluctuations. Support vector machine (SVM) and random forest (RF) methods were applied for classification. We achieved a relatively wide range of 78-87% accuracy for the various feature selection methods with SVM combining the three rs-fMRI parameters. In the ADNI datasets case we can also see even 90% accuracy scores. RF provided a more harmonized result among the feature selection algorithms in both datasets with 80-84% accuracy for our local and 74-82% for the ADNI database. Despite some lower performance metrics of some algorithms, most of the results were positive and could be seen in two unrelated datasets which increase the validity of our methods. Our results highlight the potential of ML-based fMRI applications for automated diagnostic techniques to recognize MCI patients.

Differentiation of patients with mild cognitive impairment and healthy controls based on computer assisted hand movement analysis: a proof-of-concept study.

Mild cognitive impairment (MCI) is the prodromal phase of dementia, and it is highly underdiagnosed in the community. We aimed to develop an automated, rapid (< 5 min), electronic screening tool for the recognition of MCI based on hand movement analysis. Sixty-eight individuals participated in our study, 46 healthy controls and 22 patients with clinically defined MCI. All participants underwent a detailed medical assessment including neuropsychology and brain MRI. Significant differences were found between controls and MCI groups in mouse movement characteristics. Patients showed higher level of entropy for both the left (F = 5.24; p = 0.001) and the right hand (F = 8.46; p < 0.001). Longer time was required in MCI to perform the fine motor task (p < 0.005). Furthermore, we also found significant correlations between mouse movement parameters and neuropsychological test scores. Correlation was the strongest between motor parameters and Clinical Dementia Rating scale (CDR) score (average r: - 0.36, all p's < 0.001). Importantly, motor parameters were not influenced by age, gender, or anxiety effect (all p's > 0.05). Our study draws attention to the utility of hand movement analysis, especially to the estimation of entropy in the early recognition of MCI. It also suggests that our system might provide a promising tool for the cognitive screening of large populations.

Impact of the third wave of the COVID-19 pandemic on the lifestyle, mental and physical health of the Hungarian population over 60 / A COVID-19-pandémia harmadik hullámának hatása a 60 év feletti magyar lakosság életmódjára, mentális és fizikai egészségére

Introduction: Elderly population is the most vulnerable group of the COVID-19 pandemic, since they often live with chronic diseases. Objective: The goal of our research is to analyze the direct and indirect effects of the pandemic on the Hungarian population over 60 years of age. Method: We collected data using the authentic Hungarian translation of the,World-Wide FINGERS SARS-CoV-2 Survey between 1st of February and 1st of June 2021. Results: Our study included 431 people with a low rate of COVID infection (6%). The most marked changes were the increase in the use of digital services in 71%, increased feeling of loneliness in 46%, decrease in subjective sleep quality in 47%, and reduced contact with friends and relatives in 80% of the respondents. Eight-six percent of participants had at least one chronic illness and 23% missed an illness-related medical visit during the pandemic. In 45%, the subjective quality of life deteriorated and 25% reported impairment of memory functions. Discussion: Participants became socially isolated during the pandemic having a significant negative impact on their way of life. The changes in physical and mental health are likely to be reflected in an increased incidence and accelerated progression of age-related diseases in the elderly. Conclusion: In order to reduce the direct and indirect harmful effects of the COVID-19 pandemic, it is of paramount importance to know how the pandemic and the following restrictions affect the behavior and lifestyle of the elderly as well as the care of patients living with chronic diseases.

Alteration of Visuospatial System as an Early Marker of Cognitive Decline: A Double-Center Neuroimaging Study.

Amnestic-type mild cognitive impairment (a-MCI) represents the prodromal phase of Alzheimer's disease associated with a high conversion rate to dementia and serves as a potential golden period for interventions. In our study, we analyzed the role of visuospatial (VS) functions and networks in the recognition of a-MCI. We examined 78 participants (32 patients and 46 controls) in a double-center arrangement using neuropsychology, structural, and resting-state functional MRI. We found that imaging of the lateral temporal areas showed strong discriminating power since in patients only the temporal pole (F = 5.26, p = 0.034) and superior temporal gyrus (F = 8.04, p < 0.001) showed reduced cortical thickness. We demonstrated significant differences between controls and patients in various neuropsychological results; however, analysis of cognitive subdomains revealed that the largest difference was presented in VS skills (F = 8.32, p < 0.001). Functional connectivity analysis of VS network showed that patients had weaker connectivity between the left and right frontotemporal areas, while stronger local connectivity was presented between the left frontotemporal structures (FWE corrected p < 0.05). Our results highlight the remarkable potential of examining the VS system in the early detection of cognitive decline. Since resting-state setting of functional MRI simplifies the possible automatization of data analysis, detection of VS system alterations might provide a non-invasive biomarker of a-MCI.

The Association of Periodontitis and Alzheimer's Disease: How to Hit Two Birds with One Stone.

Alzheimer's disease (AD) is the leading cause of cognitive impairment in the elderly. Recent evidence suggests that preventive interventional trials could significantly reduce the risk for development of dementia. Periodontitis is the most common dental disease characterized by chronic inflammation and loss of alveolar bone and perialveolar attachment of teeth. Growing number of studies propose a potential link between periodontitis and neurodegeneration. In the first part of the paper, we overview case-control studies analyzing the prevalence of periodontitis among AD patients and healthy controls. Second, we survey observational libraries and cross-sectional studies investigating the risk of cognitive decline in patients with periodontitis. Next, we describe the current view on the mechanism of periodontitis linked neural damage, highlighting bacterial invasion of neural tissue from dental plaques, and periodontitis induced systemic inflammation resulting in a neuroinflammatory process. Later, we summarize reports connecting the four most common periodontal pathogens to AD pathology. Finally, we provide a practical guide for further prevalence and interventional studies on the management of cognitively high-risk patients with and without periodontitis. In this section, we highlight strategies for risk control, patient information, dental evaluation, reporting protocol and dental procedures in the clinical management of patients with a risk for periodontitis and with diagnosed periodontitis. In conclusion, our review summarizes the current view on the association between AD and periodontitis and provides a research and intervention strategy for harmonized interventional trials and for further case-control or cross-sectional studies.

The Assessment of Visuospatial Skills and Verbal Fluency in the Diagnosis of Alzheimer's Disease.

BACKGROUND: In the diagnosis of Alzheimer's disease (AD), examining memory is predominant. Our aim was to analyze the potential role of various cognitive domains in the cognitive evaluation of AD. METHODS: In total, 110 individuals with clinically defined AD and 45 healthy control participants underwent neuropsychological evaluation including Addenbrooke's Cognitive Examination (ACE). Patients with AD were selected in three groups based on disease duration in years (Group 1: ≤2 years, n = 36; Group 2: 2-4 years, n = 44; Group 3: ≥4 years, n = 30). Covariance-weighted intergroup comparison was performed on the global cognitive score and subscores of cognitive domains. Spearman's rho was applied to study the correlation between cognitive subscores and disease duration. The Wilcoxon signed-rank test was used for within-group analysis among ACE cognitive subscores. RESULTS: Significant difference was found between ACE total scores among groups (χ2 = 119.1; p < 0.001) with a high negative correlation (p < 0.001; r = -0.643). With a longer disease duration, all the subscores of ACE significantly decreased (p-values < 0.001). The visuospatial score showed the strongest negative correlation with disease duration with a linear trajectory in decline (r = -0.85). In the early phase of cognitive decline, verbal fluency was the most impaired cognitive subdomain (normalized value = 0.64), and it was significantly reduced compared to all other subdomains (p-values < 0.05). CONCLUSION: We found that the impairment of verbal fluency is the most characteristic feature of early cognitive decline; therefore, it might have crucial importance in the early detection of AD. Based on our results, the visuospatial assessment might be an ideal marker to monitor the progression of cognitive decline in AD.