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Dementia and Cognitive Ageing

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A special issue of Brain Sciences (ISSN 2076-3425).

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 53326

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Special Issue Editors

Dr. Dorina Cadar

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Guest Editor

UCL Department of Behavioural Science and Health, 1-19 Torrington Place, London, UK
Interests: dementia; cognitive impairment; neuropscyhological screening; lifestyle behaviours

Dr. Eddy J. Davelaar

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Guest Editor

Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK
Interests: memory; cognitive training; cogntive decline

Special Issue Information

Dear Colleagues,

Dementia and cognitive impairment are not an inevitable part of the ageing process. To better understand the relationship between cognitive ageing and dementia, we pose the following questions: Why do some individuals have a faster than normal cognitive decline, leading to dementia, whereas others maintain good performance until the end of their life? There are many non-modifiable (e.g., genes) and modifiable (e.g., lifestyle) risk factors, but yet it remains unclear how they interact and whether they exert their influence during a critical age period of life (early, midlife, or early-later life). What is the evidence base for interventions targeting modifiable risk factors (e.g., diet, physical activity) offsetting the risk? What is the current state-of-the-art in behavioural or biological testing to identify people at risk of cognitive impairment and dementia?

In this Special Issue, we are seeking submissions that address some of the questions raised above. We aim to bring together new research from different fields, such as epidemiology, neurobiology, and neuropsychology to provide a multidisciplinary evidence base.

Thus, we are inviting research articles, reviews, and commentaries on a broad range of cognitive ageing and dementia research from observational, experimental and clinical studies.

Dr. Dorina Cadar
Dr. Eddy J. Davelaar
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

cognitive ageing
dementia
neuropscyhological screening
modifiable risk factors

Published Papers (10 papers)

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Research

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15 pages, 1309 KiB

Open AccessArticle

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

by Atif Mehmood, Muazzam Maqsood, Muzaffar Bashir and Yang Shuyuan

Brain Sci. 2020, 10(2), 84; https://doi.org/10.3390/brainsci10020084 - 05 Feb 2020

Cited by 129 | Viewed by 11685

Abstract

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

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12 pages, 241 KiB

Open AccessArticle

Polypharmacy Is Associated with Lower Memory Function in African American Older Adults

by Shervin Assari, Cheryl Wisseh, Mohammed Saqib and Mohsen Bazargan

Brain Sci. 2020, 10(1), 49; https://doi.org/10.3390/brainsci10010049 - 16 Jan 2020

Cited by 11 | Viewed by 3695

Abstract

Although previous research has linked polypharmacy to lower cognitive function in the general population, we know little about this association among economically challenged African American (AA) older adults. This study explored the link between polypharmacy and memory function among AA older adults. This community-based study recruited 399 AA older adults who were 65+ years old and living in economically disadvantaged areas of South Los Angeles. Polypharmacy (taking 5+ medications) was the independent variable, memory function was the outcome variable (continuous variable), and gender, age, living arrangement, socioeconomic status (educational attainment and financial strain), health behaviors (current smoking and any binge drinking), and multimorbidity (number of chronic diseases) were the covariates. Linear regression was used for data analyses. Polypharmacy was associated with lower scores on memory function, above and beyond covariates. Among AA older adults, polypharmacy may be linked to worse cognitive function. Future research should test the mechanisms by which polypharmacy is associated with lower levels of cognitive decline. There is a need for screening for memory problems in AA older adults who are exposed to polypharmacy. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

9 pages, 1256 KiB

Open AccessArticle

Characteristics of the Uncinate Fasciculus and Cingulum in Patients with Mild Cognitive Impairment: Diffusion Tensor Tractography Study

by Chan-Hyuk Park, Su-Hong Kim and Han-Young Jung

Brain Sci. 2019, 9(12), 377; https://doi.org/10.3390/brainsci9120377 - 14 Dec 2019

Cited by 11 | Viewed by 3373

Abstract

Many studies have examined the relationship between cognition, and the cingulum and uncinate fasciculus (UF). In this study, diffusion tensor tractography (DTT) was used to investigate the correlation between fractional-anisotropy (FA) values and the number of fibers in the cingulum and UF in patients with and without cognitive impairment. The correlation between cognitive function, and the cingulum and UF was also investigated. Thirty patients (14 males, age = 70.68 ± 7.99 years) were divided into a control group (n = 14) and mild-cognitive-impairment (MCI) group (n = 16). The Seoul Neuropsychological Screening Battery (SNSB) and DTT were performed to assess cognition and bilateral tracts of the cingulum and UF. The relationship between SNSB values and the cingulum and UF was analyzed. The number of fibers in the right cingulum and right UF were significantly different between the two groups. The MCI group showed thinner tracts in both the cingulum and UF compared to the control group. A significant relationship was found between the number of fibers in the right UF and delayed memory recall. In conclusion, memory loss in MCI was associated with a decreased number of fibers in the right UF, while language and visuospatial function were related to the number of fibers in the right cingulum. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

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18 pages, 1254 KiB

Open AccessArticle

Does Empirically Derived Classification of Individuals with Subjective Cognitive Complaints Predict Dementia?

by Eduardo Picón, Onésimo Juncos-Rabadán, Cristina Lojo-Seoane, María Campos-Magdaleno, Sabela C. Mallo, Ana Nieto-Vietes, Arturo X. Pereiro and David Facal

Brain Sci. 2019, 9(11), 314; https://doi.org/10.3390/brainsci9110314 - 07 Nov 2019

Cited by 8 | Viewed by 2490

Abstract

(1) Background: Early identification of mild cognitive impairment (MCI) in people reporting subjective cognitive complaints (SCC) and the study of progression of cognitive decline are important issues in dementia research. This paper examines whether empirically derived procedures predict progression from MCI to dementia. (2) Methods: At baseline, 192 participants with SCC were diagnosed according to clinical criteria as cognitively unimpaired (70), single-domain amnestic MCI (65), multiple-domain amnestic MCI (33) and multiple-domain non-amnestic MCI (24). A two-stage hierarchical cluster analysis was performed for empirical classification. Categorical regression analysis was then used to assess the predictive value of the clusters obtained. Participants were re-assessed after 36 months. (3) Results: Participants were grouped into four empirically derived clusters: Cluster 1, similar to multiple-domain amnestic MCI; Cluster 2, characterized by subjective cognitive decline (SCD) but with low scores in language and working memory; Cluster 3, with specific deterioration in episodic memory, similar to single-domain amnestic MCI; and Cluster 4, with SCD but with scores above the mean in all domains. The majority of participants who progressed to dementia were included in Cluster 1. (4) Conclusions: Cluster analysis differentiated between MCI and SCD in a sample of people with SCC and empirical criteria were more closely associated with progression to dementia than standard criteria. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

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19 pages, 4149 KiB

Open AccessArticle

An Investigation of Limbs Exercise as a Treatment in Improving the Psychomotor Speed in Older Adults with Mild Cognitive Impairment

by Hao Jiang, Shihui Chen, Lina Wang and Xiaolei Liu

Brain Sci. 2019, 9(10), 277; https://doi.org/10.3390/brainsci9100277 - 16 Oct 2019

Cited by 9 | Viewed by 3643

Abstract

Objectives: This study investigated the effects of therapeutic structured limb exercises intended to improve psychomotor speed in older adults with mild cognitive impairment (MCI). Methods: Forty-four patients with mild cognitive impairment who met the inclusion criteria were selected and assigned randomly to either an experimental group (22 patients) or a control group (22 patients). The numbers of participants were selected based on the calculated sample effect size (N = 38). The study involved a 10-week intervention, in which participants completed structured limb exercises during 60-min training sessions delivered three times per week. Forty-one subjects completed the experimental programme. Scores in the Finger Tapping Test (FTT), Purdue Pegboard Test (PPT) and Montreal Cognitive Assessment (MoCA), along with electroencephalography (EEG) data, were collected before, during and after the intervention. The experimental and control groups were compared using repeated measures analysis of variance. Results: The patients with MCI in the experimental group achieved significantly improved scores in the FTT, the PPT and all dimensions of the MoCA. Moreover, these patients exhibited significant increases in the alpha and beta EEG wave power values in all brain areas of MCI patients, indicating that limb exercise training positively influenced their brain functions. Conclusions: The results conclude that a structured therapeutic limb exercise intervention can effectively improve psychomotor speed in patients with MCI and mitigate declines in cognitive function. This training intervention appears to be effective as a treatment for community-dwelling patients with MCI. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

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14 pages, 1568 KiB

Open AccessArticle

A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images

by Hamed Taheri Gorji and Naima Kaabouch

Brain Sci. 2019, 9(9), 217; https://doi.org/10.3390/brainsci9090217 - 28 Aug 2019

Cited by 59 | Viewed by 4936

Abstract

Mild cognitive impairment (MCI) is an intermediary stage condition between healthy people and Alzheimer’s disease (AD) patients and other dementias. AD is a progressive and irreversible neurodegenerative disorder, which is a significant threat to people, age 65 and older. Although MCI does not always lead to AD, an early diagnosis at the stage of MCI can be very helpful in identifying people who are at risk of AD. Moreover, the early diagnosis of MCI can lead to more effective treatment, or at least, significantly delay the disease’s progress, and can lead to social and financial benefits. Magnetic resonance imaging (MRI), which has become a significant tool for the diagnosis of MCI and AD, can provide neuropsychological data for analyzing the variance in brain structure and function. MCI is divided into early and late MCI (EMCI and LMCI) and sadly, there is no clear differentiation between the brain structure of healthy people and MCI patients, especially in the EMCI stage. This paper aims to use a deep learning approach, which is one of the most powerful branches of machine learning, to discriminate between healthy people and the two types of MCI groups based on MRI results. The convolutional neural network (CNN) with an efficient architecture was used to extract high-quality features from MRIs to classify people into healthy, EMCI, or LMCI groups. The MRIs of 600 individuals used in this study included 200 control normal (CN) people, 200 EMCI patients, and 200 LMCI patients. This study randomly selected 70 percent of the data to train our model and 30 percent for the test set. The results showed the best overall classification between CN and LMCI groups in the sagittal view with an accuracy of 94.54 percent. In addition, 93.96 percent and 93.00 percent accuracy were reached for the pairs of EMCI/LMCI and CN/EMCI, respectively. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

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13 pages, 594 KiB

Open AccessArticle

Relation of Serum Plasmalogens and APOE Genotype to Cognition and Dementia in Older Persons in a Cross-Sectional Study

by Dayan B. Goodenowe and Vijitha Senanayake

Brain Sci. 2019, 9(4), 92; https://doi.org/10.3390/brainsci9040092 - 24 Apr 2019

Cited by 14 | Viewed by 7152

Abstract

Using a community sample of 1205 elderly persons, we investigated the associations and potential interactions between Apolipoprotein E (APOE) genotype and serum phosphatidylethanolamine (PlsEtn) on cognition and dementia. For each person, APOE genotype, PlsEtn Biosynthesis value (PBV, the combination of three key PlsEtn species), cognition (the combination of five specific cognitive domains), and diagnosis of dementia was determined. APOE genotype and PBV were observed to be non-interacting (p > 0.05) and independently associated with cognition: APOE (relative to ε3ε3:ε2ε3 (Coef = 0.14, p = 4.2 × 10⁻²); ε3ε4/ε4ε4 (Coef = −0.22, p = 6.2 × 10⁻⁵); PBV (Coef = 0.12, p = 1.7 × 10⁻⁷) and dementia: APOE (relative to ε3ε3:ε2ε3 (Odds Ratio OR = 0.44, p = 3.0 × 10⁻²); ε3ε4/ε4ε4 (OR = 2.1, p = 2.2 × 10⁻⁴)); PBV (OR = 0.61, p = 3.3 × 10⁻⁶). Associations are expressed per standard deviation (SD) and adjusted for serum lipids and demographics. Due to the independent and non-interacting nature of the APOE and PBV associations, the prevalence of dementia in APOE ε3ε4/ε4ε4 persons with high PBV values (>1 SD from mean) was observed to be the same as APOE ε3ε3 persons (14.3% versus 14.0%). Similarly, the prevalence of dementia in APOE ε3ε3 persons with high PBV values was only 5.7% versus 6.7% for APOE ε2ε3 persons. The results of these analyses indicate that the net effect of APOE genotype on cognition and the prevalence of dementia is dependent upon the plasmalogen status of the person. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

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15 pages, 6500 KiB

Open AccessArticle

N-Acetyl Cysteine Supplement Minimize Tau Expression and Neuronal Loss in Animal Model of Alzheimer’s Disease

by Teresa Joy, Muddanna S. Rao and Sampath Madhyastha

Brain Sci. 2018, 8(10), 185; https://doi.org/10.3390/brainsci8100185 - 11 Oct 2018

Cited by 17 | Viewed by 5157

Abstract

Alzheimer’s disease (AD) is characterized by the accumulation of neurofibrillary tangles (NFT), deposition of beta amyloid plaques, and consequent neuronal loss in the brain tissue. Oxidative stress to the neurons is often attributed to AD, but its link to NFT and β-amyloid protein (BAP) still remains unclear. In an animal model of AD, we boosted the oxidative defense by N-Acetyl cysteine (NAC), a precursor of glutathione, a powerful antioxidant and free radical scavenger, to understand the link between oxidative stress and NFT. In mimicking AD, intracerebroventricular (ICV) colchicine, a microtubule disrupting agent also known to cause oxidative stress was administered to the rats. The animal groups consisted of an age-matched control, sham operated, AD, and NAC treated in AD models of rats. Cognitive function was evaluated in a passive avoidance test; neuronal degeneration was quantified using Nissl staining. NFT in the form of abnormal tau expression in different regions of the brain were evaluated through immunohistochemistry using rabbit anti-tau antibody. ICV has resulted in significant cognitive and neuronal loss in medial prefrontal cortex (MFC) and all the regions of the hippocampus. It has also resulted in increased accumulation of intraneuronal tau in the hippocampus and MFC. NAC treatment in AD model rats has reversed the cognitive loss and neuronal degeneration. The intraneuronal tau expression also minimized with NAC treatment in AD model rats. Thus, our findings suggest that an antioxidant supplement during the progression of AD is likely to prevent neuronal degeneration by minimizing the neurofibrillary degeneration in the form of tau accumulation. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

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Review

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13 pages, 1439 KiB

Open AccessReview

Overcoming Alzheimer’s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies

by Alexander Pilozzi and Xudong Huang

Brain Sci. 2020, 10(3), 183; https://doi.org/10.3390/brainsci10030183 - 23 Mar 2020

Cited by 14 | Viewed by 4819

Abstract

Alzheimer’s disease (AD) imposes a considerable burden on those diagnosed. Faced with a neurodegenerative decline for which there is no effective cure or prevention method, sufferers of the disease are subject to judgement, both self-imposed and otherwise, that can have a great deal of effect on their lives. The burden of this stigma is more than just psychological, as reluctance to face an AD diagnosis can lead people to avoid early diagnosis, treatment, and research opportunities that may be beneficial to them, and that may help progress towards fighting AD and its progression. In this review, we discuss how recent advents in information technology may be employed to help fight this stigma. Using artificial intelligence (AI) technologies, specifically natural language processing (NLP), to classify the sentiment and tone of texts, such as those of online posts on various social media sites, has proven to be an effective tool for assessing the opinions of the general public on certain topics. These tools can be used to analyze the public stigma surrounding AD. Additionally, there is much concern among individuals that an AD diagnosis, or evidence of pre-clinical AD such as a biomarker or imaging test results, may wind up unintentionally disclosed to an entity that may discriminate against them. The lackluster security record of many medical institutions justifies this fear to an extent. Adopting more secure and decentralized methods of data transfer and storage, and giving patients enhanced ability to control their own data, such as a blockchain-based method, may help to alleviate some of these fears. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

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Other

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9 pages, 960 KiB

Open AccessPerspective

An Overview of Experimental and Clinical Spinal Cord Findings in Alzheimer’s Disease

by Qing Xie, Wei-Jiang Zhao, Guan-Yong Ou and Wei-Kang Xue

Brain Sci. 2019, 9(7), 168; https://doi.org/10.3390/brainsci9070168 - 17 Jul 2019

Cited by 12 | Viewed by 5188

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder that occurs mainly in the elderly and presenile life stages. It is estimated that by the year 2050, 135 million people will be affected by AD worldwide, representing a huge burden to society. The pathological hallmarks of AD mainly include intracellular neurofibrillary tangles (NFTs) caused by hyperphosphorylation of tau protein, formation of extracellular amyloid plaques, and massive neural cell death in the affected nervous system. The pathogenesis of AD is very complicated, and recent scientific research on AD is mainly concentrated on the cortex and hippocampus. Although the spinal cord is a pivotal part of the central nervous system, there are a limited number of studies focusing on the spinal cord. As an extension of the brain, the spinal cord functions as the bridge between the brain and various parts of the body. However, pathological changes in the spinal cord in AD have not been comprehensively and systematically studied at present. We here review the existing progress on the pathological features of AD in the spinal cord. Full article

(This article belongs to the Special Issue Dementia and Cognitive Ageing)

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