Research

19 pages, 314 KiB

Open AccessArticle

Cognitive Test Scores and Progressive Cognitive Decline in the Aberdeen 1921 and 1936 Birth Cohorts

by Lawrence J. Whalley, Roger T. Staff, Helen Lemmon, Helen C. Fox, Chris McNeil and Alison D. Murray

Brain Sci. 2022, 12(3), 318; https://doi.org/10.3390/brainsci12030318 - 26 Feb 2022

Cited by 1 | Viewed by 2571

Abstract

The Aberdeen birth cohorts of 1921 and 1936 (ABC21 and ABC36) were subjected to IQ tests in 1932 or 1947 when they were aged about 11y. They were recruited between 1997–2001 among cognitively healthy community residents and comprehensively phenotyped in a long-term study [...] Read more.

The Aberdeen birth cohorts of 1921 and 1936 (ABC21 and ABC36) were subjected to IQ tests in 1932 or 1947 when they were aged about 11y. They were recruited between 1997–2001 among cognitively healthy community residents and comprehensively phenotyped in a long-term study of brain aging and health up to 2017. Here, we report associations between baseline cognitive test scores and long-term cognitive outcomes. On recruitment, significant sex differences within and between the ABC21 and ABC36 cohorts supported advantages in verbal ability and learning among the ABC36 women that were not significant in ABC21. Comorbid physical disorders were self-reported in both ABC21 and ABC36 but did not contribute to differences in terms of performance in cognitive tests. When used alone without other criteria, cognitive tests scores which fell below the −1.5 SD criterion for tests of progressive matrices, namely verbal learning, digit symbol and block design, did not support the concept that Mild Cognitive Impairment (MCI) is a stable class of acquired loss of function with significant links to the later emergence of a clinical dementia syndrome. This is consistent with many previous reports. Furthermore, because childhood IQ-type data were available, we showed that a lower cognitive performance at about 64 or 78 y than that predicted by IQ at 11 ± 0.5 y did not improve the prediction of progress to MCI or greater cognitive loss. We used binary logistic regression to explore how MCI might contribute to the prediction of later progress to a clinical dementia syndrome. In a fully adjusted model using ABC21 data, we found that non-amnestic MCI, along with factors such as female sex and depressive symptoms, contributed to the prediction of later dementia. A comparable model using ABC36 data did not do so. We propose that (1) MCI criteria restricted to cognitive test scores do not improve the temporal stability of MCI classifications; (2) pathways towards dementia may differ according to age at dementia onset and (3) the concept of MCI may require measures (not captured here) that underly self-reported subjective age-related cognitive decline. Full article

(This article belongs to the Special Issue Early Recognition of Alzheimer´s Disease)

8 pages, 552 KiB

Open AccessCommunication

Differences in Cognitive Functioning in Two Birth Cohorts Born 20 Years Apart: Data from the Interdisciplinary Longitudinal Study of Ageing

by Christina Degen, Claudia Frankenberg, Pablo Toro and Johannes Schröder

Brain Sci. 2022, 12(2), 271; https://doi.org/10.3390/brainsci12020271 - 15 Feb 2022

Cited by 4 | Viewed by 2369

Abstract

We compared neuropsychological functioning and prevalence of mild cognitive impairment (MCI) in two birth cohorts born 20 years apart when participants had reached the same age, i.e., the mid-60s. The study followed up 500 volunteers born 1930–1932 (C30) and 502 born 1950–1952 (C50). [...] Read more.

We compared neuropsychological functioning and prevalence of mild cognitive impairment (MCI) in two birth cohorts born 20 years apart when participants had reached the same age, i.e., the mid-60s. The study followed up 500 volunteers born 1930–1932 (C30) and 502 born 1950–1952 (C50). Participants underwent medical, neuropsychological, and psychiatric examinations in 1993–1996 (T1), 1997–2000 (T2), 2005–2008 (T3), and 2014–2016 (T4), including assessment of abstract thinking, memory performance, verbal fluency, visuo-spatial thinking, psychomotor speed, and attention. Healthy participants from C30 at T2 (n = 298) and from C50 at T4 (n = 205) were compared using multivariate ANCOVAs. Groups slightly differed with respect to age (C50: 63.86 ± 1.14 vs. C30: 66.80 ± 0.91; p < 0.05) and years of education (13.28 ± 2.89 vs. 14.56 ± 2.45). After correcting for age, C50 significantly outperformed C30 in all domains except concentration and verbal fluency. After additionally adjusting for education, C50 significantly outperformed C30 in declarative memory performances and abstract thinking only. Prevalence rates of MCI were 25.2% in C30 and 9.6% in C50 (p < 0.001). Our findings confirm the association between better educational attainment and enhanced cognitive performance in “younger” old individuals. While this association corresponds to the Flynn effect, various life course influences may have also contributed to better performance, including improvements in healthcare provision, medication, and lifestyle factors. Their overall effects may foster cognitive reserve and thus translate into the decline in MCI prevalence reported here. Full article

(This article belongs to the Special Issue Early Recognition of Alzheimer´s Disease)

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18 pages, 17913 KiB

Open AccessEditor’s ChoiceArticle

Semantic Feature Extraction Using SBERT for Dementia Detection

by Yamanki Santander-Cruz, Sebastián Salazar-Colores, Wilfrido Jacobo Paredes-García, Humberto Guendulain-Arenas and Saúl Tovar-Arriaga

Brain Sci. 2022, 12(2), 270; https://doi.org/10.3390/brainsci12020270 - 15 Feb 2022

Cited by 15 | Viewed by 3654

Abstract

Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia. It is currently considered one of the most significant major medical problems worldwide, primarily affecting the elderly. This condition gradually impairs the patient’s cognition, [...] Read more.

Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia. It is currently considered one of the most significant major medical problems worldwide, primarily affecting the elderly. This condition gradually impairs the patient’s cognition, eventually leading to the inability to perform everyday tasks without assistance. Since dementia is an incurable disease, early detection plays an important role in delaying its progression. Because of this, tools and methods have been developed to help accurately diagnose patients in their early stages. State-of-the-art methods have shown that the use of syntactic-type linguistic features provides a sensitive and noninvasive tool for detecting dementia in its early stages. However, these methods lack relevant semantic information. In this work, we propose a novel methodology, based on the semantic features approach, by using sentence embeddings computed by Siamese BERT networks (SBERT), along with support vector machine (SVM), K-nearest neighbors (KNN), random forest, and an artificial neural network (ANN) as classifiers. Our methodology extracted 17 features that provide demographic, lexical, syntactic, and semantic information from 550 oral production samples of elderly controls and people with Alzheimer’s disease, provided by the DementiaBank Pitt Corpus database. To quantify the relevance of the extracted features for the dementia classification task, we calculated the mutual information score, which demonstrates a dependence between our features and the MMSE score. The experimental classification performance metrics, such as the accuracy, precision, recall, and F1 score (77, 80, 80, and 80%, respectively), validate that our methodology performs better than syntax-based methods and the BERT approach when only the linguistic features are used. Full article

(This article belongs to the Special Issue Early Recognition of Alzheimer´s Disease)

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18 pages, 2329 KiB

Open AccessArticle

Changes in Pronoun Use a Decade before Clinical Diagnosis of Alzheimer’s Dementia—Linguistic Contexts Suggest Problems in Perspective-Taking

by Dagmar Bittner, Claudia Frankenberg and Johannes Schröder

Brain Sci. 2022, 12(1), 121; https://doi.org/10.3390/brainsci12010121 - 17 Jan 2022

Cited by 5 | Viewed by 2155

Abstract

The use of pronouns has been shown to change pathologically in the early phases of Alzheimer’s Dementia (AD). So far, the findings have been of a quantitative nature. Little is known, however, about the developmental path of the change, its onset, the domains [...] Read more.

The use of pronouns has been shown to change pathologically in the early phases of Alzheimer’s Dementia (AD). So far, the findings have been of a quantitative nature. Little is known, however, about the developmental path of the change, its onset, the domains in which it initially occurs, and if and how it spreads to other linguistic domains. The present study investigates pronoun use in six speakers of German a decade before they were clinically diagnosed with AD (LAD) and six biographically matched healthy controls (CTR). The data originate from monologic spoken language elicited by semi-spontaneous biographical interviews. Investigation of nine pronoun types revealed group differences in the use of three pronoun types: D-pronouns—a specific pronoun type of German for reference to persons and objects; the impersonal pronoun man ‘one’, and the propositional pronoun das ‘this/that’. Investigation of the linguistic contexts in which these three pronoun types were used revealed a correlation with declines in elaborative and evaluative information; that is, information the hearer would benefit from in creating an informed model of the discourse. We, therefore, hypothesize that the early changes in language use due to AD point to problems in perspective-taking, specifically in taking the hearer’s perspective. Full article

(This article belongs to the Special Issue Early Recognition of Alzheimer´s Disease)

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13 pages, 634 KiB

Open AccessArticle

fMRI Investigation of Semantic Lexical Processing in Healthy Control and Alzheimer’s Disease Subjects Using Naming Task: A Preliminary Study

by Yen-Ting Chen, Chun-Ju Hou, Natan Derek and Min-Wei Huang

Brain Sci. 2021, 11(6), 718; https://doi.org/10.3390/brainsci11060718 - 28 May 2021

Cited by 1 | Viewed by 2840

Abstract

For decades, scientists have been trying to solve the problem of dementia, with no cure currently available. Semantic–lexical impairment is well established as the early critical sign of dementia, although there are still gaps in knowledge that must be investigated. In this study, [...] Read more.

For decades, scientists have been trying to solve the problem of dementia, with no cure currently available. Semantic–lexical impairment is well established as the early critical sign of dementia, although there are still gaps in knowledge that must be investigated. In this study, we used fMRI to observe the neural activity of 31 subjects, including 16 HC (Healthy Control) and 15 AD (Alzheimer’s Disease), who participated in the naming task. The neuropsychology profile of HC (Healthy Control) and AD (Alzheimer’s Disease) are discussed in this study. The involvement of FG (Fusiform Gyrus) and IFG (Inferior Frontal Gyrus) shows dominant activation in both of the groups. We observed a decrease in neural activity in the AD group, resulting in semantic deficit problems in this preliminary study. Furthermore, ROI analysis was performed and revealed both hyperactivation and hypoactivation in the AD group. The compensatory mechanism demonstrated during the task, due to the effort required to identify an animal’s name, represents the character profile of AD. Full article

(This article belongs to the Special Issue Early Recognition of Alzheimer´s Disease)

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Graphical abstract

14 pages, 1772 KiB

Open AccessArticle

Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype

by Hai-Tao Li, Shao-Xun Yuan, Jian-Sheng Wu, Yu Gu and Xiao Sun

Brain Sci. 2021, 11(6), 674; https://doi.org/10.3390/brainsci11060674 - 21 May 2021

Cited by 9 | Viewed by 2838

Abstract

Alzheimer’s disease (AD) is a neurodegenerative brain disease in the elderly. Identifying patients with mild cognitive impairment (MCI) who are more likely to progress to AD is a key step in AD prevention. Recent studies have shown that AD is a heterogeneous disease. [...] Read more.

Alzheimer’s disease (AD) is a neurodegenerative brain disease in the elderly. Identifying patients with mild cognitive impairment (MCI) who are more likely to progress to AD is a key step in AD prevention. Recent studies have shown that AD is a heterogeneous disease. In this study, we propose a subtyping-based prediction strategy to predict the conversion from MCI to AD in three years according to MCI patient subtypes. Structural magnetic resonance imaging (sMRI) data and multi-omics data, including genotype data and gene expression profiling derived from peripheral blood samples, from 125 MCI patients were used in the Alzheimer’s Disease Neuroimaging Initiative (ADNI)-1 dataset and from 98 MCI patients in the ADNI-GO/2 dataset. A variational Bayes approximation model based on the multiple kernel learning method was constructed to predict whether an MCI patient will progress to AD within three years. In internal fivefold cross-validation within ADNI-1, we achieved an overall AUC of 0.83 (79.20% accuracy, 81.25% sensitivity, 77.92% specificity) compared to the model without subtyping, which achieved an AUC of 0.78 (76.00% accuracy, 77.08% sensitivity, 75.32% specificity). In external validation using ADNI-1 as a training set and ADNI-GO/2 as an independent test set, we attained an AUC of 0.78 (74.49% accuracy, 74.19% sensitivity, 74.63% specificity). Identifying MCI patient subtypes with omics data would improve the accuracy of predicting the conversion from MCI to AD. In addition to evaluating statistics, obtaining the significant sMRI, single nucleotide polymorphism (SNP) and mRNA expression data from peripheral blood of MCI patients is noninvasive and cost-effective for predicting conversion from MCI to AD. Full article

(This article belongs to the Special Issue Early Recognition of Alzheimer´s Disease)

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Review

Jump to: Research, Other

18 pages, 24707 KiB

Open AccessReview

Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease

by Karl Herholz

Brain Sci. 2022, 12(2), 146; https://doi.org/10.3390/brainsci12020146 - 23 Jan 2022

Cited by 2 | Viewed by 5036

Abstract

Alzheimer’s disease (AD) does not present uniform symptoms or a uniform rate of progression in all cases. The classification of subtypes can be based on clinical symptoms or patterns of pathological brain alterations. Imaging techniques may allow for the identification of AD subtypes [...] Read more.

Alzheimer’s disease (AD) does not present uniform symptoms or a uniform rate of progression in all cases. The classification of subtypes can be based on clinical symptoms or patterns of pathological brain alterations. Imaging techniques may allow for the identification of AD subtypes and their differentiation from other neurodegenerative diseases already at an early stage. In this review, the strengths and weaknesses of current clinical imaging methods are described. These include positron emission tomography (PET) to image cerebral glucose metabolism and pathological amyloid or tau deposits. Magnetic resonance imaging (MRI) is more widely available than PET. It provides information on structural or functional changes in brain networks and their relation to AD subtypes. Amyloid PET provides a very early marker of AD but does not distinguish between AD subtypes. Regional patterns of pathology related to AD subtypes are observed with tau and glucose PET, and eventually as atrophy patterns on MRI. Structural and functional network changes occur early in AD but have not yet provided diagnostic specificity. Full article

(This article belongs to the Special Issue Early Recognition of Alzheimer´s Disease)

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Other

Jump to: Research, Review

11 pages, 2554 KiB

Open AccessCase Report

A Pathogenic Presenilin-1 Val96Phe Mutation from a Malaysian Family

by Eva Bagyinszky, Gaik-Siew Ch’ng, Mei-Yan Chan, Seong Soo A. An and SangYun Kim

Brain Sci. 2021, 11(10), 1328; https://doi.org/10.3390/brainsci11101328 - 08 Oct 2021

Cited by 2 | Viewed by 1884

Abstract

Presenilin-1 (PSEN1) is one of the causative genes for early onset Alzheimer’s disease (EOAD). Recently, emerging studies have reported several novel PSEN1 mutations among Asians. In this study, a PSEN1 Val96Phe mutation was discovered in two siblings from Malaysia with a [...] Read more.

Presenilin-1 (PSEN1) is one of the causative genes for early onset Alzheimer’s disease (EOAD). Recently, emerging studies have reported several novel PSEN1 mutations among Asians. In this study, a PSEN1 Val96Phe mutation was discovered in two siblings from Malaysia with a strong family history of disease. This is the second report of PSEN1 Val96Phe mutation among EOAD patients in Asia and in the world. Patients presented symptomatic changes in their behaviors and personality, such as apathy and withdrawal in their 40s. Previous cellular studies with COS1 cell lines revealed the mutation increased the amyloid-β42 (Aβ42) productions. In the present study, whole-exome sequencing was performed on the two siblings with EOAD, and they were analyzed against the virtual panel of 100 genes from various neurodegenerative diseases. In silico modeling was also performed on PSEN1 Val96Phe mutation. This mutation was located on the first transmembrane helix of PSEN1 protein, resulting significant intramolecular stresses in the helices. This helical domain would play a significant role in γ-secretase cleavage for the increased Aβ42 productions. Several other adjacent mutations were reported in this helical domain, including Ile83Thr or Val89Leu. Our study suggested that perturbations in TMI-HLI-TMII regions could also be associated with C-terminal fragment accumulation of APP and enhanced amyloid productions. Full article

(This article belongs to the Special Issue Early Recognition of Alzheimer´s Disease)

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