Search

The Trail Making Test (TMT) is a common neuropsychological test that assesses selective attention and executive functions and is used to detect dementia. However, the test lacks efficiency due to its paper-based format. In this study, we digitally transformed (DX) the TMT using Scratch, a high-level block-based visual programming language. This is a user-friendly programming language and allows beginners to create advanced games and programs. In the DX-TMT we developed, two new tasks, one of which with an increased difficulty level, were added in addition to the two original tasks from the paper-based TMT. The results showed that the DX-TMT reproduced the basic features of the paper-based TMT. The additional task with increased difficulty was associated with a longer completion time than the original tasks, especially in elderly subjects, with greater individual differences. These results suggest that the DX-TMT could be utilized for the early detection of mild cognitive impairment. [ABSTRACT FROM AUTHOR]

ABSTRACT Being an ϵ4 carrier in the Apoϵ gene has been suggested as a modifying factor for the interaction between cardio-metabolic, social risk factors, and the development of cognitive impairment. Objective: The main objective of this study was to assess the existence of such interaction in a sample of Bogota’s elderly population. Methods: A cross-sectional study was conducted with 1,263 subjects older than 50 years. Each participant was diagnosed by consensus, after neuropsychological and neuropsychiatric evaluations, under a diagnosis of normal cognition, mild cognitive impairment (MCI) according to Petersen’s criteria, or dementia according to DSM-IV criteria. Apoϵ was typified and an analysis of MoCA test was performed in each group carrying or not ϵ4 allele. Results: Our study showed that 75% were women with a median age of 68 years (interquartile range 62–74 years) and a median schooling for 6 years (interquartile range 4–12 years). Dementia was related to low education level of ≤5 years OR=11.20 (95%CI 4.99–25.12), high blood pressure (HBP) OR=1.45 (95%CI 1.03–2.05), and age over 70 years OR=7.68 (95%CI 3.49–16.90), independently of being or not an ϵ4 allele carrier. Diabetic subjects with dementia carrying ϵ4 allele showed a tendency to exhibit lower scores on the MoCA test, when compared with noncarriers’ diabetic subjects with dementia. Conclusions: The presence of ϵ4 allele does not modify the relationship between cognitive impairment and the different cardio-metabolic and social risk factors, except in diabetic subjects ϵ4 carriers with dementia who showed a tendency to exhibit lower scores of the MoCA test, when compared with noncarriers’ diabetic subjects with dementia.

Kim H Tran,1,2 Andrew P McDonald,1,3 Ryan CN D’Arcy,2,3 Xiaowei Song1,2 1Clinical Research Centre, Surrey Memorial Hospital, Fraser Health Authority, Surrey, BC, Canada; 2Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada; 3Department of Medicine, University of British Columbia, Vancouver, BC, CanadaCorrespondence: Xiaowei Song 13750 96th Avenue, Surrey, BC V3V 1Z2, CanadaTel +1 604 585 5666 ext. 774 986Email xiaowei.song@fraserhealth.caAbstract: Contextual processing (or context processing; CP) is an integral component of cognition. CP allows people to manage their thoughts and actions by adjusting to surroundings. CP involves the formation of an internal representation of context in relation to the environment, maintenance of this information over a period of time, and the updating of mental representations to reflect changes in the environment. Each of these functions can be affected by aging and associated conditions. Here, we introduced contextual processing research and summarized the literature studying the impact of normal aging and neurodegeneration-related cognitive decline on CP. Through searching the PubMed, PsycINFO, and Google Scholar databases, 23 studies were retrieved that focused on the impact of aging, mild cogniitve impairment (MCI), Alzheimer’s disease (AD), and Parkinson’s disease (PD) on CP. Results indicated that CP is particularly vulnerable to aging and neurodegeneration. Older adults had a delayed onset and reduced amplitude of electrophysiological response to information detection, comparison, and execution. MCI patients demonstrated clear signs of impaired CP compared to normal aging. The only study on AD suggested a decreased proactive control in AD participants in maintaining contextual information, but seemingly intact reactive control. Studies on PD restricted to non-demented older participants, who showed limited ability to use contextual information in cognitive and motor processes, exhibiting impaired reactive control but more or less intact proactive control. These data suggest that the decline in CP with age is further impacted by accelerated aging and neurodegeneration, providing insights for improving intervention strategies. This review highlights the need for increased attention to research this important but understudied field.Keywords: Alzheimer’s disease, Parkinson’s disease, mild-cognitive impairment, aging, dementia, contextual processing, EEG-ERP, behavior

This work proposes the application of a new electroencephalogram (EEG) signal processing tool - the lacsogram - to characterize the Alzheimer's disease (AD) activity and to assist on its diagnosis at different stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). Statistical analyzes are performed to lacstral distances between conventional EEG subbands to find measures capable of discriminating AD in all stages and characterizing the AD activity in each electrode. Cepstral distances are used for comparison. Comparing all AD stages and Controls (C), the most important significances are the lacstral distances between subbands $\theta$ and $\alpha$ ($p$ = 0.0014 $< $ 0.05). The topographic maps show significant differences in parietal, temporal and frontal regions as AD progresses. Machine learning models with a leave-one-out cross-validation process are applied to lacstral/cepstral distances to develop an automatic method for diagnosing AD. The following classification accuracies are obtained with an artificial neural network: 95.55% for All vs All, 98.06% for C vs MCI, 95.99% for C vs ADM, 93.85% for MCI vs ADM-ADA. In C vs MCI, C vs ADM and MCI vs ADM-ADA, the proposed method outperforms the state-of-art methods by 5%, 1%, and 2%, respectively. In All vs All, it outperforms the state-of-art EEG and non-EEG methods by 6% and 2%, respectively. These results indicate that the proposed method represents an improvement in diagnosing AD. [ABSTRACT FROM AUTHOR]

A worldwide increase in longevity is bringing novel challenges to public health and health care professionals. Cognitive impairment in the elderly may compromise living conditions and precede Alzheimer’s disease (AD), the most prevalent form of dementia. Therefore, finding molecular markers associated with cognitive impairment is of crucial importance. Lipocalin 2 (LCN2), an iron-related protein, has been suggested as a potential marker for mild cognitive impairment (MCI) and AD. This study aimed at investigating the association between LCN2 measured in serum and cerebrospinal fluid (CSF) with cognitive impairment. A cross-sectional design based on two aging cohorts was used: individuals diagnosed with subjective cognitive complaints (SCC), MCI, and AD from a Swedish memory clinic-based cohort, and individuals diagnosed with SCC and AD from a Portuguese cohort. Binary logistic [for the outcome cognitive impairment (MCI + AD) in the Swedish cohort and AD in the Portuguese cohort] and multinomial logistic (for the outcomes MCI and AD) regression analyses were used. No associations were found in both cohorts when controlling for sex, education, and age. This explanatory study suggests that the association between serum and CSF LCN2 concentrations with cognitive impairment reported in the literature must be further analyzed for confounders.

A worldwide increase in longevity is bringing novel challenges to public health and health care professionals. Cognitive impairment in the elderly may compromise living conditions and precede Alzheimer's disease (AD), the most prevalent form of dementia. Therefore, finding molecular markers associated with cognitive impairment is of crucial importance. Lipocalin 2 (LCN2), an iron-related protein, has been suggested as a potential marker for mild cognitive impairment (MCI) and AD. This study aimed at investigating the association between LCN2 measured in serum and cerebrospinal fluid (CSF) with cognitive impairment. A cross-sectional design based on two aging cohorts was used: individuals diagnosed with subjective cognitive complaints (SCC), MCI, and AD from a Swedish memory clinic-based cohort, and individuals diagnosed with SCC and AD from a Portuguese cohort. Binary logistic [for the outcome cognitive impairment (MCI + AD) in the Swedish cohort and AD in the Portuguese cohort] and multinomial logistic (for the outcomes MCI and AD) regression analyses were used. No associations were found in both cohorts when controlling for sex, education, and age. This explanatory study suggests that the association between serum and CSF LCN2 concentrations with cognitive impairment reported in the literature must be further analyzed for confounders. [ABSTRACT FROM AUTHOR]

Random Forest (RF) is a bagging ensemble model and has many important advantages, such as robustness to noise, an effective structure for complex multimodal data and parallel computing, and also provides important features that help investigate biomarkers. Despite these benefits, RF is not used actively to predict Alzheimer’s disease (AD) with brain MRIs. Recent studies have reported RF’s effectiveness in predicting AD, but the test sample sizes were too small to draw any solid conclusions. Thus, it is timely to compare RF with other learning model methods, including deep learning, particularly with large amounts of data. In this study, we tested RF and various machine learning models with regional volumes from 2250 brain MRIs: 687 normal controls (NC), 1094 mild cognitive impairment (MCI), and 469 AD that ADNI (Alzheimer’s Disease Neuroimaging Initiative database) provided. Three types of features sets (63, 29, and 22 features) were selected, and classification accuracies were computed with RF, Support vector machine (SVM), Multi-layer perceptron (MLP), and Convolutional neural network (CNN). As a result, RF, MLP, and CNN showed high performances of 90.2%, 89.6%, and 90.5% with 63 features. Interestingly, when 22 features were used, RF showed the smallest decrease in accuracy, −3.8%, and the standard deviation did not change significantly, while MLP and CNN yielded decreases in accuracy of −6.8% and −4.5% with changes in the standard deviation from 3.3% to 4.0% for MLP and 2.1% to 7.0% for CNN, indicating that RF predicts AD more reliably with fewer features. In addition, we investigated the importance of the features that RF provides, and identified the hippocampus, amygdala, and inferior lateral ventricle as the major contributors in classifying NC, MCI, and AD. On average, AD showed smaller hippocampus and amygdala volumes and a larger volume of inferior lateral ventricle than those of MCI and NC.

Introduction Alzheimer Disease (AD) standard diagnosis is based on evaluations and biomarkers that are non-specific, expensive, or requires invasive sampling. Therefore, an early, and non-invasive diagnosis is required. As regards molecular mechanisms, recent research has shown that lipid peroxidation plays an important role. Methods Well-defined participants groups were recruited. Lipid peroxidation compounds were determined in plasma using a validated analytical method. Statistical studies consisted of an elastic-net-penalized logistic regression adjustment. Results The regression model fitted to the data included six variables (lipid peroxidation biomarkers) as potential predictors of early AD. This model achieved an apparent area under the receiver operating characteristics (AUC-ROCs) of 0.883 and a bootstrap-validated AUC-ROC of 0.817. Calibration of the model showed very low deviations from real probabilities. Conclusion A satisfactory early diagnostic model has been obtained from plasma levels of 6 lipid peroxidation compounds, indicating the individual probability of suffering from early AD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is associated with well-established macrostructural and cellular markers, including localized brain atrophy and deposition of amyloid. However, there is growing recognition of the link between cerebrovascular dysfunction and AD, supported by continuous experimental evidence in the animal and human literature. As a result, neuroimaging studies of AD are increasingly aiming to incorporate vascular measures, exemplified by measures of cerebrovascular reactivity (CVR). CVR is a measure that is rooted in clinical practice, and as non-invasive CVR-mapping techniques become more widely available, routine CVR mapping may open up new avenues of investigation into the development of AD. This review focuses on the use of MRI to map CVR, paying specific attention to recent developments in MRI methodology and on the emerging stimulus-free approaches to CVR mapping. It also summarizes the biological basis for the vascular contribution to AD, and provides critical perspective on the choice of CVRmapping techniques amongst frail populations. [ABSTRACT FROM AUTHOR]

Mathias Schlögl,1 Michael F Holick21University Center for Medicine of Aging Basel, University of Basel, Basel, Switzerland; 2Department of Medicine, Section of Endocrinology, Nutrition, and Diabetes, Vitamin D, Skin, and Bone Research Laboratory, Boston University Medical Center, Boston, MA, USAAbstract: In recent years, emerging evidence has linked vitamin D not only to its known effects on calcium and bone metabolism, but also to many chronic illnesses involving neurocognitive decline. The importance of vitamin D3 in reducing the risk of these diseases continues to increase due to the fact that an increasing portion of the population in developed countries has a significant vitamin D deficiency. The older population is at an especially high risk for vitamin D deficiency due to the decreased cutaneous synthesis and dietary intake of vitamin D. Recent studies have confirmed an association between cognitive impairment, dementia, and vitamin D deficiency. There is a need for well-designed randomized trials to assess the benefits of vitamin D and lifestyle interventions in persons with mild cognitive impairment and dementia.Keywords: vitamin D, 25(OH)D level, cognition, mild cognitive impairment, Alzheimer's disease, vascular dementia

Background: Presenilin-1 (PS1), the active component of the intramembrane γ-secretase complex, can be detected as soluble heteromeric aggregates in cerebrospinal fluid (CSF). The aim of this study was to examine the different soluble PS1 complexes in the lumbar CSF (CSF-PS1) of individuals with Alzheimer's disease (AD), particularly in both symptomatic and asymptomatic genetically determined AD, in order to evaluate their potential as early biomarkers. Methods: Western blotting, differential centrifugation and co-immunoprecipitation served to determine and characterize CSF-PS1 complexes. We also monitored the assembly of soluble PS1 into complexes in a cell model, and the participation of Aβ in the dynamics and robustness of the stable PS1 complexes. Results: There was an age-dependent increase in CSF-PS1 levels in cognitively normal controls, the different complexes represented in similar proportions. The total levels of CSF-PS1, and in particular the proportion of the stable 100--150 kDa complexes, increased in subjects with autosomal dominant AD that carried PSEN1 mutations (eight symptomatic and six asymptomatic ADAD) and in Down syndrome individuals (ten demented and ten nondemented DS), compared with age-matched controls (n = 23), even prior to the appearance of symptoms of dementia. The proportion of stable CSF-PS1 complexes also increased in sporadic AD (n = 13) and mild-cognitive impaired subjects (n = 12), relative to age-matched controls (n = 17). Co-immunoprecipitation demonstrated the association of Aβ oligomers with soluble PS1 complexes, particularly the stable complexes. Conclusions: Our data suggest that CSF-PS1 complexes may be useful as an early biomarker for AD, reflecting the pathology at asymptomatic state. [ABSTRACT FROM AUTHOR]

The Montreal Cognitive Assessment (MoCA) has been proved brief and sensitive to screen for mild cognitive impairment (MCI) and early dementia in some developed countries or areas. However, little MoCA data are available from mainland China. In this study, the MoCA was applied to 8411 Chinese community dwellers aged 65 or older (6283 = cognitively normal [CN], 1687 = MCI, and 441 = dementia). The MoCA norms were established considering significant influential factors. The optimal cutoff points were 13/14 for illiterate individuals, 19/20 for individuals with 1 to 6 years of education, and 24/25 for individuals with 7 or more years of education. With the optimal cutoffs, the sensitivity of the MoCA was 83.8% for all cognitive impairments, 80.5% for MCI and 96.9% for dementia, and the specificity for identifying CN was 82.5%. These indicate that with optimal cutoffs, the MoCA is valid to screen for cognitive impairment in elderly Chinese living in communities. [ABSTRACT FROM PUBLISHER]

Abstract: Cholinergic neurons express choline acetyltransferase (ChAT) which synthesizes acetylcholine. We show here for the first time that primate-specific 82-kDa ChAT is expressed in nuclei of cholinergic neurons in human brain and spinal cord; isoform-specific antibodies were used to compare localization patterns and temporal expression of the more abundant 69-kDa ChAT and primate-specific 82-kDa ChAT in necropsy tissues. The 82-kDa ChAT co-localizes with 69-kDa ChAT in well-characterized cholinergic areas, but is also found in the claustrum which does not contain 69-kDa ChAT. Cholinergic neuron function changes with increasing age and are targeted in neurodegenerative diseases such as AD, thus we compared expression and subcellular localization of 69- and 82-kDa ChAT in necropsy brain samples from control subjects of varying ages and from Alzheimer disease (AD) subjects. The 82-kDa ChAT protein was expressed in cholinergic neurons in brain from birth until the eighth decade of life and in AD, but the subcellular staining pattern and proportion of neurons that were immunopositive changed with increasing age and in AD. [Copyright &y& Elsevier]

Being an ϵ4 carrier in the Apoϵ gene has been suggested as a modifying factor for the interaction between cardio-metabolic, social risk factors, and the development of cognitive impairment., Objective: The main objective of this study was to assess the existence of such interaction in a sample of Bogota's elderly population., Methods: A cross-sectional study was conducted with 1,263 subjects older than 50 years. Each participant was diagnosed by consensus, after neuropsychological and neuropsychiatric evaluations, under a diagnosis of normal cognition, mild cognitive impairment (MCI) according to Petersen's criteria, or dementia according to DSM-IV criteria. Apoϵ was typified and an analysis of MoCA test was performed in each group carrying or not ϵ4 allele., Results: Our study showed that 75% were women with a median age of 68 years (interquartile range 62-74 years) and a median schooling for 6 years (interquartile range 4-12 years). Dementia was related to low education level of ≤5 years OR=11.20 (95%CI 4.99-25.12), high blood pressure (HBP) OR=1.45 (95%CI 1.03-2.05), and age over 70 years OR=7.68 (95%CI 3.49-16.90), independently of being or not an ϵ4 allele carrier. Diabetic subjects with dementia carrying ϵ4 allele showed a tendency to exhibit lower scores on the MoCA test, when compared with noncarriers' diabetic subjects with dementia., Conclusions: The presence of ϵ4 allele does not modify the relationship between cognitive impairment and the different cardio-metabolic and social risk factors, except in diabetic subjects ϵ4 carriers with dementia who showed a tendency to exhibit lower scores of the MoCA test, when compared with noncarriers' diabetic subjects with dementia., Competing Interests: Disclosure: The authors report no conflicts of interest.

Random Forest (RF) is a bagging ensemble model and has many important advantages, such as robustness to noise, an effective structure for complex multimodal data and parallel computing, and also provides important features that help investigate biomarkers. Despite these benefits, RF is not used actively to predict Alzheimer's disease (AD) with brain MRIs. Recent studies have reported RF's effectiveness in predicting AD, but the test sample sizes were too small to draw any solid conclusions. Thus, it is timely to compare RF with other learning model methods, including deep learning, particularly with large amounts of data. In this study, we tested RF and various machine learning models with regional volumes from 2250 brain MRIs: 687 normal controls (NC), 1094 mild cognitive impairment (MCI), and 469 AD that ADNI (Alzheimer's Disease Neuroimaging Initiative database) provided. Three types of features sets (63, 29, and 22 features) were selected, and classification accuracies were computed with RF, Support vector machine (SVM), Multi-layer perceptron (MLP), and Convolutional neural network (CNN). As a result, RF, MLP, and CNN showed high performances of 90.2%, 89.6%, and 90.5% with 63 features. Interestingly, when 22 features were used, RF showed the smallest decrease in accuracy, −3.8%, and the standard deviation did not change significantly, while MLP and CNN yielded decreases in accuracy of −6.8% and −4.5% with changes in the standard deviation from 3.3% to 4.0% for MLP and 2.1% to 7.0% for CNN, indicating that RF predicts AD more reliably with fewer features. In addition, we investigated the importance of the features that RF provides, and identified the hippocampus, amygdala, and inferior lateral ventricle as the major contributors in classifying NC, MCI, and AD. On average, AD showed smaller hippocampus and amygdala volumes and a larger volume of inferior lateral ventricle than those of MCI and NC. [ABSTRACT FROM AUTHOR]

Brain iron dyshomeostasis is a feature of Alzheimer's disease. Conventionally, research has focused on non-heme iron although degradation of heme from hemoglobin subunits can generate iron to augment the redox-active iron pool. Hemopexin both detoxifies heme to maintain iron homeostasis and bolsters antioxidant capacity via catabolic products, biliverdin and carbon monoxide to combat iron-mediated lipid peroxidation. The aim of the present study was to examine the association of cerebrospinal fluid levels (CSF) hemopexin and hemoglobin subunits (α and β) to Alzheimer's pathological proteins (amyloid and tau), hippocampal volume and metabolism, and cognitive performance. We analyzed baseline CSF heme/iron proteins (multiplexed mass spectrometry-based assay), amyloid and tau (Luminex platform), baseline/longitudinal neuroimaging (MRI, FDG-PET) and cognitive outcomes in 86 cognitively normal, 135 mild-cognitive impairment and 66 Alzheimer's participants from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) cohort. Multivariate regression analysis was performed to delineate differences in CSF proteins between diagnosis groups and evaluated their association to amyloid and tau, neuroimaging and cognition. A p -value ≤ 0.05 was considered significant. Higher hemopexin was associated with higher CSF amyloid (implying decreased brain amyloid deposition), improved hippocampal metabolism and cognitive performance. Meanwhile, hemoglobin subunits were associated with increased CSF tau (implying increased brain tau deposition). When dichotomizing individuals with mild-cognitive impairment into stable and converters to Alzheimer's disease, significantly higher baseline hemoglobin subunits were observed in the converters compared to non-converters. Heme/iron dyshomeostasis is an early and crucial event in AD pathophysiology, which warrants further investigation as a potential therapeutic target., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Ashraf, Dani and So.)