scholarly journals Roles of Selenoproteins in Brain Function and the Potential Mechanism of Selenium in Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhong-Hao Zhang ◽  
Guo-Li Song
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Function ◽  
Recent Literature ◽  
Brain Regions ◽  
Potential Mechanism ◽  
Biological Functions ◽  
Synaptic Functions ◽  
New Directions ◽  
The Brain

Selenium (Se) and its compounds have been reported to have great potential in the prevention and treatment of Alzheimer’s disease (AD). However, little is known about the functional mechanism of Se in these processes, limiting its further clinical application. Se exerts its biological functions mainly through selenoproteins, which play vital roles in maintaining optimal brain function. Therefore, selenoproteins, especially brain function-associated selenoproteins, may be involved in the pathogenesis of AD. Here, we analyze the expression and distribution of 25 selenoproteins in the brain and summarize the relationships between selenoproteins and brain function by reviewing recent literature and information contained in relevant databases to identify selenoproteins (GPX4, SELENOP, SELENOK, SELENOT, GPX1, SELENOM, SELENOS, and SELENOW) that are highly expressed specifically in AD-related brain regions and closely associated with brain function. Finally, the potential functions of these selenoproteins in AD are discussed, for example, the function of GPX4 in ferroptosis and the effects of the endoplasmic reticulum (ER)-resident protein SELENOK on Ca2+ homeostasis and receptor-mediated synaptic functions. This review discusses selenoproteins that are closely associated with brain function and the relevant pathways of their involvement in AD pathology to provide new directions for research on the mechanism of Se in AD.

scholarly journals Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

2021 ◽  
Author(s):  
Antonio Giovannetti ◽  
Gianluca Susi ◽  
Paola Casti ◽  
Arianna Mencattini ◽  
Sandra Pusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Classifiers ◽  
Deep Convolutional Neural Networks ◽  
Early Signs ◽  
Data Representations ◽  
The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

scholarly journals Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer’s disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Angela M. Crist ◽  
Kelly M. Hinkle ◽  
Xue Wang ◽  
Christina M. Moloney ◽  
Billie J. Matchett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Digital Pathology ◽  
Selective Vulnerability ◽  
Brain Regions ◽  
Biochemical Analyses ◽  
The Brain ◽  
Relevant Gene ◽  
Tau Expression

AbstractSelective vulnerability of different brain regions is seen in many neurodegenerative disorders. The hippocampus and cortex are selectively vulnerable in Alzheimer’s disease (AD), however the degree of involvement of the different brain regions differs among patients. We classified corticolimbic patterns of neurofibrillary tangles in postmortem tissue to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. We identified hippocampal gene expression changes associated with hippocampal vulnerability and used machine learning to identify genes that were associated with AD neuropathology, including SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Further histologic and biochemical analyses suggested SERPINA5 expression is associated with tau expression in the brain. Our study highlights the importance of embracing heterogeneity of the human brain in disease to identify disease-relevant gene expression.

Intranasal administration of mitochondria improves spatial memory in olfactory bulbectomized mice

2021 ◽  
pp. 153537022110568
Author(s):  
Natalia V Bobkova ◽  
Daria Y Zhdanova ◽  
Natalia V Belosludtseva ◽  
Nikita V Penkov ◽  
Galina D Mironova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Memory ◽  
Intranasal Administration ◽  
Brain Structures ◽  
Brain Regions ◽  
Dependent Manner ◽  
Behavioral Experiments ◽  
Hippocampal Cell Culture ◽  
The Brain

Here, we found that functionally active mitochondria isolated from the brain of NMRI donor mice and administrated intranasally to recipient mice penetrated the brain structures in a dose-dependent manner. The injected mitochondria labeled with the MitoTracker Red localized in different brain regions, including the neocortex and hippocampus, which are responsible for memory and affected by degeneration in patients with Alzheimer's disease. In behavioral experiments, intranasal microinjections of brain mitochondria of native NMRI mice improved spatial memory in the olfactory bulbectomized (OBX) mice with Alzheimer’s type degeneration. Control OBX mice demonstrated loss of spatial memory tested in the Morris water maze. Immunocytochemical analysis revealed that allogeneic mitochondria colocalized with the markers of astrocytes and neurons in hippocampal cell culture. The results suggest that a non-invasive route intranasal administration of mitochondria may be a promising approach to the treatment of neurodegenerative diseases characterized, like Alzheimer's disease, by mitochondrial dysfunction.

scholarly journals Ceramide and Related-Sphingolipid Levels Are Not Altered in Disease-Associated Brain Regions of APPSLand APPSL/PS1M146LMouse Models of Alzheimer's Disease: Relationship with the Lack of Neurodegeneration?

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Laurence Barrier ◽  
Bernard Fauconneau ◽  
Anastasia Noël ◽  
Sabrina Ingrand
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Animal Models ◽  
Neuronal Death ◽  
Time Course ◽  
Neuronal Loss ◽  
Brain Regions ◽  
App Processing ◽  
Ceramide Accumulation ◽  
The Brain

There is evidence linking sphingolipid abnormalities, APP processing, and neuronal death in Alzheimer's disease (AD). We previously reported a strong elevation of ceramide levels in the brain of the APPSL/PS1Ki mouse model of AD, preceding the neuronal death. To extend these findings, we analyzed ceramide and related-sphingolipid contents in brain from two other mouse models (i.e., APPSLand APPSL/PS1M146L) in which the time-course of pathology is closer to that seen in most currently available models. Conversely to our previous work, ceramides did not accumulate in disease-associated brain regions (cortex and hippocampus) from both models. However, the APPSL/PS1Ki model is unique for its drastic neuronal loss coinciding with strong accumulation of neurotoxic Aβisoforms, not observed in other animal models of AD. Since there are neither neuronal loss nor toxic Aβspecies accumulation in APPSLmice, we hypothesized that it might explain the lack of ceramide accumulation, at least in this model.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

Delirium, dementia, and other cognitive disorders

Psychiatry ◽  
2019 ◽  
Author(s):  
Rebecca McKnight ◽  
Jonathan Price ◽  
John Geddes
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Psychiatric Disorders ◽  
Brain Function ◽  
Healthcare Services ◽  
Common Condition ◽  
Organic Brain Disease ◽  
The Brain

Organic psychiatric disorders result from brain dys­function caused by organic pathology inside or outside the brain. Dementia is the most common condition, with Alzheimer’s disease alone affecting 1 per cent of the population at 60 years, rising to 40 per cent over 80 years. Many of the rarer organic psychiatric dis­orders tend to affect a wider age range, but present in similar ways. Given the changing demographics of most developed countries, disorders producing cognitive im­pairment in older adults are becoming increasingly important for provision of healthcare services and in daily clinical practice. This chapter will cover the more common causes of cognitive impairment, and there is additional information in Chapters 18 and 20 on psych­iatry of older adults in psychiatry and medicine. There are three common clinical presentations of or­ganic psychiatric disorders: … 1 Delirium— an acute generalized impairment of brain function, in which the most important feature is impairment of consciousness. The disturbance of brain function is generalized, and the primary cause is often outside the brain; for example, sepsis due to a urinary tract infection. 2 Dementia— chronic generalized impairment, in which the main clinical feature is global intellectual impairment. There are also changes in mood and behaviour. The brain dysfunction is generalized, and the primary cause is within the brain; for example, a degenerative condition such as Alzheimer’s disease. 3 Specific syndromes— which include disorders with a predominant impairment of isolated areas; for example, memory (amnesic syndrome), thought, mood, or personality change. These include neurological disorders that frequently result in organic psychological complications; for example, epilepsy…. Table 26.1 lists the main categories of psychiatric disorder associated with organic brain disease. The following sections describe these syndromes and the psychiatric consequences of a number of neurological conditions. Organic causes of other core psychiatric conditions (e.g. anxiety and psychosis) are covered in the relevant specific chapters. Delirium is characterized by an acute impairment of consciousness producing a generalized cognitive impairment. The word delirium is derived from the Latin, ‘lira’, which means to wander from the furrow. Delirium is a common condition, affecting up to 30 per cent of patients in general medical or surgical wards, with the primary cause often being a sys­temic illness. The term ‘acute confusional state’ is a synonym for delirium.

scholarly journals The Presence of Human Herpesvirus 6 in the Brain in Health and Disease

Biomolecules ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1520
Author(s):  
Gabriel Santpere ◽  
Marco Telford ◽  
Pol Andrés-Benito ◽  
Arcadi Navarro ◽  
Isidre Ferrer
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Current Knowledge ◽  
Human Herpesvirus ◽  
Brain Regions ◽  
Specific Cell ◽  
Human Herpesvirus 6 ◽  
Detection Techniques ◽  
Herpesvirus 6 ◽  
The Brain

The human herpesvirus 6 (HHV‐6) ‐A and ‐B are two dsDNA beta‐herpesviruses infectingalmost the entire worldwide population. These viruses have been implicated in multipleneurological conditions in individuals of various ages and immunological status, includingencephalitis, epilepsy, and febrile seizures. HHV‐6s have also been suggested as playing a role inthe etiology of neurodegenerative diseases such as multiple sclerosis and Alzheimer’s disease. Theapparent robustness of these suggested associations is contingent on the accuracy of HHV‐6detection in the nervous system. The effort of more than three decades of researching HHV‐6 in thebrain has yielded numerous observations, albeit using variable technical approaches in terms oftissue preservation, detection techniques, sample sizes, brain regions, and comorbidities. In thisreview, we aimed to summarize current knowledge about the entry routes and direct presence ofHHV‐6 in the brain parenchyma at the level of DNA, RNA, proteins, and specific cell types, inhealthy subjects and in those with neurological conditions. We also discuss recent findings relatedto the presence of HHV‐6 in the brains of patients with Alzheimer’s disease in light of availableevidence.

scholarly journals Alzheimer’s disease under the purview of Graph Theory Centric Genetic Networks

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Yegnanarayanan Venkatraman ◽  
◽  
Narayanaa Y Krithicaa ◽  
Valentina E. Balas ◽  
Marius M. Balas ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Network Theory ◽  
Memory Loss ◽  
Amyloid Plaque ◽  
Brain Regions ◽  
Protein Protein Interaction ◽  
Communication Demands ◽  
The Brain

Notice that the synapsis of brain is a form of communication. As communication demands connectivity, it is not a surprise that "graph theory" is a fastest growing area of research in the life sciences. It attempts to explain the connections and communication between networks of neurons. Alzheimer’s disease (AD) progression in brain is due to a deposition and development of amyloid plaque and the loss of communication between nerve cells. Graph/network theory can provide incredible insights into the incorrect wiring leading to memory loss in a progressive manner. Network in AD is slanted towards investigating the intricate patterns of interconnections found in the pathogenesis of brain. Here, we see how the notions of graph/network theory can be prudently exploited to comprehend the Alzheimer’s disease. We begin with introducing concepts of graph/network theory as a model for specific genetic hubs of the brain regions and cellular signalling. We begin with a brief introduction of prevalence and causes of AD followed by outlining its genetic and signalling pathogenesis. We then present some of the network-applied outcome in assessing the disease-signalling interactions, signal transduction of protein-protein interaction, disturbed genetics and signalling pathways as compelling targets of pathogenesis of the disease.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

scholarly journals Predicting Brain Regions Related to Alzheimer’s Disease Based on Global Feature

2020 ◽  
Author(s):  
Qi Wang ◽  
Siwei Chen ◽  
He Wang ◽  
Luzeng Chen ◽  
Yongan Sun ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Diffusion Tensor ◽  
The Elderly ◽  
Brain Regions ◽  
Imaging Method ◽  
Global Feature ◽  
Objective Monitoring ◽  
The Brain

AbstractAlzheimer’s disease (AD) is a common neurodegenerative disease in the elderly, early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most of the brain regions related to AD were identified based on the imaging method, which can only identify some atrophic brain regions. In this work, we used mathematical models to find out the potential brain regions related to AD. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, we set a new local feature index 2hop-connectivity to measure the correlation among different areas. And for this, we proposed a novel algorithm named 2hopRWR to measure 2hop-connectivity. At last, we proposed a new index GFS (Global Feature Score) based on global feature by combing 5 local features: degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are likely related to Alzheimer’s Disease. As a result, all the top ten brain regions in GFS scoring difference between the AD group and the non-AD group were related to AD by literature verification. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the mini-mental state examination (MMSE) scale and montreal cognitive assessment (MoCA) scale. So, we believe the GFS can also be used as a new index to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method in other areas of network analysis.

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