Estrogenic Regulation of Synaptic Health and Cognition in Aging Rhesus Monkeys

2020 ◽  
pp. 303-334
Author(s):  
Johanna L. Crimins ◽  
Yuko Hara ◽  
John H. Morrison
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Aging ◽  
Rhesus Monkeys ◽  
Memory Systems ◽  
Brain Regions ◽  
Clinical Literature ◽  
Menopausal Women ◽  
Dorsolateral Prefrontal ◽  
Estrogenic Regulation

A compelling case can be made for estrogen’s role in maintaining synaptic health in the context of cognitive aging. This chapter first reviews clinical literature pertinent to estrogenic actions on cognition in menopausal women. Next, the authors provide a comprehensive summary of recent investigations in aging rhesus monkeys, which have emerged as a particularly powerful model for the study of synaptic and cognitive effects of both natural and surgical menopause. In particular, we focus on hippocampal and dorsolateral prefrontal cortex neurons and circuits that degenerate in normal aging and Alzheimer’s disease. The responsiveness of these brain regions to estrogen and implications for their related memory systems are discussed. Finally, the chapter highlights work that needs to be done to more fully understand the molecular basis for the complex interplay between menopause, aging, and vulnerability to Alzheimer’s disease in higher cognitive function and synaptic health.

scholarly journals Deciphering cellular transcriptional alterations in Alzheimer’s disease brains

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Xue Wang ◽  
Mariet Allen ◽  
Shaoyu Li ◽  
Zachary S. Quicksall ◽  
Tulsi A. Patel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Complex Disease ◽  
Individual Cell ◽  
Cell Types ◽  
Brain Regions ◽  
Bioinformatics Pipeline ◽  
Dorsolateral Prefrontal ◽  
Transcriptional Alterations

Abstract Large-scale brain bulk-RNAseq studies identified molecular pathways implicated in Alzheimer’s disease (AD), however these findings can be confounded by cellular composition changes in bulk-tissue. To identify cell intrinsic gene expression alterations of individual cell types, we designed a bioinformatics pipeline and analyzed three AD and control bulk-RNAseq datasets of temporal and dorsolateral prefrontal cortex from 685 brain samples. We detected cell-proportion changes in AD brains that are robustly replicable across the three independently assessed cohorts. We applied three different algorithms including our in-house algorithm to identify cell intrinsic differentially expressed genes in individual cell types (CI-DEGs). We assessed the performance of all algorithms by comparison to single nucleus RNAseq data. We identified consensus CI-DEGs that are common to multiple brain regions. Despite significant overlap between consensus CI-DEGs and bulk-DEGs, many CI-DEGs were absent from bulk-DEGs. Consensus CI-DEGs and their enriched GO terms include genes and pathways previously implicated in AD or neurodegeneration, as well as novel ones. We demonstrated that the detection of CI-DEGs through computational deconvolution methods is promising and highlight remaining challenges. These findings provide novel insights into cell-intrinsic transcriptional changes of individual cell types in AD and may refine discovery and modeling of molecular targets that drive this complex disease.

The Impact of Transcranial Direct Current Stimulation on the Language Production of Healthy Adults and Individuals With Alzheimer's Disease

2016 ◽  
Vol 1 (2) ◽  
pp. 138-144
Author(s):  
Amy Vogel-Eyny ◽  
Elizabeth E. Galletta ◽  
Loraine K. Obler
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Direct Current ◽  
Transcranial Direct Current Stimulation ◽  
Language Production ◽  
Memory Systems ◽  
Brain Regions ◽  
Healthy Adults ◽  
Direct Current Stimulation ◽  
The Impact

Transcranial direct current stimulation (tDCS) is a non-invasive form of brain stimulation that is a technique for modulating cognitive and linguistic functions. Researchers employ tDCS as a way of examining the language system and related memory systems. Although the field is in its infancy, there is evidence to suggest that tDCS applied to language-related brain regions has the potential to exert a beneficial influence on the language and memory functioning of healthy adults as well as individuals with Alzheimer's disease (AD). The purpose of the present review is to critically evaluate the current body of literature on the impact of tDCS on language production in healthy adults and the related memory performance of individuals with AD.

scholarly journals Deciphering cellular transcriptional alterations in Alzheimer’s disease brains

2020 ◽  
Author(s):  
Xue Wang ◽  
Mariet Allen ◽  
Shaoyu Li ◽  
Zachary S. Quicksall ◽  
Tulsi A. Patel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Complex Disease ◽  
Individual Cell ◽  
Cell Types ◽  
Brain Regions ◽  
Bioinformatics Pipeline ◽  
Dorsolateral Prefrontal ◽  
Transcriptional Alterations

AbstractLarge-scale brain bulk-RNAseq studies identified molecular pathways implicated in Alzheimer’s disease (AD), however these findings can be confounded by cellular composition changes in bulk-tissue. To identify cell intrinsic gene expression alterations of individual cell types, we designed a bioinformatics pipeline and analyzed three AD and control bulk-RNAseq datasets of temporal and dorsolateral prefrontal cortex from 685 brain samples. We detected cell-proportion changes in AD brains that are robustly replicable across the three independently assessed cohorts. We applied three different algorithms including our in-house algorithm to identify cell intrinsic differentially expressed genes in individual cell types (CI-DEGs). We assessed the performance of all algorithms by comparison to single nucleus RNAseq data. We identified consensus CI-DEGs that are common to multiple brain regions. Despite significant overlap between consensus CI-DEGs and bulk-DEGs, many CI-DEGs were absent from bulk-DEGs. Consensus CI-DEGs and their enriched GO terms include genes and pathways previously implicated in AD or neurodegeneration, as well as novel ones. We demonstrated that the detection of CI-DEGs through computational deconvolution methods is promising and highlight remaining challenges. These findings provide novel insights into cell-intrinsic transcriptional changes of individual cell types in AD and may refine discovery and modeling of molecular targets that drive this complex disease.

A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

2018 ◽  
Vol 15 (5) ◽  
pp. 429-442 ◽  
Author(s):  
Nishant Verma ◽  
S. Natasha Beretvas ◽  
Belen Pascual ◽  
Joseph C. Masdeu ◽  
Mia K. Markey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Latent Variable ◽  
Brain Regions ◽  
Disease Assessment ◽  
Latent Variable Analysis ◽  
The Relationship ◽  
Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

Covert versus overt self-recognition in late stage Alzheimer's disease

1997 ◽  
Vol 3 (2) ◽  
pp. 195-198 ◽  
Author(s):  
SANDRA M. BOLOGNA ◽  
CAMERON J. CAMP
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Stage ◽  
Memory Systems ◽  
Self Recognition ◽  
Nondeclarative Memory

Some persons with Alzheimer's disease (AD) lose the ability to recognize themselves, as when they cannot overtly recognize their reflection in a mirror. There is evidence, however, that covert or unconscious self-recognition might be displayed in such individuals. In this study, 3 persons with AD lacking the ability to overtly self-recognize demonstrated multiple instances of unconscious or covert self-recognition. A variety of interventions, inspired by research with prosopagnosics, was implemented to remediate this loss. Interventions enabled all participants to exhibit overt self-recognition, though each did so with the aid of a different intervention. In addition, successful overt self-recognition required a verbal probe and was entirely intervention-dependent: When the intervention was removed, overt self-recognition was lost. Results support a dissociation between explicit–declarative versus implicit–nondeclarative memory systems, and extends this dissociation into the realm of self-recognition in AD. (JINS, 1997, 3, 195–198.)

scholarly journals Genome-wide analysis identifies a novel LINC-PINT splice variant associated with vascular amyloid pathology in Alzheimer’s disease

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Joseph S. Reddy ◽  
Mariet Allen ◽  
Charlotte C. G. Ho ◽  
Stephanie R. Oatman ◽  
Özkan İş ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Transcriptome Profiling ◽  
Brain Regions ◽  
Common Finding ◽  
Amyloid Angiopathy ◽  
Genome Wide ◽  
A Genome ◽  
Male Sex

AbstractCerebral amyloid angiopathy (CAA) contributes to accelerated cognitive decline in Alzheimer’s disease (AD) dementia and is a common finding at autopsy. The APOEε4 allele and male sex have previously been reported to associate with increased CAA in AD. To inform biomarker and therapeutic target discovery, we aimed to identify additional genetic risk factors and biological pathways involved in this vascular component of AD etiology. We present a genome-wide association study of CAA pathology in AD cases and report sex- and APOE-stratified assessment of this phenotype. Genome-wide genotypes were collected from 853 neuropathology-confirmed AD cases scored for CAA across five brain regions, and imputed to the Haplotype Reference Consortium panel. Key variables and genome-wide genotypes were tested for association with CAA in all individuals and in sex and APOEε4 stratified subsets. Pathway enrichment was run for each of the genetic analyses. Implicated loci were further investigated for functional consequences using brain transcriptome data from 1,186 samples representing seven brain regions profiled as part of the AMP-AD consortium. We confirmed association of male sex, AD neuropathology and APOEε4 with increased CAA, and identified a novel locus, LINC-PINT, associated with lower CAA amongst APOEε4-negative individuals (rs10234094-C, beta = −3.70 [95% CI −0.49—−0.24]; p = 1.63E-08). Transcriptome profiling revealed higher LINC-PINT expression levels in AD cases, and association of rs10234094-C with altered LINC-PINT splicing. Pathway analysis indicates variation in genes involved in neuronal health and function are linked to CAA in AD patients. Further studies in additional and diverse cohorts are needed to assess broader translation of our findings.

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 Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 216 ◽  
Author(s):  
Jianjia Wang ◽  
Xichen Wu ◽  
Mingrui Li ◽  
Hui Wu ◽  
Edwin Hancock
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ensemble Methods ◽  
Brain Regions ◽  
Fmri Data ◽  
Thermal Systems ◽  
Network Construction ◽  
Network Characteristics ◽  
Network Entropy ◽  
Canonical Ensembles

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

scholarly journals Repetitive Transcranial Magnetic Stimulation in the Treatment of Alzheimer’s Disease and Other Dementias

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 949
Author(s):  
Athina-Maria Aloizou ◽  
Georgia Pateraki ◽  
Konstantinos Anargyros ◽  
Vasileios Siokas ◽  
Christos Bakirtzis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Lewy Body Dementia ◽  
Standard Of Care ◽  
Central Position ◽  
Beneficial Effects ◽  
Dorsolateral Prefrontal

Dementia is a debilitating impairment of cognitive functions that affects millions of people worldwide. There are several diseases belonging to the dementia spectrum, most prominently Alzheimer’s disease (AD), vascular dementia (VD), Lewy body dementia (LBD) and frontotemporal dementia (FTD). Repetitive transcranial magnetic stimulation (rTMS) is a safe, non-invasive form of brain stimulation that utilizes a magnetic coil to generate an electrical field and induce numerous changes in the brain. It is considered efficacious for the treatment of various neuropsychiatric disorders. In this paper, we review the available studies involving rTMS in the treatment of these dementia types. The majority of studies have involved AD and shown beneficial effects, either as a standalone, or as an add-on to standard-of-care pharmacological treatment and cognitive training. The dorsolateral prefrontal cortex seems to hold a central position in the applied protocols, but several parameters still need to be defined. In addition, rTMS has shown potential in mild cognitive impairment as well. Regarding the remaining dementias, research is still at preliminary phases, and large, randomized studies are currently lacking.

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