scholarly journals Communicability Characterization of Structural DWI Subcortical Networks in Alzheimer’s Disease

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 475 ◽  
Author(s):  
Eufemia Lella ◽  
Nicola Amoroso ◽  
Domenico Diacono ◽  
Angela Lombardi ◽  
Tommaso Maggipinto ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Brain Network ◽  
Data Set ◽  
Brain Connectome ◽  
Subcortical Brain ◽  
Classification Framework ◽  
Subcortical Regions ◽  
Cortical Regions

In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer’s disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network composed only by the subcortical regions, especially using diffusion-weighted imaging (DWI) data. In this work, we examine a mixed cohort including 46 healthy controls (HC) and 40 AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set. We reconstruct the brain connectome through the use of state of the art tractography algorithms and we propose a method based on graph communicability to enhance the information content of subcortical brain regions in discriminating AD. We develop a classification framework, achieving 77% of area under the receiver operating characteristic (ROC) curve in the binary discrimination AD vs. HC only using a 12 × 12 subcortical features matrix. We find some interesting AD-related connectivity patterns highlighting that subcortical regions tend to increase their communicability through cortical regions to compensate the physical connectivity reduction between them due to AD. This study also suggests that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity.

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.

scholarly journals Altered neuronal gene expression in brain regions differentially affected by Alzheimer's disease: a reference data set

2008 ◽  
Vol 33 (2) ◽  
pp. 240-256 ◽  
Author(s):  
Winnie S. Liang ◽  
Travis Dunckley ◽  
Thomas G. Beach ◽  
Andrew Grover ◽  
Diego Mastroeni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Expression Profiles ◽  
Brain Regions ◽  
Elderly Persons ◽  
Data Set ◽  
Healthy Elderly ◽  
Neuronal Gene ◽  
Laser Capture

Alzheimer's Disease (AD) is the most widespread form of dementia during the later stages of life. If improved therapeutics are not developed, the prevalence of AD will drastically increase in the coming years as the world's population ages. By identifying differences in neuronal gene expression profiles between healthy elderly persons and individuals diagnosed with AD, we may be able to better understand the molecular mechanisms that drive AD pathogenesis, including the formation of amyloid plaques and neurofibrillary tangles. In this study, we expression profiled histopathologically normal cortical neurons collected with laser capture microdissection (LCM) from six anatomically and functionally discrete postmortem brain regions in 34 AD-afflicted individuals, using Affymetrix Human Genome U133 Plus 2.0 microarrays. These regions include the entorhinal cortex, hippocampus, middle temporal gyrus, posterior cingulate cortex, superior frontal gyrus, and primary visual cortex. This study is predicated on previous parallel research on the postmortem brains of the same six regions in 14 healthy elderly individuals, for which LCM neurons were similarly processed for expression analysis. We identified significant regional differential expression in AD brains compared with control brains including expression changes of genes previously implicated in AD pathogenesis, particularly with regard to tangle and plaque formation. Pinpointing the expression of factors that may play a role in AD pathogenesis provides a foundation for future identification of new targets for improved AD therapeutics. We provide this carefully phenotyped, laser capture microdissected intraindividual brain region expression data set to the community as a public resource.

scholarly journals Gender-Dependent Deregulation of Linear and Circular RNA Variants of HOMER1 in the Entorhinal Cortex of Alzheimer’s Disease

2021 ◽  
Vol 22 (17) ◽  
pp. 9205
Author(s):  
Amaya Urdánoz-Casado ◽  
Javier Sánchez-Ruiz de Gordoa ◽  
Maitane Robles ◽  
Blanca Acha ◽  
Miren Roldan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Entorhinal Cortex ◽  
Circular Rna ◽  
Brain Regions ◽  
Mrna Levels ◽  
C Protein ◽  
Amyloid Aβ ◽  
Cortical Regions ◽  
Female Specific

The HOMER1 gene is involved in synaptic plasticity, learning and memory. Recent studies show that circular RNA derived from HOMER1 (circHOMER1) expression is altered in some Alzheimer’s disease (AD) brain regions. In addition, HOMER1 messenger (mRNA) levels have been associated with β-Amyloid (Aβ) deposits in brain cortical regions. Our aim was to measure the expression levels of HOMER1 circRNAs and their linear forms in the human AD entorhinal cortex. First, we showed downregulation of HOMER1B/C and HOMER1A mRNA and hsa_circ_0006916 and hsa_circ_0073127 levels in AD female cases compared to controls by RT-qPCR. A positive correlation was observed between HOMER1B/C, HOMER1A mRNA, and hsa_circ_0073128 with HOMER1B/C protein only in females. Global average area of Aβ deposits in entorhinal cortex samples was negatively correlated with HOMER1B/C, HOMER1A mRNA, and hsa_circ_0073127 in both genders. Furthermore, no differences in DNA methylation were found in two regions of HOMER1 promoter between AD cases and controls. To sum up, we demonstrate that linear and circular RNA variants of HOMER1 are downregulated in the entorhinal cortex of female patients with AD. These results add to the notion that HOMER1 and its circular forms could be playing a female-specific role in the pathogenesis of AD.

scholarly journals Cognition at Each Stage of Lewy Body Disease with Co-occurring Alzheimer’s Disease Pathology

2021 ◽  
Vol 80 (3) ◽  
pp. 1243-1256
Author(s):  
Sephira G. Ryman ◽  
Maya Yutsis ◽  
Lu Tian ◽  
Victor W. Henderson ◽  
Thomas J. Montine ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lewy Body ◽  
Lewy Bodies ◽  
Lewy Body Disease ◽  
Cognitive Domain ◽  
Data Set ◽  
Pathologic Stage ◽  
Cortical Regions ◽  
The Impact

Background: Alzheimer’s disease neuropathologic change (ADNC) may contribute to dementia in patients with Lewy body disease (LBD) pathology. Objective: To examine how co-occurring ADNC impacts domain specific cognitive impairments at each pathologic stage (brainstem, limbic, cerebral cortical) of LBD. Methods: 2,433 participants with antemortem longitudinal neuropsychological assessment and postmortem neuropathological assessment from the National Alzheimer’s Coordinating Center’s Uniform Data Set were characterized based on the evaluation of ADNC and LBD. Longitudinal mixed-models were used to derive measures of cumulative cognitive deficit for each cognitive domain at each pathologic stage of LBD (brainstem, limbic, and cerebral cortical). Results: 111 participants with a pathologic diagnosis of LBD, 741 participants with combined LBD and ADNC, 1,357 participants with ADNC only, and 224 with no pathology (healthy controls) were included in the analyses. In the executive/visuospatial domain, combined LBD and ADNC showed worse deficits than LBD only when Lewy bodies were confined to the brainstem, but no difference when Lewy bodies extended to the limbic or cerebral cortical regions. The cerebral cortical LBD only group exhibited greater executive/visuospatial deficits than the ADNC only group. By contrast, the ADNC only group and the combined pathology group both demonstrated significantly greater cumulative memory deficits relative to Lewy body disease only, regardless of stage. Conclusion: The impact of co-occurring ADNC on antemortem cumulative cognitive deficits varies not only by domain but also on the pathological stage of Lewy bodies. Our findings stress the cognitive impact of different patterns of neuropathological progression in Lewy body diseases.

scholarly journals Communicability distance reveals hidden patterns of Alzheimer’s disease

2020 ◽  
Vol 4 (4) ◽  
pp. 1007-1029 ◽  
Author(s):  
Eufemia Lella ◽  
Ernesto Estrada
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Path Length ◽  
Brain Regions ◽  
Brain Network ◽  
Efficient System ◽  
Brain Hemispheres ◽  
Disconnection Syndrome ◽  
Path Lengths ◽  
The Brain

The communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer’s disease. Using this distance, we obtain the shortest communicability path lengths between different regions of brain networks from patients with Alzheimer’s disease (AD) and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach, we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only, and 20% affect vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of AD. Finally, we show that in 76.9% of damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.

scholarly journals A meta-analysis of brain DNA methylation across sex, age and Alzheimer’s disease points for accelerated epigenetic aging in neurodegeneration

2020 ◽  
Author(s):  
C Pellegrini ◽  
C Pirazzini ◽  
C Sala ◽  
L Sambati ◽  
I Yusipov ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Meta Analysis ◽  
Brain Regions ◽  
Epigenetic Aging ◽  
High Concordance ◽  
Males And Females ◽  
Cortical Regions ◽  
The Brain

AbstractAlzheimer’s disease (AD) is characterized by specific alterations of brain DNA methylation (DNAm) patterns. Age and sex, two major risk factors for AD, are also known to largely affect the epigenetic profiles in the brain, but their contribution to AD-associated DNAm changes has been poorly investigated. In this study we considered publicly available DNAm datasets of 4 brain regions (temporal, frontal, entorhinal cortex and cerebellum) from healthy adult subjects and AD patients, and performed a meta-analysis to identify sex-, age- and AD-associated epigenetic profiles. We showed that DNAm differences between males and females tend to be shared between the 4 brain regions, while aging differently affects cortical regions compared to cerebellum. We found that the proportion of sex-dependent probes whose methylation changes also during aging is higher than expected, but that differences between males and females tend to be maintained, with only few probes showing sex-by-age interaction. We did not find significant overlaps between AD- and sex-associated probes, nor disease-by-sex interaction effects. On the contrary, we found that AD-related epigenetic modifications are significantly enriched in probes whose DNAm changes with age and that there is a high concordance between the direction of changes (hyper or hypo-methylation) in aging and AD, supporting accelerated epigenetic aging in the disease.In conclusion, we demonstrated that age-associated, but not sex-associated DNAm concurs to the epigenetic deregulation observed in AD, providing new insight on how advanced age enables neurodegeneration.

Disrupted Time-Dependent and Functional Connectivity Brain Network in Alzheimer's Disease: A Resting-State fMRI Study Based on Visibility Graph

2020 ◽  
Vol 17 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Zhongke Gao ◽  
Yanhua Feng ◽  
Chao Ma ◽  
Kai Ma ◽  
Qing Cai ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Complex Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Visibility Graph ◽  
Time Dependent ◽  
Biological Information ◽  
Local Complex

Background: Alzheimer's Disease (AD) is a progressive neurodegenerative disease with insidious onset, which is difficult to be reversed and cured. Therefore, discovering more precise biological information from neuroimaging biomarkers is crucial for accurate and automatic detection of AD. Methods: We innovatively used a Visibility Graph (VG) to construct the time-dependent brain networks as well as functional connectivity network to investigate the underlying dynamics of AD brain based on functional magnetic resonance imaging. There were 32 AD patients and 29 Normal Controls (NCs) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. First, the VG method mapped the time series of single brain region into networks. By extracting topological properties of the networks, the most significant features were selected as discriminant features into a supporting vector machine for classification. Furthermore, in order to detect abnormalities of these brain regions in the whole AD brain, functional connectivity among different brain regions was calculated based on the correlation of regional degree sequences. Results: According to the topology abnormalities exploration of local complex networks, we found several abnormal brain regions, including left insular, right posterior cingulate gyrus and other cortical regions. The accuracy of characteristics of the brain regions extracted from local complex networks was 88.52%. Association analysis demonstrated that the left inferior opercular part of frontal gyrus, right middle occipital gyrus, right superior parietal gyrus and right precuneus played a tremendous role in AD. Conclusion: These results would be helpful in revealing the underlying pathological mechanism of the disease.

scholarly journals Brain network remodelling reflects tau-related pathology prior to memory deficits in Thy-Tau22 mice

Brain ◽  
2020 ◽  
Author(s):  
Laetitia Degiorgis ◽  
Meltem Karatas ◽  
Marion Sourty ◽  
Emilie Faivre ◽  
Julien Lamy ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Dorsal Hippocampus ◽  
Brain Network ◽  
Prodromal Phase ◽  
Compensatory Response ◽  
Major Mechanism ◽  
Brain Connectome

Abstract In Alzheimer’s disease, the tauopathy is known as a major mechanism responsible for the development of cognitive deficits. Early biomarkers of such affectations for diagnosis/stratification are crucial in Alzheimer’s disease research, and brain connectome studies increasingly show their potential establishing pathology fingerprints at the network level. In this context, we conducted an in vivo multimodal MRI study on young Thy-Tau22 transgenic mice expressing tauopathy, performing resting state functional MRI and structural brain imaging to identify early connectome signatures of the pathology, relating with histological and behavioural investigations. In the prodromal phase of tauopathy, before the emergence of cognitive impairments, Thy-Tau22 mice displayed selective modifications of brain functional connectivity involving three main centres: hippocampus (HIP), amygdala (AMG) and the isocortical areas, notably the somatosensory (SS) cortex. Each of these regions showed differential histopathological profiles. Disrupted ventral HIP-AMG functional pathway and altered dynamic functional connectivity were consistent with high pathological tau deposition and astrogliosis in both hippocampus and amygdala, and significant microglial reactivity in amygdalar nuclei. These patterns were concurrent with widespread functional hyperconnectivity of memory-related circuits of dorsal hippocampus—encompassing dorsal HIP-SS communication—in the absence of significant cortical histopathological markers. These findings suggest the coexistence of two intermingled mechanisms of response at the functional connectome level in the early phases of pathology: a maladaptive and a likely compensatory response. Captured in the connectivity patterns, such first responses to pathology could further be used in translational investigations as a lead toward an early biomarker of tauopathy as well as new targets for future treatments.

scholarly journals EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions

2019 ◽  
Vol 9 (4) ◽  
pp. 81 ◽  
Author(s):  
Katerina D. Tzimourta ◽  
Nikolaos Giannakeas ◽  
Alexandros T. Tzallas ◽  
Loukas G. Astrakas ◽  
Theodora Afrantou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Spectral Features ◽  
Classification Problems ◽  
Whole Brain ◽  
Central Regions ◽  
Eeg Recordings ◽  
Cortical Regions ◽  
The Right

Alzheimer’s Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.

scholarly journals A Meta-Analysis of Brain DNA Methylation Across Sex, Age, and Alzheimer's Disease Points for Accelerated Epigenetic Aging in Neurodegeneration

2021 ◽  
Vol 13 ◽  
Author(s):  
Camilla Pellegrini ◽  
Chiara Pirazzini ◽  
Claudia Sala ◽  
Luisa Sambati ◽  
Igor Yusipov ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Healthy Adult ◽  
Meta Analysis ◽  
Brain Regions ◽  
Epigenetic Aging ◽  
High Concordance ◽  
Males And Females ◽  
Cortical Regions

Alzheimer's disease (AD) is characterized by specific alterations of brain DNA methylation (DNAm) patterns. Age and sex, two major risk factors for AD, are also known to largely affect the epigenetic profiles in brain, but their contribution to AD-associated DNAm changes has been poorly investigated. In this study we considered publicly available DNAm datasets of four brain regions (temporal, frontal, entorhinal cortex, and cerebellum) from healthy adult subjects and AD patients, and performed a meta-analysis to identify sex-, age-, and AD-associated epigenetic profiles. In one of these datasets it was also possible to distinguish 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) profiles. We showed that DNAm differences between males and females tend to be shared between the four brain regions, while aging differently affects cortical regions compared to cerebellum. We found that the proportion of sex-dependent probes whose methylation is modified also during aging is higher than expected, but that differences between males and females tend to be maintained, with only a few probes showing age-by-sex interaction. We did not find significant overlaps between AD- and sex-associated probes, nor disease-by-sex interaction effects. On the contrary, we found that AD-related epigenetic modifications are significantly enriched in probes whose DNAm varies with age and that there is a high concordance between the direction of changes (hyper or hypo-methylation) in aging and AD, supporting accelerated epigenetic aging in the disease. In summary, our results suggest that age-associated DNAm patterns concur to the epigenetic deregulation observed in AD, providing new insights on how advanced age enables neurodegeneration.

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