scholarly journals Bispectrum-based Cross-frequency Functional Connectivity: A Study of Alzheimer's Disease

2021 ◽  
Author(s):  
Dominik Klepl ◽  
Fei He ◽  
Min Wu ◽  
Daniel J Blackburn ◽  
Ptolemaios G Sarrigiannis
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
High Frequency ◽  
Neurodegenerative Disorder ◽  
Low Frequency ◽  
Brain Regions ◽  
Frequency Bands ◽  
Frequency Coupling ◽  
Eeg Recordings

Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the first use of cross-bispectrum to reconstruct a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. An increase of within-band FC in AD is observed in low-frequency bands using both methods. Bispectrum also detects multiple cross-frequency differences, mainly increased FC in AD in delta-theta coupling. An increased importance of low-frequency coupling and decreased importance of high-frequency coupling is observed in AD. Integration properties of AD networks are more vulnerable than HC, while the segregation property is maintained in AD. Moreover, the segregation property of γ is less vulnerable in AD, suggesting the shift of importance from high-frequency activity towards low-frequency components. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD. Moreover, the results demonstrate the advantages and limitations of using bispectrum to reconstruct FC networks.

scholarly journals A Machine Learning Approach to Unmask Novel Gene Signatures and Prediction of Alzheimer’s Disease Within Different Brain Regions

2021 ◽  
Author(s):  
Abhibhav Sharma ◽  
Pinki Dey
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Non Coding Rna ◽  
Machine Learning Approach ◽  
Microarray Datasets ◽  
Rna Genes

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disorder whose aetiology is currently unknown. Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing their clinical significance. Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus, and Entorhinal cortex- to discover novel genetic biomarkers through a machine learning-based feature-selection classification scheme. The proposed scheme unrevealed the most optimum and biologically significant classifiers within each brain region, which achieved by far the highest prediction accuracy of AD in 5-fold cross-validation (99% average). Interestingly, along with the novel and prominent biomarkers including CORO1C, SLC25A46, RAE1, ANKIB1, CRLF3, PDYN, numerous non-coding RNA genes were also observed as discriminator, of which AK057435 and BC037880 are uncharacterized long non-coding RNA genes.

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 Functional connectivity among brain regions affected in Alzheimer's disease is associated with CSF TNF-α in APOE4 carriers

2020 ◽  
Vol 86 ◽  
pp. 112-122 ◽  
Author(s):  
Joey Annette Contreras ◽  
Vahan Aslanyan ◽  
Melanie D. Sweeney ◽  
Lianne M.J. Sanders ◽  
Abhay P. Sagare ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Tnf Α

scholarly journals Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer’s disease

2018 ◽  
Author(s):  
Stephen A. Semick ◽  
Rahul A. Bharadwaj ◽  
Leonardo Collado-Torres ◽  
Ran Tao ◽  
Joo Heon Shin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Neurodegenerative Disorder ◽  
Late Onset ◽  
Brain Regions ◽  
Epigenetic Mechanism ◽  
Novel Genes ◽  
Age Related

AbstractBackgroundLate-onset Alzheimer’s disease (AD) is a complex age-related neurodegenerative disorder that likely involves epigenetic factors. To better understand the epigenetic state associated with AD represented as variation in DNA methylation (DNAm), we surveyed 420,852 DNAm sites from neurotypical controls (N=49) and late-onset AD patients (N=24) across four brain regions (hippocampus, entorhinal cortex, dorsolateral prefrontal cortex and cerebellum).ResultsWe identified 858 sites with robust differential methylation, collectively annotated to 772 possible genes (FDR<5%, within 10kb). These sites were overrepresented in AD genetic risk loci (p=0.00655), and nearby genes were enriched for processes related to cell-adhesion, immunity, and calcium homeostasis (FDR<5%). We analyzed corresponding RNA-seq data to prioritize 130 genes within 10kb of the differentially methylated sites, which were differentially expressed and had expression levels associated with nearby DNAm levels (p<0.05). This validated gene set includes previously reported (e.g. ANK1, DUSP22) and novel genes involved in Alzheimer’s disease, such as ANKRD30B.ConclusionsThese results highlight DNAm changes in Alzheimer’s disease that have gene expression correlates, implicating DNAm as an epigenetic mechanism underlying pathological molecular changes associated with AD. Furthermore, our framework illustrates the value of integrating epigenetic and transcriptomic data for understanding complex 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.

P2-448: CSF SPDGFRB, A MEASURE OF VASCULAR DYSFUNCTION, IS RELATED TO DISRUPTED FUNCTIONAL CONNECTIVITY AMONG BRAIN REGIONS ASSOCIATED WITH ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_16) ◽  
pp. P889-P890
Author(s):  
Joey A. Contreras ◽  
Melanie D. Sweeney ◽  
Abhay Sagare ◽  
John C. Morris ◽  
Anne M. Fagan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Vascular Dysfunction ◽  
Brain Regions

scholarly journals Subcortical connectivity in dementia with Lewy bodies and Alzheimer's disease

2013 ◽  
Vol 203 (3) ◽  
pp. 209-214 ◽  
Author(s):  
Eva R. Kenny ◽  
John T. O'Brien ◽  
Michael J. Firbank ◽  
Andrew M. Blamire
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Dementia With Lewy Bodies ◽  
Lewy Bodies ◽  
Low Frequency ◽  
Blood Oxygen Level Dependent ◽  
Bold Signal ◽  
Blood Oxygen Level

BackgroundResting-state functional magnetic resonance imaging (fMRI) can be used to measure correlations in spontaneous low-frequency fluctuations in the blood oxygen level-dependent (BOLD) signal which represent functional connectivity between key brain areas.AimsTo investigate functional connectivity with regions hypothesised to be differentially affected in dementia with Lewy bodies (DLB) compared with Alzheimer's disease and controls.MethodFifteen participants with probable DLB, 16 with probable Alzheimer's disease and 16 controls were scanned in the resting-state using a 3T scanner. The BOLD signal time-series of fluctuations in seed regions were correlated with all other voxels to measure functional connectivity.ResultsParticipants with DLB and Alzheimer's disease showed greater caudate and thalamic connectivity compared with controls. Those with DLB showed greater putamen connectivity compared with those with Alzheimer's disease and the controls. No regions showed less connectivity in DLB or Alzheimer's disease v. controls, or in DLB v. Alzheimer's disease.ConclusionsAltered connectivity in DLB and Alzheimer's disease provides new insights into the neurobiology of these disorders and may aid in earlier diagnosis.

IC-P-030: CSF SPDGFRB, A MEASURE OF VASCULAR DYSFUNCTION, IS RELATED TO DISRUPTED FUNCTIONAL CONNECTIVITY AMONG BRAIN REGIONS ASSOCIATED WITH ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_1) ◽  
pp. P34-P35
Author(s):  
Joey A. Contreras ◽  
Melanie D. Sweeney ◽  
Abhay Sagare ◽  
John C. Morris ◽  
Anne M. Fagan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Vascular Dysfunction ◽  
Brain Regions

scholarly journals Probiotics: An Adjuvant therapy for D-Galactose induced Alzheimer's disease

2017 ◽  
Vol 1 (1) ◽  
pp. 30-33 ◽  
Author(s):  
Varshil Mehta ◽  
Kavya Bhatt ◽  
Nimit Desai ◽  
Mansi Naik
Keyword(s):  
Oxidative Stress ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Health Concern ◽  
Major Health ◽  
Acetyl Choline ◽  
Glycation End Products ◽  
Anti Oxidant

Alzheimer’s disease (AD) is a chronic and slowly progressing neurodegenerative disorder which has become a major health concern worldwide. The literature has shown that oxidative stress is one of the most important risk factors behind the cause of AD. Oxidative stress often leads to the production of Reactive Oxygen Species (ROS). D-Galactose, a physiological nutrient and reducing sugar, non-enzymatically reacts with amines of amino acids in proteins and peptides to form Advanced Glycation End products which activate its receptors coupled to Biochemical pathways that stimulate free radical production and induces mitochondrial dysfunction which damages the neuron intracellularly. High dosage of D-Galactose also suppresses the expression of nerve growth factors and its associated protein which results in the degeneration of nerve cells and reduction of acetylcholine levels in brain regions. This article put forwards the advantages of using Lactic Acid Bacteria (Probiotics) possessing anti-oxidant properties and which produces Acetyl Choline against D-Galactose induced Alzheimer’s disease.

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