scholarly journals EPIGENETIC PROFILES OF ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S937-S937
Author(s):  
Kyra Thrush ◽  
Morgan E Levine
Keyword(s):  
Network Analysis ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Clear Understanding ◽  
Clinical Biomarkers ◽  
Age Related ◽  
Epigenetic Age ◽  
Lifestyle Choices ◽  
Level Information ◽  
Highly Correlated

Abstract Although age is highly correlated with incidence of Alzheimer’s Dementia (AD), the field continues to lack a clear understanding of how either normal and/or pathological aging processes drive neurodegeneration. As such, there remains a clear lack of valid and reliable clinical biomarkers to predict that disease’s future development and severity. Epigenetic age based on DNA methylation (DNAm) in brain have been shown to relate to AD neuropathology and cognitive decline. However, they were not initially designed as AD biomarkers. We hypothesized that supervised and unsupervised machine learning techniques (e.g. network analysis, clustering, and regressed-based techniques) could be used to build composite scoring variables from DNAm data that are predictive of AD progression. This work analyzes the methylation of 3 brain regions (cerebellum (CBM), prefrontal cortex (PFC), striatum (ST))—totaling 1,047 brain methylation samples. The samples contain neuropathologically confirmed AD cases and controls, and is enriched for APOE4+ carriers. Detailed subject-level information concerning cognitive measures, lifestyle choices, medications, and neuropathology at death were also considered. Based on epigenome-wide association study (EWAS), we identified a CpG in AIMP2 that is a robust predictor of AD-related phenotypes. Using network analysis, we have also identified co-methylation modules that relate to multifactorial AD phenotypes. Following validation, we intend to follow-up on the biological processes and molecular pathways associated with these epigenetic signatures. In moving forward, predictors of AD diagnosis and prognostication have major implications for early detection and treatment of this major age-related disease.

scholarly journals Mammalian Co-methylation Network Analysis of Species Characteristics

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 676-676
Author(s):  
Amin Haghani ◽  
Steve Horvath
Keyword(s):  
Dna Methylation ◽  
Network Analysis ◽  
Growth Hormone Receptor ◽  
Mammalian Species ◽  
Tissue Type ◽  
Maximum Lifespan ◽  
Age Related ◽  
Species Analysis ◽  
Species Characteristics ◽  
Highly Correlated

Abstract The comparative cross-species analysis is a powerful tool to resolve the mysteries of evolution and phenotypic disparities among animals. This is the first network analysis of 10,000 DNA methylome data from 176 mammalian species to identify co-methylation modules that relate to individual (age, sex, tissue type) and species characteristics (e.g. phylogenetic order, maximum lifespan, adult weight). The unexpected correlation between DNA methylation and species were sufficiently strong to allow the construction of phyloepigenetic trees that parallel the phylogenetic tree. Weighted correlation network analysis identified 55 distinct co-methylation modules, i.e. sets of highly correlated CpGs. 31 of these modules are readily interpretable in terms of their relationship to age, maximum lifespan, tissue type etc. An age-related module was perturbed by gold standard anti-aging interventions in mice such as caloric restriction or growth hormone receptor knock outs. Our module-based analysis greatly enhances our biological understanding of age-related changes in DNA methylation across many species.

Inefficient Encoding as an Explanation for Age-Related Deficits in Recollection-Based Processing

2014 ◽  
Vol 28 (3) ◽  
pp. 148-161 ◽  
Author(s):  
David Friedman ◽  
Ray Johnson
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Cognitive Processes ◽  
Brain Activity ◽  
Memory Performance ◽  
Brain Regions ◽  
Semantic Retrieval ◽  
Age Related ◽  
The Face ◽  
Episodic Memories

A cardinal feature of aging is a decline in episodic memory (EM). Nevertheless, there is evidence that some older adults may be able to “compensate” for failures in recollection-based processing by recruiting brain regions and cognitive processes not normally recruited by the young. We review the evidence suggesting that age-related declines in EM performance and recollection-related brain activity (left-parietal EM effect; LPEM) are due to altered processing at encoding. We describe results from our laboratory on differences in encoding- and retrieval-related activity between young and older adults. We then show that, relative to the young, in older adults brain activity at encoding is reduced over a brain region believed to be crucial for successful semantic elaboration in a 400–1,400-ms interval (left inferior prefrontal cortex, LIPFC; Johnson, Nessler, & Friedman, 2013 ; Nessler, Friedman, Johnson, & Bersick, 2007 ; Nessler, Johnson, Bersick, & Friedman, 2006 ). This reduced brain activity is associated with diminished subsequent recognition-memory performance and the LPEM at retrieval. We provide evidence for this premise by demonstrating that disrupting encoding-related processes during this 400–1,400-ms interval in young adults affords causal support for the hypothesis that the reduction over LIPFC during encoding produces the hallmarks of an age-related EM deficit: normal semantic retrieval at encoding, reduced subsequent episodic recognition accuracy, free recall, and the LPEM. Finally, we show that the reduced LPEM in young adults is associated with “additional” brain activity over similar brain areas as those activated when older adults show deficient retrieval. Hence, rather than supporting the compensation hypothesis, these data are more consistent with the scaffolding hypothesis, in which the recruitment of additional cognitive processes is an adaptive response across the life span in the face of momentary increases in task demand due to poorly-encoded episodic memories.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

scholarly journals Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance

2021 ◽  
Author(s):  
Adeline Jabès ◽  
Giuliana Klencklen ◽  
Paolo Ruggeri ◽  
Christoph M. Michel ◽  
Pamela Banta Lavenex ◽  
...  
Keyword(s):  
Older Adults ◽  
Working Memory ◽  
Resting State ◽  
Cognitive Aging ◽  
Temporal Dynamics ◽  
Spatial Working Memory ◽  
Brain Activity ◽  
Memory Performance ◽  
Brain Regions ◽  
Age Related

AbstractAlterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.

scholarly journals Scotopic thresholds on dark-adapted chromatic perimetry in healthy aging and age-related macular degeneration

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Manjot Kaur Grewal ◽  
Shruti Chandra ◽  
Alan Bird ◽  
Glen Jeffery ◽  
Sobha Sivaprasad
Keyword(s):  
Macular Degeneration ◽  
Healthy Aging ◽  
Intraclass Correlation ◽  
Age Related Macular Degeneration ◽  
Healthy Individuals ◽  
Age Related ◽  
Rod Function ◽  
The Mean ◽  
Highly Correlated ◽  
Effect Of Age

AbstractTo evaluate the effect of aging, intra- and intersession repeatability and regional scotopic sensitivities in healthy and age-related macular degeneration (AMD) eyes. Intra- and intersession agreement and effect of age was measured in healthy individuals. The mean sensitivity (MS) and pointwise retinal sensitivities (PWS) within the central 24° with 505 nm (cyan) and 625 nm (red) stimuli were evaluated in 50 individuals (11 healthy and 39 AMD eyes). The overall intra- and intersession had excellent reliability (intraclass correlation coefficient, ICC > 0.90) and tests were highly correlated (Spearman rs = 0.75–0.86). Eyes with subretinal drusenoid deposit (SDD) had reduced PWS centrally, particularly at inferior and nasal retinal locations compared with controls and intermediate AMD (iAMD) without SDD. There was no difference in MS or PWS at any retinal location between iAMD without SDD and healthy individuals nor between iAMD with SDD and non-foveal atrophic AMD groups. Eyes with SDD have reduced rod function compared to iAMD without SDD and healthy eyes, but similar to eyes with non-foveal atrophy. Our results highlight rod dysfunction is not directly correlated with drusen load and SDD location.

scholarly journals Age-related but not longevity-related genes are found by weighted gene co-expression network analysis in the peripheral blood cells of humans

2018 ◽  
Vol 93 (6) ◽  
pp. 221-228 ◽  
Author(s):  
Chunhong Li ◽  
Dan Mo ◽  
Meiqin Li ◽  
Yanyan Zheng ◽  
Qiao Li ◽  
...  
Keyword(s):  
Network Analysis ◽  
Peripheral Blood ◽  
Blood Cells ◽  
Peripheral Blood Cells ◽  
Age Related

Cortical Thinning Associated with Age and CSF Biomarkers in Early Parkinson’s Disease Is Modified by the SNCA rs356181 Polymorphism

2018 ◽  
Vol 18 (5-6) ◽  
pp. 233-238
Author(s):  
Frederic Sampedro ◽  
Juan Marín-Lahoz ◽  
Saul Martínez-Horta ◽  
Javier Pagonabarraga ◽  
Jaime Kulisevsky
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Decline ◽  
De Novo ◽  
Brain Regions ◽  
Cortical Atrophy ◽  
Csf Biomarkers ◽  
Gene Encoding ◽  
Cortical Thinning ◽  
Age Related

The role of cerebrospinal fluid (CSF) biomarkers such as CSF α-synuclein and CSF tau in predicting cognitive decline in Parkinson’s disease (PD) continues to be inconsistent. Here, using a cohort of de novo PD patients with preserved cognition from the Parkinson’s Progression Markers Initiative (PPMI), we show that the SNCA rs356181 single nucleotide polymorphism (SNP) modulates the effect of these CSF biomarkers on cortical thinning. Depending on this SNP’s genotype, cortical atrophy was associated with either higher or lower CSF biomarker levels. Additionally, this SNP modified age-related atrophy. Importantly, the integrity of the brain regions where this phenomenon was observed correlated with cognitive measures. These results suggest that this genetic variation of the gene encoding the α-synuclein protein, known to be involved in the development of PD, also interferes in its subsequent neurodegeneration. Overall, our findings could shed light on the so far incongruent association of common CSF biomarkers with cognitive decline in PD.

scholarly journals Epigenetic Age Acceleration and Hearing: Observations From the Baltimore Longitudinal Study of Aging

2021 ◽  
Vol 13 ◽  
Author(s):  
Pei-Lun Kuo ◽  
Ann Zenobia Moore ◽  
Frank R. Lin ◽  
Luigi Ferrucci
Keyword(s):  
Dna Methylation ◽  
Longitudinal Study ◽  
Smoking History ◽  
Future Research ◽  
Age Related ◽  
Epigenetic Age ◽  
Epigenetic Age Acceleration ◽  
Baltimore Longitudinal Study ◽  
Age Related Hearing Loss ◽  
The Relationship

Objectives: Age-related hearing loss (ARHL) is highly prevalent among older adults, but the potential mechanisms and predictive markers for ARHL are lacking. Epigenetic age acceleration has been shown to be predictive of many age-associated diseases and mortality. However, the association between epigenetic age acceleration and hearing remains unknown. Our study aims to investigate the relationship between epigenetic age acceleration and audiometric hearing in the Baltimore Longitudinal Study of Aging (BLSA).Methods: Participants with both DNA methylation and audiometric hearing measurements were included. The main independent variables are epigenetic age acceleration measures, including intrinsic epigenetic age acceleration—“IEAA,” Hannum age acceleration—“AgeAccelerationResidualHannum,” PhenoAge acceleration—“AgeAccelPheno,” GrimAge acceleration—“AgeAccelGrim,” and methylation-based pace of aging estimation—“DunedinPoAm.” The main dependent variable is speech-frequency pure tone average. Linear regression was used to assess the association between epigenetic age acceleration and hearing.Results: Among the 236 participants (52.5% female), after adjusting for age, sex, race, time difference between measurements, cardiovascular factors, and smoking history, the effect sizes were 0.11 995% CI: (–0.00, 0.23), p = 0.054] for Hannum’s clock, 0.08 [95% CI: (–0.03, 0.19), p = 0.143] for Horvath’s clock, 0.10 [95% CI: (–0.01, 0.21), p = 0.089] for PhenoAge, 0.20 [95% CI: (0.06, 0.33), p = 0.004] for GrimAge, and 0.21 [95% CI: (0.09, 0.33), p = 0.001] for DunedinPoAm.Discussion: The present study suggests that some epigenetic age acceleration measurements are associated with hearing. Future research is needed to study the potential subclinical cardiovascular causes of hearing and to investigate the longitudinal relationship between DNA methylation and hearing.

scholarly journals Weighted Gene Co-Expression Network Analysis and Machine learning Identify Critical Genes in the Development of Osteomyelitis after Staphylococcus aureus Infections

2021 ◽  
Author(s):  
Baixing Chen ◽  
Shaoshuo Li ◽  
Zhaoqi Lu ◽  
Mingling Huang ◽  
Shi Lin ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Network Analysis ◽  
Protein Function ◽  
Actin Polymerization ◽  
Rna Binding ◽  
Area Under The Curve ◽  
Recursive Feature Elimination ◽  
Protein Protein Interaction ◽  
External Data ◽  
Highly Correlated

Abstract Background: Staphylococcus aureus (S. aureus) is the most common pathogen that causes osteomyelitis (OM). However, OM's pathogenesis, which is not clear, involves many factors such as environment, genetics and immunity dysregulation. This study aims to explore the key genes involved in the pathogenesis and development of OM following S. aureus infection. Methods: After obtaining the datasets of GSE6269 and GSE16129, we performed weighted gene co-expression network analysis (WGCNA) to find clusters modules of highly correlated genes and recursive feature elimination (RFE) method to narrow the range of feature genes. For determining the effect of feature genes, we constructed a random forest (RF) model with feature genes and validated the predictive validity of the RF model using independent data from GSE11908. The protein-protein interaction (PPI) network identifies essential proteins that contributed to OM development. Results: There were 12,401 genes from 77 samples that 48 S. aureus patients developed to OM and 29 of those without OM. We divided 31 significant gene modules into different modules, and the brown module significantly related to OM. Biological Functions of the brown module mainly enriched in the inflammatory response, metabolic, cancer, viral pathways, protein binding and RNA binding. After screening, 19 genes, including CYP2E1, BBS10, ARPC5L, GAPVD1, PURA, RBMS1, BTN2A2, EXOSC8, METTL8, FYCO1, KHK, PRPF38B, CD72, C2CD5, ABHD6, CD200, FAM53C, HCP5 and ELP1, were defined as feature genes for constructing RF model. After validating the external data, the average area under the curve was 85%, and the accuracy of the RF model was 85.7%. The protein function of modules enriched in the RNA exosome complex's catalytic component and regulation of actin polymerization. Conclusions: This study aimed to identify related genes involved in the occurrence and development of OM. We constructed the RF model with 19 genes, which effectively classify the patients with OM or non-OM. Despite its limitations, the study certainly adds to our understanding of OM's pathogenesis, and therefore, has significant implications for potential therapeutic targets and the predicted value of OM.

scholarly journals Evaluation of Effect of Ninjin'yoeito on Regional Brain Glucose Metabolism by 18F-FDG Autoradiography With Insulin Loading in Aged Mice

2021 ◽  
Vol 8 ◽  
Author(s):  
Jingmin Zhao ◽  
Ryota Imai ◽  
Naoyuki Ukon ◽  
Saki Shimoyama ◽  
Chengbo Tan ◽  
...  
Keyword(s):  
Glucose Metabolism ◽  
Brain Regions ◽  
Cognitive Outcome ◽  
Brain Diseases ◽  
Control Group ◽  
Brain Glucose ◽  
Brain Glucose Metabolism ◽  
Aged Mice ◽  
Regional Brain ◽  
Age Related

Introduction: A recent clinical study revealed that Ninjin'yoeito (NYT) may potentially improve cognitive outcome. However, the mechanism by which NYT exerts its effect on elderly patients remains unclear. The aim of this study is to evaluate the effect of Ninjin'yoeito on regional brain glucose metabolism by 18F-FDG autoradiography with insulin loading in aged wild-type mice.Materials and Methods: After 12 weeks of feeding NYT, mice were assigned to the control and insulin-loaded groups and received an intraperitoneal injection of human insulin (2 U/kg body weight) 30 min prior to 18F-FDG injection. Ninety minutes after the injection, brain autoradiography was performed.Results: After insulin loading, the 18F-FDG accumulation showed negative changes in the cortex, striatum, thalamus, and hippocampus in the control group, whereas positive changes were observed in the NYT-treated group.Conclusions: Ninjin'yoeito may potentially reduce insulin resistance in the brain regions in aged mice, thereby preventing age-related brain diseases.

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