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Article ; Online: Neuroimaging Advances in Neurologic and Neurodegenerative Diseases.

Risacher, Shannon L / Saykin, Andrew J

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics

2021 Volume 18, Issue 2, Page(s) 659–660

MeSH term(s)	Brain/diagnostic imaging ; Humans ; Nervous System Diseases/diagnostic imaging ; Neurodegenerative Diseases/diagnostic imaging ; Neuroimaging/methods ; Neuroimaging/trends
Language	English
Publishing date	2021-08-19
Publishing country	United States
Document type	Editorial ; Research Support, N.I.H., Extramural
ZDB-ID	2316693-9
ISSN	1878-7479 ; 1933-7213
ISSN (online)	1878-7479
ISSN	1933-7213
DOI	10.1007/s13311-021-01105-7
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Article: Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation.

Pugalenthi, Pradeep Varathan / He, Bing / Xie, Linhui / Nho, Kwangsik / Saykin, Andrew J / Yan, Jingwen

Research square

2024

Abstract: Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a significant set of SNPs associated with AD and related traits. GWAS ... ...

Abstract	Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a significant set of SNPs associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.
Language	English
Publishing date	2024-02-08
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-3871665/v1
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Article: Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation.

Varathan, Pradeep / Xie, Linhui / He, Bing / Saykin, Andrew J / Nho, Kwangsik / Yan, Jingwen

medRxiv : the preprint server for health sciences

2023

Abstract: Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWAS) have led to a significant set of SNPs associated with AD and related traits. GWAS hits ...

Abstract	Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWAS) have led to a significant set of SNPs associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed with even the strongest associations in the GWAS, the lead SNPs have been historically the focus of the field, with the remaining associations inferred as redundant. Recent deep genome annotation tools enable the prediction of function from a segment of DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits on the chromatin functions, and whether it will be altered by the genomic context (i.e., alleles of neighborhood SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impact on the downstream functions. Although some GWAS lead SNPs showed dominating functional effect regardless of the neighborhood SNP alleles, several other ones do get enhanced loss or gain of function under certain genomic context, suggesting potential extra information hidden in the LD blocks.
Language	English
Publishing date	2023-10-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.10.23.23297399
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Article ; Online: miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease.

Han, Sang-Won / Pyun, Jung-Min / Bice, Paula J / Bennett, David A / Saykin, Andrew J / Kim, Sang Yun / Park, Young Ho / Nho, Kwangsik

Alzheimer's research & therapy

2024 Volume 16, Issue 1, Page(s) 5

Abstract: Background: Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic ... ...

Abstract	Background: Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers. Methods: We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification. Results: Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs. Conclusions: Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.
MeSH term(s)	Humans ; Alzheimer Disease/genetics ; Reactive Oxygen Species ; Cognitive Dysfunction ; MicroRNAs/genetics ; Biomarkers
Chemical Substances	Reactive Oxygen Species ; MicroRNAs ; Biomarkers ; Mirn129 microRNA, human ; MIRN433 microRNA, human
Language	English
Publishing date	2024-01-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2506521-X
ISSN	1758-9193 ; 1758-9193
ISSN (online)	1758-9193
ISSN	1758-9193
DOI	10.1186/s13195-023-01366-8
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Article: Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales.

Xu, Frederick / Garai, Sumita / Duong-Tran, Duy / Saykin, Andrew J / Zhao, Yize / Shen, Li

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

2023 Volume 2022, Page(s) 1323–1328

Abstract: Graph theoretical measures have frequently been used to study disrupted connectivity in Alzheimer's disease human brain connectomes. However, prior studies have noted that differences in graph creation methods are confounding factors that may alter the ... ...

Abstract	Graph theoretical measures have frequently been used to study disrupted connectivity in Alzheimer's disease human brain connectomes. However, prior studies have noted that differences in graph creation methods are confounding factors that may alter the topological observations found in these measures. In this study, we conduct a novel investigation regarding the effect of parcellation scale on graph theoretical measures computed for fiber density networks derived from diffusion tensor imaging. We computed 4 network-wide graph theoretical measures of average clustering coefficient, transitivity, characteristic path length, and global efficiency, and we tested whether these measures are able to consistently identify group differences among healthy control (HC), mild cognitive impairment (MCI), and AD groups in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort across 5 scales of the Lausanne parcellation. We found that the segregative measure of transtivity offered the greatest consistency across scales in distinguishing between healthy and diseased groups, while the other measures were impacted by the selection of scale to varying degrees. Global efficiency was the second most consistent measure that we tested, where the measure could distinguish between HC and MCI in all 5 scales and between HC and AD in 3 out of 5 scales. Characteristic path length was highly sensitive to the variation in scale, corroborating previous findings, and could not identify group differences in many of the scales. Average clustering coefficient was also greatly impacted by scale, as it consistently failed to identify group differences in the higher resolution parcellations. From these results, we conclude that many graph theoretical measures are sensitive to the selection of parcellation scale, and further development in methodology is needed to offer a more robust characterization of AD's relationship with disrupted connectivity.
Language	English
Publishing date	2023-01-02
Publishing country	United States
Document type	Journal Article
ISSN	2156-1125
ISSN	2156-1125
DOI	10.1109/bibm55620.2022.9995657
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Article ; Online: Discovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations.

Lee, Brian N / Wang, Junwen / Nho, Kwangsik / Saykin, Andrew J / Shen, Li

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

2023 Volume 2023, Page(s) 340–349

Abstract: Alzheimer's Disease (AD) is a highly heritable neurodegenerative disorder characterized by memory impairments. Understanding how genetic factors contribute to AD pathology may inform interventions to slow or prevent the progression of AD. We performed ... ...

Abstract	Alzheimer's Disease (AD) is a highly heritable neurodegenerative disorder characterized by memory impairments. Understanding how genetic factors contribute to AD pathology may inform interventions to slow or prevent the progression of AD. We performed stratified genetic analyses of 1,574 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants to examine associations between levels of quantitative traits (QT's) and future diagnosis. The Chow test was employed to determine if an individual's genetic profile affects identified predictive relationships between QT's and future diagnosis. Our chow test analysis discovered that cognitive and PET-based biomarkers differentially predicted future diagnosis when stratifying on allelic dosage of AD loci. Post-hoc bootstrapped and association analyses of biomarkers confirmed differential effects, emphasizing the necessity of stratified models to realize individualized AD diagnosis prediction. This novel application of the Chow test allows for the quantification and direct comparison of genetic-based differences. Our findings, as well as the identified QT-future diagnosis relationships, warrant future investigation from a biological context.
Language	English
Publishing date	2023-06-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	2676378-3
ISSN	2153-4063 ; 2153-4063
ISSN (online)	2153-4063
ISSN	2153-4063
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Article: Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood.

Chen, Li / Saykin, Andrew J / Yao, Bing / Zhao, Fengdi

Computational and structural biotechnology journal

2022 Volume 20, Page(s) 5761–5774

Abstract: Traditional approaches for diagnosing Alzheimer's disease (AD) such as brain imaging and cerebrospinal fluid are invasive and expensive. It is desirable to develop a useful diagnostic tool by exploiting biomarkers obtained from peripheral tissues due to ... ...

Abstract	Traditional approaches for diagnosing Alzheimer's disease (AD) such as brain imaging and cerebrospinal fluid are invasive and expensive. It is desirable to develop a useful diagnostic tool by exploiting biomarkers obtained from peripheral tissues due to their noninvasive and easily accessible characteristics. However, the capacity of using DNA methylation data in peripheral blood for predicting AD progression is rarely known. It is also challenging to develop an efficient prediction model considering the complex and high-dimensional DNA methylation data in a longitudinal study. Here, we develop two multi-task deep autoencoders, which are based on the convolutional autoencoder and long short-term memory autoencoder to learn the compressed feature representation by jointly minimizing the reconstruction error and maximizing the prediction accuracy. By benchmarking on longitudinal DNA methylation data collected from the peripheral blood in Alzheimer's Disease Neuroimaging Initiative, we demonstrate that the proposed multi-task deep autoencoders outperform state-of-the-art machine learning approaches for both predicting AD progression and reconstructing the temporal DNA methylation profiles. In addition, the proposed multi-task deep autoencoders can predict AD progression accurately using only the historical DNA methylation data and the performance is further improved by including all temporal DNA methylation data.
Language	English
Publishing date	2022-10-23
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2022.10.016
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Article: Myelin repair in Alzheimer's disease: a review of biological pathways and potential therapeutics.

Hirschfeld, Lauren Rose / Risacher, Shannon L / Nho, Kwangsik / Saykin, Andrew J

Translational neurodegeneration

2022 Volume 11, Issue 1, Page(s) 47

Abstract: This literature review investigates the significant overlap between myelin-repair signaling pathways and pathways known to contribute to hallmark pathologies of Alzheimer's disease (AD). We discuss previously investigated therapeutic targets of amyloid, ... ...

Abstract	This literature review investigates the significant overlap between myelin-repair signaling pathways and pathways known to contribute to hallmark pathologies of Alzheimer's disease (AD). We discuss previously investigated therapeutic targets of amyloid, tau, and ApoE, as well as other potential therapeutic targets that have been empirically shown to contribute to both remyelination and progression of AD. Current evidence shows that there are multiple AD-relevant pathways which overlap significantly with remyelination and myelin repair through the encouragement of oligodendrocyte proliferation, maturation, and myelin production. There is a present need for a single, cohesive model of myelin homeostasis in AD. While determining a causative pathway is beyond the scope of this review, it may be possible to investigate the pathological overlap of myelin repair and AD through therapeutic approaches.
MeSH term(s)	Humans ; Myelin Sheath/metabolism ; Myelin Sheath/pathology ; Alzheimer Disease/drug therapy ; Alzheimer Disease/genetics ; Oligodendroglia/metabolism ; Oligodendroglia/pathology ; Remyelination ; Apolipoproteins E/metabolism
Chemical Substances	Apolipoproteins E
Language	English
Publishing date	2022-10-26
Publishing country	England
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural
ZDB-ID	2653701-1
ISSN	2047-9158
ISSN	2047-9158
DOI	10.1186/s40035-022-00321-1
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Article ; Online: Author Correction: The human connectome in Alzheimer disease - relationship to biomarkers and genetics.

Yu, Meichen / Sporns, Olaf / Saykin, Andrew J

Nature reviews. Neurology

2021 Volume 17, Issue 9, Page(s) 592

Language	English
Publishing date	2021-08-11
Publishing country	England
Document type	Published Erratum
ZDB-ID	2491514-2
ISSN	1759-4766 ; 1759-4758
ISSN (online)	1759-4766
ISSN	1759-4758
DOI	10.1038/s41582-021-00554-0
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Article ; Online: The human connectome in Alzheimer disease - relationship to biomarkers and genetics.

Yu, Meichen / Sporns, Olaf / Saykin, Andrew J

Nature reviews. Neurology

2021 Volume 17, Issue 9, Page(s) 545–563

Abstract: The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the ...

Abstract	The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.
MeSH term(s)	Alzheimer Disease/diagnostic imaging ; Alzheimer Disease/genetics ; Alzheimer Disease/metabolism ; Amyloid beta-Peptides/genetics ; Amyloid beta-Peptides/metabolism ; Apolipoproteins E/genetics ; Apolipoproteins E/metabolism ; Biomarkers/metabolism ; Brain/diagnostic imaging ; Brain/metabolism ; Connectome/methods ; Connectome/trends ; Humans ; Nerve Net/diagnostic imaging ; Nerve Net/metabolism ; Neuroimaging/methods ; Neuroimaging/trends
Chemical Substances	Amyloid beta-Peptides ; Apolipoproteins E ; Biomarkers
Language	English
Publishing date	2021-07-20
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	2491514-2
ISSN	1759-4766 ; 1759-4758
ISSN (online)	1759-4766
ISSN	1759-4758
DOI	10.1038/s41582-021-00529-1
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