Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Amyloid Fibrils

Amyloid Fibrils

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals.

JAMA psychiatry·2023

Same author

White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer's disease continuum.

medRxiv : the preprint server for health sciences·2023

Same author

Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2023

Same author

Social enrichment on the job: Complex work with people improves episodic memory, promotes brain reserve, and reduces the risk of dementia.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2023

Same author

Novel <i>CYP1B1-RMDN2</i> Alzheimer's disease locus identified by genome-wide association analysis of cerebral tau deposition on PET.

medRxiv : the preprint server for health sciences·2023

Same author

Aberrant GAP43 Gene Expression Is Alzheimer Disease Pathology-Specific.

Annals of neurology·2023

Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 25, 2026

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Published on: April 19, 2021

Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm.

Jingwen Yan¹, Lei Du², Sungeun Kim²

¹BioHealth, Indiana University School of Informatics & Computing, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA, Computer Science & Engineering, The University of Texas at Arlington, TX 76019, USA and Genetics, Community & Family Medicine, Dartmouth Medical School, Lebanon, NH 03756, USA BioHealth, Indiana University School of Informatics & Computing, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA, Computer Science & Engineering, The University of Texas at Arlington, TX 76019, USA and Genetics, Community & Family Medicine, Dartmouth Medical School, Lebanon, NH 03756, USA.

Bioinformatics (Oxford, England)

|August 28, 2014

Summary

This summary is machine-generated.

This study introduces a new Knowledge-Guided Sparse Canonical Correlation Analysis (KG-SCCA) to find associations between genetic variations and brain imaging traits. The KG-SCCA method improves upon existing techniques by incorporating prior biological knowledge, leading to more accurate and meaningful results in imaging genetics research.

More Related Videos

Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections

Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections

Published on: May 19, 2022

Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging

Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging

Published on: October 20, 2017

Related Experiment Videos

Last Updated: Apr 25, 2026

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Published on: April 19, 2021

Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections

Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections

Published on: May 19, 2022

Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging

Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging

Published on: October 20, 2017

Area of Science:

Neuroscience
Genetics
Bioinformatics

Background:

Imaging genetics links genetic variations to brain structure and function.
Sparse Canonical Correlation Analysis (SCCA) is used to find associations between genetic markers (SNPs) and brain imaging traits (QTs).
Existing SCCA methods assume feature independence, which is unsuitable for complex imaging genetic data.

Purpose of the Study:

To propose a novel Knowledge-Guided Sparse Canonical Correlation Analysis (KG-SCCA) algorithm.
To overcome the independence assumption limitation in current SCCA methods.
To improve the accuracy of identifying multi-SNP and multi-QT associations by incorporating prior biological knowledge.

Main Methods:

Developed KG-SCCA to model group structures (e.g., linkage disequilibrium) and network structures (e.g., gene co-expression).
Introduced new regularization terms to encourage weight similarity between grouped or connected features.
Designed a new algorithm to solve the KG-SCCA model without the independence constraint.

Main Results:

Demonstrated the effectiveness of KG-SCCA using both synthetic and real Alzheimer's disease (AD) cohort data.
Applied KG-SCCA to analyze associations between APOE gene SNPs and amyloid deposition in cortical regions.
KG-SCCA showed improved cross-validation performance and yielded biologically meaningful results compared to standard SCCA.

Conclusions:

KG-SCCA effectively integrates prior biological knowledge into association analysis.
The proposed method overcomes limitations of existing SCCA algorithms for imaging genetics.
KG-SCCA offers a powerful tool for uncovering complex genetic influences on brain imaging phenotypes, particularly in diseases like Alzheimer's.