Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Methods of Obtaining Topography01:25

Methods of Obtaining Topography

190
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
190

You might also read

Related Articles

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

Sort by
Same author

Brain regional susceptibility to tauopathy in individuals at risk for chronic traumatic encephalopathy.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Dose-dependent white matter changes associated with repetitive head impacts in former American football players.

Brain communications·2026
Same author

Cognitive, biomarker, and neuroimaging indices associated with traumatic encephalopathy syndrome across two independent athlete cohorts.

Alzheimer's research & therapy·2026
Same author

The word Dementia should be retired.

Communications medicine·2026
Same author

A plasma protein signature for cerebral amyloid angiopathy.

Acta neuropathologica·2026
Same author

Advancing biomarker development for chronic traumatic encephalopathy: Summary and recommendations from the 2025 Leon Thal Summit.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

From Geometry to Intensity: A Coarse-to-Fine Pipeline for Unsupervised 3D Ultrasound Stitching.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
See all related articles

Related Experiment Video

Updated: Nov 28, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Patch-Based Surface Morphometry Feature Selection with Federated Group Lasso Regression.

Jianfeng Wu1, Jie Zhang1, Qingyang Li1

  • 1School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA.

Proceedings of Spie--The International Society for Optical Engineering
|November 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a federated learning framework for brain imaging analysis, enabling Alzheimer's disease (AD) research without sharing private patient data. The method effectively identifies key brain morphometry changes linked to AD progression.

Keywords:
Alzheimer’s DiseaseAmyloid BurdenFeature SelectionFederated LearningGroup LassoSurface-Based Morphometry

More Related Videos

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

10.0K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Related Experiment Videos

Last Updated: Nov 28, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K
Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

10.0K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Area of Science:

  • Neuroimaging
  • Machine Learning
  • Computational Neuroscience

Background:

  • Distributed brain imaging datasets offer valuable resources for studying neurological disorders like Alzheimer's disease (AD).
  • Centralizing these datasets is often infeasible due to privacy concerns, data restrictions, and legal regulations.
  • Existing methods require direct data access, limiting collaborative research and large-scale analysis.

Purpose of the Study:

  • To propose a novel federated feature selection framework for analyzing distributed brain imaging data without compromising patient privacy.
  • To develop and validate a privacy-preserving method for identifying neuroimaging biomarkers associated with Alzheimer's disease.
  • To investigate the relationship between hippocampal morphometry and cognitive decline in AD using federated analysis.

Main Methods:

  • A federated group lasso optimization method based on block coordinate descent was developed.
  • Stability selection was employed for statistically significant feature identification, accelerated by a federated screening rule.
  • The framework was applied to patch-based feature selection on hippocampal morphometry (radial distance, surface area) from T1-weighted MRI using tensor-based morphometry (TBM).

Main Results:

  • The federated framework successfully performed feature selection on distributed datasets without data sharing.
  • Analysis of 1,127 brain MRIs revealed associations between hippocampal morphometry and cognitive assessment/amyloid burden.
  • Significant morphometry changes related to AD deterioration and plaque accumulation were identified and visualized on hippocampal surfaces.

Conclusions:

  • The proposed federated feature selection framework is efficient and effective for privacy-preserving analysis of distributed neuroimaging data.
  • This approach facilitates collaborative research into Alzheimer's disease and other brain disorders.
  • The findings highlight the potential of federated learning in advancing neurological research while upholding data privacy.