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

You might also read

Related Articles

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

Sort by

Same author

Sparsity-guided multiple functional connectivity patterns for classification of schizophrenia via convolutional network.

Human brain mapping·2023

Same author

Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets.

Computational diffusion MRI : MICCAI Workshop·2017

Same author

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017

Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017

Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017

Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017

Same journal

Benchmarking fMRI Denoising Pipelines.

Human brain mapping·2026

Same journal

Modeled Long-Term Effects of Psilocybin on Dynamic Activity and Effective Connectivity of Fronto-Striatal-Thalamic Circuits.

Human brain mapping·2026

Same journal

Intrinsic Functional Architecture Reflects Individual Differences in Passive Working Memory: An Exploratory Resting-State fMRI Study.

Human brain mapping·2026

Same journal

Symptom Overlap and Neurobiological Similarities Between Posttraumatic Stress Disorder and Tinnitus.

Human brain mapping·2026

Same journal

Test-Retest Reliability of Sensorimotor Activity Measured With Spinal Cord fMRI.

Human brain mapping·2026

Same journal

The Human Visual Claustrum Responses to Physical Stimulus Properties and Subjective Content During Movie Viewing.

Human brain mapping·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 8, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.

Renping Yu^1,2, Han Zhang², Le An²

¹School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

Human Brain Mapping

|February 3, 2017

Summary

This summary is machine-generated.

This study introduces a new method for building brain networks, improving early diagnosis of mild cognitive impairment (MCI). The novel approach enhances accuracy in identifying brain disease biomarkers.

Keywords:

brain network disease classification functional connectivity mild cognitive impairment (MCI)sparse representation

More Related Videos

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Mar 8, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroimaging
Computational Neuroscience
Biomedical Engineering

Background:

Brain functional network analysis aids in understanding brain function and disease biomarkers.
Accurate brain network construction is crucial for applications like Alzheimer's disease (AD) and mild cognitive impairment (MCI) detection.
Existing sparse learning methods for brain networks do not account for inherent connectivity strength or group structures.

Purpose of the Study:

To propose a novel brain functional network modeling framework incorporating a connectivity strength-weighted sparse group constraint.
To enhance the accuracy and biological meaningfulness of brain network construction.
To improve the classification of mild cognitive impairment (MCI) for early Alzheimer's disease (AD) diagnosis.

Main Methods:

Developed a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint."
Optimized network modeling by considering raw connectivity strength and group structure simultaneously with sparsity.
Applied the proposed method to resting-state functional MRI data for MCI classification.

Main Results:

The proposed method achieved a significantly higher classification accuracy of 84.8% for MCI detection compared to competing methods (65.6%).
Experimental results demonstrated the effectiveness of the novel framework using resting-state fMRI data from 50 MCI patients and 49 healthy controls.
Post hoc analysis revealed more biologically meaningful brain functional connectivities identified by the proposed method.

Conclusions:

The novel brain functional network modeling framework with a connectivity strength-weighted sparse group constraint is effective for MCI classification.
The method enhances the accuracy and biological interpretability of brain network analysis for early AD diagnosis.
This approach offers a promising tool for identifying biomarkers in neurodegenerative diseases.