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Related Experiment Video

Updated: Feb 2, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification.

Yang Li¹, Jingyu Liu¹, Xinqiang Gao¹

¹School of Automation Sciences and Electrical Engineering, Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing Advanced Innovation Center for Big Date-based Precision Medicine, Beihang University, Beijing, China.

Medical Image Analysis

|November 26, 2018

Summary

This summary is machine-generated.

This study introduces a new method integrating BOLD and ASL fMRI for enhanced brain network analysis in MCI classification. Multimodal hyper-networks show superior performance over single-modality approaches.

Keywords:

Arterial spin labeling (ASL)Hyper-connectivity network Mild cognitive impairment (MCI)Multimodality Ultra-least squares (ULS)Weighted LASSO

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Last Updated: Feb 2, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Area of Science:

Neuroimaging
Biomarkers
Machine Learning

Background:

Blood-oxygen-level-dependent (BOLD) fMRI hyper-networks show promise for MCI classification but lack specificity.
Arterial spin labeling (ASL) fMRI offers quantitative cerebral blood flow (CBF) measurements, complementing BOLD's limitations.

Purpose of the Study:

To develop a novel sparse regression algorithm for inferring integrated hyper-connectivity networks from BOLD and ASL fMRI.
To enhance the discriminative power of brain network biomarkers for MCI classification using multimodal data.

Main Methods:

A constrained LASSO algorithm integrates ASL-derived functional connectivity to estimate BOLD-based hyper-connectivity.
ASL functional connectivity is constructed using Ultra-GroupLASSO-UOLS for topology detection and UOLS for connectivity strength estimation.

Main Results:

Multimodal hyper-networks combining BOLD and ASL fMRI exhibit superior discriminative characteristics compared to unimodal or pairwise networks.
The proposed method outperforms existing single-modality sparse functional connectivity inference techniques on the ADNI dataset.

Conclusions:

Integrated multimodal hyper-networks provide a more robust and discriminative approach for MCI classification.
Combining BOLD and ASL fMRI enhances the characterization of brain network physiology and functional interactions.