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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Aug 29, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Consistent connectome landscape mining for cross-site brain disease identification using functional MRI.

Mingliang Wang¹, Daoqiang Zhang², Jiashuang Huang³

¹School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China; Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, china; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Medical Image Analysis

|September 7, 2022

Summary

This summary is machine-generated.

This study introduces Connectome Landscape Modeling (CLM) to identify brain disorders by analyzing functional connectivity networks. CLM overcomes data inconsistencies across sites, offering a more reliable method for disease biomarker discovery.

Keywords:

Autism spectrum disorder Consistent connectome landscape Cross-site analysis Functional connectivity

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Last Updated: Aug 29, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

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

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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:

Neuroscience
Medical Imaging
Computational Biology

Background:

Brain disorders often exhibit altered functional connectivity, prompting biomarker research for patient identification.
Previous studies yield inconsistent findings due to data variability and reliance on human-engineered features.
Existing methods struggle with multi-site data heterogeneity and lack data-driven feature extraction.

Purpose of the Study:

To propose a novel Connectome Landscape Modeling (CLM) method for mining cross-site consistent connectome landscapes.
To extract data-driven representations of functional connectivity networks for improved brain disorder identification.
To develop a robust method that addresses inconsistencies in multi-site neuroimaging data.

Main Methods:

Developed Connectome Landscape Modeling (CLM) to learn a weight matrix for joint learning, feature extraction, and disease identification.
Employed a row-column overlap norm penalty to ensure cross-site consistent connectome landscape learning.
Utilized an ℓ1-norm penalty to capture site-specific patterns and an ADMM algorithm for efficient optimization.

Main Results:

Demonstrated the potential of CLM in cross-site brain disorder analysis using three real-world fMRI datasets.
Showcased CLM's ability to mine consistent connectome landscapes across different data acquisition sites.
Validated the effectiveness of data-driven feature extraction for enhanced disease identification.

Conclusions:

Connectome Landscape Modeling (CLM) offers a promising approach for robust brain disorder identification across multiple sites.
The method effectively addresses data heterogeneity and provides data-driven insights into functional connectivity networks.
CLM has the potential to improve the reliability and generalizability of biomarker discovery in neuroscience research.