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Related Experiment Video

Updated: May 16, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

A radiomics-based method for studying seed-based voxel-wise morphological connectivity.

Cheng Jiang¹, Xin Wang², Ning Pan²

¹Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China.

|May 14, 2026

Summary

This study introduces a novel radiomics approach to map brain networks at the voxel level, offering an atlas-free method for individualized brain connectivity. The framework shows high reliability and clinical potential for studying neurological conditions.

Keywords:

Autism spectrum disorder Morphological connectivity Radiomics Structural MRI Test-retest reliability

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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

Area of Science:

Neuroimaging
Radiomics
Connectomics

Background:

Current brain network analysis often relies on predefined atlases, limiting spatial specificity and overlooking within-region details.
Individualized brain network mapping is crucial for understanding human connectome variations.

Purpose of the Study:

To develop and validate a radiomics-based framework for voxel-level morphological connectivity (MC) mapping.
To assess the feasibility, reproducibility, functional relevance, and clinical utility of this atlas-free approach.

Main Methods:

Extracted 93 radiomic features from gray matter volume maps.
Estimated MC by correlating voxel-wise radiomic feature vectors.
Validated the framework using three independent datasets and analyzed the default mode network (DMN), central executive network (CEN), and salience network (SN).

Main Results:

The radiomics-based MC framework successfully mapped canonical network patterns with high between-subject consistency and within-subject reliability.
Seed-based MC showed significant coupling with functional connectivity (FC), particularly in the CEN.
In autism spectrum disorder, MC revealed distinct alterations in the CEN and DMN compared to FC.

Conclusions:

The proposed radiomics-based, seed-based, voxel-wise MC framework is a novel, atlas-free, and scalable method for brain network analysis.
This approach demonstrates high reproducibility and reliability, offering potential for studying both healthy and diseased brain states.
It provides a more spatially specific and detailed view of neural circuit coordination than traditional region-level methods.