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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: Jun 16, 2025

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

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Mapping effective connectivity by virtually perturbing a surrogate brain.

Zixiang Luo^1,2, Kaining Peng¹, Zhichao Liang¹

¹Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.

|April 22, 2025

Summary

This summary is machine-generated.

This study introduces Neural Perturbational Inference (NPI), a novel framework for mapping whole-brain effective connectivity (EC). NPI accurately infers causal brain interactions using a computational surrogate, advancing neuroscience and clinical applications.

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Effective connectivity (EC) is crucial for understanding brain function.
Traditional EC methods are invasive or lack whole-brain coverage.
A non-invasive method for whole-brain EC mapping is needed.

Purpose of the Study:

To introduce Neural Perturbational Inference (NPI), a data-driven framework for whole-brain EC mapping.
To develop a computational surrogate brain model for simulating neural dynamics.
To enable causal inference of brain-wide interactions.

Main Methods:

Developed an artificial neural network as a computational surrogate of the brain.
Systematically perturbed regions in the surrogate model to analyze responses.
Trained the network to model large-scale neural dynamics for EC inference.

Main Results:

NPI demonstrated superiority over Granger causality and dynamic causal modeling in validation studies.
Applied to resting-state fMRI data, NPI revealed consistent, structurally supported EC patterns.
NPI-inferred EC closely matched real stimulation propagation patterns from evoked potential data.

Conclusions:

NPI provides a powerful, non-invasive tool for mapping whole-brain effective connectivity.
This framework facilitates a transition from correlational to causal understanding of brain function.
NPI holds significant potential for neuroscience research and clinical applications.