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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 Stimulation (TMS).

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Toward Optimizing Thalamic Deep Brain Stimulation for Cortical Modulation: A Surrogate Brain Approach.

Raunak Ahmed, Yuqi Feng, Anna Wang Roe

Biorxiv : the Preprint Server for Biology

|July 10, 2026

Summary

This study introduces Neural Perturbational Inference (NPI) to map brain connections for personalized thalamic deep brain stimulation (DBS). NPI accurately predicts how stimulating the thalamus can precisely modulate brain activity for better treatment outcomes.

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Analysis of Gene Expression Changes in the Rat Hippocampus After Deep Brain Stimulation of the Anterior Thalamic Nucleus

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Published on: March 8, 2015

Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

The thalamus is a critical brain hub connecting cortical and subcortical regions.
Thalamic deep brain stimulation (DBS) offers potential for widespread brain modulation by targeting specific thalamic nuclei.
Accurate subject-specific effective connectivity (EC) mapping is crucial for optimizing DBS parameters.

Purpose of the Study:

To develop and validate a computational framework for estimating subject-specific thalamocortical EC.
To identify optimal thalamic stimulation targets for achieving desired cortical responses.
To advance personalized therapeutic strategies for thalamic DBS.

Main Methods:

Extended Neural Perturbational Inference (NPI) to a high-resolution thalamocortical network model.
Incorporated tSNR-weighted loss and multi-resolution, cross-scale consistency loss for improved model training.
Formulated a constrained linear control problem to determine stimulation targets based on inferred EC.

Main Results:

The enhanced NPI model demonstrated improved performance in synthetic benchmarks.
Inferred subject-specific thalamocortical EC profiles were validated against independent macaque and human datasets.
Results showed interpretable EC predictions aligning with known brain structures.

Conclusions:

This work establishes a computational method for personalized thalamic DBS optimization.
The approach provides a pathway for precise modulation of cortical activity via thalamic stimulation.
The findings support the development of tailored DBS therapies in humans and nonhuman primates.