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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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Related Experiment Video

Updated: Jul 17, 2025

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Published on: May 12, 2014

Prior elicitation for Gaussian spatial process: An application to TMS brain mapping.

Osafu Augustine Egbon^1,2,3, Diego Nascimento⁴, Francisco Louzada¹

¹Institute of Mathematical and Computer Sciences, Universidade de São Paulo, São Carlos, Brazil.

Statistics in Medicine

|September 4, 2023

Summary

This summary is machine-generated.

This study introduces new informative prior distributions for spatial Bayesian analysis, improving brain mapping with transcranial magnetic stimulation (TMS) data. Findings clarify motor cortex function for better health treatments.

Keywords:

commensurate prior motor evoked potential power prior spatial model transcranial magnetic stimulation

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Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging

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Area of Science:

Bayesian statistics
Geostatistics
Neuroscience

Background:

Informative prior distributions enhance Bayesian analysis by incorporating historical data.
Existing methods primarily focus on non-spatial data, neglecting spatial geostatistical applications.
Prior knowledge elicitation from historical geostatistical data is crucial for spatial modeling.

Purpose of the Study:

Extend informative prior distributions to Gaussian spatial processes for geostatistical data.
Develop novel prior distributions for spatial modeling.
Apply these methods to transcranial magnetic stimulation (TMS) brain mapping data.

Main Methods:

Developed three informative prior distributions for spatial modeling.
Implemented an efficient Markov Chain Monte Carlo algorithm for Bayesian analysis.
Utilized hierarchical models combined with developed prior distributions.

Main Results:

Simulation studies confirmed the adequacy of the informative prior distributions.
Analysis of TMS data revealed spatial patterns of motor cortex response.
Quantified uncertainty in motor response, identifying the primary motor cortex for hand movement.

Conclusions:

The developed methods effectively incorporate historical geostatistical data into spatial Bayesian analysis.
The study provides deeper insights into neural mechanisms of motor function.
Findings can aid in improving treatment strategies for neurological conditions.