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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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Related Experiment Video

Updated: May 20, 2026

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

Brain imaging analysis can identify participants under regular mental training.

João R Sato¹, Elisa H Kozasa, Tamara A Russell

¹UFABC-Univ. Federal do ABC, Santo André, Brazil.

|July 18, 2012

Summary

Brain imaging accurately identifies regular meditators using multivariate pattern recognition and MRI analysis. This neuroimaging technique achieved 94.87% accuracy in distinguishing meditators from non-meditators.

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Last Updated: May 20, 2026

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Published on: June 20, 2012

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Area of Science:

Neuroimaging
Cognitive Neuroscience
Machine Learning in Medicine

Background:

Multivariate pattern recognition is increasingly used in neuroimaging data analysis.
These methods classify participant groups based on whole-brain patterns.
Structural MRI combined with automated morphometric analysis offers insights into brain structure.

Purpose of the Study:

To investigate the efficacy of multivariate pattern recognition with structural MRI for identifying individuals engaged in regular mental training.
To determine if brain imaging can differentiate regular meditators from non-meditators based on neural patterns.

Main Methods:

Utilized multivariate pattern recognition techniques.
Employed automated morphometric analysis of structural Magnetic Resonance Imaging (MRI) data.
Applied these methods to a sample of regular meditators and non-meditators.

Main Results:

The combined approach achieved high accuracy (94.87%) in classifying participants.
Statistical significance was confirmed (p<0.001).
Successfully distinguished regular meditators from non-meditators based on brain patterns.

Conclusions:

Multivariate pattern recognition and structural MRI analysis can accurately identify individuals based on their mental training history.
This study suggests a potential new application of neuroimaging for identifying individuals by their mental experiences.
Brain imaging may evolve to include classification based on cognitive engagement and mental practices.