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Related Concept Videos

Brain Imaging01:14

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).
Organization of the Brain01:30

Organization of the Brain

The brain is an integral component of the nervous system and serves as the center for processing sensory inputs, making decisions, and directing bodily actions. This complex organ is organized into three primary sections: the hindbrain, midbrain, and forebrain, each responsible for a range of vital functions.
Hindbrain
The hindbrain, located at the base of the brain, plays a vital role in regulating automatic processes that sustain life. It includes the medulla oblongata, which is essential for...

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Building bridges between brain and behavior: An open-source toolbox for joint modeling with fMRI.

Niek Stevenson1, Steven Miletić1,2, Birte U Forstmann1

  • 1University of Amsterdam, Amsterdam, The Netherlands.

Imaging Neuroscience (Cambridge, Mass.)
|June 18, 2026
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Summary

This study introduces EMC2, an R package for joint modeling of brain and behavior data. It helps researchers better understand cognitive neuroscience by improving brain-behavior correlation analysis.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Psychometrics

Background:

  • Relating neural activity to behavior is a key challenge in cognitive neuroscience.
  • Joint modeling simultaneously analyzes behavioral and fMRI data, accounting for errors and variability.
  • Existing methods can struggle with parameter stability and bias in brain-behavior correlations.

Purpose of the Study:

  • To present EMC2, an R package simplifying joint model estimation for behavioral and neural data.
  • To demonstrate the application of EMC2 using a perceptual decision-making task in an fMRI setting.
  • To provide researchers with tools for robust brain-behavior link analysis.

Main Methods:

  • Developed an R package, EMC2, for joint modeling.
  • Utilized a hierarchical modeling framework for stable parameter estimation.
  • Applied the toolbox to fMRI and behavioral data from a perceptual decision-making task.

Main Results:

  • EMC2 streamlines the estimation of behavioral, neural, and joint models.
  • Joint estimation stabilizes parameters and reduces attenuation bias in brain-behavior correlations.
  • The toolbox facilitates group-level and individual-difference analyses.

Conclusions:

  • EMC2 offers an accessible introduction to joint modeling for researchers.
  • The toolbox enables richer, more reliable insights into cognitive and neural mechanisms.
  • Adoption of joint models can advance understanding of the brain-behavior relationship.