Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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).

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Simulating the Effect of Environmental Change on Evolving Populations.

Artificial life·2024

Same author

Effect of Environmental Change Distribution on Artificial Life Simulations.

Artificial life·2022

Same author

A longitudinal study of theory of mind and listening comprehension: Is preschool theory of mind important?

Journal of experimental child psychology·2022

Same author

Imitative and Direct Learning as Interacting Factors in Life History Evolution.

Artificial life·2017

Same author

Theory of mind in emerging reading comprehension: A longitudinal study of early indirect and direct effects.

Journal of experimental child psychology·2017

Same author

Inclined to see it your way: Do altercentric intrusion effects in visual perspective taking reflect an intrinsically social process?

Quarterly journal of experimental psychology (2006)·2015

Search research articles

Related Experiment Video

Updated: May 13, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Limiting factors for mapping corpus-based semantic representations to brain activity.

John A Bullinaria¹, Joseph P Levy

¹School of Computer Science, University of Birmingham, Birmingham, United Kingdom. j.a.bullinaria@cs.bham.ac.uk

|March 26, 2013

Summary

This summary is machine-generated.

This study reveals that the quality of fMRI data, not semantic representations, limits brain activation prediction accuracy for concrete nouns. Improving fMRI data is key for advancing computational neuroscience research.

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Functional Mapping with Simultaneous MEG and EEG

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Related Experiment Videos

Last Updated: May 13, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Functional Mapping with Simultaneous MEG and EEG

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Neuroimaging

Background:

Previous studies mapped semantic representations to fMRI brain activations for concrete nouns.
Performance improvements in predicting brain activations have plateaued in recent years.
Current model accuracies are limited, necessitating identification of performance bottlenecks.

Purpose of the Study:

To systematically identify limiting factors in predicting brain activations from semantic representations.
To differentiate the impact of fMRI data quality versus semantic input quality.
To guide future research directions for improved predictive models.

Main Methods:

Introduced artificial brain activation vectors with varying noise levels.
Developed improved corpus-based semantic vectors.
Conducted computational experiments to isolate performance constraints.

Main Results:

Current semantic representations perform near-optimally for non-ambiguous concrete nouns.
fMRI data quality is the primary constraint on predictive model performance.
Noise in brain activation data significantly restricts achievable accuracy.

Conclusions:

Future research should prioritize enhancing fMRI data quality.
Semantic representation quality is less critical than previously assumed for this task.
Empirical findings suggest a clear path for improving brain-based semantic decoding.