Search research articles

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...

You might also read

Related Articles

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

Sort by

Same author

Neural Representations of Death-Related Concepts Identify Conceptual Alteration of Self in Suicidal Youth.

Human brain mapping·2026

Same author

The neural and cognitive basis of expository text comprehension.

NPJ science of learning·2024

Same author

Retraction Note: Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth.

Nature human behaviour·2023

Same author

Similarities and differences in the neural representations of abstract concepts across English and Mandarin.

Human brain mapping·2022

Same author

Publisher Correction: The neuroscience of advanced scientific concepts.

NPJ science of learning·2021

Same author

The neuroscience of advanced scientific concepts.

NPJ science of learning·2021

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

Search research articles

Related Experiment Video

Updated: Mar 27, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Modality-independent representations of small quantities based on brain activation patterns.

Saudamini Roy Damarla¹, Vladimir L Cherkassky¹, Marcel Adam Just¹

¹Department of Psychology, Center for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, Pennsylvania.

Human Brain Mapping

|January 11, 2016

Summary

This study shows that the brain represents quantities similarly across vision and hearing. Machine learning decodes quantity information between visual dots and auditory tones, revealing shared neural patterns in the parietal cortex.

Keywords:

cross-modality fMRI multivoxel pattern analysis number representation

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

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

Related Experiment Videos

Last Updated: Mar 27, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

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

Area of Science:

Cognitive Neuroscience
Neuroimaging
Computational Neuroscience

Background:

Previous machine learning (ML) and Multi-Voxel Pattern Analysis (MVPA) studies successfully decoded neural representations of visual quantities from fMRI data.
The neural basis for representing abstract concepts like quantity across different sensory modalities remains largely unexplored.

Purpose of the Study:

To investigate if neural representations of quantity are shared across visual and auditory sensory modalities.
To determine if quantity information can be decoded between modalities using fMRI and ML techniques.
To identify brain regions involved in cross-modal quantity representation.

Main Methods:

Functional magnetic resonance imaging (fMRI) was used to record brain activity.
Machine learning classifiers, specifically MVPA, were trained on fMRI data.
Classifiers trained on neural patterns from one modality (visual dots) were tested on patterns from another modality (auditory tones), and vice-versa.

Main Results:

For the first time, quantity information was successfully decoded across visual and auditory modalities.
Classifiers trained on visual quantity patterns could predict auditory quantity patterns, and vice-versa.
Common cross-modal quantity representations were found predominantly in right-lateralized frontal and parietal regions.
Neural patterns representing quantity in the parietal cortex were consistent across different participants.

Conclusions:

The findings demonstrate a common neural foundation for representing quantity information across different sensory modalities (vision and audition).
This suggests abstract quantity representations are not strictly modality-specific.
The parietal cortex plays a crucial role in supporting these shared, abstract quantity representations, consistent across individuals.