Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 13, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

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

Published on: October 24, 2012

Gaussian process methods for estimating cortical maps.

Jakob H Macke1, Sebastian Gerwinn, Leonard E White

  • 1Gatsby Computational Neuroscience Unit, University College London, London, UK. jakob@gatsby.ucl.ac.uk

Neuroimage
|May 18, 2010
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Neural timescales from a computational perspective.

Nature neuroscience·2026
Same author

Retinal circuits in silico: A review of modern retina models and a vision for their future.

Vision research·2026
Same author

Non-inferiority of digitally assisted outpatient rehabilitation in patients with back pain: 12-month follow-up of a randomized controlled trial.

Journal of rehabilitation medicine·2026
Same author

Corrigendum to "Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes" [Medical Image Analysis 103 (2025) 103580].

Medical image analysis·2026
Same author

Millimeter-scale selective amplification in the developing visual cortex.

bioRxiv : the preprint server for biology·2026
Same author

[Impact of psychological comorbidity in women with breast cancer on work participation after medical rehabilitation: A routine data analysis].

Die Rehabilitation·2026
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
See all related articles

This study introduces a novel probabilistic model for analyzing functional imaging data, improving cortical map estimation and providing uncertainty measures. This method enhances the understanding of neural coding and noise correlations in brain activity.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Cortical organization features topographic maps for stimulus feature encoding (e.g., orientation, direction selectivity).
  • Functional imaging techniques like optical imaging and fMRI are crucial for mapping cortical structures.
  • Functional imaging data is often noisy, necessitating robust statistical processing for accurate map extraction.

Purpose of the Study:

  • To develop a probabilistic model for analyzing functional imaging data.
  • To improve the accuracy and efficiency of cortical map estimation.
  • To quantify uncertainty in estimated cortical map properties.

Main Methods:

  • Development of a probabilistic model utilizing Gaussian processes for functional imaging data.

More Related Videos

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
09:57

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

Brain Mapping Using a Graphene Electrode Array
10:32

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

Related Experiment Videos

Last Updated: Jun 13, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

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

Published on: October 24, 2012

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
09:57

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

Brain Mapping Using a Graphene Electrode Array
10:32

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

  • Application of the model to estimate cortical maps from noisy imaging data.
  • Utilizing the model to decode stimulus information from single-trial imaging experiments.

    • Main Results:

    • The Gaussian process model provides superior cortical map estimates compared to conventional methods, especially with limited data.
    • The model generates quantitative uncertainty estimates (error bars) for map properties.
    • The model facilitates the study of coding properties and noise correlations by enabling single-trial stimulus decoding.

    Conclusions:

    • Probabilistic Gaussian process modeling offers a powerful approach for analyzing noisy functional imaging data.
    • This method enhances the precision of cortical map reconstruction and provides valuable uncertainty quantification.
    • The developed model advances the investigation of neural coding mechanisms and the impact of noise correlations in the cortex.