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

Search research articles

Related Experiment Video

Updated: Jul 25, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

A topography-based predictive framework for naturalistic viewing fMRI.

Xuan Li¹, Patrick Friedrich¹, Kaustubh R Patil¹

¹Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research CentreJülich, Jülich 52428, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf,Düsseldorf 40225, Germany.

|June 23, 2023

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

Biological brain aging, cognitive-motor decline and vascular risk: a multivariate imaging analysis of 40,579 individuals.

Frontiers in aging neuroscience·2026

Same author

Linking human brain functional connectivity to underlying neurotransmission.

bioRxiv : the preprint server for biology·2026

Same author

Test-retest reliability of meta analytic networks during naturalistic viewing.

PloS one·2026

Same author

Predicting brain volumes from anthropometric and demographic features: insights from UK biobank neuroimaging data.

Brain structure & function·2026

Same author

Longitudinal brain-age predictions comprising long-duration spaceflight missions.

NPJ microgravity·2026

Same author

Can we predict sleep health based on brain features? A large-scale machine learning study using the UK Biobank.

Brain communications·2026

Same journal

Lifespan Trajectories of the Brain's Functional Complexity Characterized by Multiscale Sample Entropy.

NeuroImage·2026

Same journal

Pleasant fragrance modulates dyadic social sharing of positive emotion: Sharer-centered socioemotional enhancement effect and its neural couplings.

NeuroImage·2026

Same journal

Altered Functional Hierarchical and Sequential Organization in Individuals with Schizophrenia during Auditory Processing.

NeuroImage·2026

Same journal

Mechanical Deformation Explains Distinct Neuroimaging Patterns and Etiologies in Brain Trauma.

NeuroImage·2026

Same journal

Ventral striatum temporal interference brain stimulation enhances the reward-positivity event-related potential and reduces anxiety.

NeuroImage·2026

Same journal

NeuroHarm‑Kit: An Open‑Source Toolbox for Benchmarking Deep‑Learning Harmonization of Multi‑Site T1‑Weighted MRI.

NeuroImage·2026

See all related articles

A new method, topography-based predictive framework (TOPF), identifies individual brain activity patterns during naturalistic viewing (NV). This framework effectively predicts behavioral traits from brain activity topographies, advancing brain-behavior relationship studies.

Area of Science:

Neuroimaging
Cognitive Neuroscience
Machine Learning

Background:

Functional magnetic resonance imaging (fMRI) during naturalistic viewing (NV) offers ecologically valid insights into brain function.
Understanding individual differences in brain activity and their behavioral relevance is a key goal in neuroscience.
Current methods for analyzing individual brain function during NV are limited.

Purpose of the Study:

To introduce a novel topography-based predictive framework (TOPF) for analyzing individual brain activity during fMRI.
To assess the ability of TOPF to capture individual differences in evoked brain activity.
To investigate the behavioral relevance of these individual brain activity topographies.

Main Methods:

Developed a data-driven topography-based predictive framework (TOPF).

Keywords:

Behavior prediction Evoked activity Individual differences Naturalistic viewing fMRI Topography

More Related Videos

fMRI Validation of fNIRS Measurements During a Naturalistic Task

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jul 25, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

fMRI Validation of fNIRS Measurements During a Naturalistic Task

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Utilized machine learning to predict behavioral phenotypes from individual-specific evoked activity topographies.

Validated TOPF on both naturalistic viewing and task-based fMRI datasets.

Main Results:

TOPF effectively and stably captures individual differences in evoked brain activity.
The framework successfully predicts cognitive, emotional, and personality phenotypes from activity topographies in unseen subjects.
TOPF outperforms functional connectivity-based approaches and identifies neurobiologically interpretable brain regions.

Conclusions:

TOPF provides a powerful tool for understanding individual brain-behavior relationships using fMRI.
Examining individual evoked brain activity topographies is crucial for advancing neuroscience.
The approach holds significant potential for clinical applications in understanding individual brain function.