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...

You might also read

Related Articles

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

Sort by

Same author

Statistically efficient neural encoding of natural object variability shapes the temporal dynamics of visual processing.

NeuroImage·2026

Same author

Patterns of functional connectivity differentiate individuals and individual regions in face and scene selective networks.

Neuropsychologia·2026

Same author

Hyperfamiliarity for faces: Preserved core face processing with altered medial temporal lobe connectivity in a single case study.

Cortex; a journal devoted to the study of the nervous system and behavior·2026

Same author

Differences in face recognition predict the understanding of events during natural viewing.

Perception·2025

Same author

The perceptual and neural processing of familiar faces is shaped by the statistical regularities of real-world viewing.

Cerebral cortex (New York, N.Y. : 1991)·2025

Same author

Correction: Positive and negative impacts of electrical infrastructure on animal biodiversity: A systematic review.

Oecologia·2025

Same journal

Brain-Inspired Large Model Mindreading.

NeuroImage·2026

Same journal

Light on Broken Networks: Resting-State fNIRS as a Tool for Connectivity Mapping.

NeuroImage·2026

Same journal

Criticism-Evoked Rumination Is Linked to Dynamic adjustments of the Left Superficial Amygdala in Adolescents.

NeuroImage·2026

Same journal

GeNED.ar cohort: Neuroimaging Resource for Aging Studies in an Admixed Population from Argentina.

NeuroImage·2026

Same journal

DTI-ALPS index correlates with poor neuromodulation outcomes of bilateral STN-DBS in Parkinson's disease patients: a prospective cohort study.

NeuroImage·2026

Same journal

Decoding neuronal criticality firing patterns for large brain based EEG models.

NeuroImage·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 26, 2025

Functional Mapping with Simultaneous MEG and EEG

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Connectopic mapping techniques do not reflect functional gradients in the brain.

David M Watson¹, Timothy J Andrews¹

¹Department of Psychology and York Neuroimaging Centre, University of York, York YO10 5DD, UK.

|June 20, 2023

Summary

This summary is machine-generated.

Artificial spatial autocorrelations from data analysis can create illusory functional gradients in the brain. These connectopic gradients, identified using connectopic mapping, may be unreliable and require cautious interpretation.

Keywords:

Connectopic mapping Functional connectivity Neural gradients

More Related Videos

Brain Mapping Using a Graphene Electrode Array

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

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: Jul 26, 2025

Functional Mapping with Simultaneous MEG and EEG

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Brain Mapping Using a Graphene Electrode Array

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

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:

Neuroscience
Brain Imaging
Data Analysis

Background:

Functional gradients are proposed as a key brain organization principle.
Connectopic mapping reconstructs these gradients from functional connectivity.
Potential confounds like spatial autocorrelation exist in data analysis.

Purpose of the Study:

To investigate if spatial autocorrelations can create illusory connectopic gradients.
To assess the impact of smoothing and interpolation on gradient reconstruction.
To determine the reliability of connectopic gradients across different analysis pipelines.

Main Methods:

Generated random white noise datasets.
Applied spatial smoothing and/or interpolation.
Performed connectopic mapping on simulated and real data.
Reconstructed volume- and surface-based gradients.

Main Results:

Smoothing and interpolation induced spatial autocorrelations, creating illusory gradients.
Simulated gradients resembled those from real data but were statistically different.
Global gradients were less susceptible to artificial autocorrelations.
Reproducibility of gradients depended on analysis pipeline specifics.

Conclusions:

Connectopic gradients may be confounded by artificial spatial autocorrelations.
Illusory gradients can be generated by common data analysis techniques.
Connectopic gradients require cautious interpretation due to potential analysis confounds.
Gradient reproducibility varies significantly with analysis pipeline choices.