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

You might also read

Related Articles

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

Sort by

Same author

Early skill learning is shaped by the offline emergence of expert synergies.

Current biology : CB·2026

Same author

White matter microstructure fingerprint of cerebral small vessel disease.

Brain : a journal of neurology·2026

Same author

Reducing Noise Induced by Cardiac Pulsatility in Brain Maps of R<sub>2</sub>* and Magnetic Susceptibility Using Tailored k-space Sampling.

NMR in biomedicine·2026

Same author

Impaired reward-based learning but preserved motor invigoration in chronic stroke.

Brain : a journal of neurology·2026

Same author

Expert Motor Synergies Emerge Predominantly Offline During Early Skill Learning.

bioRxiv : the preprint server for biology·2026

Same author

Neural signatures of major depression across the episodic memory circuitry.

Journal of affective disorders·2026

Same journal

Investigating the Neural Origins of Ear-EEG: A Correlation Study Using Scalp EEG Source Reconstruction.

NeuroImage·2026

Same journal

Hysteresis effects in visual and auditory perception and the comparison of underlying neural mechanisms - an EEG study.

NeuroImage·2026

Same journal

Short-term audio-tactile training affects cortical auditory speech-envelope tracking for incongruent but not congruent stimuli.

NeuroImage·2026

Same journal

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

NeuroImage·2026

Same journal

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026

Same journal

Whole-brain network dynamics underlying intolerance of uncertainty.

NeuroImage·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 9, 2026

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

High precision anatomy for MEG.

Luzia Troebinger¹, José David López², Antoine Lutti³

¹Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London, WC1N 3BG, UK.

|August 6, 2013

Summary

This summary is machine-generated.

Subject-specific 3D-printed head casts significantly improve magnetoencephalography (MEG) accuracy by minimizing head movement and co-registration errors. This enhances signal-to-noise ratio and supports individualized anatomical models for better brain activity analysis.

Keywords:

3D printer Coregistration Head movement Longitudinal MEG MEG MSP Magnetoencephalography Spatial resolution

More Related Videos

Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans

Published on: September 27, 2020

Successful Orthotopic Liver Transplantation in Mice Utilizing Microcomputed Tomography Angiography

Successful Orthotopic Liver Transplantation in Mice Utilizing Microcomputed Tomography Angiography

Published on: September 22, 2023

Related Experiment Videos

Last Updated: May 9, 2026

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans

Published on: September 27, 2020

Successful Orthotopic Liver Transplantation in Mice Utilizing Microcomputed Tomography Angiography

Successful Orthotopic Liver Transplantation in Mice Utilizing Microcomputed Tomography Angiography

Published on: September 22, 2023

Area of Science:

Neuroscience
Biomedical Engineering
Medical Imaging

Background:

Accurate magnetoencephalography (MEG) source localization relies on precise head position relative to sensors.
Head movements during scanning and co-registration errors to anatomical scans limit MEG data reliability.

Purpose of the Study:

To introduce and validate a method using 3D-printed, subject-specific head casts to minimize head movement and co-registration errors in MEG.
To assess the impact of this method on data quality, signal-to-noise ratio (SNR), and the use of individualized anatomical models.

Main Methods:

Subject-specific head casts were created using 3D printing, fitting both the subject's scalp and the MEG dewar.
MRI-visible fiducial markers were incorporated into the casts to improve co-registration accuracy.
Bootstrap methods and analysis of MEG data over six months were used to estimate co-registration errors and data variability.

Main Results:

Head casts reduced within- and between-session head movements, achieving absolute co-registration errors around 1mm and relative errors <1.5mm.
Between-session sensor variability in evoked responses was comparable to within-session noise, indicating minimal movement-related artifact.
Simulations showed a five-fold improvement in sensor-level amplitude SNR compared to conventional methods.
High co-registration accuracy (<5mm) favored individualized anatomical models, while larger errors diminished this advantage.

Conclusions:

3D-printed head casts offer a robust solution for minimizing MEG head localization errors, significantly improving data quality and SNR.
This technique enables more sensitive longitudinal MEG studies and supports the use of precise, individualized anatomical models for brain source reconstruction.
The improved accuracy paves the way for advanced MEG source analysis leveraging high SNR signals and accurate subject-specific anatomy.