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Related Experiment Video

Updated: Sep 8, 2025

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging

Published on: February 27, 2011

A longitudinal multi-scanner multimodal human neuroimaging dataset.

Colin Hawco^1,2, Erin W Dickie^3,4, Gabrielle Herman³

¹Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada. colin.hawco@camh.ca.

Scientific Data

|June 14, 2022

Summary

This summary is machine-generated.

This study used "travelling phantoms" across multiple MRI scanners and sites over four years. The resulting dataset helps evaluate scanner consistency and harmonization methods for neuroimaging research.

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Last Updated: Sep 8, 2025

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging

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Published on: February 27, 2011

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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

Area of Science:

Neuroimaging
Brain Function Research

Background:

Small sample sizes limit neuroimaging studies.
Multi-site, multi-scanner protocols are increasingly used to overcome these limitations.
Understanding human brain function requires robust, reproducible data.

Purpose of the Study:

To create a dataset for evaluating multi-scanner and multi-site neuroimaging protocols.
To assess scanner consistency and harmonization techniques.
To support research into social cognition in schizophrenia.

Main Methods:

Acquired structural T1, diffusion, and resting-state functional MRIs annually.
Used "travelling human phantoms" across three sites.
Harmonized scan protocols across sites and managed scanner upgrades (PRISMA MRIs).

Main Results:

Collected 30 MRI scans across 4 individuals over 4 years.
Dataset includes multiple neuroimaging modalities and repeated scans.
Data accounts for variations due to 6 different MRIs.

Conclusions:

The dataset is valuable for testing neuroimaging pipeline consistency.
Enables evaluation of differences across scanners.
Facilitates the development and validation of multi-scanner harmonization approaches.