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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 Stimulation (TMS).

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Related Experiment Video

Updated: Jun 24, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Replicable brain-phenotype associations require large-scale neuroimaging data.

Shu Liu^1,2, Abdel Abdellaoui^3,4, Karin J H Verweij^3,4

¹Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands. s.liu@amsterdamumc.nl.

Nature Human Behaviour

|June 26, 2023

Summary

This summary is machine-generated.

Large neuroimaging studies are essential for reliable brain-phenotype associations. Increasing sample sizes or preselecting individuals significantly improves replicability, reducing false positives in smaller studies.

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Last Updated: Jun 24, 2026

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Area of Science:

Neuroimaging
Cognitive Neuroscience
Psychiatry

Background:

Replicability of brain-phenotype associations is crucial for understanding interindividual differences.
Previous neuroimaging studies have not adequately addressed the sample size requirements for robust findings.

Purpose of the Study:

To determine the sample sizes needed for replicable brain-phenotype associations.
To investigate the impact of sample size and individual preselection on replicability.
To assess the relationship between effect size and required sample size.

Main Methods:

Utilized the UK Biobank neuroimaging dataset (N=37,447).
Examined associations between neuroimaging data and six health-related variables: age, body mass index, intelligence, memory, neuroticism, and alcohol consumption.
Assessed the improvement in replicability with increasing sample sizes and compared findings between upper and lower quartiles of individuals.

Main Results:

Highly replicable associations for age required approximately 300 individuals.
Other phenotypes required sample sizes ranging from 1,500 to 3,900 individuals.
Required sample size demonstrated a negative power law relationship with effect size, and preselection reduced sample size needs by 15-75%.

Conclusions:

Large-scale neuroimaging datasets are necessary for reproducible brain-phenotype associations.
Preselection of participants can mitigate the need for extremely large sample sizes.
Small-scale studies may be prone to reporting false positive findings due to insufficient sample sizes.