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Related Experiment Video

Updated: May 27, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Large-sample PCA eigenvectors stabilize cortical thickness components and improve small sample brain behavior

Zhang Yun Feng^1,2, Kenchi Hosokawa^1,2, Chihiro Hosoda^3,4,5

¹Graduate School of Information Sciences, Tohoku University, 6-3-09 Aoba, Aramaki-Aza Aoba-Ku, Sendai, 980-8579, Japan.

Scientific Reports

|May 25, 2026

Summary

This summary is machine-generated.

Principal component analysis (PCA) in brain imaging is more stable with larger sample sizes. Using eigenvectors from large samples improves brain-behavior prediction in smaller studies, enhancing reproducibility.

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Area of Science:

Neuroimaging
Quantitative Neuroscience
Brain-Behavior Relationships

Background:

Reproducibility in brain-wide association studies using structural MRI is hindered by unstable eigenspaces from high-dimensional data in small samples.
Principal Component Analysis (PCA) is a common dimensionality reduction technique in neuroimaging, but its stability is sample-dependent.

Purpose of the Study:

To investigate the impact of sample size on the stability of PCA eigenspaces in structural MRI data.
To determine if eigenvectors derived from large samples can enhance brain-behavior prediction in smaller, independent samples.

Main Methods:

Cortical thickness data from the Human Connectome Project Young Adult cohort (N=1,113) were used.
PCA stability was assessed using resampling schemes and quantified with cosine similarity and Hungarian matching.
Brain-behavior prediction models (linear regression, machine learning) were compared across different sample size pairings (500 vs. 500, 500 vs. 100, 100 vs. 100) for 65 cognitive and personality traits.

Main Results:

PCA stability significantly increased with sample size; small subsamples (<100 participants) yielded unstable components, while larger samples produced dozens of stable components.
Transferring eigenvectors from larger samples to smaller samples consistently improved brain-behavior prediction compared to using PCA components derived solely within small samples.
Optimal prediction performance was observed at an intermediate dimensionality of approximately 30 principal components.

Conclusions:

Eigenspace stability in PCA is crucial for reproducible brain-behavior inference in neuroimaging.
Reusing PCA eigenvectors derived from large samples is a practical strategy to stabilize feature extraction in resource-limited neuroimaging studies.
The findings highlight the importance of sample size for reliable feature representation in neuroimaging analyses.