Topological false discovery rates for brain mapping based on signal height

Area of Science:

• Neuroimaging analysis
• Statistical modeling
• Brain mapping

Background:

• Multiple testing correction is crucial in statistical parametric mapping to avoid spurious findings or missed true effects.
• Current False Discovery Rate (FDR) methods, particularly those based on peak height, may be overly conservative and lack detection power.
• Combining random field theory with FDR is desirable for enhanced detection under topological errors.

Purpose of the Study:

• To introduce and validate a novel topological FDR method based on signal height for statistical parametric mapping.
• To improve the accuracy of error rate control compared to existing peak-based FDR methods.
• To increase the detection power in neuroimaging analyses.

Main Methods:

• Development of a new topological FDR method utilizing signal height.
• Theoretical analysis of the proposed method's properties.
• Extensive experimental validation using simulated and real neuroimaging data.

Main Results:

• The new topological FDR method demonstrates more accurate error rate control than the peak FDR method.
• Substantial gains in detection power are achieved with the proposed signal height-based FDR method.
• Reasons for the under-performance of the peak FDR method were identified and explained.

Conclusions:

• The proposed topological FDR method offers a more powerful and accurate approach for statistical parametric mapping.
• This method addresses limitations of existing peak-based FDR techniques, enhancing neuroimaging research.
• Understanding the behavior of different FDR methods is essential for reliable statistical inference.