Adjusting effective multiplicity (Meff) for family-wise error rate in functional near-infrared spectroscopy data with a small sample size

Abstract

Significance: The advancement of multichannel functional near-infrared spectroscopy (fNIRS) has enabled measurements across a wide range of brain regions. This increase in multiplicity necessitates the control of family-wise errors in statistical hypothesis testing. To address this issue, the effective multiplicity (M eff) method designed for channel-wise analysis, which considers the correlation between fNIRS channels, was developed. However, this method loses reliability when the sample size is smaller than the number of channels, leading to a rank deficiency in the eigenvalues of the correlation matrix and hindering the accuracy of M eff calculations. Aim: We aimed to reevaluate the effectiveness of the M eff method for fNIRS data with a small sample size. Approach: In experiment 1, we used resampling simulations to explore the relationship between sample size and M eff values. Based on these results, experiment 2 employed a typical exponential model to investigate whether valid M eff could be predicted from a small sample size. Results: Experiment 1 revealed that the M eff values were underestimated when the sample size was smaller than the number of channels. However, an exponential pattern was observed. Subsequently, in experiment 2, we found that valid M eff values can be derived from sample sizes of 30 to 40 in datasets with 44 and 52 channels using a typical exponential model. Conclusions: The findings from these two experiments indicate the potential for the effective application of M eff correction in fNIRS studies with sample sizes smaller than the number of channels. © 2024 Elsevier B.V., All rights reserved.