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

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SignificanceThe 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 (Meff) 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 Meff calculations.AimWe aimed to reevaluate the effectiveness of the Meff method for fNIRS data with a small sample size.ApproachIn experiment 1, we used resampling simulations to explore the relationship between sample size and Meff values. Based on these results, experiment 2 employed a typical exponential model to investigate whether valid Meff could be predicted from a small sample size.ResultsExperiment 1 revealed that the Meff 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 Meff values can be derived from sample sizes of 30 to 40 in datasets with 44 and 52 channels using a typical exponential model.ConclusionsThe findings from these two experiments indicate the potential for the effective application of Meff correction in fNIRS studies with sample sizes smaller than the number of channels.

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