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

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 ( ) 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 calculations.

AIM

We aimed to reevaluate the effectiveness of the 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 values. Based on these results, experiment 2 employed a typical exponential model to investigate whether valid could be predicted from a small sample size.

RESULTS

Experiment 1 revealed that the 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 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 correction in fNIRS studies with sample sizes smaller than the number of channels.