Exploring effective multiplicity in multichannel functional near-infrared spectroscopy using eigenvalues of correlation matrices

Recent advances in multichannel functional near-infrared spectroscopy (fNIRS) allow wide coverage of cortical areas while entailing the necessity to control family-wise errors (FWEs) due to increased multiplicity. Conventionally, the Bonferroni method has been used to control FWE. While Type I errors (false positives) can be strictly controlled, the application of a large number of channel settings may inflate the chance of Type II errors (false negatives). The Bonferroni-based methods are especially stringent in controlling Type I errors of the most activated channel with the smallest [Formula: see text] value. To maintain a balance between Types I and II errors, effective multiplicity ([Formula: see text]) derived from the eigenvalues of correlation matrices is a method that has been introduced in genetic studies. Thus, we explored its feasibility in multichannel fNIRS studies. Applying the [Formula: see text] method to three kinds of experimental data with different activation profiles, we performed resampling simulations and found that [Formula: see text] was controlled at 10 to 15 in a 44-channel setting. Consequently, the number of significantly activated channels remained almost constant regardless of the number of measured channels. We demonstrated that the [Formula: see text] approach can be an effective alternative to Bonferroni-based methods for multichannel fNIRS studies.