Bayes factor analysis
Bayes Factor (BF) refers to the ratio of the marginal likelihood of alternate hypothesis to the likelihood of null hypothesis, given the data. The measure compares the alternate against the null hypothesis, rather than limiting the inference from the null hypothesis evidence (Jarosz & Wiley, 2014). In the present study, we used the Bayesian paired t-test with a Cauchy prior scaled to 1. Under this scheme, BF values below 1 suggests the absence of effect and evidence in favor of the null hypothesis, BF values between 1 and 3 provide anecdotal evidence in favor of the alternate hypothesis, while values between 3 and 10 provide substantial evidence and values above 10 provide strong evidence in favor of the alternative (Jeffreys, 1998). We calculated BF for right-tailed paired t-test (connectivity(controls) > connectivity(cases)) or (connectivity(Middle-aged) > connectivity(elderly)) for all the frequency bands. We used the MATLAB toolbox on BF by Bart Krekelberg (Krekelberg, 2021) based on (Rouderet al. , 2012).