# Correlation analysis and correcting p-values for multiple testing II

I am currently facing problems dealing with lots of individual simple correlations (28 individual DTI connections strengths, individual behavioural scores on 31 time points). That would be 28x31 individual correlations. Referring to a previous discussion on that, I just want to make sure that I understood correctly and am looking forward for your short answers whether this way would be fine:

1. Individual correlations 28x31 are computed, giving R and p values (original data).
2. Subjects are shuffled on 1 side (e.g. for all 28 connections, pseudosample), all correlations are computed again, giving a new 28x31x5000 matrix with R and p-val. 2a. This is done 5000x.
3. The number R perm > R obs (N) is collected for each correlation (hence max. 5000).
4. Emperical p val is computed as N/5000 for each correlation.
5. A common threshold of p-val of e.g. 0.05 is applied to find significant correlations based on all individual 28x31 p-vals.

A. What about negative R? What about the case when abs(R) would be larger in perm. than in original?
B. How to adress R perm -0.05 and R original -0.6, hence stronger negative correlation? At this stage, I only count the # of Rorig > Rperm, should I also count abs(Rorig> abs (Rperm)?

I am still a bit confused and unsure whether I would report correct statistics.

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If you have negative correlations than logically you should change the sign $>$ to $<$ on step 3 for this particular comparison. To this end use $\left|\frac{R_{\text{perm}}}{R_{\text{obs}}}\right|>1$ rule in step 3.