Power of detecting affected atoms and partially affected subsets depending on the proportion .

<p>For the multiplicity correction, the Bonferroni procedure is used. Three different values of the subsets' size (4, 8 or 16) and two different values of the raw effect Δ (1 or 2). The other parameters are: .</p

The general outcome of a multiple comparisons.

<p>A total of <i>m</i> null hypotheses are tested. FP is the number of Type I errors or the number of false positives (rejected true hypotheses). Physical significance as indicated in the first column means the existence of a real effect, whereas statistical significance refers to the detection of such effect by means of measurements. FN is the number of Type II errors or the number of false negatives (false hypotheses not rejected). The number <i>R</i> of rejected hypotheses is an observable random variable, while FP, FN, TP and TN are unobservable random variables. The number of true null hypotheses is also unknown in practice. The empirical type I error rate is defined by FP/, while the empirical type II error rate is defined by FN/ and the estimated average power is TP/. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023009#pone.0023009-Benjamini1" target="_blank">[12]</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023009#pone.0023009-Dudoit1" target="_blank">[13]</a>.</p

Illustration of the different types of subnetworks within a brain network.

<p>In the right side, a connection matrix is presented. In the left side, the connectivity between two groups of node is presented which defines three subnetworks of two types. The first type represents the intra-connection within the same subset of nodes (the red and the green subnetworks) and whose corresponding blocks are localized on the diagonal of the global connection matrix (the red and the green blocks). The second type represents the interconnections between the two subsets of nodes (the yellow subset). Its corresponding block is localized out of the diagonal in the global connection matrix (the yellow block).</p

Extraction of a Whole Brain Structural Connection Matrix.

<p><b>A–B.</b> MRI Acquisition: (A) high-resolution T1-weighted image and (B) diffusion images. The T1 is registered on the diffusion images. In every imaged voxel the Orientation Density Function (ODF) is extracted from the diffusion images. <b>C.</b> Whole brain tractography provides an estimate of axonal trajectories across the WM. <b>D.</b> Cortex partitioning into 83 gyral-based parcels using the Freesurfer software (<a href="http://surfer.nmr.mgh.harvard.edu" target="_blank">http://surfer.nmr.mgh.harvard.edu</a>). <b>E.</b> Creation of the low-resolution structural connection matrix, representing the fiber density between every pair of the 83 parcels (upper left and lower right blocks: connections in the right, respectively left hemisphere; off-diagonal blocks: inter-hemispheric connections).</p

Power of detecting affected atoms and partially affected subsets depending on the proportion .

<p>For the multiplicity correction, the BH95 procedure is used. Three different values of the subsets' size (4, 8 or 16) and two different values of the raw effect Δ (1 or 2) are used. The other parameters are: .</p