In particular, we generated an average network by
averaging the correlation matrices from all the subjects (n = 194).
Although
averaging correlation matrices across subjects can represent the connectivity between two nodes as an element in the averaged matrix, such an approach may not accurately summarize the consistent network structure.
This was done by
averaging the correlation matrices from all the subjects, element by element.
An average network, which is produced by
averaging correlation matrices across subjects, does not properly represent the characteristics of the individual networks [26].
For instance,
averaging correlation matrices across individual subjects resulted in the separation of the left from the right dorsal lateral prefrontal cortex.
Thus, in the calculation of
the average correlation matrix, the denominator was adjusted for the number of all valid correlation coefficients at each element of the matrix.
This average correlation matrix was then thresholded (see Materials and Methods) and modular organization was then detected on the resulting adjacency matrix.
Here is
the average correlation matrix:
Not exact matches
We defined a similarity measure between the HICA
matrices of two individuals as the
correlation coefficient of their corresponding elements, and computed for each individual an index of intra-hemispheric intrinsic connectivity asymmetry as the
average similarity measure of his HICA
matrix to those of the other subjects of the sample (HICAs).