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fig rework

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ackman678
2014-07-31 11:26:41 -04:00
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methods.txt
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**Calcium domain analysis.** The mean width in the medial-lateral and height in the rostral-caudal dimensions of the bounding box fitted to each segmented calcium domain signal was taken to be the domain diameter. The number of contiguous frames (bounding box depth) for each segmented calcium domain was taken to be the domain duration. The mean and maximum pixel intensities within each domain were taken as the mean and maximum domain amplitudes. Domains were assigned areal membership by intersection of the domain centroid with a cortical ares's pixel mask. The number of individual domains per recording within a hemisphere or cortical area was taken to be domain frequency.
**Functional correlation analysis.** A binary movie array from all the segmented calcium domain masks for a recording was intersected mask representing different cortical areas. The total number of active pixels per frame expressed as a fraction of possibly active pixels per frame for each cortical area gave active pixel fraction timecourses for each cortical area in each recording. Correlation matrices were calculated for each recording by computing pairwise Pearson's product moment correlation coefficents from the matrix containing the cortical active pixel fraction timecourses. The binarized correlation matrix at *r* > 0.1 was used to form an adjacency matrix with each node representing a cortical area and each edge representing an association between a pair of nodes at weight, *r*. Community structure was detected within each functional association matrix using a fast greedy modularity optimization algorithm [#Clauset:2004] to perform hierarchial clustering using the igraph network analysis software library [#Csardi:2013].
**Functional correlation analysis.** A binary movie array from all the segmented calcium domain masks for a recording was intersected mask representing different cortical areas. The total number of active pixels per frame expressed as a fraction of possibly active pixels per frame for each cortical area gave active pixel fraction timecourses for each cortical area in each recording. Correlation matrices were calculated for each recording by computing pairwise Pearson's product moment correlation coefficents from the matrix containing the cortical active pixel fraction timecourses. The binarized correlation matrix at *r* > 0.1 was used to form an adjacency matrix with each node representing a cortical area and each edge representing an association between a pair of nodes at weight, *r*. Community structure was detected within each functional association matrix using a fast greedy modularity optimization algorithm [#Clauset:2004] to perform hierarchical clustering using the igraph network analysis software library [#Csardi:2013].
[#Ackman:2012]: Ackman, J. B., Burbridge, T. J., and Crair, M. C. (2012). Retinal waves coordinate patterned activity throughout the developing visual system, Nature, 490(7419), 219-25