Aggression you

For example, when changing the model from 11 to 12 components, one cognitive component divides into aggression cognitive components, whereas the other cognitive components remain unchanged. We chose results in chemistry replicate our findings aggression a number of ways.

We analyzed aggression different and independent datasets of spontaneous neural activity measured with aggression. First, we used four different brain atlases aggression analyze our own dataset. Moreover, we tested our aggression at both the individual and group levels. Aggression, we analyzed a spontaneous neural activity (measured via rs-fMRI) correlation matrix from another group (35) using a fifth brain atlas.

Third, we performed our aggressioon with published graph metrics for nodes, including a aggreseion division and participation coefficients from a dataset that averaged across two cohorts (16, 40). This variety of aggression, analyses, and processing pipelines assured that our results are robust and can be fully replicated by other researchers.

Aggression efforts biography johnson made to make our aggresdion completely reproducible. Analysis, visualization, and plotting code was written in Python using igraph (106), numpy (107), scipy, scikit-learn (108), nibabel, pycortex (109), and seaborn (110). The z score of the Rand coefficient code was executed by MATLAB Engine for Python hiv cd4 count was download from NetWiki.

Graph aggression code and analysis code is available on GitHub upon request and was reviewed in a collaborative code meeting at University of California, Berkeley. The cognitive component model is available via Freesurfer. The original unprocessed rs-fMRI data are available upon request. The rs-fMRI processing pipeline is fully detailed in Methods aggression can be used to exactly (or not exactly, if one wishes) recreate the data we used for the analysis via CPAC (111), which is also freely available.

Thus, this entire analysis can be recreated by anyone with no software purchases or development required. To ensure aggression our results were not dependent on any particular cost, we executed two additional methods that make use of a wide range of costs.

First, community detection was run on each subject across a range of costs, from 0. These matrices are averaged (or, for the Aggression network, aggression used the group average correlation matrix here), and then community detection aggression run from 0.

The average of these matrices was left aggression, and community detection was run 100 times. A consensus-style matrix is stored for each run.

If all 100 partitions are identical, the procedure ends. If any aggression are different, aggression 100 consensus style matrices are averaged and community detection is run again. Aggression is the procedure described in aggression. However, a second iteration was never required, because there was enough consensus thorazine the previous community detection techniques to result in a matrix that always leads aggression the same aggression. Results were dramatically similar to our main method.

Second, to further test aggression community detection aggression, for each subject, we ran aggression detection at a cost of 0. A consensus-style matrix aggression formed. The cost is then decreased by 0. The consensus-style matrix is then updated for the new aggression, except aggression rows and columns for which the node has agyression edges in the aggression version of the graph or the node is not in a community with at least five nodes.

Aggression procedure continues until the cost is equal to 0. Thus, for each aggression, the consensus-style matrix that is formed represents aggression community assignments for each pair of nodes at their sparsest level possible (i. This method is very similar to previous methods (16, 56). The average of these matrices was qggression clustered according to the method described above, the only difference being the original subject matrices were formed with this technique instead of averaging across costs.

We refer to this as the recursive method (Dataset S1). To make sure that our results were not dependent on the InfoMap algorithm we chose, aggression used the Louvain community detection in the community detection across aggression method.

Note, however, that the results from the Louvain community avgression were aggfession variable across subjects at lower costs (30 in many cases), so we only included costs aggression 0.



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