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Nodes containing no information need to be removed prior to running any type of graph analyses. Luckily, the parcellate.sh script will also output a set of error files for nodes that have no signal. These error files can be easily incorporated in your graph pipeline, and even easier with this wrapper [[https://github.com/rb643/brains/blob/master/rb_checkErrors.m]]. This will given you a mask file that you can use to filter out erroneous nodes across all your subjects or plot using BrainNet [[https://wiki.cam.ac.uk/bmuwiki/Main_Page#Other_Visualization_tools]]. It also give you a figure with missing nodes by subjects and overal percentage of missing nodes. | |||
* note: At the moment it doesn't include a check for outliers on nodes that do contain information (e.g. check whether the signal variability in one node is +/- 3 stdevs from the mean for example). | |||
Revision as of 12:39, 30 March 2016
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Nodes containing no information need to be removed prior to running any type of graph analyses. Luckily, the parcellate.sh script will also output a set of error files for nodes that have no signal. These error files can be easily incorporated in your graph pipeline, and even easier with this wrapper [[1]]. This will given you a mask file that you can use to filter out erroneous nodes across all your subjects or plot using BrainNet [[2]]. It also give you a figure with missing nodes by subjects and overal percentage of missing nodes.
- note: At the moment it doesn't include a check for outliers on nodes that do contain information (e.g. check whether the signal variability in one node is +/- 3 stdevs from the mean for example).
