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==Drop outs== | |||
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 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 [[Main_Page#Other_Visualization_tools|BrainNet]]. It also give you a figure with missing nodes by subjects and overall percentage of missing nodes. | |||
:'''NB:''' 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). | |||
===Back To Main Page=== | ===Back To Main Page=== | ||
[[Main Page]] | [[Main Page]] | ||
Latest revision as of 19:45, 1 April 2016
Drop outs
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 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. It also give you a figure with missing nodes by subjects and overall percentage of missing nodes.
- NB: 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).
