clpipe 1.5.0 Release: New Command fmri_postprocess2
This update combines the functionalities of glm_setup and fmri_postprocess into one command, fmri_postprocess2*
fmri_postprocess2 also provides the ability to customize which processing steps to use, and in which order. These configurations and their outputs can be handled as independent pipelines using the processing streams feature.
Image postprocessing steps provided by fmri_postprocess2:
- Temporal Filtering
- Intensity Normalization
- Spatial Smoothing
- AROMA Regression
- Confound Regression
- Timepoint Trimming
- Masking
In addition to processing images, fmri_postprocess2 provides a processing pipeline for each image's corresponding confound file. When enabled, fmri_postprocess2 will apply any temporally relevant postprocessing steps to the confounds that were applied to the images. It also can automatically provide spike regressors to replicate glm_setup's functionality.
fmri_postprocess2 is implemented completely with nipype, which, among other features, provides a framework for automatic parallelization of processing steps and caching for quick-reruns of pipelines. Nipype also makes it easier to implement additional processing steps. Additionally, nipype allows for visualization of processing streams, and some examples are provided below:
An example of a pipeline which performs smoothing, AROMA regression, temporal filtering, and normalization. Note how the temporally relevant processing steps are also applied to the confounds, in the same order:
An example of a pipeline providing only normalization and masking, as well as providing spike regressors:
- fmri_postprocess2 will replace fmri_postprocess and deprecate glm_setup in v2.0.0