NeuroElf Features
While this list might not be complete, it tries to give an overview of what the NeuroElf toolbox can do (and, to some extent, what it cannot do), linking to other wiki pages containing information on how the user can achieve the required tasks with the toolbox.
Next to the purely file-operation (xff class to read and write file formats commonly used in fMRI data analyses) functionality, features can be mainly split into two categories: analysis (which includes any kind of processing and manipulation of data, such as preprocessing, filtering, etc.) and result generation and visualization (which is at the “far end” of the pipeline between coming up with a project/study idea and submitting a paper).
Data analysis
limited capabilities for
data import with functions
dcm2nii (DICOM to NIftI import via SPM5/8 code) or
dicom2nii (import without SPM code, still not fully functional),
createfmr (DICOM to FMR import, not all types of DICOM images supported),
createvmr (DICOM to VMR import, not all types of DICOM images supported)
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batched preprocessing of fMRI data (re-using
SPM5/SPM8 code) with
spm5_preprojobs
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morph (
smoothing, inflation, sphere-morph)
surface objects using the
SRF::Morph method
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performing
robust regression and computing t-statistics based on those results with the
fitrobustbisquare (single sample regression),
fitrobustbisquare_img (robust regression with one common model over multi-dimensional data), and
fitrobustbisquare_multi (robust regression with variable model over multi-dimensional data) functions, followed by using the
robustt function
first-level regression (
beta-estimation), either via the
Compute Multi-Study GLM dialog or the
MDM::ComputeGLM method on the command line (which creates a BrainVoyager QX-compatible RFX-GLM object)
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single-run ICA using the
FastICA algorithm of the group of Aapo Hyvärinen either using the
ne_fastica function or the
VTC::ICA method
RFX
contrast computation and
correlation with (behavioral) covariates using the
Contrast Manager UI or the
GLM::RFX_tMap and
GLM::RFX_rMap methods (incl. group comparison as well as robust regression and rank correlation features)
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estimating true-Null
cluster-size thresholds using using
alphasim
classifying data with a Support-Vector-Machine
(SVM) classifier using the
svmtrain and
svmpredict functions (which re-use the
libSVM code by Chih-Jen Lin and his group)
Result generation
Data visualization
creating
high-resolution slice images (overlaid statistics, montage images) using the
Image Montage UI, incl. multi-map overlay and integration into variable background
creating
high-resolution surface images (overlaid statistics) using the screenshot feature of the
NeuroElf GUI satellite window
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creating
high-quality 3D renderings of brains (incl. statistics) using the
Rendering UI
creating GLM
beta bar or scatter plots using the
GLM beta plotter feature (supports subject groups and plotting of contrast values)
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flexible time-course plotting using the
tcplot function
plotting of (conditional)
confidence ellipses using the
cellipse function
Not (yet) implemented features
segmentation (only via SPM's preprocessing)
inter-subject normalization (only via SPM's preprocessing)
group-based ICA
multi-level mediation analysis
Granger Causality (network analysis and whole-brain mapping)
Please note, that at the moment I have too little time to develop and/or integrate these features into the toolbox!