NeuroElf wiki

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====== 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)
  * **fMRI quality control** with [[fmriquality]] (computation) and [[fmriqasheet]] (assessment)
  * **batched preprocessing** of fMRI data (re-using **SPM5/SPM8** code) with [[spm5_preprojobs]]
  * attempting to **correct for magnetic field inhomogeneities** (intensity bias field) using the [[pmbfilter]] function or the [[vmr.InhomogeneityCorrect|VMR::InhomogeneityCorrect]] method
  * **reconstructing a segmentation** as a surface using the [[vmr.DBReco|VMR::DBReco]] method to generate a [[xff - SRF format|BrainVoyager QX SRF]] object
  * morph (**smoothing**, inflation, sphere-morph) **surface objects** using the [[srf.Morph|SRF::Morph]] method
  * **SPM-to-BVQX data import** with [[importvtcfromanalyze]] (time courses), [[importvmpfromspms]] (maps), and [[importrfxglmfromspms]] (entire set of first-level stats)
  * 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 [[NeuroElf GUI - compute multi-study GLM|Compute Multi-Study GLM]] dialog or the [[mdm.ComputeGLM|MDM::ComputeGLM]] method on the command line (which creates a BrainVoyager QX-compatible RFX-GLM object)
  * **extract time-course data** from VTC files using either the [[vtc.VOITimeCourse|VTC::VOITimeCourse]] (single file) or [[mdm.VOITimeCourses|MDM::VOITimeCourses]] (multiple files) methods
  * VOI-based GLM computation (regression) using the [[mdm.ComputeVOIGLM]] method
  * **single-run ICA** using the [[http://research.ics.tkk.fi/ica/fastica/|FastICA]] algorithm of the group of Aapo Hyvärinen either using the [[ne_fastica]] function or the [[vtc.ICA|VTC::ICA]] method
  * RFX **contrast computation** and **correlation with (behavioral) covariates** using the [[NeuroElf GUI - contrast manager|Contrast Manager UI]] or the [[glm.RFX_tMap|GLM::RFX_tMap]] and [[glm.RFX_rMap|GLM::RFX_rMap]] methods (incl. group comparison as well as robust regression and rank correlation features)
  * RFX **(single-level) mediation analysis** (whole-brain mediation mapping) using the [[NeuroElf GUI - RFX mediation|RFX mediation analysis]] dialog for data stored in [[xff - GLM format|BrainVoyager QX's GLM format]] (or the [[mediationpset]] function on the command line for extracted data)
  * computing **[[Working with statistics|conjunction maps]]**
  * estimating true-Null **cluster-size thresholds** using using [[alphasim - extended uses|alphasim]]
  * classifying data with a Support-Vector-Machine **(SVM) classifier** using the [[svmtrain]] and [[svmpredict]] functions (which re-use the [[http://www.csie.ntu.edu.tw/~cjlin/libsvm/|libSVM]] code by Chih-Jen Lin and his group)

===== Result generation =====
  * creating **cluster tables** with the [[NeuroElf GUI|main GUI]] or using the [[vmp.ClusterTable|VMP::ClusterTable]] method
  * **extracting beta values** from clusters with the [[NeuroElf GUI|main GUI]] or using the [[glm.VOIBetas|GLM::VOIBetas]] method

===== Data visualization =====
  * creating **high-resolution slice images** (overlaid statistics, montage images) using the [[NeuroElf GUI - image montage UI|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
  * visualizing activation maps as flatmaps using the files created by [[neuroelf_makefiles]] and the SMP sampling either via the [[NeuroElf GUI|main GUI]] or the [[vmp.CreateSMP|VMP::CreateSMP]] method
  * creating **high-quality 3D renderings** of brains (incl. statistics) using the [[NeuroElf GUI - rendering UI|Rendering UI]]
  * creating GLM **beta bar or scatter plots** using the [[NeuroElf GUI - GLM beta plotter|GLM beta plotter]] feature (supports subject groups and plotting of contrast values)
  * creating **trial/event-related averaging plots** using the [[NeuroElf GUI - MDM VOI condition average UI|MDM VOI condition average UI]]
  * **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!