Allows users to improve signal-to-noise-ratio (SNR) by automatically deriving the noise regressors entered in the general linear model (GLM). GLMdenoise is a denoising technique that can encompass many different types of noise, including motion-related noise, physiological noise, and neural noise. Moreover, this method can be applied to existing functional magnetic resonance imaging (fMRI) datasets.
Aims to facilitate data processing. BRANT is a MATLAB-based toolbox that integrates (i) batch processing pipelines for resting-state functional magnetic resonance imaging (rs-fMRI) data preprocessing, (ii) voxel-wise spontaneous activity analysis, (iii) functional connectivity analysis, (iv) complex network analysis, as well as (v) statistical analysis and results visualization. It was designed using dynamically generated GUIs. Users can generate their own GUIs by adding a few lines of MATLAB code.
Allows characterization of functional magnetic resonance imaging (fMRI) activation patterns based on functionally-derived atlases. ICN_Atlas is an atlasing tool providing a collection of scripts that serves as an extension to the SPM toolbox. The software allows interpretation of blood oxygenation-level dependent (BOLD) maps based on the objective quantification of the degree of engagement of a set of intrinsic connectivity networks. It can be applied to activation studies of any nature, providing reproducible descriptions of fMRI maps.
Treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. The tool provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques.
Performs functional magnetic resonance imaging (fMRI) analysis computation. Fiswidgets provides a desktop style framework containing more than 100 subcomponents of fMRI analysis software packages. This software facilitates the interoperability and usability of tools developed by different laboratories and it is built on open architecture and source, modularity and extensibility. It allows users to dynamically construct, execute, monitor, and log multi-step and iterative data processing sequences.
Automates preprocessing and analysis of resting-state functional magnetic resonance imaging (fMRI) data. C-PAC allows users to assess the impact of processing decisions on their findings by specifying multiple analysis pipelines to be run simultaneously. This tool provides a quality control functionality that simplifies manual examination of pipeline outputs. It can be used to select subjects to be included in group comparisons.
Improves the two-threshold (TT) approach using an iterative choice of individually optimized thresholds. iTT can be employed to adjust statistical thresholds to variations in the noise characteristics of the actual data. This tool is useful for the estimation of height thresholds in SPM. It preserves statistical power in situations where conventional approaches fail.
Manages task-based and resting-state functional magnetic resonance imaging (fMRI) for analysis. FMRIPrep is divided into anatomical and functional preprocessing tasks which are both composed of modules that can be merged differently according to the user input data. It encompasses several features such as voxel-based, resting-state connectivity or surface-based analysis. The software can also be run through the OpenNeuro platform or the Singularity container.
Offers a collection of filters for 3D images supported by Java Native Interface. FastFilter3D performs several 3D filtering on 8-bits and 16-16-bits gray-levels stacks. This ImageJ plugin performs the following 3D filters: median, mean, minimum, maximum, maximum local and topchat.
Provides users a medical imaging file format and Toolbox for use in medical imaging. The original MINC file format and tools were based upon the NetCDF data format. The actual version was changed to HDF in order to support large files and other new features.
Optimizes functional magnetic resonance imaging (fMRI) analysis of data derived from high motion pediatric and clinical subjects. ArtRepair is a module, compatible with SPM2, 5, 8 and 12, intending to identify and correct artifacts at three different levels (voxel, volume and slice). This application comprises five mains functions that includes noise filtering, an utility to repair outlier volumes as well as a feature to display the quality of estimates produced by SPM.
Extends the SPM software by enabling the conservation of the shape and spatial extent of the activation areas during signal detection in functional magnetic resonance imaging (fMRI) data processing. Aws4SPM is a module built around a structural adaptive smoothing algorithm which is compatible with both SPM8 and 12. It permits users to assess linear model’s settings as well as novel features for selecting for the smoothing procedure pops up.
Serves for iterative image deblurring. PID allows users to deconvolve a color image by splitting the channels and deblur each channel separately. It contains a graphical user interface (GUI) permitting users to specify several types of details such as stopping tolerance, threshold, or log convergence.
Delivers a collection of tool for neuroimaging analysis. NIAK can be used to preprocess large functional magnetic resonance imaging data. It can conduct complex tasks such as registration and denoising of brain images, as well as data-driven functional brain parcellation or connectome-wide association studies.
Can process parallel analysis of functional magnetic resonance imaging (fMRI) data on a large variety of hardware configurations. BROCCOLI can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. It aims to demonstrate the advantages of parallel processing and to enable the neuroimaging field to avail itself of more computationally demanding normalization algorithms, and statistical methods that are based on a smaller number of assumptions.