A core idea of the technique is to compute the spatiotemporal spectral profile (STSP), two dimensional representation of four dimensional fMRI data (X x Y x Z x Time). Toolbox runs on MATLAB R2013a and higher.

Simulation and Data for the Average Sliding Window Correlation method. doi: 10.1002/hbm.24509

Cortex is a wrapper around Pytorch that makes training, managing, and visualizing models more convenient.

MATLAB toolbox which implements multiple algorithms for independent component analysis and blind source separation of group (and single subject) electro encephalogram data.

Matlab toolbox which implements the joint ICA, parallel ICA and CCA-Joint ICA methods.

FNC is a Matlab toolbox which finds and displays temporal relations amongst components. This can help determine causal relations in the brain. Using GIFT to preprocess the raw data for FNC is recommended.

GIPC is a Matlab toolbox that can compare spatial activation similarities amongst subjects in a study group, as well as comparing group spatial activations amongst different study groups.

MATLAB toolbox which implements multiple algorithms for independent component analysis and blind source separation of group (and single subject) functional magnetic resonance imaging data.

LUI is a Matlab toolbox that generates the lateral difference maps of brain images.

MANCOVAN provides a suite of tools for testing for group, group-group interaction, covariate, covariate-covariate interaction, and group-covariate interaction effects in the context of a multivariate response and it does so without using the Statistics Toolbox. Because MANCOVAN represents such a general model, it can be used for ANOVA, ANOVAN, ANCOVA, ANCOVAN, MANOVA, MANOVAN, and MANCOVA as well without loss of power or precision. In addition to MANCOVAN, this suite of tools includes MSTEPWISE for multivariate stepwise regression, MT for t-tests among levels of a group or for the slope of the regression line associated with a covariate, a variety of functions for creating and using custom design matrices, and plenty of examples.

This repository contains Torch implementation of MeshNet architecture. MeshNet is volumetric convolutional neural network based on dilated kernels [1] for image segmentation. Model has been trained for brain tissue segmentation from imperfect labeling obtained using FreeSurfer automatic approach. The repository also contains weights of trained model with volumetric dropout for gray and white matter.

pl2mind is an extension for Pylearn2 specifically for neuroimaging and other brain data applications. It contains unique datasets directed to incorporating 3d brain data into deep models implemented in Pylearn2. pl2mind is currently under rapid development and will continue to expand as it is used more to facilitate different kinds of brain research.

Run a multitude of classifiers on your data and get an AUC report

This toolbox contains implementation of square-root Cubature Kalman Filter and square-root Rauch-Tang-Striebel smoother (SCKF-SCKS). These algorithms perform joint estimation of the states, input and parameters of stochastic continuous-discrete state-space models. The state equations must have a form of ordinary differential equations, where their discretization is performed through an    efficient local-linearization scheme. Additionally, the parameter noise covariance is estimated dynamicaly via stochastic Robbins-Monro approximation method, and the measurement noise covariance is estimated online as well, using combination of varitional Bayesian (VB) approach with nonlinear filter/smoother. In particular, this method was designed to perform the nonlinear blind deconvolution of hemodynamic responses from fMRI data to estimate the underlying neuronal signal.

SimTB is a Matlab toolbox allowing for flexible generation of fMRI datasets under a model of spatiotemporal separability and is designed to facilitate the testing of a variety of analytic     methods. Users have full control over generated datasets, including the creation and manipulation of spatial sources,implementation of block- and event-related experimental designs, inclusion of tissue-specific baselines, simulated head movement, and more.

Data driven simulator

WaveIDioT is a Matlab toolbox allowing for improved 3-D denoising of fMRI data sets using a wavelet-based hierarchical approach. With the help of this toolbox, the user can easily smooth the data without losing essential spatial details (edges, shape of gyri etc.) right after the other preprocessing steps have been applied.

A wavelet-based approach was implemented to generate simulated EEG data. This approach is based upon the notion that continuous EEG may be decomposed as a convolution of a series of basis functions (i.e. wavelets) which have defined temporal and frequency properties. The distribution of the associated coefficients was estimated within select frequency bands from real data. Then, simulated wavelet coefficients were generated by randomly drawing samples from that distribution. The simulated coefficients were reconstructed within the separate frequency bands, generating simulated EEG data with temporal and spectral properties that are consistent with the EEG segment that was used to estimate the coefficient distributions.