Brain imaging software produced by the Brain Research Imaging Centre of The University of Edinburgh.
BioSig is a software library for processing of biomedical signals (EEG, ECG, etc.) with Matlab, Octave, C/C++ and Python. A standalone signal viewer supporting more than 30 different data formats is also provided.
intracranial electrodes localization toolbox
iElectrodes is an open source toolbox to obtain intracranial electrode coordinates in a semiautomatic way with minimal user intervention. It uses MRI and CT coregistered images. It is possible to add brain parcellations and brain surfaces. It works in the native or normalized MNI space The toolbox is capable to work with ECoG (subdural grids and strips) and SEEG (depth) electrodes. In the presurgical evaluation of patients with drug resistant epilepsy is of great importance to know the intracranial electrodes coordinates and the anatomical description of these locations. Additionally, you can plan depth electrode implantation and then compare it with the real locations. Visit iElectrodes Wiki tab to learn more about the toolbox. Please report any bugs to email@example.com We are trying to make iElectrodes a great toolbox everyday! Thanks for your help! Additional scripts are provided to make image corregistations using SPM software.
Obsolete project - archived at https://github.com/darrenleeweber/bioelectromagnetism This is a public release of a Matlab toolbox for working with data from electroencephalography (EEG/ERP) and magnetic resonance imaging (MRI). It contains functions to process and visualize ERP/MRI data and associated electrode positions.
Projeny (Probablistic Networks Generator in Java) is a graphical (Java SWT) front-end to BNT (Bayes Net Toolbox for Matlab). Projeny requires BNT, JMatLink and a Matlab back-end. There is no installable release package, but source code is available on SVN - please check out from SVN to use Projeny. Projeny was started with BNJ as the base.
Probabilistic natural mapping for gene based association study.
PALM is a software that conducts gene-based association analysis. PALM exploits Hidden Markov Models (HMM) to capture the inner genomic structures. Then, unique gene features (termed "palm prints") are extracted for each gene set by calculating the natural gradient. Then palm-prints are tested with chi-square significance test.
A tool to register and superpose CTF MEG SAM(g2) result and MRI image into one gray-level image.
Proyecto de colaboración entre estudiantes de la universidad politecnica de valencia que realizan la materia de informatica medica.
Cell tracking and segmentation software
Toolboxes for SPM (http://www.fil.ion.ucl.ac.uk/spm/) developed at Freiburg Brain Imaging (http://fbi.uniklinik-freiburg.de/)
The MATLAB software toolbox for MEG and EEG analysis
FieldTrip is the Matlab toolbox for EEG and MEG data. It includes algorithms for simple and advanced analysis, such as importing, preprocessing, time-frequency analysis, source reconstruction, statistical testing and connectivity analysis.
This project will be moving to GitHub. If you want to contribute or get the latest source go to: https://github.com/jramshur/HRVAS HRVAS is a heart rate variability (HRV) analysis tool developed using MATLAB. HRVAS can detrend and filter IBI and can perform time domain, frequency domain, time-frequency, Poincare', and nonlinear HRV analysis. If you use this application or modify this application for your research, please reference the thesis entitled "DESIGN, EVALUATION, AND APPLICAION OF HEART RATE VARIABILITY ANALYSIS SOFTWARE (HRVAS)". The thesis can be found here: http://iweb.dl.sourceforge.net/project/hrvas/Documents/ramshur_thesis.pdf
This project, developed at UCL London, provides code for tomographic reconstruction. NiftyRec is written in C and has Python and Matlab extensions. Computationally intensive functions have a GPU accelerated version based on CUDA.
NBT is an open source Matlab toolbox for the computation and integration of neurophysiological biomarkers. NBT allows for easy implementation of new biomarkers, and incorporates an online wiki with extensive help and tutorials.
This project is a lab prototype of MEG default mode network in MATLAB.
Default mode network (DMN) or task negative network (TNN) may provide useful information on state of brain. Normal brain is constantly busy and demonstrates a network of synchronized activity at distinct brain regions. Trauma, injury, or resection ruptures this natural network of neuronal connectivity, subsequently altering the default scheme of synchronized activation. This utility is designed to quickly analyze and visualize the default mode network, at current state of brain, for MEG data acquired from any subject. Customization of threshold and sampling rate, followed by complete circular visualizations for quick clinical references are a plus. The program is primarily designed to facilitate clinicians, and researchers. The researchers can further analyze, if required, using the MAT file stored from the last analysis. Moreover, the framework can be used to estimate functional connectivity while performing any particular task.
SVMT: A MATLAB toolbox for Stereo-vision motion tracking of motor reactivity elicited by sensory stimulation.
Analysis of drug resistance and epidemiology of STI (STD) in Russia
The STIanafor is a tool for spatio-temporal data vizualization, analysis and forecast of incidence of sexually-transmitted diseases and drug resistance of pathogens in the Russian Federation. Currently supports gonorrhoea (N. gonorrhoeae) and syphilis (T. pallidum). A set of real data for testing is provided.
Open Source White Matter Hyperintensities Segmentation Toolbox
Wisconsin White Matter Hyperintensity Segmentation [W2MHS] and Quantification Toolbox is an open source MatLab toolbox designed for detecting and quantifying White Matter Hyperintensities (WMH) in Alzheimer’s and aging related neurological disorders. WMHs arise as bright regions on T2- weighted FLAIR images. They reflect comorbid neural injury or cerebral vascular disease burden. Their precise detection is of interest in Alzheimer’s disease (AD) with regard to its prognosis. Our toolbox provides a self-sufficient set of tools for segmenting these WMHs reliably and further quantifying their burden for down-processing studies.
A collection of functions, in the end to be wrapped in a gui, that allow users to analyze networks from global (eigenvector centrality) and local (degree dist, statistical significance) perspectives. Implemented in matlab.
Detect sphere-shaped paramagnetic deposits in MRI datasets
Moved to http://parkermills.github.io/MRI-PDQ/
striatal subregional analysis tool for dopamine transporter PET
DAT_Analyzer (DANA) is fully automated tool for analyzing dopamine transporter PET. DANA utilizes coregistration and spatial normalization functions in SPM8 and therefore requires MATLAB 7.1 (or higher) and SPM8 in 64bit environment with more than 8 GB RAM. MAC OSX OS is recommended. It includes various custom templates for conventional MR, DARTEL MR, two type of CT, and ligand specific PET and also includes various types of striatal subregional VOI templates. It supports five types of spatial normalization methods. (This project will be uploaded soon. It's now under construction.)
This program has been written having in mind to create an useful tool to evaluate the Motor Evoked Potentials (MEPs) generated by Transcranial Magnetic Stimulation (TMS) and recorded with the program "Signal" (version 2.xx).
A matlab software platform (GUI included) for implementing literature-inspired/novel models of contrast dynamics for -- then ultimately analyzing series generated by -- the dynamic contrast-enhanced MR modality.
Cupid: simultaneous reconstruction of miRNA-target and ceRNA networks
Chiu et. al., Genome Research 2014 Cupid is a method for simultaneous prediction of miRNA-target interactions and their mediated competitive endogenous RNA (ceRNA) interactions. We showed that our integrative approach significantly improves on miRNA-target prediction accuracy as assessed by both mRNA and protein level measurements in breast cancer cell lines. Cupid is implemented in 3 steps: Step 1: re-evaluate candidate miRNA binding sites in 3’ UTRs, as inferred by TargetScan, miRanda and PITA by integrating their scores, location in the 3’ UTR, and cross-species conservation. Step2: interactions are predicted by integrating information about selected sites, their multiplicity, and the statistical dependency between the expression profiles of miRNA and putative targets. Likelihoods for each predictive feature are computed based on a positive gold standard set. Step 3: Cupid assesses whether inferred targets compete for predicted miRNA regulators.
Generate positive-contrast images emphasizing magnetic field variation
Moved to: http://parkermills.github.io/MRI-PSM/