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Software algorithm that automatically estimates and removes physiological noise in BOLD fMRI data, including the effects of heartbeat and respiration. This algorithm (1) masks out high-variance CSF and vascular tracts that may otherwise confound analyses, and (2) regresses out noise timeseries in grey matter tissue, using an adaptive multivariate component decomposition (Canonical Autocorrelations Analysis). PHYCAA+ is an efficient, automated procedure that does NOT require external measures of physiology, nor does it require the user to manually identify noise components. Based on the peer-reviewed article: Churchill & Strother (2013). PHYCAA+: An Optimized, Adaptive Procedure for Measuring and Controlling Physiological Noise in BOLD fMRI. NeuroImage 82: 306-325

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• SciCrunch
• 13 years ago - by Anonymous

Software tool to detect physiologic signals from the data itself as well as an adaptive physiologic noise removal tool (Impulse Response Function or IRF-RETROICOR) that zooms in on noise with only 6 regressors, getting all the noise that 5th order RETROICOR gets. These tools will allow you to correct your data for physiologic noise with what you currently have. These signals are equivalent to a parallel monitored pulse signal and a respiratory chest-bellows signal. Do you have 3D+time EPI data (BOLD or perfusion) but no usable physio signals for pulse and respiration? Are you concerned about the effect of physio noise on your data but don't know what to do but regress data-derived signals that mix unknown functional signal with possible physio noise signal? Are you concerned about the number of regressors you're incorporating once you add 5th order RETROICOR (20 more regressors!)? This is for you.

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• SciCrunch
• 13 years ago - by Anonymous

Software that simulates PacBio reads by using either a model-based or sampling-based simulation.

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• SciCrunch
• 13 years ago - by Anonymous

Software matlab toolbox for pipeline processing of diffusion MRI images. For each subject, PANDA can provide outputs in 2 types: i) diffusion parameter data that is ready for statistical analysis; ii) brain anatomical networks constructed by using diffusion tractography. Particularly, there are 3 types of resultant diffusion parameter data: WM atlas-level, voxel-level and TBSS-level. The brain network generated by PANDA has various edge definitions, e.g. fiber number, length, or FA-weighted. The key advantages of PANDA are as follows: # fully-automatic processing from raw DICOM/NIFTI to final outputs; # Supporting both sequential and parallel computation. The parallel environment can be a single desktop with multiple-cores or a computing cluster with a SGE system; # A very friendly GUI (graphical user interface).

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• SciCrunch
• 13 years ago - by Anonymous

A C++ package for low-frequency bio-electromagnetism solving forward problems in the field of EEG and MEG with very high accuracy.

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• SciCrunch
• 13 years ago - by Anonymous

Software with advanced visualization techniques and state-of-the-art volume rendering provide unparalleled insight into the details and properties of neurological data acquired by CT, micro-CT, MRI, PET, SPECT, microscopy and other modalities. With data fusion tools, intramodality and multimodality registration of MR/CT or PET/CT is easily accomplished, while semi-automatic VOI delineation on fused datasets can improve analysis. Standard formats, such as DICOM, RAW, JPEG, NIFTI, Analyze are supported and 3D/4D sequences can be played. Other features include MPR, oblique, CPR, volume clipping, and surface visualization of cortex, skull, and scalp models. Also standard are easy-to-use tools for voxel-based delineation of features and the measurement of properties, including areas, volumes, counts, and intensity profiles. Present your findings by creating annotated animations or high-resolution images for posters. An SDK is also available to create plug-ins that provide new workflows or functionalities.

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• SciCrunch
• 13 years ago - by Anonymous

Stand-alone and multiplatform application for the integrated analysis of NGS data. It implements pipelines for the analysis of single nucleotide and copy-number variation and bisulfite-seq and ChIP-seq experiments.

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• SciCrunch
• 13 years ago - by Anonymous

A computerized, odor delivery device that can be used for basic research applications including mapping olfactory centers, cognitive / learning research, neuro-marketing among other uses. Additional products including tests for odor threshold, odor identification, odor discrimination and odor memory.

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• SciCrunch
• 13 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 13, 2026. Software program, executable under any Windows32 OS, performs Group BSS (Blind Source Separation) analysis comparing two groups of individuals and it performs NICA (Normative ICA) analysis where individuals are compared individually to a (normative) group. All analysis is performed in the frequency domain, that is, for all frequencies. The program also performs all these analysis for qEEG, that is, at the electrode level, without any BSS. The program does all computations, saves and displays results. The rationale and methods used in this program are explained in all details in the following paper: Congedo M, John ER, De Ridder D, Prichep L (2010) Group Independent Component Analysis of Resting-State EEG in Large Normative Samples International Journal of Psychophysiology 78, 89-99.

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• SciCrunch
• 13 years ago - by Anonymous

Project to develop software tools and provide shared image validation databases for rigorous testing of non-rigid image registration algorithms. NIREP will extend the scope of prior validation projects by developing evaluation criteria and metrics using large image populations, using richly annotated image databases, using computer simulated data, and increasing the number and types of evaluation criteria. The goal of this project is to establish, maintain, and endorse a standardized set of relevant benchmarks and metrics for performance evaluation of nonrigid image registration algorithms. Furthermore, these standards will be incorporated into an exportable computer program to automatically evaluate the registration accuracy of nonrigid image registration algorithms.

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• SciCrunch
• 13 years ago - by Anonymous

Software library for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code.

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• SciCrunch
• 13 years ago - by Anonymous

Software package for modeling Near-Infrared light transport in tissue and image reconstruction. This includes: Standard single wavelength absorption and reduced scatter, Multi-wavelength spectrally constrained models and Fluorescence models.

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• SciCrunch
• 13 years ago - by Anonymous

A package for writing fMRI analysis pipelines and interfacing with external analysis packages (SPM, FSL, AFNI). Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface. Nipype, an open-source, community-developed initiative under the umbrella of Nipy, is a Python project that solves these issues by providing a uniform interface to existing neuroimaging software and by facilitating interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., SPM, FSL), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.

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• SciCrunch
• 13 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 13, 2026. Bookmarklet suitable for all main browsers, that, when invoked, highlights concepts of scientific significance in text and offers links to many open information resources about any concept highlighted.

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• SciCrunch
• 13 years ago - by Anonymous

Python module to parse mzML data in Python based on cElementTree. It is an extension to Python that offers (i) an easy access to mass spectrometry (MS) data that allows the rapid development of tools, (ii) a very fast parser for mzML data and (iii) a set of functions to compare or handle spectra., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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• SciCrunch
• 12 years ago - by Anonymous

Software toolbox that includes reconstruction tools for emission and transmission imaging modalities, including Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), cone-beam X-Ray CT and parallel-beam X-Ray CT. At the core of NiftyRec are efficient, GPU accelerated, projection, back-projection and iterative reconstruction algorithms. The easy to use Matlab and Python interfaces of NiftyRec enable fast prototyping and development of reconstruction algorithms. NiftyRec includes standard iterative reconstruction algorithms such as Maximum Likelihood Expectation Maximisation (MLEM), Ordered Subsets Expectation Maximisation (OSEM) and One Step Late Maximum A Posteriori Expectation Maximisation (OSL-MAPEM), for multiple imaging modalities.

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• SciCrunch
• 13 years ago - by Anonymous

A Python package for reading and writing a variety of medical and neuroimaging file formats. This includes: ANALYZE (plain, SPM99, SPM2), NIfTI1, as well as MINC. NiBabel is the successor of PyNIfTI.

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• SciCrunch
• 13 years ago - by Anonymous

Software library of modules and pipelines for fMRI processing with Octave or Matlab(r) that can run in parallel either locally or in a supercomputing environment. Linux OS and MINC file format are supported. NIAK currently includes a preprocessing, a region growing and a connectome pipelines. NIAK features powerful pipeline management capabilities, including parallel computing, generation of detailed logs and automatic handling of pipeline failures or updates.

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• SciCrunch
• 13 years ago - by Anonymous

Non-rigid registration / distortion correction tools for enhanced functional localization through the registration of EPI fMRI to high-resolution anatomical MRI.

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• SciCrunch
• 13 years ago - by Anonymous

Software repository of custom extensions to the GForge collaborative environment.

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• SciCrunch
• 13 years ago - by Anonymous