Package: afni Version: 0.20091204~dfsg.1-1~maverick.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 27116 Depends: afni-common (= 0.20091204~dfsg.1-1~maverick.nd1), tcsh, gifsicle, libjpeg-progs, freeglut3, lesstif2 (>= 1:0.94.4), libc6 (>= 2.11), libexpat1 (>= 1.95.8), libf2c2, libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libglw1-mesa | libglw1, libgomp1 (>= 4.2.1), libgsl0ldbl (>= 1.9), libgts-0.7-5 (>= 0.7.6), libice6 (>= 1:1.0.0), libnetcdf4, libnifti1 (>> 1.1.0-2), libsm6, libvolpack1, libx11-6 (>= 0), libxext6 (>= 0), libxi6, libxmu6, libxt6, zlib1g (>= 1:1.1.4) Recommends: nifti-bin, bzip2, ffmpeg Homepage: http://afni.nimh.nih.gov Priority: extra Section: science Filename: pool/main/a/afni/afni_0.20091204~dfsg.1-1~maverick.nd1_amd64.deb Size: 10718856 SHA256: cb984f78c088ee1691f5c55fdf79e4d4810c107c5857d1c243b49ca8c7071a75 SHA1: 4af6ad94ffb3b3aecd31abefcddf4098841c96c5 MD5sum: 96b32a692e03b33c5b992caca5154f01 Description: toolkit for analyzing and visualizing functional MRI data AFNI is an environment for processing and displaying functional MRI data. It provides a complete analysis toolchain, including 3D cortical surface models, and mapping of volumetric data (SUMA). . In addition to its own format AFNI understands the NIfTI format and is therefore easily usable in combination with FSL and Freesurfer. Package: afni-common Source: afni Version: 0.20091204~dfsg.1-1~maverick.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 5312 Depends: python, tcsh Homepage: http://afni.nimh.nih.gov Priority: extra Section: science Filename: pool/main/a/afni/afni-common_0.20091204~dfsg.1-1~maverick.nd1_all.deb Size: 3059780 SHA256: 1d0f277216732bbd00cbe4b5689b4d979a5e41f6bc859389986dec37caaa820f SHA1: a190d139d3aa5fb1a0c02e50559b947dafb61f23 MD5sum: 7f1b539f0a9aa39cb1a4f01cfdb0c4e2 Description: miscellaneous scripts and data files for AFNI This package provides the required architecture independent parts of AFNI. Package: afni-dev Source: afni Version: 0.20091204~dfsg.1-1~maverick.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 14744 Homepage: http://afni.nimh.nih.gov Priority: extra Section: science Filename: pool/main/a/afni/afni-dev_0.20091204~dfsg.1-1~maverick.nd1_amd64.deb Size: 3762678 SHA256: 1986ad5c37faab74adcf79a903b7057b412147b041f3cc81391e1f036e22d4cb SHA1: 20a47df8c2ec82c3d2190bc53680225c24e9b308 MD5sum: e8bbb681d003fa65d2edb11621e1cb1f Description: header and static libraries for AFNI plugin development AFNI is an environment for processing and displaying functional MRI data. It provides a complete analysis toolchain, including 3D cortical surface models, and mapping of volumetric data (SUMA). . This packages provides the necessary libraries and header files for AFNI plugin development. Package: ants Version: 1.9+svn532-4~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 39164 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libinsighttoolkit3.18, libstdc++6 (>= 4.4.0) Suggests: fsl, gridengine-client Homepage: http://www.picsl.upenn.edu/ANTS/ Priority: extra Section: science Filename: pool/main/a/ants/ants_1.9+svn532-4~maverick.nd1_amd64.deb Size: 11582794 SHA256: 4ee7d22095b46eb667dcaabc179c8471daf5c69fa421e4a8ec75d568d7457e5f SHA1: 5e050077a9a211a57b70a6af35237a72b3686e40 MD5sum: 77a12ef20bc37f427f0b72e2d596efd9 Description: advanced normalization tools for brain and image analysis Advanced Normalization Tools (ANTS) is an ITK-based suite of normalization, segmentation and template-building tools for quantitative morphometric analysis. Many of the ANTS registration tools are diffeomorphic, but deformation (elastic and BSpline) transformations are available. Unique components of ANTS include multivariate similarity metrics, landmark guidance, the ability to use label images to guide the mapping and both greedy and space-time optimal implementations of diffeomorphisms. The symmetric normalization (SyN) strategy is a part of the ANTS toolkit as is directly manipulated free form deformation (DMFFD). Package: caret-data Version: 5.6~dfsg.1-1 Architecture: all Maintainer: Michael Hanke Installed-Size: 236780 Homepage: http://brainmap.wustl.edu/caret Priority: optional Section: science Filename: pool/main/c/caret-data/caret-data_5.6~dfsg.1-1_all.deb Size: 175205418 SHA256: 329a14cfd5547064496d4f6909db62578412857ad7d5f73e129335481f550b47 SHA1: 7d6cbdd77b04f258327d2bc9fcc4b0494fcf71bc MD5sum: e5f41497554088124975dfc27ba6378b Description: common data files for Caret This package provides online help, tutorials and atlas datasets for Caret. Package: dicomnifti Version: 2.28.14-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 524 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti1 (>> 1.1.0-2), libstdc++6 (>= 4.4.0) Homepage: http://cbi.nyu.edu/software/dinifti.php Priority: optional Section: science Filename: pool/main/d/dicomnifti/dicomnifti_2.28.14-2~maverick.nd1_amd64.deb Size: 158698 SHA256: 9de31342677564bbd2c46db4c101360ba62dc0859527112e462a106307bcab65 SHA1: bfd0234348776fde2a5f160fe8ee5dd7b30d1f84 MD5sum: 8bd2a7a3900e37490b2c03a1ea9b3083 Description: converts DICOM files into the NIfTI format The dinifti program converts MRI images stored in DICOM format to NIfTI format. The NIfTI format is thought to be the new standard image format for medical imaging and can be used with for example with FSL, AFNI, SPM, Caret or Freesurfer. . dinifti converts single files, but also supports fully automatic batch conversions of complete dicomdirs. Additionally, converted NIfTI files can be properly named, using image series information from the DICOM files. Package: fslview Version: 3.1.8+4.1.6-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 4168 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti1 (>> 1.1.0-2), libqt3-mt (>= 3:3.3.8-b), libqwt4c2, libstdc++6 (>= 4.4.0), libvtk5.4, libvtk5.4-qt3 Recommends: fslview-doc Suggests: fsl-atlases Conflicts: fsl-fslview Replaces: fsl-fslview Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: science Filename: pool/main/f/fslview/fslview_3.1.8+4.1.6-2~maverick.nd1_amd64.deb Size: 1524246 SHA256: f91230348eba03a775059d469f0ca881861af545b7084d3f181e60853701a689 SHA1: 231e4dcf8fa221603446bb875a45f65bd1fd4f60 MD5sum: 4d71f47153aad52bd6293eb15a5bd555 Description: viewer for (f)MRI and DTI data This package provides a viewer for 3d and 4d MRI data as well as DTI images. FSLView is able to display ANALYZE and NIFTI files. The viewer supports multiple 2d viewing modes (orthogonal, lightbox or single slices), but also 3d volume rendering. Additionally FSLView is able to visualize timeseries and can overlay metrical and stereotaxic atlas data. . FSLView is part of FSL. Package: fslview-doc Source: fslview Version: 3.1.8+4.1.6-2~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3216 Depends: qt3-assistant Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_3.1.8+4.1.6-2~maverick.nd1_all.deb Size: 2378930 SHA256: 0619f0704097efa80d21adb5e852e5ea97a00b84eee6a6c90cb5825b80b6b68e SHA1: e4dbfb09e83e7593d3036b3abd9af6ec4d93fa2c MD5sum: 963b9b6b17837cf2aced5b54ac1b22ca Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gifti-bin Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 128 Depends: libc6 (>= 2.3.4), libexpat1 (>= 1.95.8), libgiftiio0, libnifti1 (>> 1.1.0-2), zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: utils Filename: pool/main/g/gifticlib/gifti-bin_1.0.9-1~maverick.nd1_amd64.deb Size: 29248 SHA256: 0e3f17a174bdf3fbc004c4c8b3fea6b1de90d5e745355e9907eecccc3c45aa79 SHA1: 5a1f62520a7089fc0f7779a6aad302eb8e8623f9 MD5sum: 371cc7de70de99fd19a3b4aef2fb0940 Description: tools shipped with the GIFTI library GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the tools that are shipped with the library (gifti_tool and gifti_test). Package: libgiftiio-dev Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 256 Depends: libgiftiio0 (= 1.0.9-1~maverick.nd1) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libdevel Filename: pool/main/g/gifticlib/libgiftiio-dev_1.0.9-1~maverick.nd1_amd64.deb Size: 65020 SHA256: a5e63ac7d35ef813eed1d81d98075530acbe2f058a945bd212566b4d468bcfc2 SHA1: 47c3332fe15460f79c90c5ca42a2059c93e8e2e9 MD5sum: 6861952fd85b1efebfc95d6a120d9efb Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the header files and static library. Package: libgiftiio0 Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 180 Depends: libc6 (>= 2.4), libexpat1 (>= 1.95.8), libnifti1 (>> 1.1.0-2), zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libs Filename: pool/main/g/gifticlib/libgiftiio0_1.0.9-1~maverick.nd1_amd64.deb Size: 57604 SHA256: e4754cde9e0089b5774f5bbd3739171f5a68a0115cd50fd84a8c9aa1035fbfcb SHA1: 11983a5c86ae4fab0008551c4f17a5cc0d7ccb1e MD5sum: e7eacce11ea15f9b8b97ea6a242a36c0 Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package contains the shared library. Package: libnifti-dev Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 624 Depends: libnifti2 (= 2.0.0-1~maverick.nd1) Conflicts: libfslio-dev, libnifti0-dev, libnifti1-dev, libniftiio-dev Replaces: libnifti1-dev Homepage: http://niftilib.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/n/nifticlib/libnifti-dev_2.0.0-1~maverick.nd1_amd64.deb Size: 171296 SHA256: a10c25e55440328b1c4ac917735541ee5640176d41cc940c8c6564718d728bf6 SHA1: f0df2a274d65f9c6725a2769503bbbfbb2848624 MD5sum: fc52429aa09b32657fe7d09d3bc8ed11 Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the header files and static libraries of libniftiio, znzlib and libnifticdf. Package: libnifti-doc Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1896 Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-1~maverick.nd1_all.deb Size: 245398 SHA256: ee170c249d6406abcf28bf82179b9327507e20bc341314191e9339ddbdf725f5 SHA1: 255d82f6d5115000ec47d28dd096cea340261cfc MD5sum: 6293a28d6b4ab6f3e08ee6f766fdd757 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libnifti2 Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 336 Depends: libc6 (>= 2.7), zlib1g (>= 1:1.1.4) Homepage: http://niftilib.sourceforge.net Priority: optional Section: libs Filename: pool/main/n/nifticlib/libnifti2_2.0.0-1~maverick.nd1_amd64.deb Size: 123028 SHA256: 6dc383c542f510dfe43d769101d51d117497011604bbe6dedd2dc2a2ea5e59e1 SHA1: 7e51b50bb5c2e73003a779dcb5818de3ded222d6 MD5sum: c00979d1767d6b98c166fea6528485b8 Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package contains the shared library of the low-level IO library niftiio, low-level IO library znzlib and the nifticdf shared library that provides functions to compute cumulative distributions and their inverses. Package: libopenmeeg-dev Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 276 Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: libdevel Filename: pool/main/o/openmeeg/libopenmeeg-dev_2.0.0.dfsg-2~maverick.nd1_amd64.deb Size: 43850 SHA256: c65760742eb06e6a12fd758b5db143564e27dd25b2eff3f98854e035566991f0 SHA1: 0175dc1e5a5701a201119d7901e256882549e439 MD5sum: 6c45ad3f49003ead9939df00c72abada Description: library for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides static libraries and header files. Package: libopenmeeg1 Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 960 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmatio0, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/libopenmeeg1_2.0.0.dfsg-2~maverick.nd1_amd64.deb Size: 252568 SHA256: 87d6e3f667c493b8615eadbf411b841fde7c47364991ee9256b7e13479a90175 SHA1: 13b3b2307374b46e0421791084bae02fed18238e MD5sum: 30c910d0f9bda2c9d2a4b47cc5812920 Description: library for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. Package: lipsia Version: 1.6.0-4~maverick.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 3940 Depends: libc6 (>= 2.7), libdcmtk1 (>= 3.5.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libice6 (>= 1:1.0.0), libnifti1 (>> 1.1.0-2), libqt3-mt (>= 3:3.3.8-b), libsm6, libstdc++6 (>= 4.4.0), libvia0, libx11-6, libxext6, zlib1g (>= 1:1.1.4), via-bin Recommends: dcmtk, lipsia-doc Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: science Filename: pool/main/l/lipsia/lipsia_1.6.0-4~maverick.nd1_amd64.deb Size: 1350080 SHA256: d6e5814498a44d6f9aa68c26dfe86e5a24398c6727efb40240ab872fad14e217 SHA1: 2887d9c808cbc0861958043d8dea9eee1ccc9c3a MD5sum: 109c0c9b499315d58dad5395c035eb9d Description: analysis suite for MRI and fMRI data Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This is a software package for the data processing and evaluation of functional magnetic resonance images. The analysis of fMRI data comprises various aspects including filtering, spatial transformation, statistical evaluation as well as segmentation and visualization. All these aspects are covered by LIPSIA. For the statistical evaluation, a number of well established and peer-reviewed algorithms were implemented in LIPSIA that allow an efficient and user-friendly processing of fMRI data sets. As the amount of data that must be handled is enormous, an important aspect in the development of LIPSIA was the efficiency of the software implementation. . LIPSIA operates exclusively on data in the VISTA data format. However, the package contains converters for medical image data in iBruker, ANALYZE and NIfTI format -- converting VISTA images into NIfTI files is also supported. Package: lipsia-doc Source: lipsia Version: 1.6.0-4~maverick.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 7004 Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/l/lipsia/lipsia-doc_1.6.0-4~maverick.nd1_all.deb Size: 5539260 SHA256: 82338bf6b7642c81e3d25791b543695be0cfdce051003635d2635dd36680651e SHA1: 4605c3847915a816361fb32d35551861a05db84d MD5sum: 405aaaf745427fdfe430d25bb7e8b238 Description: documentation for LIPSIA Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This package provides the LIPSIA documentation in HTML format. Package: mni-colin27-minc Source: mni-colin27 Version: 1.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 12064 Homepage: http://packages.bic.mni.mcgill.ca/tgz/ Priority: extra Section: science Filename: pool/main/m/mni-colin27/mni-colin27-minc_1.1-1_all.deb Size: 12274320 SHA256: 53c6b97ed6d4182fd4da2502377bc1f32de4a816952eab6037e8791d85828fd0 SHA1: 467a57c00040530e387ed1183815d8591b32b2e6 MD5sum: 1ea73688b743b36778bee148076ebd4d Description: Talairach stereotaxic space template This template MRI volume was created from 27 T1-weighted MRI scans of a single individual that have been transformed into the Talairach stereotaxic space. The anatomical image is complemented by a brain and a head mask. All images are in 1x1x1 mm resolution. . This package provides the template in MINC format. Package: mni-colin27-nifti Source: mni-colin27 Version: 1.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 11748 Homepage: http://packages.bic.mni.mcgill.ca/tgz/ Priority: extra Section: science Filename: pool/main/m/mni-colin27/mni-colin27-nifti_1.1-1_all.deb Size: 11952134 SHA256: 73bbe01f4f42fe966fc9308b46cedca16cda981da0492975afaaa9f731bf5581 SHA1: e1fa1c293312ea699493d2158efe70dd6649840e MD5sum: b9228cbbbd551e91de94f28d8f4da2ea Description: Talairach stereotaxic space template This template MRI volume was created from 27 T1-weighted MRI scans of a single individual that have been transformed into the Talairach stereotaxic space. The anatomical image is complemented by a brain and a head mask. All images are in 1x1x1 mm resolution. . This package provides the template in NIfTI format. Package: mni-icbm152-nlin-2009a Source: mni-icbm152-nlin Version: 0.20090623.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 120332 Homepage: http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 Priority: extra Section: science Filename: pool/main/m/mni-icbm152-nlin/mni-icbm152-nlin-2009a_0.20090623.1-1_all.deb Size: 122770998 SHA256: d1cab63c136b6898ce133ae0ac5309b77ac11774317e4710a3c567689bc64435 SHA1: 3c310421152cc482d8d99dca51d3c0145dab6553 MD5sum: dc79aa787955aa03e0d60c98cc5c3da3 Description: MNI stereotaxic space human brain template This is an unbiased standard magnetic resonance imaging template volume for the normal human population. It has been created by the Montreal Neurological Institute (MNI) using anatomical data from the International Consortium for Brain Mapping (ICBM). . The package provides a 1x1x1 mm resolution template (hemissphere-symetric and asymetric non-linearily co-registered versions), including T1w, T2w, PDw modalities, T2 relaxometry, and tissue probability maps. In addition, it contains a lobe atlas, and masks for brain, eyes and face. . The template is similar to the one in the mni-icbm152-nlin-2009c package. However, the sampling of the ICBM data is different and here intensity inhomogeneity correction was performed by N3 version 1.10.1, leading to different tissue probability maps. Package: mni-icbm152-nlin-2009b Source: mni-icbm152-nlin Version: 0.20090623.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 722392 Homepage: http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 Priority: extra Section: science Filename: pool/main/m/mni-icbm152-nlin/mni-icbm152-nlin-2009b_0.20090623.1-1_all.deb Size: 739142896 SHA256: d00b100f1a65e1e3909adddd1f9a9d3fb4c717ced6f425089a276b64780cd13d SHA1: 5d4b4d340be14ed062a5e96bc43af33d2fd9ec40 MD5sum: 0e906ff84b016a127d7cc1ecf03dfbef Description: MNI stereotaxic space human brain template This is an unbiased standard magnetic resonance imaging template volume for the normal human population. It has been created by the Montreal Neurological Institute (MNI) using anatomical data from the International Consortium for Brain Mapping (ICBM). . The package provides a 0.5x0.5x0.5 mm resolution template (hemissphere-symetric and asymetric non-linearily co-registered versions), including T1w, T2w, PDw modalities. Package: mni-icbm152-nlin-2009c Source: mni-icbm152-nlin Version: 0.20090623.1-1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 113888 Homepage: http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 Priority: extra Section: science Filename: pool/main/m/mni-icbm152-nlin/mni-icbm152-nlin-2009c_0.20090623.1-1_all.deb Size: 116182926 SHA256: 08c25ff6564fd6860d96bf32a182ce5922aaadd2b584850d82762aa9bb4fefd5 SHA1: f515a99b9edc7dfabfb058a21acbab12a2031c1a MD5sum: c1ec21de0bd62ab68c1d2a84655d4891 Description: MNI stereotaxic space human brain template This is an unbiased standard magnetic resonance imaging template volume for the normal human population. It has been created by the Montreal Neurological Institute (MNI) using anatomical data from the International Consortium for Brain Mapping (ICBM). . The package provides a 1x1x1 mm resolution template (hemissphere-symetric and asymetric non-linearily co-registered versions), including T1w, T2w, PDw modalities, and tissue probability maps. In addition, it contains a lobe atlas, and masks for brain, eyes and face. . The template is similar to the one in the mni-icbm152-nlin-2009a package. However, the sampling of the ICBM data is different and here intensity inhomogeneity correction was performed by N3 version 1.11, leading to different tissue probability maps. Package: nifti-bin Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 200 Depends: libc6 (>= 2.7), libnifti2 Homepage: http://niftilib.sourceforge.net Priority: optional Section: utils Filename: pool/main/n/nifticlib/nifti-bin_2.0.0-1~maverick.nd1_amd64.deb Size: 62302 SHA256: e74b6f17efa9e03e859a450192fe35c0be66c33552d59123271c6704f169758a SHA1: b43f633e1de2af8b1463260231d9e49d819776ef MD5sum: 7947b46fb4d71466cde1e787e1b4f51a Description: tools shipped with the NIfTI library Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the tools that are shipped with the library (nifti_tool, nifti_stats and nifti1_test). Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/openmeeg-tools_2.0.0.dfsg-2~maverick.nd1_amd64.deb Size: 163230 SHA256: fad9d47731fc75238a1a6da647637e110faa81579ba4ea54e5cd1d1e2423a911 SHA1: 724af280bad2953af20b8db0f737b6dde84908dc MD5sum: cde12d4b4d0cd81fb8f41175c72a78df Description: tools for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides command line tools. Package: psychopy Version: 1.61.02.dfsg-2~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 2748 Depends: python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-imaging, python-serial, python-scipy, libavbin0 Suggests: python-pyepl Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.61.02.dfsg-2~maverick.nd1_all.deb Size: 1088438 SHA256: c9d5f9d8bbf345df256d43553cb091cc9a54d143175d37dfaad8cf43f3e37818 SHA1: 9d0180b0b25433933c3b678e1fde0651331721e7 MD5sum: 6df6b34113e031d1f84d9383266ff8ef Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features - High-level powerful scripting language (Python) - Simple syntax - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.6 Package: python-dicom Source: pydicom Version: 0.9.4.1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1804 Depends: python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.4.1-1~maverick.nd1_all.deb Size: 360124 SHA256: e11a5ca1d87ad166eefa550498743db961ae2727733b6f01d40d177c7d9c53dd SHA1: 3f767eb7480ef44c6c7a56a839f01e99f8cf849d MD5sum: 0adfc3a91a5b7419208ef94bb7a699a9 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-mlpy Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 424 Depends: python (>= 2.4), python-support (>= 0.90.0), python2.6, python-numpy, python-mlpy-lib (>= 2.2.0~dfsg1-1~maverick.nd1) Suggests: python-mvpa Provides: python2.6-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy_2.2.0~dfsg1-1~maverick.nd1_all.deb Size: 55840 SHA256: 8283e4ed0f79671d42b030290729eda5c540214d36125e1b06fd0b5b3c9a21c7 SHA1: 4bd22103a057fe6eecf855acf12e7e077d38fc19 MD5sum: b0171fc77f895c31946c6d7aed80768d Description: high-performance Python package for predictive modeling mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . mlpy includes: SVM (Support Vector Machine), KNN (K Nearest Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression, Penalized, Diagonal Linear Discriminant Analysis) for classification and feature weighting, I-RELIEF, DWT and FSSun for feature weighting, *RFE (Recursive Feature Elimination) and RFS (Recursive Forward Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated, Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time Warping), Hierarchical Clustering, k-medoids, Resampling Methods, Metric Functions, Canberra indicators. Python-Version: 2.6 Package: python-mlpy-doc Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1132 Depends: libjs-jquery Suggests: python-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: doc Filename: pool/main/m/mlpy/python-mlpy-doc_2.2.0~dfsg1-1~maverick.nd1_all.deb Size: 478862 SHA256: d4a1ca68b49b3b1fa7a35abdc1e263f00970d4a6cfde9fd5388508b5c3273ce9 SHA1: 3a203bde333a49868ea27467bb04fa9eb6870af6 MD5sum: da3c2ef1ca5363c2774440218a32fb73 Description: documention and examples for mlpy mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . This package provides user documentation for mlpy in various formats (HTML, PDF). Package: python-mlpy-lib Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 312 Depends: libc6 (>= 2.3.4), libgsl0ldbl (>= 1.9), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-mlpy-lib Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy-lib_2.2.0~dfsg1-1~maverick.nd1_amd64.deb Size: 70820 SHA256: bbfb06fd94be6d0d3288014ace6eece19424cb7b7c2fe7743673d8c6f9d9a568 SHA1: 01d9f84c07de1f9b4e987bf20d41c9df399364e9 MD5sum: 25a7200ed3517d83a23bc8e928a374c1 Description: low-level implementations and bindings for mlpy This is an add-on package for the mlpy providing compiled core functionality. Python-Version: 2.6 Package: python-mvpa Source: pymvpa Version: 0.4.5-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4144 Depends: python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.5-1~nd10.10+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.6-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.5-1~nd10.10+1_all.deb Size: 2156652 SHA256: 0d228449c2d9bda679310a534b862c74f86e0ee7a3659e067e77dc9559d1a363 SHA1: 3bc906f2b7eed67b9788f3adbf41e724430515eb MD5sum: 8020da8cb6a4df47fd81669bffe7ee2c Description: multivariate pattern analysis with Python Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. Python-Version: 2.6 Package: python-mvpa-doc Source: pymvpa Version: 0.4.5-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41004 Depends: libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.5-1~nd10.10+1_all.deb Size: 9012602 SHA256: 402a7262245b46a4a32abe12b7659382d1644c210cc199e713da6ac36ff16864 SHA1: 426b48e5d94d41837567eca8fd7957313c13eec9 MD5sum: 3ef659e7f06589db1fccabe36d7c9e6d Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa-lib Source: pymvpa Version: 0.4.5-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 184 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-mvpa-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa-lib_0.4.5-1~nd10.10+1_amd64.deb Size: 34792 SHA256: 9d0e277502dbe4dbcb5c91ebcf27e12574fe8c65af9554b712b099968b5b3e5a SHA1: 1aae51d858fe8fa46b36ecf454dd64d534c107bc MD5sum: 9d1a96c5343268a4bb3fed073c4a2b97 Description: low-level implementations and bindings for PyMVPA This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. Python-Version: 2.6 Package: python-networkx Version: 1.1-2~maverick.nd1 Architecture: all Maintainer: Debian Python Modules Team Installed-Size: 2628 Depends: python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-scipy, python-pygraphviz | python-pydot, python-pkg-resources, python-matplotlib, python-yaml Homepage: http://networkx.lanl.gov/ Priority: optional Section: python Filename: pool/main/p/python-networkx/python-networkx_1.1-2~maverick.nd1_all.deb Size: 679658 SHA256: 1b57041f1c6383b2c67aee7c5adced6e17c27769de5768b2a5625146b12aefed SHA1: 7dea67b649381618bd855eeea17892721e71ec39 MD5sum: ffbdff4561f896997795d265f14d2263 Description: tool to create, manipulate and study complex networks NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph we mean a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network we usually mean a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. Package: python-nifti Source: pynifti Version: 0.20100607.1-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1184 Depends: libc6 (>= 2.4), libnifti1 (>> 1.1.0-2), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, python-numpy, python-numpy-ext, libjs-jquery Provides: python2.6-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-1~maverick.nd1_amd64.deb Size: 284858 SHA256: d8e061875b3be7bfb75f72e38c359fc527ed1e4c24f097b5b436e7469a439435 SHA1: aef50ff2e8816ff31983a9b436d9809a9b520cb0 MD5sum: 922d8a157e55c3f2d56104ebf8697fd5 Description: Python interface to the NIfTI I/O libraries Using PyNIfTI one can easily read and write NIfTI and ANALYZE images from within Python. The NiftiImage class provides Python-style access to the full header information. Image data is made available via NumPy arrays. Python-Version: 2.6 Package: python-nipype Source: nipype Version: 0.3.2-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1716 Depends: python (>= 2.5), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits Recommends: python-nifti, ipython, python-nose, python-networkx Suggests: fsl, afni, lipsia, python-nipy Provides: python2.6-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.3.2-1~maverick.nd1_all.deb Size: 269190 SHA256: 34d1e732c2f6b613f9cb99cd2c3961a6283349484beef4a8265369f1961f10b7 SHA1: 811f18c612a1a546f438410fae8a234b9cbedefa MD5sum: aeec13b279567a2dca79648b3828f8e7 Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.3.2-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3224 Depends: libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.3.2-1~maverick.nd1_all.deb Size: 774716 SHA256: f64d2d2fefd479b9392f117205223ff24c117c26cdc3af6fe29b77466f650d15 SHA1: fd53363af0225c7bb44a539b22a13f9f009a9a13 MD5sum: 21bbda904e514b4c334c7e723347f666 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype documentation in various formats. Package: python-openmeeg Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-openmeeg Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: python Filename: pool/main/o/openmeeg/python-openmeeg_2.0.0.dfsg-2~maverick.nd1_amd64.deb Size: 157526 SHA256: 3a95190b1f9aa9128f1ff07a9927bf4285f605c029ff87b7f763905047efe436 SHA1: 303aa983399d068350fe8c0b289533ecfd0ee69e MD5sum: 59f8f5cd00801d0d43f458256051b35c Description: Python bindings for openmeeg library OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides Python bindings for OpenMEEG library. Python-Version: 2.6 Package: python-openpyxl Source: openpyxl Version: 1.1.0-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 448 Depends: python, python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.1.0-1~maverick.nd1_all.deb Size: 49184 SHA256: 5cdbef28c90ba9a004c40dcd1a7dbd46a47e8d071510627d43dba4a94aee6b57 SHA1: e7c9e1bcf4345c507210870199e01a8fcc514fdd MD5sum: e5783f04682ec139db7681ef9f707d32 Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pyepl Source: pyepl Version: 1.1.0-3~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1696 Depends: python (<< 2.7), python (>= 2.6), python-central (>= 0.6.11), python-pyepl-common (= 1.1.0-3~maverick.nd1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libasound2 (>> 1.0.22), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0, libsndfile1 (>= 1.0.20), libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.6-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0-3~maverick.nd1_amd64.deb Size: 381566 SHA256: 5c2db2b03d580c1c40aeb4768b772aae61ebab32a1564b46d58eab61efb2e4a1 SHA1: f900b6fb4e1cabbc59cf0d16db41753165d0422c MD5sum: 8f8968da5e76f1f8748211134d2e645a Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides PyEPL for supported versions of Python. Python-Version: 2.6 Package: python-pyepl-common Source: pyepl Version: 1.1.0-3~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 852 Depends: python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0-3~maverick.nd1_all.deb Size: 817816 SHA256: c48e4f818f9d5f08f12d59d6c1131b7f163f29c8801d3373889a7c20795d2d9f SHA1: 82228fe2202df811391f45df27ceaabcc7abca89 MD5sum: f4abbefb9d51c38b9370f48229577970 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images.