Package: afni Version: 0.20091204~dfsg.1-1~maverick.nd1 Architecture: i386 Maintainer: Michael Hanke Installed-Size: 25112 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_i386.deb Size: 9336880 SHA256: 3ab1c8ea9625245c7fd26bc376fc187382490d8260bd40b8c1ab5ca1f0463058 SHA1: 960dde0b195545370db56ed95a8f9a5642098364 MD5sum: f96725d33dd6276c3f41a155af0df92a 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: i386 Maintainer: Michael Hanke Installed-Size: 10816 Homepage: http://afni.nimh.nih.gov Priority: extra Section: science Filename: pool/main/a/afni/afni-dev_0.20091204~dfsg.1-1~maverick.nd1_i386.deb Size: 3362996 SHA256: 334098ec2af3d63d28bfac39bc7700ead517e9ba2bc6c6118c7447a1bd815ef4 SHA1: 9e48b682fa4828076f710b5a0eff59daa427f8fb MD5sum: 9ce49eae0c5a9ba0ff0a1c78b6302325 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: i386 Maintainer: NeuroDebian Team Installed-Size: 36280 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_i386.deb Size: 11264466 SHA256: 19ae2ae26e2dbdc77965cfd13127593a61c44a12fd380197406a593204412f29 SHA1: 98c6aac582676dc1116ffc15ccd0f3ba0191b567 MD5sum: 0e23be8015dc59288f9238bf14deb877 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: i386 Maintainer: NeuroDebian Team Installed-Size: 488 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_i386.deb Size: 147284 SHA256: 408383702dc93a854b0675506b660b4f769d97aa270a6115910ee62f5e63ec49 SHA1: 8787d9a7fbbe29c9ff679ceaf6bbf5ec871345cb MD5sum: 3f6038e143ce660149f1563c018a1cfb 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: i386 Maintainer: NeuroDebian Team Installed-Size: 3868 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_i386.deb Size: 1493370 SHA256: 731204fa6f615fe33b7de517619ce8fb38172ed0be35708aad8796d4bbd05837 SHA1: 24159fe931085d0b329f730ae37e89716583cf32 MD5sum: 1e85096a7518e4f12ea2c1856ef17639 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: i386 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_i386.deb Size: 28706 SHA256: ad377655a84bb88f5cc20c052cb55d7a7d274b4e88cdfccd76963fd084054207 SHA1: d8467b3f02d7abcea154d08cec72d8a8ea386569 MD5sum: 3762794dc8512d528b5f035b0d57eba2 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: i386 Maintainer: NeuroDebian Team Installed-Size: 208 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_i386.deb Size: 62362 SHA256: e7645b4762143dc3e4a6c6ecb6c88e72dc822a57921f504fb1e2ca8c9fb44698 SHA1: 6e3a1cab0bf1605f1d09636a81882c4908f9ce7e MD5sum: 5ce79f3b3df0df7d1d29590785b1a9da 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: i386 Maintainer: NeuroDebian Team Installed-Size: 176 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_i386.deb Size: 57236 SHA256: 44cf7e5acd2cb6d43f5bffbfc5e1110b5868b1a8e0ea110eed8b968574af5d49 SHA1: 34fc692fa9d906630488a5a3b8def1b36d5ba8c2 MD5sum: f14e2a0f09e5fba43c32c81d4596fb73 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: i386 Maintainer: NeuroDebian Team Installed-Size: 460 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_i386.deb Size: 151424 SHA256: 02dd63229792f2671cff56a1d0e0f670961084cd3ec39eeeaa0e438398ce3096 SHA1: b09d8a64ea93c72bd083452d2af14a696aeee2c1 MD5sum: 0e4089404ec474109398e654a5118c53 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: i386 Maintainer: NeuroDebian Team Installed-Size: 304 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_i386.deb Size: 107384 SHA256: 150bca33f426cc89796db8958d1b03730f80e4ae1d347039ba70ebfe2eabcd82 SHA1: a3a6f9736f7a0e218254e5cac55a3f2be26cdcf6 MD5sum: cea5d13d0539b1fdb36067594a636348 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: i386 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_i386.deb Size: 43856 SHA256: c92c21a2772e96d232289dadea0671887abf2b868ae988f209cd5b6c9eb574f1 SHA1: 4c7d482ca1198fd762853f13a387d0abd6a10bcd MD5sum: f51b11e44be747a9ba4f7c8d048907d2 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: i386 Maintainer: NeuroDebian Team Installed-Size: 816 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_i386.deb Size: 233088 SHA256: aaa12f77bef369fae972b97534252d484e564f16a03d1834e28c9da7c81249d1 SHA1: 31d24420ada6c4e1ab753fb7ceef7e2dede0f0fb MD5sum: 395f4db513ea6d5f2eafc4b001ed1920 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: i386 Maintainer: Michael Hanke Installed-Size: 3616 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_i386.deb Size: 1271960 SHA256: a05ad0462cbf1c0dcdc33fee836640ec263e390a3c25ee24e19381606e6f5b02 SHA1: 26976df4dd314ad7144d4d1922ceb49066dafde9 MD5sum: acab84d96e8d07cad4dbfc55f5c89c7b 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: i386 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_i386.deb Size: 59308 SHA256: 61bda2c52b962de9e33a75688f65ad0dd0df6c958007eafe8f5922e19e7163af SHA1: 71ff48ea63b49aa5cc07fad0e80347a6ed0d725d MD5sum: 3cb3c3ffaaa7a470ecfe6c83ef6ca79f 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: i386 Maintainer: NeuroDebian Team Installed-Size: 556 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_i386.deb Size: 152402 SHA256: 783356064d0921be805bb9c99ef62c6f48a210e4b39c6501a9fa3002eff59a64 SHA1: 5e22f941bd62323ab4d94da7db34036895e71df0 MD5sum: 1913d1041bc1eadff878a7839c546dd3 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: i386 Maintainer: NeuroDebian Team Installed-Size: 288 Depends: libc6 (>= 2.3.6-6~), 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_i386.deb Size: 61960 SHA256: 69fd245c477437e07fad88bfc8448283b7dac70035a9d4036cb72122d3355f27 SHA1: e1cf93b4d9c32c486d07079351bab5336b9d572c MD5sum: 76626ea954c78fb83a4782061996ff41 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-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: i386 Maintainer: NeuroDebian Team Installed-Size: 1132 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_i386.deb Size: 272608 SHA256: a63dbee756c7c65f5cb09b3793c2716a58da19ffd89c2812cffc93dfd37c93c7 SHA1: 2c28423dd5a9a4596cd7bc9ee0028f7536c2ad95 MD5sum: 7a117a3483d766faedee550e080d9ffc 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: i386 Maintainer: NeuroDebian Team Installed-Size: 560 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_i386.deb Size: 146574 SHA256: ba0240b2c518087ddee3eb74e28f819a5d9d27d616cf33bdf2f654faf9837a71 SHA1: ab0544329617edd43b16af2b4ace8c56a18eac30 MD5sum: d7ccd32f13a73b2938663c511cdbad2a 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: i386 Maintainer: NeuroDebian Team Installed-Size: 1584 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_i386.deb Size: 348724 SHA256: e492fe000c55e4d56cf178e68c68c458b42b21fbe092d83db7f6465b38ea4b8b SHA1: 02d7343f7b494907b0739c48bf3a73e600238180 MD5sum: 06ea149b83d3e816ef5ecbe05339dc96 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. Package: python-scikits-learn-lib Source: scikit-learn Version: 0.4-2~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 484 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), libsvm2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-scikits-learn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn-lib_0.4-2~maverick.nd1_i386.deb Size: 158188 SHA256: 287b8dd046569e565c490e3c8eade2d9626ded1364597a7cb4f5fcd290aefa54 SHA1: e9f05233f97c4d665d81e992f2691635e6596536 MD5sum: 3141b4d6264f42768e5137d8d0900581 Description: low-level implementations and bindings for scikits-learn This is an add-on package for python-scikits-learn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Python-Version: 2.6