Perelman School of Medicine at the University of Pennsylvania

Section for Biomedical Image Analysis (SBIA)

ODVBA: Optimally-Discriminative Voxel-based Analysis


This software package implements ODVBA [1-2], which is used to determine the optimal spatially adaptive smoothing of images, followed by applying a voxel-based group analysis.

Voxel-based Analysis and Statistical Parametric Mapping (VBA-SPM) of imaging data have offered the potential to analyze structural and functional data in great spatial detail, without the need to define a priori regions of interest (ROIs) and assumptions. Gaussian smoothing of images is an important step in VBA-SPM; it accounts for registration errors and integrates imaging signals from a region around each voxel being analyzed. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically, non-optimally, and lacks spatial adaptivity to the shape and spatial extent of the region of interest.

ODVBA provides a mathematically rigorous framework for determining the optimal spatial smoothing of structural and functional images, prior to applying voxel-based group analysis. In order to determine the optimal smoothing kernel, in the first stage of ODVBA (shown in Figure 1.) a local discriminative analysis, restricted by appropriate nonnegativity constraints, is applied to a spatial neighborhood around each voxel, aiming to find the direction best highlights the difference between two groups in that neighborhood. In the second stage of ODVBA, since each voxel belongs to a large number of such neighborhoods, each centered on one of its neighboring voxels, the group difference at each voxel is determined by a composition of all these optimal smoothing directions. In the final stage of ODVBA, permutation tests are used to obtain the statistical significance of the resulting Optimally-Discriminative VBM (ODVBA) maps.

[1] Zhang, T., Davatzikos, C. (2011). ODVBA: optimally-discriminative voxel-based analysis. IEEE Transactions on Medical Imaging, 30(8), 1441-1454.

[2] Zhang, T., Davatzikos, C. (2013). Optimally-Discriminative Voxel-Based Morphometry significantly increases the ability to detect group differences in schizophrenia, mild cognitive impairment, and Alzheimer's disease. Neuroimage, 79, 94-110.

 

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Software License

ODVBA software is freely available under a BSD-style open source license that is compatible with the Open Source Definition by The Open Source Initiative and contains no restrictions on use of the software. The full license text is included with the distribution package and available online.

To download please visit our ODVBA NITRC page.

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Installation

Introduction

This software package implements ODVBA [1], which is used to determine the optimal spatially adaptive smoothing of images, followed by applying a voxel-based group analysis.

Voxel-based Analysis and Statistical Parametric Mapping (VBA-SPM) [2] of imaging data have offered the potential to analyze structural and functional data in great spatial detail, without the need to define a priori regions of interest (ROIs) and assumptions. Gaussian smoothing of images is an important step in VBA-SPM; it accounts for registration errors and integrates imaging signals from a region around each voxel being analyzed. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically, non-optimally, and lacks spatial adaptivity to the shape and spatial extent of the region of interest.

ODVBA provides a mathematically rigorous framework for determining the optimal spatial smoothing of structural and functional images, prior to applying voxel-based group analysis. In order to determine the optimal smoothing kernel, a local discriminative analysis, restricted by appropriate nonnegativity constraints, is applied to a spatial neighborhood around each voxel, aiming to find the direction best highlights the difference between two groups in that neighborhood. Since each voxel belongs to a large number of such neighborhoods, each centered on one of its neighboring voxels, the group difference at each voxel is determined by a composition of all these optimal smoothing directions. Permutation tests are used to obtain the statistical significance of the resulting Optimally-Discriminative VBM (ODVBA) maps.

Build Dependencies

The following software has to be installed (if not optional).

  • Linux OS
  • CMake 2.8.12+
  • CXX compiler which supports multi threading (GCC 4.7+)
  • ATLAS 3.8.3+

Web Site: http://math-atlas.sourceforge.net/
Download: https://sourceforge.net/projects/math-atlas/files/Stable/
Ubuntu: libatlas-dev

  • Boost 1.33+

Web Site: http://www.boost.org/
Download: http://www.boost.org/users/download/
Ubuntu: libboost-dev

  • boost-numeric-bindings 20081116+ (already provided in the ATLAS/ directory - see ATLAS/boost-numeric-bindings/LICENSE_1_0.txt for details)

Web Site: http://mathema.tician.de/software/boost-numeric-bindings
Download: http://mathema.tician.de/dl/software/boost-numeric-bindings

In particular, the ATLAS bindings are used by this software.

  • nifticlib 1.1+

Web Site: http://niftilib.sourceforge.net/
Download: http://sourceforge.net/projects/niftilib/files/nifticlib/
Ubuntu: libnifti1-dev or nifticlib

An example installation chain (for Ubuntu 14.04) is given below:

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install build-essential
sudo apt-get install g++-4.9 cmake # check version using "gcc --version", if mismatch then change the symbolic link /usr/bin/g++ to a target of /usr/bin/g++-4.9
sudo apt-get install libblas-dev libatlas-dev libatlas-base-dev libatlas3-base liblapacke-dev libboost-all-dev libnifti-dev libnifti2
sudo apt-get install doxygen

Note for RedHat 7+ distributions (this definitely applies to CentOS 7 and Fedora 22):

The Atlas library has started building a single "master" .so file for Fedora (reference - https://www.centos.org/forums/viewtopic.php?f=47&t=48723). To mitigate the FindLapack issue with CMake, create soft links for libatlas.so, libcblas.so and libf77blas.so with /usr/lib64/atlas/libsatlas.so under /usr/lib64/ or to a directory where you have write access and append it to CMAKE_LIBRARY_PATH.

Build ODVBA

The common steps to build, test, and install software based on CMake, including this software, are as follows:

  1. Extract source files.
  2. Create build directory and change to it.
  3. Run CMake to configure the build tree.
  4. Build the software using selected build tool.
  5. Run the unit tests (optional but recommended).
  6. Install the built files (optional).

On Unix-like systems with GNU Make as build tool, these build steps can be summarized by the following sequence of commands executed in a shell, where $package and $version are shell variables which represent the name of this package and the obtained version of the software.

tar xzf $package-$version-source.tar.gz
mkdir $package-$version-build cd $package-$version-build
cmake ../$package-$version-source
make
make test     #optional but recommended
make install  #optional

Please refer to this guide first if you are uncertain about above steps or have problems to build, test, or install the software on your system. If this guide does not help you resolve the issue, please contact us at sbia-software at uphs.upenn.edu.

In case of configuration errors, please attach the following file: $build_directory/CMakeCache.txt.

In case of compilation errors, please attach the output of the following command in addition to the previous file:

make >& make.log

In case of failing tests, please attach the output of the following command to your email along of the previous two files:

ctest -V >& test.log

In the following, only package-specific CMake settings available to configure the build and installation of this software are documented.

CMake Configuration Variables

ATLAS_CBLAS_INCLUDE_DIR     CBlas header location
ATLAS_CLAPACK_INCLUDE_DIR   Lapack header location
CBLAS_LIB                   CBlas install location
ITK_DIR                     ITK install location
CMAKE_INSTALL_PREFIX        Directory to do the installation
NiftiCLib_DIR               Installation directory of the nifticlib library, e.g., /usr/local.

Advanced CMake Options:

BLAS_atlas_LIBRARY          Path of the ATLAS library.
BLAS_cblas_LIBRARY          Path of the C BLAS library.
BLAS_f77blas_LIBRARY        Path of the Fortran BLAS library.
Boost_INCLUDE_DIR           Include directory of the Boost library, e.g., /usr/include.
Boost_LIBRARY_DIRS          Directory where the Boost libraries are installed, e.g., /usr/lib.

Usage

Please follow prompts in command line

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Changes

Version 3.0.0 (August, 2015)
  • utilized MP to improve the software.
Version 2.0.0 (February, 2012)
  • utilized MPI for a parallel execution using multiple slots available on a cluster.
Version 1.0.0 (December, 2010)
  • First public release of the ODVBA software.

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People

Advisor

Software Authors

  • Tianhao Zhang (Primary developer)
    • Developed the algorithm, implemented the software.
  • Andreas Schuh
    • Revised the software and utilized MPI for a parallel execution using multiple slots available on a cluster.
  • Sarthak Pati
    • Refined the software by enabling use on local non-HPC machines using OpenMP, improved command line parsing, better documentation generation and improved comments.

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Publications

Please cite [TMI2011], [NeuroImage2013] when you used this version of ODVBA in your research:

Methodology
  • [TMI2011] Zhang, T., Davatzikos, C. (2011). ODVBA: optimally-discriminative voxel-based analysis. IEEE Transactions on Medical Imaging, 30(8), 1441-1454.
  • Zhang, T., & Davatzikos, C. (2010). Optimally-discriminative voxel-based analysis. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2010 (pp. 257-265). Springer Berlin Heidelberg.
  • Zhang, T., Satterthwaite, T. D., Davatzikos, C. (2013). ODVBA-C: Optimally-Discriminative Voxel-Based Analysis of Continuous Variables. In Pattern Recognition in Neuroimaging (PRNI), 2013 International Workshop on(pp. 161-164). IEEE.
  • Zhang, T., Satterthwaite, T. D., Elliott, M., Gur, R. C., Gur, R. E., Davatzikos, C. (2012). Multivariate fMRI analysis using optimally-discriminative voxel-based analysis. In Pattern Recognition in NeuroImaging (PRNI), 2012 International Workshop on (pp. 33-36). IEEE.
Validation
  • [NeuroImage2013] Zhang, T., Davatzikos, C. (2013). Optimally-Discriminative Voxel-Based Morphometry significantly increases the ability to detect group differences in schizophrenia, mild cognitive impairment, and Alzheimer's disease. Neuroimage, 79, 94-110.
Applications
  • Zhang, T., Koutsouleris, N., Meisenzahl, E., Davatzikos, C. (2015). Heterogeneity of Structural Brain Changes in Subtypes of Schizophrenia Revealed Using Magnetic Resonance Imaging Pattern Analysis. Schizophrenia bulletin, 41(1), 74-84.
  • Erus, G., Battapady, H., Zhang, T., Lovato, J., Miller, M. E., Williamson, J. D., Launer, L., Bryan, R., Davatzikos, C. (2015). Spatial Patterns of Structural Brain Changes in Type 2 Diabetic Patients and Their Longitudinal Progression With Intensive Control of Blood Glucose. Diabetes care, 38(1), 97-104.
  • Steinberg, S., Zhang, T., Tournier, J., Jeurissen, B., Leopold, N., Liang, T., Davatzikos, D. (2015) Optimally-Discriminative Voxel-Based Analysis and High Angular Resolution Diffusion-Weighted Imaging Reveals Connectivity Abnormalities in Early Stage Lewy Body Spectrum Disease. Annual Meeting of the American Society of Neuroradiology (ASNR).
  • Da, X., Toledo, J. B., Zee, J., Wolk, D. A., Xie, S. X., Ou, Y., Shackletta, A., Parmpia, P., Shawb, L., Trojanowski, J.Q., Davatzikos, C. (2014). Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. NeuroImage: Clinical, 4, 164-173.
  • Zanetti, M., Zhang, T., Machado-Vieira, R., Serpa, M., Doshi, J., Chaim, T., Sousa, R., Gattaz, W., Davatzikos, C., Busatto, G. (2014) Neuroanatomical Differences between Bipolar I and II Disorders, Annual Meeting of the Society of Biological Psychiatry (SOBP).
  • Chaim, T., Zhang, T., Zanetti, M., da Silva, M., Louzã, M., Doshi, J., Serpa, H., Duran, F., Caetano, S., Davatzikos, C., Busatto, G. (2014). Multimodal Magnetic Resonance Imaging Study of Treatment-Naïve Adults with Attention-Deficit/Hyperactivity Disorder. PloS one,9(10), e110199.
  • Zhang T, Casanova, R., Resnick, S., Espeland, M., Davatzikos, C. (2014). Effects of Hormone Therapy on Brain Volumes Changes of Postmenopausal Women Revealed by Optimally-Discriminative Voxel-Based Morphometry, Annual Women's Health Imitative (WHI) Investigator Meeting.

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