Keyword(s): *Algorithms,
Animals,
Brain Mapping/*methods,
Human,
Image Processing,
Computer-Assisted/*methods,
Magnetic Resonance Imaging/*methods,
Programming Languages,
Reproducibility of Results,
Signal Processing,
Computer-Assisted,
Software/*standards/trends,
Software Validation,
Support,
Non-U.S. Gov't,
User-Computer Interface.
| The enormous scale and complexity of data sets in functional neuroimaging makes it crucial to have well-designed and flexible software for image processing, modeling, and statistical analysis. At present, researchers must choose between general purpose scientific computing environments (e.g., Splus and Matlab), and specialized human brain mapping packages that implement particular analysis strategies (e.g., AFNI, SPM, VoxBo, FSL or FIASCO). For the vast majority of users in Human Brain Mapping and Cognitive Neuroscience, general purpose computing environments provide an insufficient framework for a complex data-analysis regime. On the other hand, the operational particulars of more specialized neuroimaging analysis packages are difficult or impossible to modify and provide little transparency or flexibility to the user for approaches other than massively multiple comparisons based on inferential statistics derived from linear models. In order to address these problems, we have developed open-source software that allows a wide array of data analysis procedures. The RUMBA software includes programming tools that simplify the development of novel methods, and accommodates data in several standard image formats. A scripting interface, along with programming libraries, defines a number of useful analytic procedures, and provides an interface to data analysis procedures. The software also supports a graphical functional programming environment for implementing data analysis streams based on modular functional components. With these features, the RUMBA software provides researchers programmability, reusability, modular analysis tools, novel data analysis streams, and an analysis environment in which multiple approaches can be contrasted and compared. The RUMBA software retains the flexibility of general scientific computing environments while adding a framework in which both experts and novices can develop and adapt neuroimaging-specific analyses. |
@Article{BRH+04,
Author = {Bly, B. M. and Rebbechi, D. and Hanson, S. J. and Grasso, G.},
Title = {The {RUMBA} software: tools for neuroimaging data analysis},
Journal = {Neuroinformatics},
Volume = {2},
Number = {1},
Pages = {71-100},
abstract = {The enormous scale and complexity of data sets in functional neuroimaging makes it crucial to have well-designed and flexible software for image processing, modeling, and statistical analysis. At present, researchers must choose between general purpose scientific computing environments (e.g., Splus and Matlab), and specialized human brain mapping packages that implement particular analysis strategies (e.g., AFNI, SPM, VoxBo, FSL or FIASCO). For the vast majority of users in Human Brain Mapping and Cognitive Neuroscience, general purpose computing environments provide an insufficient framework for a complex data-analysis regime. On the other hand, the operational particulars of more specialized neuroimaging analysis packages are difficult or impossible to modify and provide little transparency or flexibility to the user for approaches other than massively multiple comparisons based on inferential statistics derived from linear models. In order to address these problems, we have developed open-source software that allows a wide array of data analysis procedures. The RUMBA software includes programming tools that simplify the development of novel methods, and accommodates data in several standard image formats. A scripting interface, along with programming libraries, defines a number of useful analytic procedures, and provides an interface to data analysis procedures. The software also supports a graphical functional programming environment for implementing data analysis streams based on modular functional components. With these features, the RUMBA software provides researchers programmability, reusability, modular analysis tools, novel data analysis streams, and an analysis environment in which multiple approaches can be contrasted and compared. The RUMBA software retains the flexibility of general scientific computing environments while adding a framework in which both experts and novices can develop and adapt neuroimaging-specific analyses.},
keywords = {*Algorithms ; Animals ; Brain Mapping/*methods ; Human ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; Programming Languages ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software/*standards/trends ; Software Validation ; Support, Non-U.S. Gov't ; User-Computer Interface},
url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks\&dbfrom=pubmed\&retmode=ref\&id=15067169},
PDF = {http://www.rumba.rutgers.edu/pubs/BRH+04.pdf},
year = 2004
} Keyword(s): EEG,
fMRI,
fusion.
@Article{HPH+03,
author = {Halchenko, Y. O. and Pearlmutter, B. A. and Hanson, S. J. and Zaimi, A.},
title = {Fusion of functional brain imaging modalities via linear programming},
keywords = {EEG, fMRI, fusion},
journal = {Biomedizinische Technik (Biomedical Engineering)},
year = 2004,
volume = {48},
number = {2},
pages = {102-104},
address = {Chiety, Italy},
PDF = {http://www.rumba.rutgers.edu/pubs/lpnfsi2003.pdf}
} Keyword(s): Adult,
Brain Mapping,
Discrimination Learning/physiology,
Humans,
*Image Processing,
Computer-Assisted/statistics & numerical data,
*Magnetic Resonance Imaging/statistics & numerical data,
Mathematical Computing,
Nerve Net/physiology,
Neural Networks (Computer),
Occipital Lobe/physiology,
Orientation/physiology,
Oxygen Consumption/*physiology,
Parahippocampal Gyrus/physiology,
Pattern Recognition,
Visual/*physiology,
Reference Values,
Temporal Lobe/*physiology.
| Haxby et al. [Science 293 (2001) 2425] recently argued that category-related responses in the ventral temporal (VT) lobe during visual object identification were overlapping and distributed in topography. This observation contrasts with prevailing views that object codes are focal and localized to specific areas such as the fusiform and parahippocampal gyri. We provide a critical test of Haxby's hypothesis using a neural network (NN) classifier that can detect more general topographic representations and achieves 83% correct generalization performance on patterns of voxel responses in out-of-sample tests. Using voxel-wise sensitivity analysis we show that substantially the same VT lobe voxels contribute to the classification of all object categories, suggesting the code is combinatorial. Moreover, we found no evidence for local single category representations. The neural network representations of the voxel codes were sensitive to both category and superordinate level features that were only available implicitly in the object categories. |
@Article{HMH04b,
Author = {Hanson, S. J. and Matsuka, T. and Haxby, J. V.},
Title = {Combinatorial codes in ventral temporal lobe for object recognition: {H}axby (2001) revisited: is there a "face" area?},
Journal = {Neuroimage},
Volume = {23},
Number = {1},
Pages = {156-66},
abstract = {Haxby et al. [Science 293 (2001) 2425] recently argued that category-related responses in the ventral temporal (VT) lobe during visual object identification were overlapping and distributed in topography. This observation contrasts with prevailing views that object codes are focal and localized to specific areas such as the fusiform and parahippocampal gyri. We provide a critical test of Haxby's hypothesis using a neural network (NN) classifier that can detect more general topographic representations and achieves 83% correct generalization performance on patterns of voxel responses in out-of-sample tests. Using voxel-wise sensitivity analysis we show that substantially the same VT lobe voxels contribute to the classification of all object categories, suggesting the code is combinatorial. Moreover, we found no evidence for local single category representations. The neural network representations of the voxel codes were sensitive to both category and superordinate level features that were only available implicitly in the object categories.},
keywords = {Adult ; Brain Mapping ; Discrimination Learning/physiology ; Humans ; *Image Processing, Computer-Assisted/statistics & numerical data ; *Magnetic Resonance Imaging/statistics & numerical data ; Mathematical Computing ; Nerve Net/physiology ; Neural Networks (Computer) ; Occipital Lobe/physiology ; Orientation/physiology ; Oxygen Consumption/*physiology ; Parahippocampal Gyrus/physiology ; Pattern Recognition, Visual/*physiology ; Reference Values ; Temporal Lobe/*physiology},
url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks\&dbfrom=pubmed\&retmode=ref\&id=15325362},
year = 2004
}@Article{Mat03a,
author = {Matsuka, T.},
title = {Generalized Exploratory Model of Category Learning},
journal = {International Journal of Computational Intelligence},
year = 2004,
volume = 1,
pages = {7-15},
}@Article{MC04c,
author = {Matsuka, T. and Corter, J. E. },
title = {Stochastic learning algorithms for modeling human category learning},
Journal = {International Journal of Computational Intelligence},
year = 2004,
volume = 1,
pages = {40-48},
} @InProceedings{BHR+04,
author = {Bly, B. M. and Hillary, F. G. and Rebbechi, D. and Preciado, R. and Giangrante, H. and Rypma, B. and DeLuca, J.},
title = {Analyzing f{MRI} data in patients with diffuse brain abnormalities},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Neuroscience Society},
year = 2004,
address = {San Francisco, CA},
PDF = {http://www.rumba.rutgers.edu/pubs/cns2004_Bly.pdf}
}@InProceedings{CM04,
author = {Corter, J. E. and Matsuka, T.},
title = {Empirical measures of attention allocation in classification learning: A replication of Medin & Schaffer (1978).},
booktitle = {45th Annural Meeting of Psychonomics Society},
year = 2004,
address = {Minneapolis, MN}
} Keyword(s): EEG,
fMRI,
fusion.
@InProceedings{HHP04,
author = {Halchenko, Y. O. and Hanson, S. J. and Pearlmutter, B. A.},
title = {Fusion of Functional Brain Imaging Modalities using L-Norms Signal Reconstruction},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Neuroscience Society},
keywords = {EEG,fMRI,fusion},
year = 2004,
address = {San Francisco, CA},
PDF = {http://www.rumba.rutgers.edu/pubs/cns2004_yarik.pdf},
url-djvu = {http://www.rumba.rutgers.edu/pubs/cns2004_yarik.djvu}
} @InProceedings{HHS04,
author = {Hanson, C. and Hanson, S. J. and Schweighardt, T.},
title = {Neural Correlates of Integral and Separable Processing During Category Learning},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Neuroscience Society},
year = 2004,
address = {San Francisco, CA},
PDF = {http://www.rumba.rutgers.edu/pubs/cns2004_cat.pdf}
} Keyword(s): Modeling,
MCMC.
@InProceedings{HMH+04,
author = {Hanson, S. J. and Matsuka, T. and Hanson, C. and Rebbechi, D. and Halchenko, Y. O. and Zaimi, A. and Pearlmutter, B. A.},
title = {Structural Equation Modeling of Neuroimaging Data: Exhaustive Search and Markov Chain Monte Carlo},
keywords = {Modeling, MCMC},
booktitle = {Human Brain Mapping},
year = 2004,
address = {},
PDF = {http://www.rumba.rutgers.edu/pubs/hbm2004.pdf},
url-djvu = {http://www.rumba.rutgers.edu/pubs/hbm2004.pdf}
} @InProceedings{HMH04a,
author = {Hanson, S. J. and Matsuka, T. and Haxby, J. V.},
title = {Combinatoric Codes in Ventral Medial Temporal Lobes for Objects: Haxby 2001 revisited},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Neuroscience Society},
year = {2004},
address = {San Francisco, CA},
PDF = {http://www.rumba.rutgers.edu/pubs/cns2004_shj_tm_hjv.pdf}
} @InProceedings{HBP+04,
author = {Hillary, F. G. and Bly, B. M. and Preciado, R. and Rebbechi, D. and Genova, H. M. and Rypma, B. and DeLuca, J.},
title = {Combining f{MRI} data in cases of diffuse and focal brain abnormality},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Neuroscience Society},
year = 2004,
address = {San Francisco, CA},
PDF = {http://www.rumba.rutgers.edu/pubs/cns2004_Bly2.pdf}
} @InProceedings{Mat04b,
author = {Matsuka, T.},
title = {Biased stochastic learning in computational model of category learning},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society},
year = 2004,
pages = {915-920},
location = {Chicago, IL},
PDF = {http://psychology.rutgers.edu/~matsuka/papers/cogsci04_tm_bsl_fin.pdf}
} @InProceedings{Mat04c,
author = {Matsuka, T.},
title = {Comparisons of prototype- and exemplar-based neural network models of categorization using the {GECEL} framework},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society},
year = 2004,
pages = {909-914},
location = {Chicago, IL},
PDF = {http://psychology.rutgers.edu/~matsuka/papers/cogsci04_tm_gecle_fin.pdf}
} @InProceedings{Mat04a,
author = {Matsuka, T.},
title = {Exploratory approach for modeling human category learning},
booktitle = {Proceedings of the 6th International Conference on Cognitive Modelling},
year = 2004,
pages = {190-195},
location = {Pittsburgh,PA},
PDF = {http://psychology.rutgers.edu/~matsuka/papers/iccm04_exp_fin.pdf}
} @InProceedings{Mat04d,
author = {Matsuka, T.},
title = {Interactions between representation and attention processes in category learning},
booktitle = {Annual Meeting of the Cognitive Neuroscience Society},
year = 2004,
address = {San Francisco, CA },
PDF = {http://www.rumba.rutgers.edu/pubs/cns2004_tm.pdf}
}@InProceedings{MC04a,
author = {Matsuka, T. and Chouchourelou, A.},
title = {Survival of the fittest hypothesis: Computational model of category learning based on evolving category concepts by hypothesis testing},
booktitle = {34th Annual meeting of Society of Computers in Psychology},
year = {2004}
}@InProceedings{MC04b,
author = {Matsuka, T. and Corter, J. E.},
title = {Modeling category learning with stochastic processes},
year = 2004,
publisher = {Lawrence Erlbaum Associates},
booktitle = {Proceedings of the 6th International Conference on Cognitive Modelling},
pages = {196-201},
location = {Pittsburgh,PA},
} @InProceedings{MCH04,
author = {Matsuka, T. and Corter, J. E. and Hanson, S. J.},
title = {Irresistibly attractive fruitless feature dimensions},
publisher = {Lawrence Erlbaum Associates},
booktitle = {Proceedings of the 6th International Conference on Cognitive Modelling},
pages = {370-371},
location = {Pittsburgh,PA},
year = 2004,
PDF = {http://psychology.rutgers.edu/~matsuka/papers/iccm04_irres_fin.pdf}
} @InProceedings{SML04,
author = {Sakamoto, Y. and Matsuka, T. and Love, B. C.},
title = {Dimension-wide and exempler-speficic attention in category learning and recognition},
booktitle = {Proceedings of the 6th International Conference on Cognitive Modelling},
year = 2004,
pages = {261-266},
PDF = {http://psychology.rutgers.edu/~matsuka/papers/iccm04_ys_tm_bcl_fin.pdf}
} @InProceedings{ZHH04,
author = {Zaimi, A. and Hanson, C. and Hanson, S. J.},
title = {Event Perception of Schema-Rich and Schema-Poor Video Sequences During f{MRI} Scanning: Top Down Versus Bottom Up Processing},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Neuroscience Society},
year = 2004,
address = {San Francisco, CA},
PDF = {http://www.rumba.rutgers.edu/pubs/cns2004_adi.pdf}
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