Simple Fully Automated Group Classification on Brain fMRI

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We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical ... continued below

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Honorio, J.; Goldstein, R.; Honorio, J.; Samaras, D.; Tomasi, D. & Goldstein, R.Z. April 14, 2010.

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We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

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  • 7th International Sympsoium on Biomedical Imaging: From Nano to Macro (2010 Macro); Rotterdam, Netherlands; 20100414 through 20100417

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  • Report No.: BNL--95072-2011-CP
  • Grant Number: DE-AC02-98CH10886
  • Office of Scientific & Technical Information Report Number: 1013544
  • Archival Resource Key: ark:/67531/metadc846998

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • April 14, 2010

Added to The UNT Digital Library

  • May 19, 2016, 3:16 p.m.

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  • Aug. 30, 2016, 3:39 p.m.

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Honorio, J.; Goldstein, R.; Honorio, J.; Samaras, D.; Tomasi, D. & Goldstein, R.Z. Simple Fully Automated Group Classification on Brain fMRI, article, April 14, 2010; United States. (digital.library.unt.edu/ark:/67531/metadc846998/: accessed April 25, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.