Comparison of multivariate classifiers and response normalizations for pattern-information fMRI
A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to “decode” the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, litt...
Saved in:
| Published in | NeuroImage (Orlando, Fla.) Vol. 53; no. 1; pp. 103 - 118 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
15.10.2010
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2010.05.051 |
Cover
| Abstract | A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to “decode” the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta- or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher's linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function kernel) support vector machines. We compared these classifiers' accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher's linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60min per subject with 3T fMRI. |
|---|---|
| AbstractList | A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to "decode" the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta- or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher's linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function kernel) support vector machines. We compared these classifiers' accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher's linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60min per subject with 3T fMRI.A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to "decode" the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta- or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher's linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function kernel) support vector machines. We compared these classifiers' accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher's linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60min per subject with 3T fMRI. A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to “decode” the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta- or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher's linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function kernel) support vector machines. We compared these classifiers' accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher's linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60min per subject with 3T fMRI. A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to "decode" the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta- or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher's linear discriminant, Gaussian naive Bayes, and linear and nonlinear (radial-basis-function kernel) support vector machines. We compared these classifiers' accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3 T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher's linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60 min per subject with 3T fMRI. A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to "decode" the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta- ort-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, ak-nearest-neighbors classifier, Fisher's linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function kernel) support vector machines. We compared these classifiers' accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher's linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns byt-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based ont-value patterns may perform best in the present scenario of visual object representations measured for about 60min per subject with 3T fMRI. A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to “decode” the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher’s linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function-kernel) support vector machines. We compared these classifiers’ accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher’s linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60-minutes per subject with 3T fMRI. |
| Author | Bandettini, Peter A. Kim, Youn Kriegeskorte, Nikolaus Misaki, Masaya |
| AuthorAffiliation | 3 Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, United Kingdom 1 Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Maryland, USA 2 Department electrical engineering, University of California, San Diego, California, USA |
| AuthorAffiliation_xml | – name: 3 Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, United Kingdom – name: 2 Department electrical engineering, University of California, San Diego, California, USA – name: 1 Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Maryland, USA |
| Author_xml | – sequence: 1 givenname: Masaya surname: Misaki fullname: Misaki, Masaya email: misakim@mail.nih.gov organization: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA – sequence: 2 givenname: Youn surname: Kim fullname: Kim, Youn organization: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA – sequence: 3 givenname: Peter A. surname: Bandettini fullname: Bandettini, Peter A. organization: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA – sequence: 4 givenname: Nikolaus surname: Kriegeskorte fullname: Kriegeskorte, Nikolaus email: nikolaus.kriegeskorte@mrc-cbu.cam.ac.uk organization: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20580933$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNUl2L1DAUDbLifuhfkIIPPnW8aZM2eRF18GNhRRB9Dml6u2ZMkzFpB9Zfb-qss7ovDlxIbs7J4XLuOScnPngkpKCwokCbF5uVxzkGO-prXFWQn4Hnog_IGQXJS8nb6mS587oUlMpTcp7SBgAkZeIROa2AC5B1fUbUOoxbHW0KvghDMc5usrvc6wkL43RKdrAYU6F9X0RM2-ATFj7EUTv7U08298UQYrHV04TRl9YPC7gAxfDx8-Vj8nDQLuGT2_OCfH339sv6Q3n16f3l-vVVaRpKp1IbI6HpGNYN07KiuuUd60BKRvmgQfTYMQ5DX0MvO8F76CqRESa4FMjbob4gL_e627kbsTfop6id2sbsUbxRQVv1L-LtN3UddqrKllBWZ4HntwIx_JgxTWq0yaBz2mOYk2o5E7Jhjfg_k0moqKwW5rN7zE2Yo88-KMqhpQ3U7cJ6-vfoh5n_LCkTxJ5gYkgp4nCgUFBLHtRG3eVBLXlQwHPRO1sOX42dfm8nm2DdMQJv9gKYl7fLUVDJWPQGexvRTKoP9hiRV_dEjLPeGu2-481xEr8A3EPwbA |
| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2017_03_067 crossref_primary_10_1038_s42003_022_03858_z crossref_primary_10_1016_j_mri_2012_11_009 crossref_primary_10_1371_journal_pcbi_1005508 crossref_primary_10_1002_hbm_24581 crossref_primary_10_1073_pnas_2003480117 crossref_primary_10_1111_psyp_12665 crossref_primary_10_1523_JNEUROSCI_2857_20_2021 crossref_primary_10_1016_j_neuroimage_2017_09_001 crossref_primary_10_1016_j_neuropsychologia_2018_10_008 crossref_primary_10_1007_s12021_019_09435_w crossref_primary_10_1523_JNEUROSCI_0351_14_2014 crossref_primary_10_1371_journal_pone_0013775 crossref_primary_10_1016_j_conb_2016_02_001 crossref_primary_10_1016_j_patcog_2011_04_007 crossref_primary_10_3389_fnsys_2020_615129 crossref_primary_10_1016_j_neuroimage_2021_118033 crossref_primary_10_1002_eat_22538 crossref_primary_10_1094_PHYTO_05_20_0185_R crossref_primary_10_1038_s41467_024_47749_9 crossref_primary_10_3233_THC_171332 crossref_primary_10_3951_sobim_45_1_21 crossref_primary_10_1016_j_actpsy_2024_104569 crossref_primary_10_1038_s42003_021_02002_7 crossref_primary_10_3389_fnhum_2016_00128 crossref_primary_10_1007_s00521_016_2451_0 crossref_primary_10_1371_journal_pone_0069684 crossref_primary_10_1162_jocn_a_02021 crossref_primary_10_1371_journal_pcbi_1003553 crossref_primary_10_1002_hipo_22531 crossref_primary_10_1111_jopy_12658 crossref_primary_10_1371_journal_pone_0069566 crossref_primary_10_1162_jocn_a_00403 crossref_primary_10_3389_fninf_2014_00088 crossref_primary_10_3389_fnins_2021_715549 crossref_primary_10_1016_j_neuropsychologia_2020_107489 crossref_primary_10_1016_j_cortex_2020_02_020 crossref_primary_10_1016_j_neuroimage_2010_12_035 crossref_primary_10_1093_scan_nsz037 crossref_primary_10_1016_j_tics_2012_04_006 crossref_primary_10_1016_j_cortex_2019_11_021 crossref_primary_10_1007_s00429_022_02465_2 crossref_primary_10_3389_fpsyt_2016_00177 crossref_primary_10_1002_hbm_25575 crossref_primary_10_1016_j_neuroimage_2018_08_029 crossref_primary_10_1016_j_neuropsychologia_2018_03_023 crossref_primary_10_1097_WCO_0b013e32834028c7 crossref_primary_10_1016_j_neuroimage_2012_07_013 crossref_primary_10_1371_journal_pone_0207083 crossref_primary_10_7554_eLife_00425 crossref_primary_10_1016_j_neulet_2019_134344 crossref_primary_10_1162_netn_a_00264 crossref_primary_10_1523_JNEUROSCI_4389_13_2014 crossref_primary_10_1162_netn_a_00144 crossref_primary_10_1016_j_neuropsychologia_2019_05_032 crossref_primary_10_1016_j_cortex_2021_03_004 crossref_primary_10_1016_j_neuroimage_2011_06_053 crossref_primary_10_1523_JNEUROSCI_1061_17_2017 crossref_primary_10_1016_j_jneumeth_2018_08_021 crossref_primary_10_1080_19312458_2018_1520823 crossref_primary_10_1016_j_neuroimage_2012_10_069 crossref_primary_10_1002_hbm_25445 crossref_primary_10_1016_j_neuroimage_2016_01_045 crossref_primary_10_3389_fnins_2019_00692 crossref_primary_10_1007_s13139_019_00571_4 crossref_primary_10_1523_JNEUROSCI_3392_15_2016 crossref_primary_10_1016_j_cortex_2021_01_020 crossref_primary_10_1093_cercor_bhw379 crossref_primary_10_1093_cercor_bhw257 crossref_primary_10_1016_j_neuroimage_2015_05_002 crossref_primary_10_1523_JNEUROSCI_1318_17_2017 crossref_primary_10_1016_j_neuroimage_2016_11_019 crossref_primary_10_1109_TNNLS_2013_2253563 crossref_primary_10_1016_j_neuroimage_2021_118825 crossref_primary_10_1016_j_ajp_2023_103721 crossref_primary_10_1523_JNEUROSCI_4770_11_2012 crossref_primary_10_1002_hbm_23015 crossref_primary_10_1016_j_neuropsychologia_2017_12_027 crossref_primary_10_1016_j_neuron_2012_04_034 crossref_primary_10_1162_jocn_a_00505 crossref_primary_10_1016_j_cub_2021_08_012 crossref_primary_10_1523_JNEUROSCI_4588_10_2011 crossref_primary_10_1093_cercor_bhy345 crossref_primary_10_1146_annurev_neuro_080317_061906 crossref_primary_10_1523_JNEUROSCI_0182_15_2015 crossref_primary_10_1088_1741_2560_12_6_066026 crossref_primary_10_1523_JNEUROSCI_2562_17_2018 crossref_primary_10_1093_bjps_axx012 crossref_primary_10_1016_j_neuroimage_2014_12_062 crossref_primary_10_1162_jocn_a_01121 crossref_primary_10_1016_j_neuroimage_2014_05_034 crossref_primary_10_1016_j_neuroimage_2021_117816 crossref_primary_10_1021_acs_jpcb_0c05981 crossref_primary_10_1038_s41598_018_25127_y crossref_primary_10_1016_j_neuroimage_2016_04_061 crossref_primary_10_1162_nol_a_00035 crossref_primary_10_1016_j_eswa_2022_117417 crossref_primary_10_5607_en23016 crossref_primary_10_1007_s12161_016_0568_5 crossref_primary_10_3389_fpsyg_2020_581701 crossref_primary_10_1016_j_dcn_2014_12_003 crossref_primary_10_1016_j_neuroimage_2021_118230 crossref_primary_10_1093_scan_nsy039 crossref_primary_10_1109_TNSRE_2018_2878587 crossref_primary_10_1016_j_bandl_2023_105304 crossref_primary_10_3389_fpsyg_2015_01328 crossref_primary_10_1016_j_neuroimage_2017_01_002 crossref_primary_10_1016_j_neuroimage_2012_07_046 crossref_primary_10_1016_j_neuroimage_2025_121131 crossref_primary_10_1523_JNEUROSCI_5242_13_2014 crossref_primary_10_1016_j_dsp_2014_12_012 crossref_primary_10_1002_hbm_26391 crossref_primary_10_1162_jocn_a_00161 crossref_primary_10_3390_bioengineering11060609 crossref_primary_10_1016_j_neuroimage_2020_117410 crossref_primary_10_1007_s10548_014_0371_9 crossref_primary_10_1097_j_pain_0000000000003207 crossref_primary_10_1016_j_neuroimage_2013_10_067 crossref_primary_10_1007_s11682_018_9926_9 crossref_primary_10_1016_j_visres_2012_03_019 crossref_primary_10_1016_j_neuron_2020_04_010 crossref_primary_10_1371_journal_pone_0017191 crossref_primary_10_1002_ima_22398 crossref_primary_10_1016_j_neuroimage_2017_11_004 crossref_primary_10_1038_s41598_020_62071_2 crossref_primary_10_1016_j_neuroimage_2013_09_067 crossref_primary_10_1371_journal_pcbi_1005604 crossref_primary_10_1016_j_adhoc_2022_103026 crossref_primary_10_3389_fnhum_2018_00257 crossref_primary_10_1002_wcs_141 crossref_primary_10_1145_3051125 crossref_primary_10_1016_j_cell_2021_05_022 crossref_primary_10_1523_JNEUROSCI_1704_15_2016 crossref_primary_10_3389_fnhum_2015_00151 crossref_primary_10_1016_j_neuroimage_2016_03_006 crossref_primary_10_1016_j_neuroimage_2018_08_064 crossref_primary_10_1111_bdi_12222 crossref_primary_10_3389_fnins_2017_00543 crossref_primary_10_1162_jocn_a_01904 crossref_primary_10_1016_j_patcog_2011_04_023 crossref_primary_10_1371_journal_pbio_1002577 crossref_primary_10_1002_hbm_22490 crossref_primary_10_1155_2012_961257 crossref_primary_10_1093_cercor_bhz205 crossref_primary_10_1109_JPROC_2015_2425807 crossref_primary_10_3758_s13415_024_01232_6 crossref_primary_10_1016_j_cortex_2021_06_004 crossref_primary_10_1016_j_neuroimage_2012_03_076 crossref_primary_10_1093_bjps_axx023 crossref_primary_10_1038_s41598_021_86328_6 crossref_primary_10_1016_j_pain_2014_02_013 crossref_primary_10_1523_ENEURO_0091_24_2024 crossref_primary_10_1523_JNEUROSCI_1663_17_2017 crossref_primary_10_1016_j_neuroimage_2011_01_061 crossref_primary_10_1038_nn_3337 crossref_primary_10_1038_s41467_019_08857_z crossref_primary_10_1093_cercor_bhu302 crossref_primary_10_3389_fnhum_2017_00496 crossref_primary_10_1016_j_neuroimage_2015_10_089 crossref_primary_10_1142_S0129065720500240 crossref_primary_10_1371_journal_pone_0134717 crossref_primary_10_1093_scan_nsx037 crossref_primary_10_1016_j_neuroimage_2017_06_043 crossref_primary_10_1002_hbm_26243 crossref_primary_10_1017_S0021859618000539 crossref_primary_10_1523_JNEUROSCI_0376_23_2023 crossref_primary_10_1523_JNEUROSCI_2641_20_2021 crossref_primary_10_1016_j_eswa_2021_115549 crossref_primary_10_1371_journal_pbio_3001930 crossref_primary_10_1016_j_neuroimage_2010_07_073 crossref_primary_10_1109_JBHI_2023_3317508 crossref_primary_10_1146_annurev_psych_120710_100412 crossref_primary_10_1007_s10916_011_9788_9 crossref_primary_10_1016_j_neuroimage_2018_02_019 crossref_primary_10_1177_1754073913512519 crossref_primary_10_1016_j_neuroimage_2018_02_013 crossref_primary_10_1016_j_plrev_2024_04_012 crossref_primary_10_1097_j_pain_0000000000001237 crossref_primary_10_7554_eLife_66884 crossref_primary_10_1002_hbm_24979 crossref_primary_10_1162_imag_a_00484 crossref_primary_10_1016_j_nbd_2012_10_001 crossref_primary_10_1016_j_cortex_2015_01_020 crossref_primary_10_1016_j_neuroimage_2015_12_012 crossref_primary_10_1016_j_nicl_2013_05_001 crossref_primary_10_1016_j_neuroimage_2022_119597 crossref_primary_10_1016_j_nicl_2021_102718 crossref_primary_10_1016_j_neuroimage_2011_01_044 crossref_primary_10_1016_j_neuroimage_2017_05_068 crossref_primary_10_1038_nn_3749 crossref_primary_10_1111_j_1460_9568_2012_08053_x crossref_primary_10_3389_fnins_2020_00289 crossref_primary_10_1073_pnas_2003110117 crossref_primary_10_1111_ppa_13891 crossref_primary_10_2196_10885 crossref_primary_10_1016_j_neuroimage_2021_118565 crossref_primary_10_1016_j_neuroimage_2021_118686 crossref_primary_10_1162_nol_a_00001 crossref_primary_10_1016_j_pscychresns_2015_10_002 crossref_primary_10_1002_hbm_22421 crossref_primary_10_1002_hbm_70065 crossref_primary_10_1002_hbm_70184 crossref_primary_10_1093_cercor_bhx195 crossref_primary_10_1093_cercor_bhy289 crossref_primary_10_1016_j_neuropsychologia_2016_01_018 crossref_primary_10_1002_hbm_25851 crossref_primary_10_1523_ENEURO_0008_15_2015 crossref_primary_10_1111_ejn_12547 crossref_primary_10_1093_cercor_bhw302 crossref_primary_10_1016_j_neuroimage_2018_09_031 crossref_primary_10_1152_jn_00106_2011 crossref_primary_10_1007_s10898_013_0134_2 crossref_primary_10_1016_j_neures_2023_01_001 crossref_primary_10_1098_rspb_2012_2339 crossref_primary_10_1016_j_neuroimage_2019_04_044 crossref_primary_10_1073_pnas_1819993116 crossref_primary_10_1016_j_cortex_2020_08_025 crossref_primary_10_1093_cercor_bht029 crossref_primary_10_1007_s10548_013_0322_x crossref_primary_10_1016_j_neuroimage_2018_06_012 crossref_primary_10_1371_journal_pone_0117126 crossref_primary_10_1523_JNEUROSCI_3795_14_2015 crossref_primary_10_1080_23273798_2016_1272703 crossref_primary_10_3389_fninf_2018_00060 crossref_primary_10_1371_journal_pone_0090782 crossref_primary_10_1093_texcom_tgab049 crossref_primary_10_3389_fnins_2017_00740 crossref_primary_10_1109_ACCESS_2017_2698068 crossref_primary_10_1101_lm_023671_111 crossref_primary_10_1016_j_neuroimage_2022_119499 crossref_primary_10_1093_scan_nsz097 crossref_primary_10_3389_fpsyg_2014_01435 crossref_primary_10_1038_ncomms10904 crossref_primary_10_1016_j_neuroimage_2014_03_076 crossref_primary_10_3389_fnins_2018_00737 crossref_primary_10_1016_j_neuroimage_2014_03_074 crossref_primary_10_1016_j_neuroimage_2017_07_033 crossref_primary_10_1016_j_neuroimage_2021_118428 crossref_primary_10_1007_s00221_018_5395_z crossref_primary_10_1016_j_jneumeth_2023_110004 crossref_primary_10_1111_j_1467_7687_2011_01055_x crossref_primary_10_1523_JNEUROSCI_1128_17_2017 crossref_primary_10_1371_journal_pone_0232551 crossref_primary_10_1115_1_4030128 crossref_primary_10_1073_pnas_1818575116 crossref_primary_10_3389_fnins_2020_616906 crossref_primary_10_1093_cercor_bhae303 crossref_primary_10_1021_acsami_4c04858 crossref_primary_10_1007_s40708_016_0049_z crossref_primary_10_1002_hbm_25353 crossref_primary_10_1093_cercor_bht292 crossref_primary_10_1177_24705470231203655 crossref_primary_10_1002_hbm_26681 crossref_primary_10_1016_j_neuroimage_2017_08_005 crossref_primary_10_1371_journal_pone_0104586 crossref_primary_10_3389_fnins_2023_1233416 crossref_primary_10_1093_cercor_bhu022 crossref_primary_10_1007_s00221_016_4765_7 crossref_primary_10_1007_s11031_021_09900_7 crossref_primary_10_1007_s12021_013_9204_3 crossref_primary_10_1073_pnas_1311989110 crossref_primary_10_1093_cercor_bhab324 crossref_primary_10_1098_rstb_2017_0124 crossref_primary_10_1016_j_neuroscience_2013_05_025 crossref_primary_10_1007_s00429_014_0902_x crossref_primary_10_1016_j_neuroimage_2017_04_061 crossref_primary_10_1016_j_neuroimage_2018_06_076 crossref_primary_10_1007_s00429_018_1740_z crossref_primary_10_1016_j_neuroimage_2022_119311 crossref_primary_10_1016_j_neuroimage_2017_08_019 crossref_primary_10_1016_j_cmpb_2021_106549 crossref_primary_10_1109_JIOT_2022_3199712 crossref_primary_10_1111_ejn_12215 crossref_primary_10_1523_JNEUROSCI_1556_11_2011 crossref_primary_10_1016_j_neuropsychologia_2011_11_007 crossref_primary_10_1016_j_neuroimage_2020_116716 crossref_primary_10_1016_j_cortex_2023_08_018 crossref_primary_10_1017_S1355617718000590 crossref_primary_10_1089_brain_2012_0133 crossref_primary_10_1109_ACCESS_2020_3012918 crossref_primary_10_1371_journal_pone_0058632 crossref_primary_10_1371_journal_pone_0223660 crossref_primary_10_1016_j_neuroimage_2013_01_008 crossref_primary_10_1002_hbm_25215 crossref_primary_10_1016_j_neuroimage_2013_06_061 crossref_primary_10_1093_cercor_bhac420 crossref_primary_10_1371_journal_pone_0074665 crossref_primary_10_7554_eLife_31873 crossref_primary_10_1073_pnas_2119931119 crossref_primary_10_1016_j_neuropsychologia_2024_108815 crossref_primary_10_1007_s13534_011_0021_z crossref_primary_10_1111_desc_13409 crossref_primary_10_1016_j_jneumeth_2018_06_017 crossref_primary_10_1186_s12993_024_00233_2 crossref_primary_10_1073_pnas_2110474118 crossref_primary_10_1093_cercor_bhv136 crossref_primary_10_1016_j_neuroimage_2022_119227 crossref_primary_10_3389_fnhum_2014_00339 crossref_primary_10_1093_cercor_bhv134 crossref_primary_10_1016_j_neuropsychologia_2024_109000 crossref_primary_10_1093_cercor_bhw344 crossref_primary_10_1073_pnas_2412881121 crossref_primary_10_1093_chemse_bjaa068 crossref_primary_10_7554_eLife_63551 crossref_primary_10_1016_j_neuroimage_2012_05_057 crossref_primary_10_1016_j_neuroimage_2016_02_033 crossref_primary_10_1016_j_bpsc_2016_05_001 crossref_primary_10_1162_jocn_a_01068 crossref_primary_10_1016_j_neuropsychologia_2017_02_025 crossref_primary_10_3389_fnins_2018_00038 crossref_primary_10_1007_s11042_023_15935_4 crossref_primary_10_1093_texcom_tgaa038 crossref_primary_10_1002_hbm_25127 crossref_primary_10_1093_cercor_bhaa018 crossref_primary_10_1016_j_neuroimage_2019_02_030 crossref_primary_10_1038_s41598_017_14424_7 crossref_primary_10_1038_s41598_019_50801_0 crossref_primary_10_1088_1752_7163_aa6ac6 crossref_primary_10_1016_j_neuropsychologia_2012_07_007 crossref_primary_10_1111_ejn_14774 crossref_primary_10_1162_jocn_a_02041 crossref_primary_10_1073_pnas_1610686113 crossref_primary_10_1162_jocn_a_00787 crossref_primary_10_1016_j_jneumeth_2012_11_004 crossref_primary_10_1038_nn_3973 crossref_primary_10_1016_j_neuroimage_2015_09_031 crossref_primary_10_1016_j_neuroimage_2011_08_076 |
| Cites_doi | 10.1162/089976698300017197 10.1523/JNEUROSCI.16-13-04207.1996 10.1016/j.mri.2008.02.016 10.1016/j.tics.2006.07.005 10.1016/j.neuroimage.2007.02.022 10.1038/nrn2578 10.1126/science.1063736 10.1038/nrn1931 10.1023/A:1024068626366 10.1093/scan/nsn044 10.1038/nn.2303 10.1016/j.neuroimage.2005.06.070 10.1038/nn1445 10.1073/pnas.0600244103 10.1016/j.neuroimage.2008.06.037 10.1016/j.neuron.2008.10.043 10.1109/TPAMI.2005.127 10.1016/j.neuroimage.2005.01.048 10.2202/1544-6115.1175 10.1016/j.neuroimage.2004.05.020 10.1023/B:MACH.0000035475.85309.1b 10.1038/nature06713 10.1002/ima.20166 10.1126/science.1152876 10.1016/j.neuroimage.2008.11.007 10.1073/pnas.0705654104 10.1016/j.neuroimage.2006.01.022 10.1016/S1053-8119(03)00049-1 10.1016/S0927-5398(03)00007-0 10.1038/nn1444 |
| ContentType | Journal Article |
| Copyright | 2010 Published by Elsevier Inc. Copyright Elsevier Limited Oct 15, 2010 |
| Copyright_xml | – notice: 2010 – notice: Published by Elsevier Inc. – notice: Copyright Elsevier Limited Oct 15, 2010 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7TK 7X7 7XB 88E 88G 8AO 8FD 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2M M7P P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ Q9U RC3 7X8 7QO 5PM |
| DOI | 10.1016/j.neuroimage.2010.05.051 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Neurosciences Abstracts ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Psychology Database (Alumni) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central - New (Subscription) Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) Medical Database Psychology Database Biological Science Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology ProQuest Central Basic Genetics Abstracts MEDLINE - Academic Biotechnology Research Abstracts PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest One Psychology ProQuest Central Student Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Genetics Abstracts Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Psychology Journals (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest Psychology Journals ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic Biotechnology Research Abstracts |
| DatabaseTitleList | MEDLINE - Academic Engineering Research Database ProQuest One Psychology MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: BENPR name: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1095-9572 |
| EndPage | 118 |
| ExternalDocumentID | PMC2914143 3245854931 20580933 10_1016_j_neuroimage_2010_05_051 S1053811910007834 |
| Genre | Comparative Study Research Support, N.I.H., Intramural Evaluation Study Journal Article |
| GrantInformation_xml | – fundername: Intramural NIH HHS grantid: Z99 MH999999 – fundername: Medical Research Council grantid: MC_U105597120 – fundername: Intramural NIH HHS grantid: ZIA MH002783 |
| GroupedDBID | --- --K --M .~1 0R~ 123 1B1 1RT 1~. 1~5 4.4 457 4G. 5RE 5VS 7-5 71M 7X7 88E 8AO 8FE 8FH 8FI 8FJ 8P~ 9JM AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AATTM AAXKI AAXLA AAXUO AAYWO ABBQC ABCQJ ABFNM ABFRF ABIVO ABJNI ABMAC ABUWG ABXDB ACDAQ ACGFO ACGFS ACIEU ACLOT ACPRK ACRLP ACRPL ACVFH ADBBV ADCNI ADEZE ADFRT ADMUD ADNMO AEBSH AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFKRA AFPUW AFTJW AFXIZ AGUBO AGWIK AGYEJ AHHHB AHMBA AIEXJ AIIUN AIKHN AITUG AJRQY AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AXJTR AZQEC BBNVY BENPR BHPHI BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DM4 DU5 DWQXO EBS EFBJH EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN FYUFA G-Q GNUQQ GROUPED_DOAJ HCIFZ HMCUK HZ~ IHE J1W KOM LG5 LK8 LX8 M1P M29 M2M M2V M41 M7P MO0 MOBAO N9A O-L O9- OAUVE OVD OZT P-8 P-9 P2P PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PSYQQ Q38 ROL RPZ SAE SCC SDF SDG SDP SES SSH SSN SSZ T5K TEORI UKHRP UV1 YK3 ZU3 ~G- ~HD 3V. AACTN AADPK AAIAV ABLVK ABYKQ AFKWA AJBFU AJOXV AMFUW C45 HMQ LCYCR RIG SNS ZA5 .1- .FO 29N 53G AAFWJ AAQXK AAYXX ABMZM ADFGL ADVLN ADXHL AFPKN AFRHN AGHFR AGQPQ AIGII AJUYK AKRLJ APXCP ASPBG AVWKF AZFZN CAG CITATION COF FEDTE FGOYB G-2 GBLVA HDW HEI HMK HMO HVGLF OK1 PUEGO R2- SEW WUQ XPP Z5R ZMT AGCQF AGRNS ALIPV CGR CUY CVF ECM EIF NPM 7TK 7XB 8FD 8FK FR3 K9. P64 PKEHL PQEST PQUKI PRINS Q9U RC3 7X8 7QO 5PM |
| ID | FETCH-LOGICAL-c611t-acc906b4e364a921a75b4b099415fa08deb450fd30d9b85d0b2815f48598e57f3 |
| IEDL.DBID | AIKHN |
| ISSN | 1053-8119 1095-9572 |
| IngestDate | Tue Sep 30 16:00:48 EDT 2025 Tue Oct 07 09:21:15 EDT 2025 Thu Oct 02 11:06:43 EDT 2025 Tue Oct 07 06:38:54 EDT 2025 Mon Jul 21 05:15:45 EDT 2025 Wed Oct 01 02:58:00 EDT 2025 Thu Apr 24 22:49:23 EDT 2025 Fri Feb 23 02:20:31 EST 2024 Tue Oct 14 19:33:01 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | fMRI Multi-voxel pattern analysis pattern-information analysis normalization decoding classification analysis |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 Published by Elsevier Inc. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c611t-acc906b4e364a921a75b4b099415fa08deb450fd30d9b85d0b2815f48598e57f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Undefined-3 |
| PMID | 20580933 |
| PQID | 1507160378 |
| PQPubID | 2031077 |
| PageCount | 16 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_2914143 proquest_miscellaneous_754896468 proquest_miscellaneous_749021928 proquest_journals_1507160378 pubmed_primary_20580933 crossref_primary_10_1016_j_neuroimage_2010_05_051 crossref_citationtrail_10_1016_j_neuroimage_2010_05_051 elsevier_sciencedirect_doi_10_1016_j_neuroimage_2010_05_051 elsevier_clinicalkey_doi_10_1016_j_neuroimage_2010_05_051 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2010-10-15 |
| PublicationDateYYYYMMDD | 2010-10-15 |
| PublicationDate_xml | – month: 10 year: 2010 text: 2010-10-15 day: 15 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Amsterdam |
| PublicationTitle | NeuroImage (Orlando, Fla.) |
| PublicationTitleAlternate | Neuroimage |
| PublicationYear | 2010 |
| Publisher | Elsevier Inc Elsevier Limited |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier Limited |
| References | Norman, Polyn, Detre, Haxby (bib38) 2006; 10 Haxby, Gobbini, Furey, Ishai, Schouten, Pietrini (bib13) 2001; 293 Kriegeskorte, Bandettini (bib19) 2007; 38 Kriegeskorte, Goebel, Bandettini (bib20) 2006; 103 Ledoit, Wolf (bib30) 2003; 10 Schölkopf, Smola (bib42) 2001 Kamitani, Tong (bib16) 2005; 8 Kriegeskorte, N., 2004. Functional magnetic resonance imaging of the human object-vision system. PhD Thesis. Universiteit Maastricht. Haynes, Rees (bib14) 2005; 8 Mitchell (bib32) 1997 Guyon, Elisseeff (bib11) 2003; 3 Kriegeskorte, Mur, Ruff, Kiani, Bodurka, Esteky, Tanaka, Bandettini (bib22) 2008; 60 Mur, Bandettini, Kriegeskorte (bib36) 2009; 4 Chang, C.-C., Lin, C.-J., 2001. LIBSVM: a library for support vector machines, Software available at Mitchell, Shinkareva, Carlson, Chang, Malave, Mason, Just (bib34) 2008; 320 Nadeau, Bengio (bib37) 2003; 52 Ku, Gretton, Macke, Logothetis (bib28) 2008; 26 De Martino, Valente, Staeren, Ashburner, Goebel, Formisano (bib7) 2008; 43 Demšar (bib8) 2006; 7 Krzanowski (bib27) 1988 Pereira, Mitchell, Botvinick (bib39) 2009; 45 Kriegeskorte, Formisano, Sorger, Goebel (bib21) 2007; 104 Mourao-Miranda, Bokde, Born, Hampel, Stetter (bib35) 2005; 28 Hastie, Tibshirani, Friedman (bib12) 2009 Vapnik (bib43) 1995 Dietterich (bib9) 1998; 10 Boynton, Engel, Glover, Heeger (bib3) 1996; 16 Kriegeskorte, Simmons, Bellgowan, Baker (bib25) 2009; 12 Martínez-Ramón, Koltchinskii, Heileman, Posse (bib31) 2006; 31 Mitchell, Hutchinson, Niculescu, Pereira, Wang, Just, Newman (bib33) 2004; 57 LaConte, Strother, Cherkassky, Anderson, Hu (bib29) 2005; 26 Björnsdotter Åberg, Löken, Wessberg (bib2) 2008; 2 Duda, Hart, Stork (bib10) 2000 Cox, Savoy (bib5) 2003; 19 . Kriegeskorte, Bodurka, Bandettini (bib23) 2008; 18 Krishnapuram, Carin, Figueiredo, Hartemink (bib26) 2005; 27 Cristianini, Shawe-Taylor (bib6) 2000 Bishop (bib1) 2007 Kay, Naselaris, Prenger, Gallant (bib17) 2008; 452 Haynes, Rees (bib15) 2006; 7 Kriegeskorte, Mur, Bandettini (bib24) 2008; 2 Quian Quiroga, Panzeri (bib40) 2009; 10 Schafer, Strimmer (bib41) 2005; 4 Hanson, Matsuka, Haxby (bib44) 2004; 23 Martínez-Ramón (10.1016/j.neuroimage.2010.05.051_bib31) 2006; 31 Norman (10.1016/j.neuroimage.2010.05.051_bib38) 2006; 10 Kriegeskorte (10.1016/j.neuroimage.2010.05.051_bib22) 2008; 60 Hanson (10.1016/j.neuroimage.2010.05.051_bib44) 2004; 23 Demšar (10.1016/j.neuroimage.2010.05.051_bib8) 2006; 7 Hastie (10.1016/j.neuroimage.2010.05.051_bib12) 2009 Cox (10.1016/j.neuroimage.2010.05.051_bib5) 2003; 19 Dietterich (10.1016/j.neuroimage.2010.05.051_bib9) 1998; 10 Kriegeskorte (10.1016/j.neuroimage.2010.05.051_bib21) 2007; 104 Mitchell (10.1016/j.neuroimage.2010.05.051_bib33) 2004; 57 Pereira (10.1016/j.neuroimage.2010.05.051_bib39) 2009; 45 Kamitani (10.1016/j.neuroimage.2010.05.051_bib16) 2005; 8 Mitchell (10.1016/j.neuroimage.2010.05.051_bib32) 1997 Björnsdotter Åberg (10.1016/j.neuroimage.2010.05.051_bib2) 2008; 2 Krzanowski (10.1016/j.neuroimage.2010.05.051_bib27) 1988 De Martino (10.1016/j.neuroimage.2010.05.051_bib7) 2008; 43 10.1016/j.neuroimage.2010.05.051_bib4 Haynes (10.1016/j.neuroimage.2010.05.051_bib15) 2006; 7 Haynes (10.1016/j.neuroimage.2010.05.051_bib14) 2005; 8 Mur (10.1016/j.neuroimage.2010.05.051_bib36) 2009; 4 Quian Quiroga (10.1016/j.neuroimage.2010.05.051_bib40) 2009; 10 Duda (10.1016/j.neuroimage.2010.05.051_bib10) 2000 Ledoit (10.1016/j.neuroimage.2010.05.051_bib30) 2003; 10 Kriegeskorte (10.1016/j.neuroimage.2010.05.051_bib24) 2008; 2 LaConte (10.1016/j.neuroimage.2010.05.051_bib29) 2005; 26 Boynton (10.1016/j.neuroimage.2010.05.051_bib3) 1996; 16 Krishnapuram (10.1016/j.neuroimage.2010.05.051_bib26) 2005; 27 Schölkopf (10.1016/j.neuroimage.2010.05.051_bib42) 2001 Vapnik (10.1016/j.neuroimage.2010.05.051_bib43) 1995 Kriegeskorte (10.1016/j.neuroimage.2010.05.051_bib20) 2006; 103 Mourao-Miranda (10.1016/j.neuroimage.2010.05.051_bib35) 2005; 28 Guyon (10.1016/j.neuroimage.2010.05.051_bib11) 2003; 3 Mitchell (10.1016/j.neuroimage.2010.05.051_bib34) 2008; 320 Kriegeskorte (10.1016/j.neuroimage.2010.05.051_bib25) 2009; 12 Nadeau (10.1016/j.neuroimage.2010.05.051_bib37) 2003; 52 Bishop (10.1016/j.neuroimage.2010.05.051_bib1) 2007 Haxby (10.1016/j.neuroimage.2010.05.051_bib13) 2001; 293 Schafer (10.1016/j.neuroimage.2010.05.051_bib41) 2005; 4 Kriegeskorte (10.1016/j.neuroimage.2010.05.051_bib19) 2007; 38 Kriegeskorte (10.1016/j.neuroimage.2010.05.051_bib23) 2008; 18 Ku (10.1016/j.neuroimage.2010.05.051_bib28) 2008; 26 Cristianini (10.1016/j.neuroimage.2010.05.051_bib6) 2000 Kay (10.1016/j.neuroimage.2010.05.051_bib17) 2008; 452 10.1016/j.neuroimage.2010.05.051_bib18 |
| References_xml | – volume: 60 start-page: 1126 year: 2008 end-page: 1141 ident: bib22 article-title: Matching categorical object representations in inferior temporal cortex of man and monkey publication-title: Neuron – volume: 52 start-page: 239 year: 2003 end-page: 281 ident: bib37 article-title: Inference for the generalization error publication-title: Mach. Learn. – year: 1995 ident: bib43 article-title: The Nature of Statistical Learning Theory – volume: 19 start-page: 261 year: 2003 end-page: 270 ident: bib5 article-title: Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex publication-title: NeuroImage – volume: 26 start-page: 1007 year: 2008 end-page: 1014 ident: bib28 article-title: Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys publication-title: Magn. Reson. Imaging – volume: 28 start-page: 980 year: 2005 end-page: 995 ident: bib35 article-title: Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data publication-title: NeuroImage – volume: 27 start-page: 957 year: 2005 end-page: 968 ident: bib26 article-title: Sparse multinomial logistic regression: fast algorithms and generalization bounds publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 26 start-page: 317 year: 2005 end-page: 329 ident: bib29 article-title: Support vector machines for temporal classification of block design fMRI data publication-title: NeuroImage – volume: 320 start-page: 1191 year: 2008 end-page: 1195 ident: bib34 article-title: Predicting human brain activity associated with the meanings of nouns publication-title: Science – year: 2000 ident: bib6 article-title: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods – volume: 2 start-page: 302 year: 2008 end-page: 307 ident: bib2 article-title: An evolutionary approach to multivariate feature selection for fMRI pattern analysis publication-title: Biosignals – volume: 38 start-page: 649 year: 2007 end-page: 662 ident: bib19 article-title: Analyzing for information, not activation, to exploit high-resolution fMRI publication-title: NeuroImage – volume: 104 start-page: 20600 year: 2007 end-page: 20605 ident: bib21 article-title: Individual faces elicit distinct response patterns in human anterior temporal cortex publication-title: Proc. Natl. Acad. Sci. U. S. A. – volume: 103 start-page: 3863 year: 2006 end-page: 3868 ident: bib20 article-title: Information-based functional brain mapping publication-title: Proc. Natl. Acad. Sci. U. S. A. – volume: 2 start-page: 1 year: 2008 end-page: 28 ident: bib24 article-title: Representational similarity analysis—connecting the branches of systems neuroscience publication-title: Front. Syst. Neurosci. – volume: 4 year: 2005 ident: bib41 article-title: A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics publication-title: Stat. Appl. Genet. Mol. Biol. – year: 1988 ident: bib27 article-title: Principles of Multivariate Analysis: A User's Perspective – volume: 18 start-page: 345 year: 2008 end-page: 349 ident: bib23 article-title: Artifactual time course correlations in echo-planar fMRI with implications for studies of brain function publication-title: Int. J. Imaging Syst. Technol. – volume: 16 start-page: 4207 year: 1996 end-page: 4221 ident: bib3 article-title: Linear systems analysis of functional magnetic resonance imaging in human V1 publication-title: J. Neurosci. – volume: 8 start-page: 679 year: 2005 end-page: 685 ident: bib16 article-title: Decoding the visual and subjective contents of the human brain publication-title: Nat. Neurosci. – reference: Kriegeskorte, N., 2004. Functional magnetic resonance imaging of the human object-vision system. PhD Thesis. Universiteit Maastricht. – volume: 4 start-page: 101 year: 2009 end-page: 109 ident: bib36 article-title: Revealing representational content with pattern-information fMRI—an introductory guide publication-title: Soc. Cogn. Affect. Neurosci. – year: 2000 ident: bib10 article-title: Pattern Classification – volume: 57 start-page: 145 year: 2004 end-page: 175 ident: bib33 article-title: Learning to decode cognitive states from brain images publication-title: Mach. Learn. – reference: Chang, C.-C., Lin, C.-J., 2001. LIBSVM: a library for support vector machines, Software available at – volume: 3 start-page: 1157 year: 2003 end-page: 1182 ident: bib11 article-title: An introduction to variable and feature selection publication-title: J. Mach. Learn. Res. – volume: 23 start-page: 156 year: 2004 end-page: 166 ident: bib44 article-title: Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? publication-title: NeuroImage – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: bib8 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J. Mach. Learn. Res. – year: 2009 ident: bib12 article-title: The Elements of Statistical Learning publication-title: Data Mining, Inference, and Prediction – volume: 31 start-page: 1129 year: 2006 end-page: 1141 ident: bib31 article-title: fMRI pattern classification using neuroanatomically constrained boosting publication-title: NeuroImage – year: 2001 ident: bib42 article-title: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond publication-title: Adaptive Computation and Machine Learning – volume: 7 start-page: 523 year: 2006 end-page: 534 ident: bib15 article-title: Decoding mental states from brain activity in humans publication-title: Nat. Rev. Neurosci. – year: 1997 ident: bib32 article-title: Machine Learning – reference: . – volume: 293 start-page: 2425 year: 2001 end-page: 2430 ident: bib13 article-title: Distributed and overlapping representations of faces and objects in ventral temporal cortex publication-title: Science – volume: 10 start-page: 603 year: 2003 end-page: 621 ident: bib30 article-title: Improved estimation of the covariance matrix of stock returns with an application to portfolio selection publication-title: J. Empirical Finance – volume: 10 start-page: 173 year: 2009 end-page: 185 ident: bib40 article-title: Extracting information from neuronal populations: information theory and decoding approaches publication-title: Nat. Rev. Neurosci. – volume: 8 start-page: 686 year: 2005 end-page: 691 ident: bib14 article-title: Predicting the orientation of invisible stimuli from activity in human primary visual cortex publication-title: Nat. Neurosci. – volume: 10 start-page: 1895 year: 1998 end-page: 1923 ident: bib9 article-title: Approximate statistical tests for comparing supervised classification learning algorithms publication-title: Neural Comput. – volume: 452 start-page: 352 year: 2008 end-page: 355 ident: bib17 article-title: Identifying natural images from human brain activity publication-title: Nature – volume: 43 start-page: 44 year: 2008 end-page: 58 ident: bib7 article-title: Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns publication-title: NeuroImage – volume: 10 start-page: 424 year: 2006 end-page: 430 ident: bib38 article-title: Beyond mind-reading: multi-voxel pattern analysis of fMRI data publication-title: Trends Cogn. Sci. – volume: 45 start-page: S199 year: 2009 end-page: S209 ident: bib39 article-title: Machine learning classifiers and fMRI: a tutorial overview publication-title: NeuroImage – year: 2007 ident: bib1 article-title: Pattern Recognition and Machine Learning – volume: 12 start-page: 535 year: 2009 end-page: 540 ident: bib25 article-title: Circular analysis in systems neuroscience: the dangers of double dipping publication-title: Nat. Neurosci. – volume: 10 start-page: 1895 year: 1998 ident: 10.1016/j.neuroimage.2010.05.051_bib9 article-title: Approximate statistical tests for comparing supervised classification learning algorithms publication-title: Neural Comput. doi: 10.1162/089976698300017197 – year: 2000 ident: 10.1016/j.neuroimage.2010.05.051_bib10 – volume: 16 start-page: 4207 year: 1996 ident: 10.1016/j.neuroimage.2010.05.051_bib3 article-title: Linear systems analysis of functional magnetic resonance imaging in human V1 publication-title: J. Neurosci. doi: 10.1523/JNEUROSCI.16-13-04207.1996 – volume: 26 start-page: 1007 year: 2008 ident: 10.1016/j.neuroimage.2010.05.051_bib28 article-title: Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys publication-title: Magn. Reson. Imaging doi: 10.1016/j.mri.2008.02.016 – volume: 2 start-page: 1 issue: 4 year: 2008 ident: 10.1016/j.neuroimage.2010.05.051_bib24 article-title: Representational similarity analysis—connecting the branches of systems neuroscience publication-title: Front. Syst. Neurosci. – volume: 10 start-page: 424 year: 2006 ident: 10.1016/j.neuroimage.2010.05.051_bib38 article-title: Beyond mind-reading: multi-voxel pattern analysis of fMRI data publication-title: Trends Cogn. Sci. doi: 10.1016/j.tics.2006.07.005 – volume: 38 start-page: 649 year: 2007 ident: 10.1016/j.neuroimage.2010.05.051_bib19 article-title: Analyzing for information, not activation, to exploit high-resolution fMRI publication-title: NeuroImage doi: 10.1016/j.neuroimage.2007.02.022 – volume: 10 start-page: 173 year: 2009 ident: 10.1016/j.neuroimage.2010.05.051_bib40 article-title: Extracting information from neuronal populations: information theory and decoding approaches publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn2578 – ident: 10.1016/j.neuroimage.2010.05.051_bib18 – year: 2000 ident: 10.1016/j.neuroimage.2010.05.051_bib6 – year: 2009 ident: 10.1016/j.neuroimage.2010.05.051_bib12 article-title: The Elements of Statistical Learning – volume: 293 start-page: 2425 year: 2001 ident: 10.1016/j.neuroimage.2010.05.051_bib13 article-title: Distributed and overlapping representations of faces and objects in ventral temporal cortex publication-title: Science doi: 10.1126/science.1063736 – volume: 7 start-page: 523 year: 2006 ident: 10.1016/j.neuroimage.2010.05.051_bib15 article-title: Decoding mental states from brain activity in humans publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn1931 – volume: 52 start-page: 239 year: 2003 ident: 10.1016/j.neuroimage.2010.05.051_bib37 article-title: Inference for the generalization error publication-title: Mach. Learn. doi: 10.1023/A:1024068626366 – year: 2007 ident: 10.1016/j.neuroimage.2010.05.051_bib1 – volume: 4 start-page: 101 issue: 1 year: 2009 ident: 10.1016/j.neuroimage.2010.05.051_bib36 article-title: Revealing representational content with pattern-information fMRI—an introductory guide publication-title: Soc. Cogn. Affect. Neurosci. doi: 10.1093/scan/nsn044 – year: 1997 ident: 10.1016/j.neuroimage.2010.05.051_bib32 – volume: 12 start-page: 535 issue: 5 year: 2009 ident: 10.1016/j.neuroimage.2010.05.051_bib25 article-title: Circular analysis in systems neuroscience: the dangers of double dipping publication-title: Nat. Neurosci. doi: 10.1038/nn.2303 – volume: 28 start-page: 980 year: 2005 ident: 10.1016/j.neuroimage.2010.05.051_bib35 article-title: Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data publication-title: NeuroImage doi: 10.1016/j.neuroimage.2005.06.070 – year: 2001 ident: 10.1016/j.neuroimage.2010.05.051_bib42 article-title: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond – volume: 7 start-page: 1 year: 2006 ident: 10.1016/j.neuroimage.2010.05.051_bib8 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J. Mach. Learn. Res. – volume: 8 start-page: 686 year: 2005 ident: 10.1016/j.neuroimage.2010.05.051_bib14 article-title: Predicting the orientation of invisible stimuli from activity in human primary visual cortex publication-title: Nat. Neurosci. doi: 10.1038/nn1445 – volume: 3 start-page: 1157 year: 2003 ident: 10.1016/j.neuroimage.2010.05.051_bib11 article-title: An introduction to variable and feature selection publication-title: J. Mach. Learn. Res. – volume: 103 start-page: 3863 year: 2006 ident: 10.1016/j.neuroimage.2010.05.051_bib20 article-title: Information-based functional brain mapping publication-title: Proc. Natl. Acad. Sci. U. S. A. doi: 10.1073/pnas.0600244103 – volume: 2 start-page: 302 year: 2008 ident: 10.1016/j.neuroimage.2010.05.051_bib2 article-title: An evolutionary approach to multivariate feature selection for fMRI pattern analysis publication-title: Biosignals – volume: 43 start-page: 44 issue: 1 year: 2008 ident: 10.1016/j.neuroimage.2010.05.051_bib7 article-title: Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns publication-title: NeuroImage doi: 10.1016/j.neuroimage.2008.06.037 – volume: 60 start-page: 1126 year: 2008 ident: 10.1016/j.neuroimage.2010.05.051_bib22 article-title: Matching categorical object representations in inferior temporal cortex of man and monkey publication-title: Neuron doi: 10.1016/j.neuron.2008.10.043 – volume: 27 start-page: 957 year: 2005 ident: 10.1016/j.neuroimage.2010.05.051_bib26 article-title: Sparse multinomial logistic regression: fast algorithms and generalization bounds publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.127 – volume: 26 start-page: 317 year: 2005 ident: 10.1016/j.neuroimage.2010.05.051_bib29 article-title: Support vector machines for temporal classification of block design fMRI data publication-title: NeuroImage doi: 10.1016/j.neuroimage.2005.01.048 – volume: 4 year: 2005 ident: 10.1016/j.neuroimage.2010.05.051_bib41 article-title: A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics publication-title: Stat. Appl. Genet. Mol. Biol. doi: 10.2202/1544-6115.1175 – volume: 23 start-page: 156 year: 2004 ident: 10.1016/j.neuroimage.2010.05.051_bib44 article-title: Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? publication-title: NeuroImage doi: 10.1016/j.neuroimage.2004.05.020 – volume: 57 start-page: 145 year: 2004 ident: 10.1016/j.neuroimage.2010.05.051_bib33 article-title: Learning to decode cognitive states from brain images publication-title: Mach. Learn. doi: 10.1023/B:MACH.0000035475.85309.1b – volume: 452 start-page: 352 issue: 7185 year: 2008 ident: 10.1016/j.neuroimage.2010.05.051_bib17 article-title: Identifying natural images from human brain activity publication-title: Nature doi: 10.1038/nature06713 – year: 1988 ident: 10.1016/j.neuroimage.2010.05.051_bib27 – ident: 10.1016/j.neuroimage.2010.05.051_bib4 – volume: 18 start-page: 345 issue: 5–6 year: 2008 ident: 10.1016/j.neuroimage.2010.05.051_bib23 article-title: Artifactual time course correlations in echo-planar fMRI with implications for studies of brain function publication-title: Int. J. Imaging Syst. Technol. doi: 10.1002/ima.20166 – volume: 320 start-page: 1191 issue: 5880 year: 2008 ident: 10.1016/j.neuroimage.2010.05.051_bib34 article-title: Predicting human brain activity associated with the meanings of nouns publication-title: Science doi: 10.1126/science.1152876 – volume: 45 start-page: S199 year: 2009 ident: 10.1016/j.neuroimage.2010.05.051_bib39 article-title: Machine learning classifiers and fMRI: a tutorial overview publication-title: NeuroImage doi: 10.1016/j.neuroimage.2008.11.007 – volume: 104 start-page: 20600 year: 2007 ident: 10.1016/j.neuroimage.2010.05.051_bib21 article-title: Individual faces elicit distinct response patterns in human anterior temporal cortex publication-title: Proc. Natl. Acad. Sci. U. S. A. doi: 10.1073/pnas.0705654104 – year: 1995 ident: 10.1016/j.neuroimage.2010.05.051_bib43 – volume: 31 start-page: 1129 year: 2006 ident: 10.1016/j.neuroimage.2010.05.051_bib31 article-title: fMRI pattern classification using neuroanatomically constrained boosting publication-title: NeuroImage doi: 10.1016/j.neuroimage.2006.01.022 – volume: 19 start-page: 261 year: 2003 ident: 10.1016/j.neuroimage.2010.05.051_bib5 article-title: Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex publication-title: NeuroImage doi: 10.1016/S1053-8119(03)00049-1 – volume: 10 start-page: 603 year: 2003 ident: 10.1016/j.neuroimage.2010.05.051_bib30 article-title: Improved estimation of the covariance matrix of stock returns with an application to portfolio selection publication-title: J. Empirical Finance doi: 10.1016/S0927-5398(03)00007-0 – volume: 8 start-page: 679 year: 2005 ident: 10.1016/j.neuroimage.2010.05.051_bib16 article-title: Decoding the visual and subjective contents of the human brain publication-title: Nat. Neurosci. doi: 10.1038/nn1444 |
| SSID | ssj0009148 |
| Score | 2.5226197 |
| Snippet | A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to “decode” the stimuli from the response... A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to "decode" the stimuli from the response... |
| SourceID | pubmedcentral proquest pubmed crossref elsevier |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 103 |
| SubjectTerms | Adult Algorithms Classification classification analysis decoding Discriminant analysis Evoked Potentials, Visual - physiology Female fMRI Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Magnetic Resonance Imaging - methods Male Methods Multi-voxel pattern analysis Multivariate Analysis normalization Pattern Recognition, Automated - methods pattern-information analysis Reproducibility of Results Sensitivity and Specificity Support vector machines Visual Cortex - physiology Visual Perception - physiology |
| SummonAdditionalLinks | – databaseName: ProQuest Central - New (Subscription) dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fSxwxEA72hNKXYu0PT23Jg6-hyW6ySZBSWlFs4Y4iFXwLySZLT3Tvqqd_v5PdZK-2VA7ysiQDSzLJfLv55huEDiBiiyDrggQIx4SrhhPlrSVCOmpt5UJZxtzhybQ6PeffL8TFBprmXJhIq8xnYndQ-3kd_5F_7IBLRUupPi9-k1g1Kt6u5hIaNpVW8J86ibFnaLOIylgjtPn1ePrjbCXDy3ifHCdKohjTidvTM746BcnZNezjRPkS0Nj_Ata_gPRvXuUfgepkC71MCBN_6V3iFdoI7TZ6Pkl36K-RORpKD-J5gztG4T08A-jEdcTSsyaWx8a29fimZ9AG3EZoe5VzNjEgXbzolDlbkqRXYwduJmff3qDzk-OfR6cklVkgdcXYksDcaVo5HsqKW10wK4XjDpAjxPbGUuWD44I2vqReOyU8dYWCHq6EVkHIpnyLRu28DTsIq4J72PDUBskBmVgH8Cbe7MJwyQoXxkjmuTR10iCPpTCuTCabXZrVKpi4CoYKaGyM2GC56HU41rDReblMzjOFk9FAsFjD9nCwTVikxxhrWu9n7zDpTLg1Kw8eIzx0w26OVzS2DfO7WyO5BtCli6eGwDemrngFQ9717jZMSEGFin-oYJofOeIwIGqJP-5pZ786TXFYIw7QeffpF99DLzr2RGxiH42WN3fhPYCypfuQdtoDdyg6OQ priority: 102 providerName: ProQuest |
| Title | Comparison of multivariate classifiers and response normalizations for pattern-information fMRI |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1053811910007834 https://dx.doi.org/10.1016/j.neuroimage.2010.05.051 https://www.ncbi.nlm.nih.gov/pubmed/20580933 https://www.proquest.com/docview/1507160378 https://www.proquest.com/docview/749021928 https://www.proquest.com/docview/754896468 https://pubmed.ncbi.nlm.nih.gov/PMC2914143 |
| Volume | 53 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier ScienceDirect customDbUrl: eissn: 1095-9572 dateEnd: 20191231 omitProxy: true ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: ACRLP dateStart: 19950301 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals [SCFCJ] customDbUrl: eissn: 1095-9572 dateEnd: 20191231 omitProxy: true ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: AIKHN dateStart: 19950301 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Science Direct customDbUrl: eissn: 1095-9572 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: .~1 dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals customDbUrl: mediaType: online eissn: 1095-9572 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: AKRWK dateStart: 19920801 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1095-9572 dateEnd: 20250905 omitProxy: true ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: BENPR dateStart: 19980501 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Health & Medical Collection customDbUrl: eissn: 1095-9572 dateEnd: 20250905 omitProxy: true ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: 7X7 dateStart: 20020801 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fa9swED7aFMZexn4vXRf0sFctsi1ZMnvqQku6LaFkK-RNSLZMPTontGkf97fvZMvusrERGAgbWzqwJd3dZ-u7E8Bb9NjCyTymDt0x5arkVBXGUCEtMya1Lkl87PBsnk4v-MelWO7BpIuF8bTKYPtbm95Y63BnHHpzvK6q8RdEBuhu8Huj8XMJ34cD9D9KDeDg-OzTdH6fezfibUScSKgXCISelubVpI2svqPyBp6XwBL9zUv9iUJ_J1P-4p1OH8OjACvJcfvkT2DP1U_hwSwsnD8DPen3GySrkjQ0wju8RqRJcg-gq9LviU1MXZDrljbrSO3x7FUXqEkQ3pJ1k46zpiHfqq8g5Wxx9hwuTk--TqY07K1A8zSKNtTkecZSy12ScpPFkZHCcotwER16aZgqnOWClUXCiswqUTAbK6zhSmTKCVkmL2BQr2r3CoiKeYFazoyTHOGIsYhp_HIuNpdRbN0QZNeXOg-Jx_3-F1e6Y5h90_ejoP0oaCawREOIesl1m3xjB5msGy7dBZeiOdToIXaQfd_Lbk3CHaWPutmhgyG40Q3eTlki1RBIX40q7NdlTO1Wtzda8gyRVhb_qwl-WGYpT7HJy3a69R0SM6H8byns5q2J2DfwCcS3a-rqskkkjmPEES8f_tdrv4aHDaPCF3EEg831rXuDQG1jR7D_7keER7mUI1TKyeLz-SgoJ54_nMzPFz8B6KdFfg |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9MwELbGJsFeEL9XGOAHeLSwEzuxhSY0xqaWrRWaNmlvxk4c0WlLy9qB-Of423ZO7JSBmPoyyS-RfVHks-8-x3ffIfQGPLZweZEQB-6YcFlxIktjiMgtNSazLk197vBwlPWP-ecTcbKCfsdcGB9WGW1iY6jLSeH_kb9rgEtG01x-mH4nvmqUv12NJTRMKK1QbjUUYyGxY9_9-glHuNnW4BPo-22S7O0e7fRJqDJAioyxOQFRRTPLXZpxoxJmcmG5BeAErq0yVJbOckGrMqWlslKU1CYSergUSjqRVym89w5a4ylXcPhb-7g7-nK4oP1lvE3GEymRjKkQS9RGmDWMleNzsBshxExAY_9zkP8C4L_jOP9wjHsP0P2AaPF2uwQfohVXP0J3h-HO_jHSO12pQzypcBPB-AOeAeTiwmP3ceXLcWNTl_iijdh1uPZQ-izmiGJA1njaMIHWJFC9-g5cDQ8HT9DxrUz4U7RaT2q3gbBMeAkGhhqXc0BCxgKc8jfJMDxniXU9lMe51EXgPPelN850DG471QstaK8FTQU01kOsk5y2vB9LyKioLh3zWsESa3BOS8i-72QD9mkxzZLSm3F16GCDZnqxY3oId91gPfyVkKnd5HKmc64A5KnkpiFwplUZz2DIs3a5dROSUCH9HzGY5msLsRvgucuv99Tjbw2HOeiIA1R_fvOHv0b3-kfDA30wGO2_QOtN5IZvYhOtzi8u3UsAhHP7Kuw6jL7e9ka_ArWZdp8 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9MwELbGkCZeEL_pGOAHeLRmO3ZsCyGENqqV0QkhJvXN2ImjFY20WzsQ_xp_HefESRmIqS-T_BLZV1W-892X-Ls7hF5AxJZBFZwECMdE6EoQXTpHpPLUudyHLIu5w-Oj_OBYvJ_IyQb61eXCRFpl5xMbR13OiviNfLcBLjnNlN6tEi3i4_7wzfyMxA5S8aa1a6fRmshh-PkDXt8Wr0f7oOuXnA_ffd47IKnDAClyxpbEFYWhuRchy4UznDklvfAAmiCsVY7qMnghaVVmtDRey5J6rmFGaGl0kKrK4HdvoJsqy0ykE6qJWhX8ZaJNw5MZ0YyZxCJquWVNrcrpN_AYiVwmYbD_hcZ_oe_fDM4_QuLwDrqdsCx-2xrfXbQR6ntoa5xu6-8ju9c3OcSzCjfcxe_wDPAWFxG1T6vYiBu7usTnLVc34DqC6NMuOxQDpsbzpgZoTVKR1ziBq_Gn0QN0fC3b_RBt1rM6PEZYc1GCa6EuKAEYyHkAUvEOGZYrxn0YINXtpS1StfPYdOPUdrS2r3alBRu1YKmEwQaI9ZLztuLHGjKmU5ftMlrBB1sIS2vIvuplE-pp0cya0judddjkfRZ2dVYGCPfT4DfiZZCrw-xiYZUwAO8Mv2oJvM2aXOSw5FFrbv2GcCp1_BYG23zJEPsFsWr55Zl6etJULwcdCQDp21f_8edoC463_TA6OnyCbjWUjTjkDtpcnl-Ep4AEl_5Zc-Qw-nLdZ_w3gOp0OQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Comparison+of+multivariate+classifiers+and+response+normalizations+for+pattern-information+fMRI&rft.jtitle=NeuroImage+%28Orlando%2C+Fla.%29&rft.au=Misaki%2C+Masaya&rft.au=Kim%2C+Youn&rft.au=Bandettini%2C+Peter+A.&rft.au=Kriegeskorte%2C+Nikolaus&rft.date=2010-10-15&rft.issn=1053-8119&rft.volume=53&rft.issue=1&rft.spage=103&rft.epage=118&rft_id=info:doi/10.1016%2Fj.neuroimage.2010.05.051&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_neuroimage_2010_05_051 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-8119&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-8119&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-8119&client=summon |