Found 20 images.

ID | Name | Collection(s) | Description |
---|---|---|---|

57348 | Tmaps rBPM neural gain sensitivity HCsmAD | Reduced loss aversion in pathological gambling and alcohol dependence is associated with differential alterations in amygdala and prefrontal functioning | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC53,54, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for HC smaller than AD. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. |

57349 | Tmaps rBPM neural gain sensitivity HCsmPG | Reduced loss aversion in pathological gambling and alcohol dependence is associated with differential alterations in amygdala and prefrontal functioning | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for HC smallerr than PG. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. |

57350 | Tmaps rBPM neural gain sensitivity PGgrAD | Reduced loss aversion in pathological gambling and alcohol dependence is associated with differential alterations in amygdala and prefrontal functioning | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC53,54, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for PG greater than AD. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. |

57142 | spmT_0008_HC_smaller_AD.nii | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. This is one of the T-Tests. The map is unthresholded but the map is masked by a gray matter mask (SPM12 canonical cut off at 20% gray matter probability). | |

57351 | Tmaps rBPM neural gain sensitivity PGsmAD | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC53,54, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for PG smaller than AD. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. | |

57143 | Tmap PG gain positive/negative | The preprocessed fMRI single-subject data was modeled using a boxcar function denoting times of gamble presentation (task-on regressor) and three linearly scaled task-on regressors (gain and loss parallel to behavioral analysis plus Euclidean distance based on aggregated gamble matrix31). Note that this is model is completely in parallel with the behavioral model – only the dependent variable differs. In the behavioral model it is choice, in the neural model it is BOLD activity. The regressors were convolved with the canonical hemodynamic response function, downsampled to match the number of EPI scans and entered into a GLM. For further details on the single-subject model, please see Supplementary Methods. This is T-map for pathological gamblers T-test for more (or less) BOLD activity with rising gains in the mixed gamble (positive T-values denote more BOLD activity with rising gains, negative T-values vice versa). | |

57352 | Tmaps rBPM neural loss sensitivity HCgrAD | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC53,54, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for HC greater than AD. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. | |

57144 | Tmap HC gain positive/negative | The preprocessed fMRI single-subject data was modeled using a boxcar function denoting times of gamble presentation (task-on regressor) and three linearly scaled task-on regressors (gain and loss parallel to behavioral analysis plus Euclidean distance based on aggregated gamble matrix31). Note that this is model is completely in parallel with the behavioral model – only the dependent variable differs. In the behavioral model it is choice, in the neural model it is BOLD activity. The regressors were convolved with the canonical hemodynamic response function, downsampled to match the number of EPI scans and entered into a GLM. For further details on the single-subject model, please see Supplementary Methods. This is T-map for healthy controls T-test for more (or less) BOLD activity with rising gains in the mixed gamble (positive T-values denote more BOLD activity with rising gains, negative T-values vice versa). | |

57353 | Tmaps rBPM neural loss sensitivity HCgrPG | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC53,54, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for HC greater than PG. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. | |

57145 | Tmap AD gain positive/negative | The preprocessed fMRI single-subject data was modeled using a boxcar function denoting times of gamble presentation (task-on regressor) and three linearly scaled task-on regressors (gain and loss parallel to behavioral analysis plus Euclidean distance based on aggregated gamble matrix31). Note that this is model is completely in parallel with the behavioral model – only the dependent variable differs. In the behavioral model it is choice, in the neural model it is BOLD activity. The regressors were convolved with the canonical hemodynamic response function, downsampled to match the number of EPI scans and entered into a GLM. For further details on the single-subject model, please see Supplementary Methods. This is T-map for alcoholic dependent subjects T-test for more (or less) BOLD activity with rising gains in the mixed gamble (positive T-values denote more BOLD activity with rising gains, negative T-values vice versa). | |

57354 | Tmaps rBPM neural loss sensitivity HCsmAD | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC53,54, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for HC smaller than AD. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. | |

57146 | Tmap PG loss positive/negative | The preprocessed fMRI single-subject data was modeled using a boxcar function denoting times of gamble presentation (task-on regressor) and three linearly scaled task-on regressors (gain and loss parallel to behavioral analysis plus Euclidean distance based on aggregated gamble matrix31). Note that this is model is completely in parallel with the behavioral model – only the dependent variable differs. In the behavioral model it is choice, in the neural model it is BOLD activity. The regressors were convolved with the canonical hemodynamic response function, downsampled to match the number of EPI scans and entered into a GLM. For further details on the single-subject model, please see Supplementary Methods. This is T-map for pathological gamblers T-test for more (or less) BOLD activity with rising loss in the mixed gamble (positive T-values denote more BOLD activity with rising loss, negative T-values vice versa). | |

57355 | Tmaps rBPM neural loss sensitivity HCsmPG | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for HC smaller than PG. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. | |

57147 | Tmap HC loss positive/negative | The preprocessed fMRI single-subject data was modeled using a boxcar function denoting times of gamble presentation (task-on regressor) and three linearly scaled task-on regressors (gain and loss parallel to behavioral analysis plus Euclidean distance based on aggregated gamble matrix31). Note that this is model is completely in parallel with the behavioral model – only the dependent variable differs. In the behavioral model it is choice, in the neural model it is BOLD activity. The regressors were convolved with the canonical hemodynamic response function, downsampled to match the number of EPI scans and entered into a GLM. For further details on the single-subject model, please see Supplementary Methods. This is T-map for healthy controls T-test for more (or less) BOLD activity with rising loss in the mixed gamble (positive T-values denote more BOLD activity with rising loss, negative T-values vice versa). | |

57356 | Tmaps rBPM neural loss sensitivity PGgrAD | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC53,54, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for PG greater than AD. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. | |

57148 | Tmap AD loss positive/negative | The preprocessed fMRI single-subject data was modeled using a boxcar function denoting times of gamble presentation (task-on regressor) and three linearly scaled task-on regressors (gain and loss parallel to behavioral analysis plus Euclidean distance based on aggregated gamble matrix31). Note that this is model is completely in parallel with the behavioral model – only the dependent variable differs. In the behavioral model it is choice, in the neural model it is BOLD activity. The regressors were convolved with the canonical hemodynamic response function, downsampled to match the number of EPI scans and entered into a GLM. For further details on the single-subject model, please see Supplementary Methods. This is T-map for alcohol dependent subjects T-test for more (or less) BOLD activity with rising loss in the mixed gamble (positive T-values denote more BOLD activity with rising loss, negative T-values vice versa). | |

57357 | Tmaps rBPM neural loss sensitivity PGsmAD | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for PG smaller than AD. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. | |

57149 | Fmap main effect, spmF_0010_F_main_effect_group_sensitivity_to_loss_HC_PG_AD | The preprocessed fMRI single-subject data was modeled using a boxcar function denoting times of gamble presentation (task-on regressor) and three linearly scaled task-on regressors (gain and loss parallel to behavioral analysis plus Euclidean distance based on aggregated gamble matrix). Note that this is model is completely in parallel with the behavioral model – only the dependent variable differs. In the behavioral model it is choice, in the neural model it is BOLD activity. The regressors were convolved with the canonical hemodynamic response function, downsampled to match the number of EPI scans and entered into a GLM. For further details on the single-subject model, please see Supplementary Methods. This is T-map for the main effect group on neural sensitivity to loss. | |

57150 | Tmaps rBPM neural gain sensitivity HCgrPG | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC53,54, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for HC greater than PG. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. | |

57347 | Tmaps rBPM neural gain sensitivity HCgrAD | Contrast images for gain (“neural gain sensitivity”) and loss (“neural loss sensitivity”) of all participants were subjected to two separate one-way ANOVAs with group as predictor and assumption of non-equal variance between groups. Main effect (ME) group F-Test images were computed for gain and loss and thresholded at p < 0.05, minimum cluster extent k = 10. Group main effect F-test maps were then corrected for family-wise error (FWE) at peak level using small volume correction (SVC) with our network of interest (NOI, see Supplements and online .nii file) as small volume. Note, that since the group comparison hypotheses were the same in all of the regions within the NOI it is the most stringent approach to perform one SVC for the whole NOI in the neural gain and neural loss sensitivity analysis, respectively. Then all possible one-sided post-hoc T-test images to compare HC, PG, AD were computed and peak-level FWE corrected using the NOI. Significant peak voxels from post-hoc T-tests were only considered if the FWE corrected F-Test before yielded the respective voxel also as significant. Since gray matter density (GMD) in both AD and PG has been observed different from HC53,54, and since there were significant group differences in a covariate of no interest, all found group differences in post-hoc T-test at voxels with significant SVC correctable F-Test were checked for stability by rerunning the analyses with local GMD and age using robust Biological Parametric Mapping (rBPM) with Tukey’s biweight error function using the BPMe toolbox (https://www.nitrc.org/projects/rbpm/). This is a T-contrast for HC greater than AD. Observe: was run in SPM5. Can only be viewed/evaluated in SPM8 or SPM5. |