• fMRI Analysis

  • fMRI Data Analysis

    Processing of fMRI data took place in Statistical Parametric Mapping 8 (SPM8, London, UK).

  • ((For each functional run, data were preprocessed following standard procedure to remove sources of noise and artifact.)) First, slice time correction was performed in order to correct for acquisition delays within functional volumes. Images were then realigned within and across runs via a rigid body transformation to correct for subject motion during the scan session, and unwarped to correct for residual image distortions caused by movement-by-susceptibility interactions (Andersson, Hutton, Ashburner, Turner, & Friston, 2001). Functional data were normalized into standard space using the Montreal Neurological Institute (MNI)-152 template and spatially smoothed using a 6 mm full-width-at-half-maximum Gaussian kernel.
    For each individual analysis, a general linear model was created for each subject using a boxcar function convolved convolved with the canonical hemodynamic response function. Task effects (neutral, happy, and fearful expressions) and covariates of no interest (session mean, run regressor, linear trend, and six movement parameters derived from realignment corrections) were modeled as separate task regressors, and used to create contrast maps (weighted parameter estimates) for each participant. These linear contrast maps for each of the three task conditions versus fixation were then entered into a random effects model, which accounts for inter-subject variability and allows population based inferences to be drawn.

    ROI Analysis
    In order to assess the study’s a priori hypothesis regarding relative amygdala response, an anatomical region of interest (ROI) mask of the bilateral amygdala was created using the Wake Forest University Pick Atlas (Maldjian et al., 2003). The ROI mask was dilated by an expanding factor of 1 to fully encompass the entire amygdala (Etkin et al., 2004; Gianaros et al., 2008). A significance threshold of p < 0.05 corrected for multiple comparisons was imposed over the amygdala volume (~12,500 mm3), as determined by Monte Carlo simulations implemented in AlphaSim within AFNI software (Cox, 1996). Then the BOLD signal (beta weights) from the significantly activated voxels were extracted for each participant and submitted to subsequent offline testing. An

    To assess the relationship between expressivity and amygdala blood oxygen level dependent (BOLD) activity increases to masked fearful vs happy faces, voxelwise correlation analyses on the contrast maps (face-masked fearful vs happy faces and pattern-masked fearful vs happy faces) were performed with BEQ scores as a regressor. Based on the findings of previous backward masking studies (Whalen et al., 1998, 2004), we first sought to identify voxels in the amygdala that showed significantly increased BOLD signal to face-masked fearful vs happy faces. Then, we planned to use these voxels as a region of interest to examine the effects of using pattern masks
    on amygdala activity.

  • ROI

  • ``Leah 10:
    Processing of fMRI data took place in Statistical Parametric Mapping 2 (SPM2, London, United Kingdom) (37). Preprocessing
    steps were carried out including slice time correction, motion correction, correction of movement-by-susceptibility interactions (38), and spatial normalization. Normalized functional data were spatially smoothed (6 mm full-width at half maximum Gaussian kernel).
    Time points were categorized based on the height of the line as low (values 3–5), medium (values 5–7), high (values 7–9), and shock (values 9), with each level represented by a regressor for SELF and OTHER conditions and rest blocks serving as an implicit baseline. Regressors were convolved with the canonical hemodynamic response function to represent task effects. Task regressors were submitted to an individual subject voxelwise general linear model along with nuisance regressors (session mean, run regressor, linear trend, and six movement parameters derived from realignment corrections) to compute parameter estimates () and contrast images (containing weighted parameter
    estimates) for each comparison at each voxel. Because low-frequency drift artifacts were accounted for in the general
    linear model, high pass filtering was not performed.

  • Justin mask type:

    Anatomical and functional images were processed using Statistical Parametric Mapping software (SPM5, Wellcome Department of Imaging Neuroscience, London, UK). Raw functional data were preprocessed following standard procedures, starting with correcting for head movement. None of the subjects had head movement more than 1.5 mm in any direction. Functional images were then normalized to standard space using the Montreal Neurological Institute (MNI)-152 template. Spatial smoothing was applied to the normalized functional images using a Gaussian kernel of 6 mm full width at half maximum. By using a boxcar function convolved with the hemodynamic response function and covariates of no interests (a session mean, a linear trend for each run, and six movement parameters derived from realignment corrections), linear contrast maps [emotion (fearful, happy)] [mask type (non-face pattern, face)] were generated for each subject. Contrast maps were then entered into a random effects model, which accounts for inter-subject variability and allows population based inferences to be drawn.
    To assess the relationship between anxiety measures and amygdala blood oxygen level dependent (BOLD) signal increases to masked fearful vs happy faces, voxelwise correlation analyses on the contrast maps (face-masked fearful vs happy faces and pattern-masked fearful vs happy faces) were performed with STAI scores as a regressor. Separate voxelwise correlation analyses were performed for trait and state anxiety measures. Given the current study’s focus on the amygdala, we imposed a significance threshold of P < 0.05 corrected for multiple comparisons over the amygdala volume (4500 mm3 , defined using the Automated Anatomical Labeling atlas; Maldjian et al., 2003), as determined by Monte Carlo simulations implemented in AlphaSim within AFNI software (Cox, 1996), a strategy we have implemented in previous studies (Kim et al., 2003; Johnstone et al., 2005; Davis et al., 2009). Based on the findings of previous backward masking studies (Whalen et al., 1998, 2004), we first sought to identify voxels in the amygdala that showed significantly increased BOLD signal to face-masked fearful vs happy faces. Then, we planned to use these voxels as a region of interest to examine the effects of using pattern masks
    on amygdala activity.

  • Dylan:
    For each functional run, data were preprocessed to remove sources of noise and artifact. Images were corrected for differences in acquisition time between slices and realigned within and across runs via a rigid body transformation in order to correct for head movement. Images were then unwarped to reduce residual movement-related image distortions not corrected by realignment. Functional data were normalized into a standard stereotaxic space (3 mm isotropic voxels) based on the SPM8 echo planar imaging template that conforms to the ICBM 152 brain template space (Montreal Neurological Institute) and approximates the Talairach and Tournoux atlas space. Finally, normalized images were spatially smoothed (8-mm full-width at halfmaximum) using a Gaussian kernel to increase the signal to noise ratio and to reduce the impact of anatomical variability not corrected for by stereotaxic normalization. Volumes were inspected for scanner and motion-related artifact based on examination of the realignment parameters and voxelwise standard deviations for each run and subject.

{"cards":[{"_id":"57639e3d0eba566d0d000007","treeId":"576377eeee852dd15400007b","seq":2764677,"position":1,"parentId":null,"content":"fMRI Analysis"},{"_id":"5763a0d80eba566d0d000008","treeId":"576377eeee852dd15400007b","seq":2765262,"position":1,"parentId":"57639e3d0eba566d0d000007","content":"fMRI Data Analysis\n\nProcessing of fMRI data took place in Statistical Parametric Mapping 8 (SPM8, London, UK).\n"},{"_id":"5763a1750eba566d0d000009","treeId":"576377eeee852dd15400007b","seq":2764689,"position":1,"parentId":"5763a0d80eba566d0d000008","content":"``Leah 10:\nProcessing of fMRI data took place in Statistical Parametric Mapping 2 (SPM2, London, United Kingdom) (37). Preprocessing\nsteps were carried out including slice time correction, motion correction, correction of movement-by-susceptibility interactions (38), and spatial normalization. Normalized functional data were spatially smoothed (6 mm full-width at half maximum Gaussian kernel).\nTime points were categorized based on the height of the line as low (values 3–5), medium (values 5–7), high (values 7–9), and shock (values 9), with each level represented by a regressor for SELF and OTHER conditions and rest blocks serving as an implicit baseline. Regressors were convolved with the canonical hemodynamic response function to represent task effects. Task regressors were submitted to an individual subject voxelwise general linear model along with nuisance regressors (session mean, run regressor, linear trend, and six movement parameters derived from realignment corrections) to compute parameter estimates (\u0004) and contrast images (containing weighted parameter\nestimates) for each comparison at each voxel. Because low-frequency drift artifacts were accounted for in the general\nlinear model, high pass filtering was not performed."},{"_id":"5763ae280eba566d0d00000a","treeId":"576377eeee852dd15400007b","seq":2764706,"position":2,"parentId":"5763a0d80eba566d0d000008","content":"Justin mask type:\n\nAnatomical and functional images were processed using Statistical Parametric Mapping software (SPM5, Wellcome Department of Imaging Neuroscience, London, UK). Raw functional data were preprocessed following standard procedures, starting with correcting for head movement. None of the subjects had head movement more than 1.5 mm in any direction. Functional images were then normalized to standard space using the Montreal Neurological Institute (MNI)-152 template. Spatial smoothing was applied to the normalized functional images using a Gaussian kernel of 6 mm full width at half maximum. By using a boxcar function convolved with the hemodynamic response function and covariates of no interests (a session mean, a linear trend for each run, and six movement parameters derived from realignment corrections), linear contrast maps [emotion (fearful, happy)] [mask type (non-face pattern, face)] were generated for each subject. Contrast maps were then entered into a random effects model, which accounts for inter-subject variability and allows population based inferences to be drawn.\nTo assess the relationship between anxiety measures and amygdala blood oxygen level dependent (BOLD) signal increases to masked fearful vs happy faces, voxelwise correlation analyses on the contrast maps (face-masked fearful vs happy faces and pattern-masked fearful vs happy faces) were performed with STAI scores as a regressor. Separate voxelwise correlation analyses were performed for trait and state anxiety measures. Given the current study’s focus on the amygdala, we imposed a significance threshold of P < 0.05 corrected for multiple comparisons over the amygdala volume (\u00044500 mm3 , defined using the Automated Anatomical Labeling atlas; Maldjian et al., 2003), as determined by Monte Carlo simulations implemented in AlphaSim within AFNI software (Cox, 1996), a strategy we have implemented in previous studies (Kim et al., 2003; Johnstone et al., 2005; Davis et al., 2009). Based on the findings of previous backward masking studies (Whalen et al., 1998, 2004), we first sought to identify voxels in the amygdala that showed significantly increased BOLD signal to face-masked fearful vs happy faces. Then, we planned to use these voxels as a region of interest to examine the effects of using pattern masks\non amygdala activity."},{"_id":"576447550eba566d0d00000b","treeId":"576377eeee852dd15400007b","seq":3164341,"position":2,"parentId":"57639e3d0eba566d0d000007","content":"((For each functional run, data were preprocessed following standard procedure to remove sources of noise and artifact.)) First, slice time correction was performed in order to correct for acquisition delays within functional volumes. Images were then realigned within and across runs via a rigid body transformation to correct for subject motion during the scan session, and unwarped to correct for residual image distortions caused by movement-by-susceptibility interactions (Andersson, Hutton, Ashburner, Turner, & Friston, 2001). Functional data were normalized into standard space using the Montreal Neurological Institute (MNI)-152 template and spatially smoothed using a 6 mm full-width-at-half-maximum Gaussian kernel.\nFor each individual analysis, a general linear model was created for each subject using a boxcar function convolved convolved with the canonical hemodynamic response function. Task effects (neutral, happy, and fearful expressions) and covariates of no interest (session mean, run regressor, linear trend, and six movement parameters derived from realignment corrections) were modeled as separate task regressors, and used to create contrast maps (weighted parameter estimates) for each participant. These linear contrast maps for each of the three task conditions versus fixation were then entered into a random effects model, which accounts for inter-subject variability and allows population based inferences to be drawn.\n\nROI Analysis\nIn order to assess the study's a priori hypothesis regarding relative amygdala response, an anatomical region of interest (ROI) mask of the bilateral amygdala was created using the Wake Forest University Pick Atlas (Maldjian et al., 2003). The ROI mask was dilated by an expanding factor of 1 to fully encompass the entire amygdala (Etkin et al., 2004; Gianaros et al., 2008). A significance threshold of p < 0.05 corrected for multiple comparisons was imposed over the amygdala volume (~12,500 mm3), as determined by Monte Carlo simulations implemented in AlphaSim within AFNI software (Cox, 1996). Then the BOLD signal (beta weights) from the significantly activated voxels were extracted for each participant and submitted to subsequent offline testing. An \n\n\n\n\n\nTo assess the relationship between expressivity and amygdala blood oxygen level dependent (BOLD) activity increases to masked fearful vs happy faces, voxelwise correlation analyses on the contrast maps (face-masked fearful vs happy faces and pattern-masked fearful vs happy faces) were performed with BEQ scores as a regressor. Based on the findings of previous backward masking studies (Whalen et al., 1998, 2004), we first sought to identify voxels in the amygdala that showed significantly increased BOLD signal to face-masked fearful vs happy faces. Then, we planned to use these voxels as a region of interest to examine the effects of using pattern masks\non amygdala activity. "},{"_id":"576448e70eba566d0d00000c","treeId":"576377eeee852dd15400007b","seq":2765804,"position":1,"parentId":"576447550eba566d0d00000b","content":"Dylan:\nFor each functional run, data were preprocessed to remove sources of noise and artifact. Images were corrected for differences in acquisition time between slices and realigned within and across runs via a rigid body transformation in order to correct for head movement. Images were then unwarped to reduce residual movement-related image distortions not corrected by realignment. Functional data were normalized into a standard stereotaxic space (3 mm isotropic voxels) based on the SPM8 echo planar imaging template that conforms to the ICBM 152 brain template space (Montreal Neurological Institute) and approximates the Talairach and Tournoux atlas space. Finally, normalized images were spatially smoothed (8-mm full-width at halfmaximum) using a Gaussian kernel to increase the signal to noise ratio and to reduce the impact of anatomical variability not corrected for by stereotaxic normalization. Volumes were inspected for scanner and motion-related artifact based on examination of the realignment parameters and voxelwise standard deviations for each run and subject."},{"_id":"5764505e0eba566d0d00000d","treeId":"576377eeee852dd15400007b","seq":2765876,"position":3,"parentId":"57639e3d0eba566d0d000007","content":"ROI"}],"tree":{"_id":"576377eeee852dd15400007b","name":"Untitled tree","publicUrl":"576377eeee852dd15400007b"}}