Computational protocol: Differential psychophysiological interactions of insular subdivisions during varied oropharyngeal swallowing tasks

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[…] Generalized Psychophysiological Interactions (gPPI), based on their improved sensitivity and specificity in detecting connectivity effects (McLaren et al. ), were used to compute the context‐dependent connectivity of six insular seed regions. gPPI models were created and estimated using the publicly available gPPI Toolbox ( gPPI Toolbox uses the following equation to estimate the PPI effects: where H is the HRF in Toeplitz matrix form; Yk is the BOLD signal observed in the seed region; xa is the estimated neural activity from the BOLD signal in the seed region (Gitelman et al. ); Yi is the BOLD signal observed at each voxel in the brain; βi is a matrix of the beta estimates of the psychophysiological interaction terms; βG is a matrix of the beta estimates of the seed region BOLD signal (Yk), covariates of no interest (G), and convolution of psychological vectors H(gp); ei is a vector of the residuals of model; and gp is a matrix of N columns, where N is the number of conditions in the experiment and formed by separating the time when the conditions are present into separate columns. Additionally, if the time when the condition is present is weighted by a parametric modulator, such as swallow number, gPPI can also assess parametric changes in connectivity within a condition.For the present analysis, we chose six seed locations a priori to understand both the laterality and anterior–posterior effects of the swallowing on insular connectivity (Fig. ). Each seed region was defined as 6‐millimeters around the center of group peak activity maps or the contralateral MNI coordinate. The six insular regions were: (1) left ventral anterior insula (contralateral voxel of the group sour activation peak, MNI:−30, 22, −12); (2) left dorsal anterior insula (peak voxel from the group e‐stim activation, MNI: −28, 30, 12); (3) left posterior insula/rolandic operculum (peak voxel from the group visual biofeedback activation, MNI: −46, −4, 8); (4) right ventral anterior insula (peak voxel from the group sour activation, MNI: 30, 22, −12); (5) right dorsal anterior insula (contralateral voxel of the group e‐stim activation peak, MNI: 28, 30, 12); and (6) right posterior insula/rolandic operculum (contralateral voxel of the group visual biofeedback activation peak, MNI: 46, −4, 8). Peak voxels from the group maps and their associated spheres were labeled based on the Automated Anatomical Labeling atlas (Tzourio‐Mazoyer et al. ). We chose to select the contralateral voxel as the seed center for testing laterality as it is entirely objective and closely matches the same anatomical area. [...] Group analysis for the effects of swallowing was analyzed with GLM Flex ( that allows for the analysis of both within–subject and between–subject effects in the same repeated‐measures ANOVA, a feature not available in SPM8. Age group (old and young) added as a factor of no interest in the models. Separate models were run for the swallow and habituation/sensitization effects in each seed region. Thus, there were 12 repeated‐measure ANOVAs estimated in this analysis. Comparison from these models included: (1) comparison of each condition to no swallowing (condition‐specific effects) and (2) pair‐wise comparisons of conditions (condition comparison effects).To correct for multiple comparisons, we determined, using 3dClustSim (Analysis of Functional NeuroImages), that a threshold of P < 0.005 in at least 51 contiguous voxels (408 mm3) yields a cluster corrected P < 0.05. We use this threshold for reporting all voxel‐wise findings. Each gPPI seed region analysis can be considered to be an independent analysis, as we do not directly compare the gPPI contrasts between seeds at the voxel or cluster level. Thus, a correction for the number of seed regions is not needed in this study. As we wanted to establish the gPPI effects for each swallow biofeedback separately and provide evidence for selecting future swallow biofeedback conditions in future studies, we did not correct for the number of conditions.Additionally, we report the probability of finding the number of significant clusters for each region and condition, which is referred to at the set‐level P‐value, using random field theory in SPM8. […]

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