Computational protocol: Connectivity Analysis Reveals a Cortical Network for Eye Gaze Perception

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[…] MR imaging was performed with a 3-T Tim Trio magnetic resonance imaging scanner (Siemens, Germany) with a head coil gradient set at the MRC Cognition and Brain Sciences Unit, Cambridge. Whole-brain data were acquired with T2*-weighted echo-planar imaging (EPI), sensitive to blood oxygen level–dependent (BOLD) signal contrast (40 axial slices, 3-mm slice thickness; time repetition = 2424 ms; time echo = 30 ms; field of view = 192 mm; voxel size: 3 × 3 × 3 mm). The first 3 volumes were discarded to allow for equilibration effects. T1-weighted structural images were acquired at a resolution of 1 × 1 × 1 mm.Data were preprocessed and analyzed using SPM5 software (www.fil.ion.ucl.ac.uk/spm/). The EPI images were sinc interpolated in time to correct for slice-time differences and realigned to the first scan by rigid-body transformations to correct for head movements. EPI and structural images were coregistered and normalized to theT1 standard template in Montreal Neurological Institute (MNI) space (MNI—International Consortium for Brain mapping) using linear and nonlinear transformations and smoothed with a Gaussian kernel of full width at half maximum 8 mm. [...] The physiological connectivity between 2 brain regions can vary with the psychological context () known as a PPI. PPIs can be identified by GLMs sensitive to contextual modulation of task-related covariance. In contrast with dynamic casual modeling or structural equation modeling of network connectivity, GLMs do not require a specified anatomical model. Rather, one starts with a source region and identifies any other “target” voxels/clusters with which that source has context-dependent connectivity. Target regions need not correlate with the task or context alone but the interactions between these factors. Significant PPIs do not in themselves indicate the direction or neurochemistry of causal influences between source and target regions, nor whether the connectivity is mediated by mono- or poly-synaptic connections, nor changes in structural neuroplasticity from block to block. However, they do indicate interactions between regional systems and the results of PPIs accord with other connectivity methods such as dynamic causal modeling ().The right IOG, FG, and pSTS were used as the source regions for the analyses. Subject-wise local maxima in these regions were identified from the faces versus houses contrast from the face localizer. Next, spherical regions of interests (ROIs) with an 8-mm radius were generated around the individual local maxima for each source region. In other words, the center of each source region was the voxel with the highest statistical significance in the respective cluster, such that the position of the ROI was slightly different across individuals. A group-based analysis showed that the MNI average coordinates for the ROIs across participants were as follows: IOG: (44, −72, −6), FG (44, −46, −16), and pSTS (44, −56, 16).The time series for each participant was computed by using the first eigenvariate from all voxel time series in the ROI. This BOLD time series was deconvolved to estimate a “neuronal time series” for this region using the PPI-deconvolution parameter defaults in SPM5 (). The PPI term (PPI regressor) was calculated as the element-by-element product of the ROI neuronal time series and a vector coding for the main effect of task (i.e., 1 for gaze shifts and −1 for open/closed eye movements). This product was reconvolved by the canonical hemodynamic response function (HRF). The model also included the main effects of task convolved by the HRF, the neuronal time series for each source, and the movement regressors as effects of no interest.Subject-wise PPI models () were run, and contrast images were generated for positive and negative PPIs. The identified regions have greater or lesser change in connectivity with the source region according to context (i.e., gaze shifts vs. open/close eyes). The contrast images were then entered into second-level GLM analyses for contrasts of interest, and SPM t-maps generated using Gaussian random field theory to make statistical inferences. Two approaches to statistically threshold maps were applied. First, for small volume corrections (SVCs) within a priori ROI proposed in the model by , p. 231 (IPS and FEF) as well as the STG implicated in spatial attention () and gaze perception (), the threshold was set at P < 0.05 family-wise error corrected (). For the IPS, we defined an 8-mm sphere using as center the local maxima from a previous study assessing the role of IPS across various attentional tasks (32, −47, 56; ). For FEF, we used the same sphere size and took coordinates (35, −4, 47) from a meta-analysis on the location of the human FEF (). The coordinates reported above are in MNI space and were converted from Talairach space with the tal2icbm_spm transform (). The bilateral STG ROIs were defined using the WFU pick atlas () and AAL () atlas. To explore other possible regions, which were not predicted, a threshold of P < 0.001, uncorrected (unc.), with a minimum of 10 contiguous voxels was used. […]

Pipeline specifications

Software tools SPM, AAL
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens