Computational protocol: The Neural Correlates of Identity Faking and Concealment: An fMRI Study

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Protocol publication

[…] Functional imaging data was performed in a 3.0T Tim Trio system (Siemens Medical Systems Erlangen, Germany) using a twelve-channel head coil in the Shanghai Key Laboratory of Magnetic Resonance. In the experiment, a T2*-sensitive ultrafast multi slice echo planar imaging (EPI) sequence sensitive to blood oxygen level-dependent (BOLD) contrast was used. Thirty-three transversal slices of functional images that covered the whole brain were acquired using a single shot gradient echo EPI sequence with TR = 2000 ms, TE = 30 ms, flip angle 90°, matrix size = 64×64, slice thickness = 3 mm, field of view  = 220 mm2.The imaging data were analyzed using Statistical Parametric Mapping software (SPM8, Wellcome Department of Cognitive Neurology, London, UK. The functional data of each participant were motion and slice-time corrected, spatially normalized into the standard MNI space, and smoothed using a Gaussian kernel of 8 mm FWHM. To obtain regions differentially activated by the different item types, we modeled the four item classes (identity concealment, identity faking, control condition, and irrelevant items) as separate regressors that were convolved with the canonical hemodynamic response function using a general linear model (GLM) at the individual level. Simple contrast maps (identity concealment vs. control condition, identity faking vs. control condition, identity concealment vs. identity faking) were then entered into a random effects analysis to identify regions that showed significant activation differences between item types (t-contrast). Post hoc analyses were accomplished by separate comparisons of the average percentage signal change as a function of item type in all regions of interest (ROI), functionally defined by the t-contrast of the aforementioned random effects analysis. To minimize the biased selection of voxels for individual differences regression analysis, the functionally defined ROIs were replaced with spherical ROIs (radius 8 mm) centered on the centers of mass of the original ROIs. These values were obtained using the MarsBaR ( all random-effects SPM-analyses, p-values were corrected for multiple comparisons using a FWE of .05. Additionally, activations were required to reach a spatial extend threshold of at least 20 contiguous voxels.To distinguish the spatial patterns of brain activities between the concealment condition and the faking condition, we extracted the time course data for the ROIs from smoothed images. These features were then entered into a Support Vector Machine (SVM) with a non-linear kernel named radial basis function after using the principal component analysis according to the correlational matrix. We analyzed these data using the libsvm toolbox . We used a cross validation method called the “Out” method. Using this method, data from 11 subjects were used to train the support vector machine and then the remaining one was used to be predicted by the SVM model. This procedure was repeated 12 times. The advantage of the “Out” method is that it controls for individual differences; however the size of training samples is not as big as other methods . To further explore whether the brain activities in each ROI were different for the identity faking and concealment conditions, we performed the same SVM procedures on the data from each ROI. […]

Pipeline specifications

Software tools SPM, LIBSVM
Applications Miscellaneous, Functional magnetic resonance imaging
Organisms Homo sapiens