Computational protocol: The Structural Correlates of Statistical Information Processing during Speech Perception

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Protocol publication

[…] Whole-brain CT measures were obtained from FreeSurfer routines calculating the closest distance from the gray/white boundary to the gray/CSF boundary at each vertex on the tessellated surface []. The CT maps were created using spatial intensity gradients across tissue classes and are therefore not simply reliant on absolute signal intensity. We exported the individual maps to SUMA (AFNI’s 3D cortical surface mapping module; []) where we conducted group-level analyses on the surface. Prior to the group-level analyses, we smoothed the individual CT data using a 10-mm full-width-at-half-maximum Heat kernel.For the whole-brain CT measures, we tested, using a vertex-wise linear regression model (AFNI covariate analysis option in the 3dttest++ program), whether CT correlated with the statistical sensitivity index that we derived for each participant. Age was included in the analysis as a covariate to control for the well-established relation between age and CT [, ]. We also conducted an additional vertex-wise linear regression model in which sex was included as an additional covariate. This analysis is detailed in . The resulting group maps were corrected for multiple comparisons using the Monte Carlo simulation procedure implemented in FreeSurfer. Only areas in which CT significantly correlated with the statistical sensitivity index were included in the final corrected maps (individual vertex threshold of p < 0.05, corrected for multiple comparisons to achieve a whole-brain family-wise error (FWE) rate of p < 0.05 (clusters ≥ 437 vertices)). […]

Pipeline specifications

Software tools FreeSurfer, AFNI
Application Functional magnetic resonance imaging
Organisms Homo sapiens
Diseases Muscular Diseases