Computational protocol: Neural Primacy of the Salience Processing System in Schizophrenia

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Protocol publication

[…] fMRI data was preprocessed using SPM8 (http://www.fil.ion.ucl.ac.uk/spm and Data Processing Assistant for resting-state fMRI; ). Data were corrected for slice-timing differences and spatially realigned to the first image of the data set. Movement parameters were assessed for each participant, and participants were excluded if movement exceeded 3 mm. Further, we employed ArtRepair to correct movement artifacts using an interpolation method (http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html). The first five volumes of functional images were discarded to allow stability of the longitudinal magnetization. A single data set was produced from a weighted summation of the dual-echo dynamic time course (). Retrospective physiological correction of this data set was then performed (). The functional scans were then spatially normalized using the unified segmentation approach and smoothed using a Gaussian kernel of 8 mm full-width at half-maximum. After this, linear detrending and filtering using a band-pass filter (0.01–0.08Hz) was done to eliminate low-frequency fluctuations and high-frequency noise. Finally, variance accounted for by nuisance covariates including six head motion parameters, global mean signal, white-matter signal, and CSF signal was removed by regression before conducting a seed-based regional functional connectivity analysis. [...] The FC and GCA maps from each individual subject were analyzed using separate one-sample t test for the entire sample (both patients and controls) with an FWE corrected p < 0.05 for positive and negative coefficients. This threshold was used to ensure that the clusters emerging in the one-sample t test are unlikely to be due to a type 1 error. From the results, we derived search volume masks for the FC and GCA to constrain the subsequent between-group analyses. These masks represented regions with significant instantaneous positive correlation or anticorrelation with the seed region and significant excitatory or inhibitory influence to and from the seed region in the whole sample. Between-group analyses were conducted using an unpaired t test (FWE corrected p < 0.05), with the search volume corrected for the masks used in the analyses. For regions showing significant group differences at the FWE-corrected threshold, follow-up one-sample t tests were conducted to investigate the direction of the Granger causal influence in each group separately. These tests were Bonferroni corrected for a total of eight follow-up comparisons. In addition to such constrained analyses, we also carried out a whole-brain between-group analysis (at uncorrected p < 0.001) in order to identify informative group differences that may exist in regions outside the masks derived from one-sample t tests. As this exploratory search has a higher likelihood of identifying false-positive clusters, we applied an additional extent criterion of k = 30. Age and gender were used as covariates in all group-level analyses. Within the patient group, bivariate correlations were used to examine the influence of antipsychotic medications on the mean coefficients within the clusters that emerged as significant from the two-sample t tests in both FC and GCA comparisons. All group-level analyses were carried out using the SPM8 software and the toolboxes MarsBar (http://marsbar.sourceforge.net) and xjview (http://www.alivelearn.net/xjview8), in addition to MRICron (http://www.mccauslandcenter.sc.edu/mricro/mricron) to visualize the results. […]

Pipeline specifications

Software tools SPM, ArtRepair, MRIcron
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens
Diseases Psychoses, Substance-Induced