Functional connectivity analysis
schizophrenia, Conn
REGRESSION, PBC, BRAIN, CONN
All fMRI datasets were subjected to the default pre-processing pipeline as implemented in the MATLAB conn toolbox. (For an overview of the MRI sequence parameters and the preprocessing steps see the supplementary material (Supplementary Methods MWe conducted both ROI-to-ROI and seed-to-voxel functional connectivity analyses. In the first level analyses, Pearson correlation coefficients were calculated between the fMRI time series of the respective ROI-ROI or ROI-voxel pairs. For the pharmaco-fMRI dataset, correlation coefficients were calculated for each of the six pharmacological conditions separately (ketamine, midazolam as well as placebo pre and post infusion, respectively). In the second level analyses, the individual Fisher-z-transformed correlation coefficients served as dependent variables of a general linear model. To study the impact of the SNP on functional connectivity in the schizophrenia dataset, the number of disease-associated C-alleles (i.e. 0 for wildtype, 1 for heterozygous, and 2 for homozygous) served as the predictor of interest and the respective regression coefficients entered a one sample t-test when hypothesis testing was necessary. We chose this approach as we assumed an additive genetic model as commonly employed in Expression QTL (eQTL) analyses [In the pharmaco-fMRI dataset, the different pharmacological effects on functional connectivity were assessed by the contrastsketamine > placebo, i.e. (ketamine post – ketamine pre) – (placebo post – placebo pre),midazolam > placebo, i.e. (midazolam post – midazolam pre) – (placebo post – placebo pre)ketamine > midazolam, i.e. (ketamin post – ketamine pre) - (midazolam post – midazolam pre).For hypothesis testing, paired t-tests were employed.To assess the similarity between the effect of the genetic variant and each of the three pharmacological effects on functional connectivity, we conducted a correlation analysis. Since we hypothesized that the correlation should depend on the expression of NMDARs and the density of Parvalbumin-positive interneurons as well as glutamatergic neurons, we ranked the regions of the Harvard Oxford Atlas according to their averaged expression (i.e. averaged z-values) of the three marker genes GRIN1 (encoding the constitutive NMDAR 1 subunit), Parvalbumin and SLC17A7 (encoding the ‘vesicular glutamate transporter 1’ - VGLUT1, the main marker indicating the glutamatergic identity of a neuron), as revealed by the analysis of the Allen Brain dataset (see Supplementary Methods MSubsequently, we re-ordered the matrices of the averaged connectivity changes according to the ranks of the regions and repeatedly assessed the correlation, starting from the full 106 × 106 matrix of connectivity changes and iteratively removing the region with the lowest expression of the target genes until only the three highest expressing regions remained. Correlation was estimated by calculating Pearson correlation coefficients for the lower triangles of the respective symmetric connectivity submatrices remaining at each iteration. Due to the inherent interdependence of the whole-brain functional connectivity estimates, the significance of the correlation curves was assessed by comparing their area under the curve with a null distribution which was generated by 10,000 random permutations of genotype (i.e. in each iteration, genotype labels were randomly re-assigned to the patients and the second level analysis and all subsequent steps were re-done based on the permuted genotype, see Fig. Since the contrast ketamine > midazolam showed the highest similarity with the SNP effect in our functional connectivity analysis and—as shown before [In order to further explore the topography of the SNP-associated connectivity changes, we first conducted a seed to voxel analysis using the left cuneus as the seed, as it exhibited the highest average expression of our genes of interest in the postmortem dataset. The two strongest connectivity changes of this seed were subsequently re-assessed in the pharmaco-fMRI dataset for the contrast ketamine > midazolam as well as in the combined schizophrenia/control sample for the contrast schizophrenia > healthy controls using ROI-to-ROI analyses.Connectivity between striato-pallido-thalamic regions and cortical regions of the auditory-sensory-motor (ASM) network was assessed, applying a multivariate ROI to ROI analysis which addressed the overall connectivity between the two sets of regions (functional network connectivity analysis of the Conn toolbox [To identify further NMDAR-related imaging phenotypes, we applied another functional network connectivity analysis studying the effect of the GRIN2A variant on a whole-brain level and in a hypothesis-free manner. This time, networks of ROIs were defined using a data-driven hierarchical clustering procedure. Subsequently, for each network, the within network connectivity and for each pair of networks, the between network connectivity were assessed, respectively. FDR-correction was applied to control for multiple testing on the network level (p-FDR < 0.05). Again, the two strongest connectivity changes related to the genetic variant were subsequently re-assessed for the other datasets/contrasts of interest, using ROI-to-ROI analyses (for more details see supplementary methods MFinally, since it turned out that the GRIN2A-variant was associated with language-related negative symptomatology, we also aimed at identifying the underlying connectivity changes potentially mediating this effect. Since we hypothesized that the respective connections involved brain regions related to language function, we conducted a seed to voxel functional connectivity analysis using the left posterior superior temporal gyrus (pSTG, corresponding to Wernicke’s area) and the left inferior frontal gyrus (pars triangularis, corresponding to Broca’s area) of the Harvard Oxford Atlas as two separate seed regions. We first identified connections which were negatively affected by the genetic variant, applying a general linear model with the number of C-alleles constituting the predictor of interest Those connections were subsequently tested for their association with qualitative language symptoms (which showed strongest association with the genetic variant), applying a partial correlation analysis. Connections that exhibited a significant relationship both with the genetic variant and language symptomatology were further assessed for the two other datasets/contrasts of interestFor all seed to voxel analyses presented in this paper, we corrected for multiple testing by applying Gaussian Random Field theory with a combination of a height threshold of Statistical analyses and visualization of data were performed in Matlab 2020a (The MathWorks, Inc., Natick, USA), the Matlab conn toolbox, SPSS 27 (IBM, Armonk, USA), RStudio (RStudio Team (2021). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA URL 