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The genetic architecture of schizophrenia is based on polygenic trajectories. Indeed, genes converge on molecular co-expression pathways, which may be associated with heritable characteristics of patients and their siblings, called intermediate phenotypes, such as prefrontal anomalies and thalamic dysconnectivity during attentional control .
Here, we investigated in healthy humans association between co-expression of genes with coordinated thalamo-prefrontal (THA-PFC) expression and functional connectivity during attentional control.
We used Brainspan dataset to characterize a coordinated THA-PFC expression gene list by correlating post-mortem gene expression in both areas (Kendall's Tau>.76, Bonferroni P < .05). Then, we identified a PFC co-expression network1 and tested all gene sets for THA-PFC and PGC loci  enrichments (P < .05). SNPs associated with the first principal component of the resulting enriched gene set were combined in a Polygenic Co-Expression Index (PCI) . We conducted Independent Component Analysis (ICA) on attentional control fMRI data (n = 265) and selected Independent Components (ICs) including the thalamus and being highly correlated with an attentional control network2. Multiple regressions were conducted (predictor: PCI) using a thalamic cluster previously associated with familial risk for schizophrenia  as ROI (FWE P < .05).
In one of the 8 ICs of interest there was a positive effect of PCI on thalamic connectivity strength in a cluster overlapping with our ROI (Z = 4.3).
Decreased co-expression of genes included in PCI predicts thalamic dysconnectivity during attentional control, suggesting a novel co-regulated molecular pathway potentially implicated in genetic risk for schizophrenia.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
Neuroimaging studies have identified several candidate biomarkers of schizophrenia. However, it is unclear whether the considerable variability in these neurobiological correlates between patients can be translated into the clinical setting.
We aimed to identify neuroimaging predictors of clinical course in patients with schizophrenia. Combined with the identification of genetically determined markers of schizophrenia risk, our studies aimed to elucidate the biological basis and the clinical relevance of inter-individual variability between patients.
We included over 150 patients with schizophrenia and 279 healthy volunteers across five neuroimaging centers in the framework of the IMAGEMEND project . We performed multiple studies on MRI scans using random forests and ROC curves to predict clinical course. Data from healthy controls served to normalize the data from the clinical population and to provide a benchmark for the findings.
We identified ensembles of neuroimaging markers and of genetic variants predictive of clinical course. Results highlight that (i) brain imaging carries significant clinical information, (ii) clinical information at baseline can considerably increase prediction accuracy.
The methodological challenges and the results will be discussed in the context of recent findings from other multi-site studies. We conclude that brain imaging data on their own right are relevant to stratify patients in terms of clinical course; however, complementing these data with other modalities such as genetics and clinical information is necessary to further develop the field towards clinical application of the predictions.
Disclosure of interest
Giulio Pergola is the academic supervisor of a Hoffmann-La Roche Collaboration grant that partially funds his salary.
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