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Copy number variations (CNV) involving multiple genes are ideal models to study polygenic neuropsychiatric disorders. Since 22q11.2 deletion is regarded as the most important single genetic risk factor for developing schizophrenia, characterizing the effects of this CNV on neural networks offers a unique avenue towards delineating polygenic interactions conferring risk for the disorder. We used a Df(h22q11)/+ mouse model of human 22q11.2 deletion to dissect gene expression patterns that would spatially overlap with differential resting-state functional connectivity (FC) patterns in this model (N = 12 Df(h22q11)/+ mice, N = 10 littermate controls). To confirm the translational relevance of our findings, we analyzed tissue samples from schizophrenia patients and healthy controls using machine learning to explore whether identified genes were co-expressed in humans. Additionally, we employed the STRING protein-protein interaction database to identify potential interactions between genes spatially associated with hypo- or hyper-FC. We found significant associations between differential resting-state connectivity and spatial gene expression patterns for both hypo- and hyper-FC. Two genes, Comt and Trmt2a , were consistently over-expressed across all networks. An analysis of human datasets pointed to a disrupted co-expression of these two genes in the brain in schizophrenia patients, but not in healthy controls. Our findings suggest that COMT and TRMT2A form a core genetic component implicated in differential resting-state connectivity patterns in the 22q11.2 deletion. A disruption of their co-expression in schizophrenia patients points out a prospective cause for the aberrance of brain networks communication in 22q11.2 deletion syndrome on a molecular level. [ABSTRACT FROM AUTHOR]

Abstract: Alterations of connectivity are central to the systems-level pathophysiology of schizophrenia. One of the best-established genome-wide significant risk variants for this highly heritable disorder, the rs1344706 single nucleotide polymorphism in ZNF804A, was recently shown to modulate connectivity in healthy carriers during working memory (WM) in a pattern mirroring that which was found in overt disease. However, it was unclear whether this finding is specific to WM or if it is present regardless of cognitive state. Therefore, we examined genotype effects on connectivity in healthy carriers during rest and an emotion processing task without WM component. 111 healthy German subjects performed a battery of functional imaging tasks. Functional connectivity with the right dorsolateral prefrontal cortex during rest and an implicit emotion recognition task was determined using the seed voxel method and compared to results during WM. During rest and during the emotional task, a pattern of reduced interhemispheric prefrontal connectivity with increasing number of rs1344706 risk alleles could be seen that was close to identical to that during WM, suggesting a state-independent influence of the genetic variant on interhemispheric processing, possibly through structural effects. By contrast, the abnormal prefronto-hippocampal connectivity was only seen during the WM task, indicating a degree of task specificity in agreement with prior results in patients with schizophrenia. Our findings confirm a key role for disturbed functional connectivity in the genetic risk architecture of schizophrenia and identify cognitive state-dependent and independent components with regard to WM function. [Copyright &y& Elsevier]

Abstract: Magnetic resonance spectroscopy enables the in vivo analysis of certain aspects of brain biochemistry. Reduced N-acetylaspartate in key regions of schizophrenia has been reported repeatedly but not without controversy. Our objective is to investigate whether reduced N-acetylaspartate concentrations determined without correction for individual T2 relaxation time (referred to as ‘apparent tNAA concentration’) are due to a reduced absolute N-acetylaspartate concentration or to altered relaxation properties. For this purpose we measured absolute concentrations while evaluating individual T2 relaxation times. We evaluated the metabolite concentrations and metabolite/water relaxation times of a frontal white matter voxel from 23 patients who met DSM-IV criteria for schizophrenia and 29 healthy control subjects with similar age at a 3 T magnetic resonance scanner. A significantly reduced N-acetylaspartate concentration as well as shortened N-acetylaspartate''s T2 relaxation time in the schizophrenic patient group was found. The apparent N-acetylaspartate concentration difference between healthy controls and patients with schizophrenia increased with the echo time due to a decreased N-acetylaspartate''s T2 in the schizophrenic group. No group difference was found for any other metabolite concentration or metabolite/brain water relaxation time. These findings of reduced N-acetylaspartate as well as shortened N-acetylaspartate''s T2 relaxation time give further evidence for microstructural white matter changes in schizophrenia. Furthermore, they elucidate why reports of a reduced N-acetylaspartate concentration in schizophrenia were not always corroborated. [Copyright &y& Elsevier]

• Structural brain networks can be modeled as a trade-off between wiring cost and topological features. • Schizophrenia patients show lower spatial constraints and topological facilitation. • Individual model parameters are associated with genetic risk for schizophrenia and cognition. Alterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of altered brain network formation. We evaluated different generative network models in healthy controls (n =152), schizophrenia patients (n =66), and their unaffected first-degree relatives (n =32), and we identified spatial and topological factors contributing to network formation. We further investigated how these factors relate to cognition and to polygenic risk for schizophrenia. Our data show that among the four tested classes of generative network models, structural brain networks were optimally accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation across study groups. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. Further exploratory analyses point to potential associations of the model parameter reflecting spatial constraints with the polygenic risk for schizophrenia and cognitive performance. Our results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to regular brain network formation as well as altered connectomes in schizophrenia and healthy individuals at familial risk for schizophrenia. On an exploratory level, our data further point to the potential relevance of spatial constraints for the genetic risk for schizophrenia and general cognitive functioning, thereby encouraging future studies in following up on these observations to gain further insights into the biological basis and behavioral relevance of model parameters. [ABSTRACT FROM AUTHOR]