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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., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

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 © 2010 Elsevier Inc. All rights reserved.)

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.

Imaging genetics provides an enormous amount of functional-structural data on gene effects in living brain, but the sheer quantity of potential phenotypes raises concerns about false discovery. Here, we provide the first empirical results on false positive rates in imaging genetics. We analyzed 720 frequent coding SNPs without significant association with schizophrenia and a subset of 492 of these without association with cognitive function. Effects on brain structure (using voxel-based morphometry, VBM) and brain function, using two archival imaging tasks, the n-back working memory task and an emotional face matching task, were studied in whole brain and regions of interest and corrected for multiple comparisons using standard neuroimaging procedures. Since these variants are unlikely to impact relevant brain function, positives obtained provide an upper empirical estimate of the false positive association rate. In a separate analysis, we randomly permuted genotype labels across subjects, removing any true genotype-phenotype association in the data, to derive a lower empirical estimate. At a set correction level of 0.05, in each region of interest and data set used, the rate of positive findings was well below 5% (0.2-4.1%). There was no relationship between the region of interest and the false positive rate. Permutation results were in the same range as empirically derived rates. The observed low rates of positives provide empirical evidence that the type I error rate is well controlled by current commonly used correction procedures in imaging genetics, at least in the context of the imaging paradigms we have used. In fact, our observations indicate that these statistical thresholds are conservative.

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]