Search

Altered connectivity of dorsolateral prefrontal cortex (DLPFC) and hippocampus during working memory is considered an intermediate phenotype for schizophrenia (SCZ), but the relevance for other mental disorders with shared genetic background remains unknown. Here we investigated its presence in unaffected first-degree relatives of patients with bipolar disorder (BD) or major depressive disorder (MDD). Furthermore, we aimed to provide an independent replication of this phenotype in first-degree relatives of SCZ patients. We acquired functional magnetic resonance imaging (fMRI) data from 309 healthy controls and 218 healthy first-degree relatives of index patients with SCZ (n = 62), BD (n = 66) and MDD (n = 90), who completed the n-back working memory paradigm. We observed a significant group effect on DLPFC–hippocampus coupling (PFWE = .031, all P-values region of interest [ROI] corrected). Post hoc comparisons revealed that this effect was driven by the SCZ relatives, who showed a significant increase in the negative functional connectivity of the DLPFC and right hippocampus compared to controls (PFWE = .001), BD relatives (PFWE = .015) and trend-wise also MDD relatives (PFWE = .082). Comparison of BD and MDD relatives to the controls revealed no difference (PFWE-values > .451). Supplementary analyses suggested that the SCZ relatives finding is robust to a range of potential confounds, including structural differences. Our data further support altered DLPFC–hippocampus connectivity during working memory as an intermediate phenotype for SCZ. This suggests that this phenotype is relatively specific to SCZ and does not translate to other genetically related disorders in the mood-psychosis spectrum.

Imaging genetics is the study of how modifications of the DNA sequence may manifest in altered brain structure, function, and biochemistry. This field has been made possible through the combined analysis of genotyping and neuroimaging data in the same individual. Imaging genetics has had an especially significant impact in the realm of psychiatry, where the identification of genetic variants associated with heritable “intermediate phenotypes” provides important clues about underlying molecular mechanisms. More recently, integration of information on biological pathways, protein interactions, epigenetic factors, and gene expression has provided a holistic framework for developing biologically driven hypotheses of gene effects in the brain. This chapter gives a historical perspective on the imaging genetics approach, outlines a few key examples, and speculates on future directions for this exciting discipline.

The search for quantifiable biological mediators of genetic risk or 'intermediate phenotypes' is an essential strategy in psychiatric neuroscience and a useful tool for exploring the complex relationships between genes, neural circuits and behaviors. In recent years, the examination of connectivity-based intermediate phenotypes has gained increasing popularity in the study of schizophrenia, a brain disorder that manifests in early adulthood and disturbs a wide range of neural network functions. To date, several potential connectivity phenotypes have been identified that link neuroimaging measures of neural circuit interaction to genetic susceptibility for schizophrenia. This paper briefly reviews recent advances, current limitations and future directions in the search for functional connectivity intermediate phenotypes for schizophrenia across different cognitive domains.

Graph theoretical functional magnetic resonance imaging (fMRI) studies have demonstrated that brain networks reorganize significantly during motor skill acquisition, yet the associations between motor learning ability, brain network features, and the underlying biological mechanisms remain unclear. In the current study, we applied a visually guided sequential pinch force learning task and graph theoretical analyses to investigate the associations between short-term motor learning ability and resting-state brain network metrics in 60 healthy subjects. We further probed the test-retest reliability (n = 26) and potential effects of the N-methyl-d-aspartate (NMDA) antagonist ketamine (n = 19) in independent healthy volunteers. Our results show that the improvement of motor performance after short-term training was positively correlated with small-worldness (p = 0.032) and global efficiency (p = 0.025), whereas negatively correlated with characteristic path length (p = 0.014) and transitivity (p = 0.025). In addition, using network-based statistics (NBS), we identified a learning ability–associated (p = 0.037) and ketamine-susceptible (p = 0.027) cerebellar-cortical network with fair to good reliability (intraclass correlation coefficient [ICC] > 0.7) and higher functional connectivity in better learners. Our results provide new evidence for the association of intrinsic brain network features with motor learning and suggest a role of NMDA-related glutamatergic processes in learning-associated subnetworks., Author Summary Learning a new motor skill prompts immediate reconfigurations of distributed brain networks followed by adaptive changes in intrinsic brain circuits related to synaptic plasticity. Here, we identify global brain network properties and a cerebellar-cortical functional subnetwork that are both significantly associated with motor learning ability in a previously trained visuomotor task in humans. We further show that the associated functional subnetwork connectivity but not the global brain network properties are susceptible to ketamine. Our findings suggest a distinct functional role for learning-related global versus local network metrics and support the idea of a preferential susceptibility of learning-associated subnetworks to N-methyl-d-aspartate antagonist and plasticity-related consolidation effects.

GSK1144814 is a potent, insurmountable antagonist at human NK1 and NK3 receptors. Understanding the relationship between plasma pharmacokinetics and receptor occupancy in the human brain, was crucial for dose selection in future clinical studies. GSK1144814 occupancy data were acquired in parallel with the first-time-in-human safety and tolerability study. [11C]GR-205171 a selective NK1 receptor PET ligand was used to estimate NK1 occupancy at several time-points following single dose administration of GSK1144814. The time-plasma concentration–occupancy relationship post-single dose administration was assessed, and used to predict the plasma concentration–occupancy relationship following repeat dose administration. Repeat dose predictions were tested in a subsequent cohort of subjects examined following approximately 7 and 14 days dosing with GSK1144814. GSK1144814 was shown to demonstrate a dose-dependent occupancy of the NK1 receptor with an estimated in vivo EC50 ~0.9 ng/mL in the human brain. A direct relationship was seen between the GSK1144814 plasma concentration and its occupancy of the brain NK1 receptor, indicating that in future clinical trials the occupancy of brain receptors can be accurately inferred from the measured plasma concentration. Our data provided support for the further progression of this compound and have optimised the likely therapeutic dose range.

Functional interactions between the dorsolateral prefrontal cortex and hippocampus during working memory have been studied extensively as an intermediate phenotype for schizophrenia. Coupling abnormalities have been found in patients, their unaffected siblings, and carriers of common genetic variants associated with schizophrenia, but the global genetic architecture of this imaging phenotype is unclear. To achieve genome-wide hypothesis-free identification of genes and pathways associated with prefrontal–hippocampal interactions, we combined gene set enrichment analysis with whole-genome genotyping and functional magnetic resonance imaging data from 269 healthy German volunteers. We found significant enrichment of the synapse organization and biogenesis gene set. This gene set included known schizophrenia risk genes, such as neural cell adhesion molecule (NRCAM) and calcium channel, voltage-dependent, beta 2 subunit (CACNB2), as well as genes with well-defined roles in neurodevelopmental and plasticity processes that are dysfunctional in schizophrenia and have mechanistic links to prefrontal–hippocampal functional interactions. Our results demonstrate a readily generalizable approach that can be used to identify the neurogenetic basis of systems-level phenotypes. Moreover, our findings identify gene sets in which genetic variation may contribute to disease risk through altered prefrontal–hippocampal functional interactions and suggest a link to both ongoing and developmental synaptic plasticity.

IMPORTANCE: Although deficits in emotional processing are prominent in schizophrenia, it has been difficult to identify neural mechanisms related to the genetic risk for this highly heritable illness. Prior studies have not found consistent regional activation or connectivity alterations in first-degree relatives compared with healthy controls, suggesting that a more comprehensive search for connectomic biomarkers is warranted.OBJECTIVES: To identify a potential systems-level intermediate phenotype linked to emotion processing in schizophrenia and to examine the psychological association, task specificity, test-retest reliability, and clinical validity of the identified phenotype.DESIGN, SETTING, AND PARTICIPATIONS: The study was performed in university research hospitals from June 1, 2008, through December 31, 2013. We examined 58 unaffected first-degree relatives of patients with schizophrenia and 94 healthy controls with an emotional face-matching functional magnetic resonance imaging paradigm. Test-retest reliability was analyzed with an independent sample of 26 healthy participants. A clinical association study was performed in 31 patients with schizophrenia and 45 healthy controls. Data analysis was performed from January 1 to September 30, 2014.MAIN OUTCOMES AND MEASURES: Conventional amygdala activity and seeded connectivity measures, graph-based global and local network connectivity measures, Spearman rank correlation, intraclass correlation, and gray matter volumes.RESULTS: Among the 152 volunteers included in the relative-control sample, 58 were unaffected first-degree relatives of patients with schizophrenia (mean [SD] age, 33.29 [12.56]; 38 were women), and 94 were healthy controls without a first-degree relative with mental illness (mean [SD] age, 32.69 [10.09] years; 55 were women). A graph-theoretical connectivity approach identified significantly decreased connectivity in a subnetwork that primarily included the limbic cortex, visual cortex, and subcortex during emotional face processing (cluster-level P corrected for familywise error = .006) in relatives compared with controls. The connectivity of the same subnetwork was significantly decreased in patients with schizophrenia (F = 6.29, P = .01). Furthermore, we found that this subnetwork connectivity measure was negatively correlated with trait anxiety scores (P = .04), test-retest reliable (intraclass correlation coefficient = 0.57), specific to emotional face processing (F = 17.97, P CONCLUSIONS AND RELEVANCE: Our results indicate that altered connectivity in a visual-limbic subnetwork during emotional face processing may be a functional connectomic intermediate phenotype for schizophrenia. The phenotype is reliable, task specific, related to trait anxiety, and associated with manifest illness. These data encourage the further investigation of this phenotype in clinical and pharmacologic studies.

Summary Objective Several studies have established links between thyroid gland dysfunction and mood disorders, in particular major depressive disorder (MDD). Preliminary evidence also suggests that thyroid hormone gene variants influence grey matter (GM) volume, which is reportedly altered in patients with MDD. This study tested for associations of single nucleotide polymorphisms (SNPs) in two thyroid hormone transporter genes with regional GM volume differences in a large sample population of patients with recurrent MDD and healthy volunteers. Methods High-resolution T1-weighted magnetic resonance images were acquired at the Max Planck Institute, Munich, Germany. After quality control procedures were applied to images and genotypes, data for 134 patients and 144 well-matched controls were included in a stringent voxel-based morphometry analysis using non-stationary cluster-based inference. We first tested for associations between 10 candidate SNPs and regional GM volume differences across the combined sample population. We then tested for group-by-genotype interactions (i.e., differential associations determined by group status). Results No significant associations were found between SNPs and regional GM volume when testing across the combined sample population. However, group-by-genotype interactions for two highly correlated SNPs (rs496549 and rs479640) revealed co-localised association clusters in the left occipital cortex ( P -values 0.002 and 0.004, respectively, after full correction for whole brain and multiple SNP testing). The effect magnitudes within the average modulated GM clusters were greater in the control group relative to the MDD group. This study provides supporting evidence to the existing literature that thyroid-related gene variants influence regional GM volume. We propose that future studies should consider neuroimaging phenotypes when investigating the effects of thyroid hormones on brain structure and function.