Brain connectivity in psychiatric imaging genetics

Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics

Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject. These approaches, however, either do not accomodate a more general association between community structure and an outcome or cannot accommodate additional covariates that may confound the association of interest. We propose a semiparametric kernel machine regression model for either a continuous or binary outcome, where covariate effects are modeled parametrically and brain connectivity measures are measured nonparametrically. By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. We evaluate our proposed methodology on both simulated and real datasets.

AN INTEGRATIVE NETWORK-BASED MEDIATION MODEL (NMM) TO ESTIMATE MULTIPLE GENETIC EFFECTS ON OUTCOMES MEDIATED BY FUNCTIONAL CONNECTIVITY

Functional connectivity of the brain, characterized by interconnected neural circuits across functional networks, is a cutting-edge feature in neuroimaging. It has the potential to mediate the effect of genetic variants on behavioral outcomes or diseases. Existing mediation analysis methods can evaluate the impact of genetics and brain structurefunction on cognitive behavior or disorders, but they tend to be limited to single genetic variants or univariate mediators, without considering cumulative genetic effects and the complex matrix and group and network structures of functional connectivity. To address this gap, the paper presents an integrative network-based mediation model (NMM) that estimates the effect of multiple genetic variants on behavioral outcomes or diseases mediated by functional connectivity. The model incorporates group information of inter-regions at broad network level and imposes low-rank and sparse assumptions to reflect the complex structures of functional connectivity and selecting network mediators simultaneously. We adopt block coordinate descent algorithm to implement a fast and efficient solution to our model. Simulation results indicate the efficacy of the model in selecting active mediators and reducing bias in effect estimation. With application to the Human Connectome Project Youth Adult (HCP-YA) study of 493 young adults, two genetic variants (rs769448 and rs769449) on the APOE4 gene are identified that lead to deficits in functional connectivity within visual networks and fluid intelligence.

Long-term adaptation of prefrontal circuits in a mouse model of NMDAR hypofunction

Pharmacological approaches to induce N-methyl-d-aspartate receptor (NMDAR) hypofunction have been intensively used to understand the aetiology and pathophysiology of schizophrenia. Yet, the precise cellular and molecular mechanisms that relate to brain network dysfunction remain largely unknown. Here, we used a set of complementary approaches to assess the functional network abnormalities present in male mice that underwent a 7-day subchronic phencyclidine (PCP 10 mg/kg, subcutaneously, once daily) treatment. Our data revealed that pharmacological intervention with PCP affected cognitive performance and auditory evoked gamma oscillations in the prefrontal cortex (PFC) mimicking endophenotypes of some schizophrenia patients. We further assessed PFC cellular function and identified altered neuronal intrinsic membrane properties, reduced parvalbumin (PV) immunostaining and diminished inhibition onto L5 PFC pyramidal cells. A decrease in the strength of optogenetically-evoked glutamatergic current at the ventral hippocampus to PFC synapse was also demonstrated, along with a weaker shunt of excitatory transmission by local PFC interneurons. On a macrocircuit level, functional ultrasound measurements indicated compromised functional connectivity within several brain regions particularly involving PFC and frontostriatal circuits. Herein, we reproduced a panel of schizophrenia endophenotypes induced by subchronic PCP application in mice. We further recapitulated electrophysiological signatures associated with schizophrenia and provided an anatomical reference to critical elements in the brain circuitry. Together, our findings contribute to a better understanding of the physiological underpinnings of deficits induced by subchronic NMDAR antagonist regimes and provide a test system for characterization of pharmacological compounds.

Exploring Imaging Genetic Markers of Alzheimer's Disease Based on a Novel Nonlinear Correlation Analysis Algorithm

Alzheimer's disease (AD) is an irreversible neurological disorder characterized by insidious onset. Identifying potential markers in its emergence and progression is crucial for early diagnosis and treatment. Imaging genetics typically merges genetic variables with multiple imaging parameters, employing various association analysis algorithms to investigate the links between pathological phenotypes and genetic variations, and to unearth molecular-level insights from brain images. However, most existing imaging genetics algorithms based on sparse learning assume a linear relationship between genetic factors and brain functions, limiting their ability to discern complex nonlinear correlation patterns and resulting in reduced accuracy. To address these issues, we propose a novel nonlinear imaging genetic association analysis method, Deep Self-Reconstruction-based Adaptive Sparse Multi-view Deep Generalized Canonical Correlation Analysis (DSR-AdaSMDGCCA). This approach facilitates joint learning of the nonlinear relationships between pathological phenotypes and genetic variations by integrating three different types of data: structural magnetic resonance imaging (sMRI), single-nucleotide polymorphism (SNP), and gene expression data. By incorporating nonlinear transformations in DGCCA, our model effectively uncovers nonlinear associations across multiple data types. Additionally, the DSR algorithm clusters samples with identical labels, incorporating label information into the nonlinear feature extraction process and thus enhancing the performance of association analysis. The application of the DSR-AdaSMDGCCA algorithm on real data sets identified several AD risk regions (such as the hippocampus, parahippocampus, and fusiform gyrus) and risk genes (including VSIG4, NEDD4L, and PINK1), achieving maximum classification accuracy with the fewest selected features compared to baseline algorithms. Molecular biology enrichment analysis revealed that the pathways enriched by these top genes are intimately linked to AD progression, affirming that our algorithm not only improves correlation analysis performance but also identifies biologically significant markers.

Neurogenetic mechanisms of risk for ADHD: Examining associations of polygenic scores and brain volumes in a population cohort

BACKGROUND

ADHD polygenic scores (PGSs) have been previously shown to predict ADHD outcomes in several studies. However, ADHD PGSs are typically correlated with ADHD but not necessarily reflective of causal mechanisms. More research is needed to elucidate the neurobiological mechanisms underlying ADHD. We leveraged functional annotation information into an ADHD PGS to (1) improve the prediction performance over a non-annotated ADHD PGS and (2) test whether volumetric variation in brain regions putatively associated with ADHD mediate the association between PGSs and ADHD outcomes.

METHODS

Data were from the Philadelphia Neurodevelopmental Cohort (N = 555). Multiple mediation models were tested to examine the indirect effects of two ADHD PGSs-one using a traditional computation involving clumping and thresholding and another using a functionally annotated approach (i.e., AnnoPred)-on ADHD inattention (IA) and hyperactivity-impulsivity (HI) symptoms, via gray matter volumes in the cingulate gyrus, angular gyrus, caudate, dorsolateral prefrontal cortex (DLPFC), and inferior temporal lobe.

RESULTS

A direct effect was detected between the AnnoPred ADHD PGS and IA symptoms in adolescents. No indirect effects via brain volumes were detected for either IA or HI symptoms. However, both ADHD PGSs were negatively associated with the DLPFC.

CONCLUSIONS

The AnnoPred ADHD PGS was a more developmentally specific predictor of adolescent IA symptoms compared to the traditional ADHD PGS. However, brain volumes did not mediate the effects of either a traditional or AnnoPred ADHD PGS on ADHD symptoms, suggesting that we may still be underpowered in clarifying brain-based biomarkers for ADHD using genetic measures.

Disrupted-in-schizophrenia 1 Protein Misassembly Impairs Cognitive Flexibility and Social Behaviors in a Transgenic Rat Model

Alterations in cognitive functions, social behaviors and stress reactions are commonly diagnosed in chronic mental illnesses (CMI). Animal models expressing mutant genes associated to CMI represent either rare mutations or those contributing only minimally to genetic risk. Non-genetic causes of CMI can be modeled by disturbing downstream signaling pathways, for example through inducing protein misassembly or aggregation. The Disrupted-in-Schizophrenia 1 (DISC1) gene was identified to be disrupted and thereby haploinsufficient in a large pedigree where it associated to CMI. The DISC1 protein misassembles to an insoluble protein in a subset of CMI patients and this has been modeled in a rat (tgDISC1 rat) where the full-length, non mutant human transgene was overexpressed and cognitive impairments were observed. Here, we investigated the scope of effects of DISC1 protein misassembly by investigating spatial memory, social behavior and stress resilience. In water maze tasks, the tgDISC1 rats showed intact spatial learning and memory, but were deficient in flexible adaptation to spatial reversal learning compared to littermate controls. They also displayed less social interaction. Additionally, there was a trend towards increased corticosterone levels after restraint stress in the tgDISC1 rats. Our findings suggest that DISC1 protein misassembly leads to disturbances of cognitive flexibility and social behaviors, and might also be involved in stress sensitization. Since the observed behavioral features resemble symptoms of CMI, the tgDISC1 rat may be a valuable model for the investigation of cognitive, social and - possibly - also stress-related symptoms of major mental illnesses.

Ridge Penalization in High-Dimensional Testing With Applications to Imaging Genetics

High-dimensionality is ubiquitous in various scientific fields such as imaging genetics, where a deluge of functional and structural data on brain-relevant genetic polymorphisms are investigated. It is crucial to identify which genetic variations are consequential in identifying neurological features of brain connectivity compared to merely random noise. Statistical inference in high-dimensional settings poses multiple challenges involving analytical and computational complexity. A widely implemented strategy in addressing inference goals is penalized inference. In particular, the role of the ridge penalty in high-dimensional prediction and estimation has been actively studied in the past several years. This study focuses on ridge-penalized tests in high-dimensional hypothesis testing problems by proposing and examining a class of methods for choosing the optimal ridge penalty. We present our findings on strategies to improve the statistical power of ridge-penalized tests and what determines the optimal ridge penalty for hypothesis testing. The application of our work to an imaging genetics study and biological research will be presented.

Uncovering Cortical Units of Processing From Multi-Layered Connectomes

Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units.

The Hidden Side of NCAM Family: NCAM2, a Key Cytoskeleton Organization Molecule Regulating Multiple Neural Functions

Although it has been over 20 years since Neural Cell Adhesion Molecule 2 (NCAM2) was identified as the second member of the NCAM family with a high expression in the nervous system, the knowledge of NCAM2 is still eclipsed by NCAM1. The first studies with NCAM2 focused on the olfactory bulb, where this protein has a key role in axonal projection and axonal/dendritic compartmentalization. In contrast to NCAM1, NCAM2's functions and partners in the brain during development and adulthood have remained largely unknown until not long ago. Recent studies have revealed the importance of NCAM2 in nervous system development. NCAM2 governs neuronal morphogenesis and axodendritic architecture, and controls important neuron-specific processes such as neuronal differentiation, synaptogenesis and memory formation. In the adult brain, NCAM2 is highly expressed in dendritic spines, and it regulates synaptic plasticity and learning processes. NCAM2's functions are related to its ability to adapt to the external inputs of the cell and to modify the cytoskeleton accordingly. Different studies show that NCAM2 interacts with proteins involved in cytoskeleton stability and proteins that regulate calcium influx, which could also modify the cytoskeleton. In this review, we examine the evidence that points to NCAM2 as a crucial cytoskeleton regulation protein during brain development and adulthood. This key function of NCAM2 may offer promising new therapeutic approaches for the treatment of neurodevelopmental diseases and neurodegenerative disorders.

Optogenetic perturbation of projections from thalamic nucleus reuniens to hippocampus disrupts spatial working memory retrieval more than encoding

BACKGROUND

Working memory deficits are key cognitive symptoms of schizophrenia. Elevated delta oscillations, which are uniquely associated with the presence of the illness, may be the proximal cause of these deficits. Spatial working memory (SWM) is impaired by elevated delta oscillations projecting from thalamic nucleus reuniens (RE) to the hippocampus (HPC); these findings imply a role of the RE-HPC circuit in working memory deficits in schizophrenia, but questions remain as to whether the affected process is the encoding of working memory, recall, or both. Here, we answered this question by optogenetically inducing delta oscillations in the HPC terminals of RE axons in mice during either the encoding or retrieval phase (or both) of an SWM task.

METHODS

We transduced cells in RE to express channelrhodopsin-2 through bilateral injection of adeno-associated virus, and bilaterally implanted optical fibers dorsal to the hippocampus (HPC). While mice performed a spatial memory task on a Y-maze, the RE-HPC projections were optogenetically stimulated at delta frequency during distinct phases of the task.

RESULTS

Full-trial stimulation successfully impaired SWM performance, replicating the results of the previous study in a mouse model. Task-phase-specific stimulation significantly impaired performance during retrieval but not encoding.

CONCLUSIONS

Our results indicate that perturbations in the RE-HPC circuit specifically impair the retrieval phase of working memory. This finding supports the hypothesis that abnormal delta frequency bursting in the thalamus could have a causal role in producing the WM deficits seen in schizophrenia.

Neural and behavioral correlates associated with adolescent marijuana use

Purpose of Review

Marijuana (MJ) is one of the most commonly used drugs among adolescents. Exposure to MJ during adolescence can lead to alterations in brain development, and, subsequently to the behavioral correlates regulated by the affected brain regions. In this review, we discuss findings from preclinical and human studies examining the relationship between adolescent MJ use and the neurobiological and behavioral correlates associated with it.

Recent Findings

Current findings indicate that adolescent MJ use is associated with alterations in brain structure and function, especially in regions that express high levels of the cannabinoid 1 receptor such as the prefrontal cortex, hippocampus, cerebellum and limbic regions. These alterations are correlated with changes in affective, cognitive and reward-seeking behavior. Furthermore, evidence suggests that exposure to MJ during adolescence can have long-lasting and pronounced neural and behavioral effects into adulthood.

Summary

The wide ranging neural and behavioral correlates associated with MJ use during adolescence highlight the need for further studies to better understand the potential risk factors and/or neurotoxic effects of adolescent MJ use.

Separable neural mechanisms for the pleiotropic association of copy number variants with neuropsychiatric traits

22q11.2, 15q13.3, and 1q21.1 microdeletions attract considerable interest by conferring high risk for a range of neuropsychiatric disorders, including schizophrenia and autism. A fundamental open question is whether divergent or convergent neural mechanisms mediate this genetic pleiotropic association with the same behavioral phenotypes. We use a combination of rodent microdeletion models with high-field neuroimaging to perform a comparative whole-brain characterization of functional and structural mechanisms linked to high-risk states. Resting-state functional and structural magnetic resonance imaging data were acquired on mice carrying heterozygous microdeletions in 22q11.2 (N = 12), 15q13.3 (N = 11), and 1q21.1 (N = 11) loci. We performed network-based statistic, graph, and morphometric analyses. The three microdeletions did not share significant systems-level features. Instead, morphometric analyses revealed microcephaly in 1q21.1 and macrocephaly in 15q13.3 deletions, whereas cerebellar volume was specifically reduced in 22q11.2 deletion. In function, 22q11.2 deletion mice showed widespread cortical hypoconnectivity, accompanied by opposing hyperconnectivity in dopaminergic pathways, which was confirmed by graph analysis. 1q21.1 exhibited distinct changes in posterior midbrain morphology and function, especially in periaqueductal gray, whereas 15q13.3 demonstrated alterations in auditory/striatal system. The combination of cortical hypoconnectivity and dopaminergic hyperconnectivity and reduced cerebellum in 22q11.2 deletion mirrors key neurodevelopmental features of schizophrenia, whereas changes in midbrain and auditory/striatal morphology and topology in 1q21.1 and 15q13.3 rather indicate focal processes possibly linked to the emergence of abnormal salience perception and hallucinations. In addition to insights into pathophysiological processes in these microdeletions, our results establish the general point that microdeletions might increase risk for overlapping neuropsychiatric phenotypes through separable neural mechanisms.

Effects of BDNF Val66Met genotype and schizophrenia familial risk on a neural functional network for cognitive control in humans

Cognitive control represents an essential neuropsychological characteristic that allows for the rapid adaption of a changing environment by constant re-allocation of cognitive resources. This finely tuned mechanism is impaired in psychiatric disorders such as schizophrenia and contributes to cognitive deficits. Neuroimaging has highlighted the contribution of the anterior cingulate cortex (ACC) and prefrontal regions (PFC) on cognitive control and demonstrated the impact of genetic variation, as well as genetic liability for schizophrenia. In this study, we aimed to examine the influence of the functional single-nucleotide polymorphism (SNP) rs6265 of a plasticity-related neurotrophic factor gene, BDNF (Val⁶⁶Met), on cognitive control. Strong evidence implicates BDNF Val⁶⁶Met in neural plasticity in humans. Furthermore, several studies suggest that although the variant is not convincingly associated with schizophrenia risk, it seems to be a modifier of the clinical presentation and course of the disease. In order to clarify the underlying mechanisms using functional magnetic resonance imaging (fMRI), we studied the effects of this SNP on ACC and PFC activation, and the connectivity between these regions in a discovery sample of 85 healthy individuals and sought to replicate this effect in an independent sample of 253 individuals. Additionally, we tested the identified imaging phenotype in relation to schizophrenia familial risk in a sample of 58 unaffected first-degree relatives of schizophrenia patients. We found a significant increase in interregional connectivity between ACC and PFC in the risk-associated BDNF ⁶⁶Met allele carriers. Furthermore, we replicated this effect in an independent sample and demonstrated its independence of structural confounds, as well as task specificity. A similar coupling increase was detectable in individuals with increased familial risk for schizophrenia. Our results show that a key neural circuit for cognitive control is influenced by a plasticity-related genetic variant, which may render this circuit particular susceptible to genetic and environmental risk factors for schizophrenia.

Imaging Genetics Towards a Refined Diagnosis of Schizophrenia

Current diagnoses of schizophrenia and related psychiatric disorders are classified by phenomenological principles and clinical descriptions while ruling out other symptoms and conditions. Specific biomarkers are needed to assist the current diagnostic system. However, complicated gene and environment interactions induce great disease heterogeneity. This unclear etiology and heterogeneity raise difficulties in distinguishing schizophrenia-related effects. Simultaneously, the overlap in symptoms, genetic variations, and brain alterations in schizophrenia and related psychiatric disorders raises similar difficulties in determining disease-specific effects. Imaging genetics is a unique methodology to assess the impact of genetic factors on both brain structure and function. More importantly, imaging genetics builds a bridge to understand the behavioral and clinical implications of genetics and neuroimaging. By characterizing and quantifying the brain measures affected in psychiatric disorders, imaging genetics is contributing to identifying potential biomarkers for schizophrenia and related disorders. To date, candidate gene analysis, genome-wide association studies, polygenetic risk score analysis, and large-scale collaborative studies have made contributions to the understanding of schizophrenia with the potential to serve as biomarkers. Despite limitations, imaging genetics remains promising as more aggregative, clustering methods and imaging genetics-compatible clinical assessments are employed in future studies. We review imaging genetics' contribution to our understanding of the heterogeneity within schizophrenia and the commonalities across schizophrenia and other diagnostic borders, and we will discuss whether imaging genetics is ready to form its own diagnostic system.

Structural covariance across the lifespan: Brain development and aging through the lens of inter-network relationships

Recent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within-network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big-data framework and a novel application of covariate-adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7-90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter-network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter-network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter-network structural covariance across the lifespan.

Mapping the Schizophrenia Genes by Neuroimaging: The Opportunities and the Challenges

Schizophrenia (SZ) is a heritable brain disease originating from a complex interaction of genetic and environmental factors. The genes underpinning the neurobiology of SZ are largely unknown but recent data suggest strong evidence for genetic variations, such as single nucleotide polymorphisms, making the brain vulnerable to the risk of SZ. Structural and functional brain mapping of these genetic variations are essential for the development of agents and tools for better diagnosis, treatment and prevention of SZ. Addressing this, neuroimaging methods in combination with genetic analysis have been increasingly used for almost 20 years. So-called imaging genetics, the opportunities of this approach along with its limitations for SZ research will be outlined in this invited paper. While the problems such as reproducibility, genetic effect size, specificity and sensitivity exist, opportunities such as multivariate analysis, development of multisite consortia for large-scale data collection, emergence of non-candidate gene (hypothesis-free) approach of neuroimaging genetics are likely to contribute to a rapid progress for gene discovery besides to gene validation studies that are related to SZ.

Gyrification Connectomes in Unmedicated Patients With Schizophrenia and Following a Short Course of Antipsychotic Drug Treatment

Schizophrenia (SZ) is a d isease characterized by brain dysconnectivity and abnormal brain development. The study of cortical gyrification in schizophrenia may capture underlying alterations reflective of neurodevelopmental abnormalities more accurately than other imaging modalities. Graph-based connectomic approaches have been previously used in schizophrenia to study structural and functional brain covariance using a diversity of techniques. The goal of the present study was to evaluate morphological covariance using a measure of local gyrification index in patients with schizophrenia. The aims of this study were two-fold: (1) Evaluate the structural covariance of local gyrification index using graph theory measures of integration and segregation in unmedicated patients with schizophrenia compared to healthy controls and (2) investigate changes in these measures following a short antipsychotic drug (APD) treatment. Using a longitudinal prospective design, structural scans were obtained prior to treatment in 34 unmedicated patients with SZ and after 6 weeks of treatment with risperidone. To control for the effect of time, 23 matched healthy controls (HC) were also scanned twice, 6 weeks apart. The cortical surface of each structural image was reconstructed and local gyrification index values were computed using FreeSurfer. Local gyrification index values where then parcellated into atlas based regions and entered into a 68 × 68 correlation matrix to construct local gyrification index connectomes for each group at each time point. Longitudinal comparisons showed significant group by time interactions for measures of segregation (clustering, local efficiency) and modularity, but not for measures of integration (path length, global efficiency). Post-hoc tests showed increased clustering, local efficiency, and modularity connectomes in unmedicated patients with SZ at baseline compared to HC. Post-hoc tests did not show significant within group differences for HCs or patients with SZ. After 6 weeks of treatment, there were no significant differences between the groups on these measures. Abnormal cortical topography is detected in schizophrenia and is modified by short term APD treatment reflective of decreases in hyper-specialization in network connectivity. We speculate that changes in the structural organization of the brain is achieved through the neuroplastic effects that APDs have on brain tissue, thus promoting more efficient brain connections and, possibly, a therapeutic effect.

Imaging genetics approach to Parkinson's disease and its correlation with clinical score

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with both underlying genetic factors and neuroimaging findings. Existing neuroimaging studies related to the genome in PD have mostly focused on certain candidate genes. The aim of our study was to construct a linear regression model using both genetic and neuroimaging features to better predict clinical scores compared to conventional approaches. We obtained neuroimaging and DNA genotyping data from a research database. Connectivity analysis was applied to identify neuroimaging features that could differentiate between healthy control (HC) and PD groups. A joint analysis of genetic and imaging information known as imaging genetics was applied to investigate genetic variants. We then compared the utility of combining different genetic variants and neuroimaging features for predicting the Movement Disorder Society-sponsored unified Parkinson's disease rating scale (MDS-UPDRS) in a regression framework. The associative cortex, motor cortex, thalamus, and pallidum showed significantly different connectivity between the HC and PD groups. Imaging genetics analysis identified PARK2, PARK7, HtrA2, GIGYRF2, and SNCA as genetic variants that are significantly associated with imaging phenotypes. A linear regression model combining genetic and neuroimaging features predicted the MDS-UPDRS with lower error and higher correlation with the actual MDS-UPDRS compared to other models using only genetic or neuroimaging information alone.

A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes

Imaging genetics is an emerging field for the investigation of neuro-mechanisms linked to genetic variation. Although imaging genetics has recently shown great promise in understanding biological mechanisms for brain development and psychiatric disorders, studying the link between genetic variants and neuroimaging phenotypes remains statistically challenging due to the high-dimensionality of both genetic and neuroimaging data. This becomes even more challenging when studying gene-environment interaction (G×E) on neuroimaging phenotypes. In this study, we proposed a set-based mixed effect model for gene-environment interaction (MixGE) on neuroimaging phenotypes, such as structural volumes and tensor-based morphometry (TBM). MixGE incorporates both fixed and random effects of G×E to investigate homogeneous and heterogeneous contributions of multiple genetic variants and their interaction with environmental risks to phenotypes. We discuss the construction of score statistics for the terms associated with fixed and random effects of G×E to avoid direct parameter estimation in the MixGE model, which would greatly increase computational cost. We also describe how the score statistics can be combined into a single significance value to increase statistical power. We evaluated MixGE using simulated and real Alzheimer's Disease Neuroimaging Initiative (ADNI) data, and showed statistical power superior to other burden and variance component methods. We then demonstrated the use of MixGE for exploring the voxelwise effect of G×E on TBM, made feasible by the computational efficiency of MixGE. Through this, we discovered a potential interaction effect of gene ABCA7 and cardiovascular risk on local volume change of the right superior parietal cortex, which warrants further investigation.

Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations

Major psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), autism (AUT), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SZ), are highly heritable and polygenic. Evidence suggests that these five disorders have both shared and distinct genetic risks and neural connectivity abnormalities. To measure aggregate genetic risks, the polygenic risk score (PGRS) was computed. Two independent general populations (N = 360 and N = 323) were separately examined to investigate whether the cross-disorder PGRS and PGRS for a specific disorder were associated with individual variability in functional connectivity. Consistent altered functional connectivity was found with the bilateral insula: for the left supplementary motor area and the left superior temporal gyrus with the cross-disorder PGRS, for the left insula and right middle and superior temporal lobe associated with the PGRS for autism, for the bilateral midbrain, posterior cingulate, cuneus, and precuneus associated with the PGRS for BD, and for the left angular gyrus and the left dorsolateral prefrontal cortex associated with the PGRS for schizophrenia. No significant functional connectivity was found associated with the PGRS for ADHD and MDD. Our findings indicated that genetic effects on the cross-disorder and disorder-specific neural connectivity of common genetic risk loci are detectable in the general population. Our findings also indicated that polygenic risk contributes to the main neurobiological phenotypes of psychiatric disorders and that identifying cross-disorder and specific functional connectivity related to polygenic risks may elucidate the neural pathways for these disorders.

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Altered cross-disorder functional connectivity related to PGRSs is detected.

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Altered disorder-specific functional connectivity related to PGRSs is detected.

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Altered functional connectivity related to PGRSs is involved in brain networks.

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Polygenic risk contributes to neurobiological phenotypes of psychiatric disorders.

Differential Effects of Brain Disorders on Structural and Functional Connectivity

Different measures of brain connectivity can be defined based on neuroimaging read-outs, including structural and functional connectivity. Neurological and psychiatric conditions are often associated with abnormal connectivity, but comparing the effects of the disease on different types of connectivity remains a challenge. In this paper, we address the problem of quantifying the relative effects of brain disease on structural and functional connectivity at a group level. Within the framework of a graph representation of connectivity, we introduce a kernel two-sample test as an effective method to assess the difference between the patients and control group. Moreover, we propose a common representation space for structural and functional connectivity networks, and a novel test statistics to quantitatively assess differential effects of the disease on different types of connectivity. We apply this approach to a dataset from BTBR mice, a murine model of Agenesis of the Corpus Callosum (ACC), a congenital disorder characterized by the absence of the main bundle of fibers connecting the two hemispheres. We used normo-callosal mice (B6) as a comparator. The application of the proposed methods to this data-set shows that the two types of connectivity can be successfully used to discriminate between BTBR and B6, meaning that both types of connectivity are affected by ACC. However, our novel test statistics shows that structural connectivity is significantly more affected than functional connectivity, consistent with the idea that functional connectivity has a robust topology that can tolerate substantial alterations in its structural connectivity substrate.

Dopaminergic receptor blockade changes a functional connectivity network centred on the amygdala

Resting-state connectivity has become an increasingly important measure in characterizing the functional integrity of brain circuits in neuro-psychiatric conditions. One approach that has recently gained prominence in this regard-and which we use in this study-is to investigate how resting-state connectivity depends on the integrity of certain neuromodulator systems. Here, we use a pharmacological challenge in combination with functional magnetic resonance imaging to investigate the impact of dopaminergic receptor blockade on whole brain functional connectivity in twenty healthy human subjects. Administration of the D2-receptor antagonist haloperidol led to a profound change in functional integration in network nodes linked to the amygdala. Compared to placebo and baseline measurements, network-based statistics and pairwise connectivity analyses revealed reduced connectivity and decreased link strength between the amygdala and the bilateral posterior cingulate cortex and other cortical areas. This was complemented by less extensive but very circumscribed enhanced connectivity between the amygdala and the right putamen during D2-receptor blockade. It will be interesting to investigate whether these pharmacologically induced shifts in resting-state connectivity will similarly be evident in clinical conditions that involve a dysfunction of the dopaminergic system. Our findings might also aid in interpreting alterations in more complex states, such as those seen psychiatric conditions and their treatment. Hum Brain Mapp 37:4148-4157, 2016. © 2016 Wiley Periodicals, Inc.

Structural covariance of neostriatal and limbic regions in patients with obsessive-compulsive disorder

BACKGROUND

Frontostriatal and frontoamygdalar connectivity alterations in patients with obsessive-compulsive disorder (OCD) have been typically described in functional neuroimaging studies. However, structural covariance, or volumetric correlations across distant brain regions, also provides network-level information. Altered structural covariance has been described in patients with different psychiatric disorders, including OCD, but to our knowledge, alterations within frontostriatal and frontoamygdalar circuits have not been explored.

METHODS

We performed a mega-analysis pooling structural MRI scans from the Obsessive-compulsive Brain Imaging Consortium and assessed whole-brain voxel-wise structural covariance of 4 striatal regions (dorsal and ventral caudate nucleus, and dorsal-caudal and ventral-rostral putamen) and 2 amygdalar nuclei (basolateral and centromedial-superficial). Images were preprocessed with the standard pipeline of voxel-based morphometry studies using Statistical Parametric Mapping software.

RESULTS

Our analyses involved 329 patients with OCD and 316 healthy controls. Patients showed increased structural covariance between the left ventral-rostral putamen and the left inferior frontal gyrus/frontal operculum region. This finding had a significant interaction with age; the association held only in the subgroup of older participants. Patients with OCD also showed increased structural covariance between the right centromedial-superficial amygdala and the ventromedial prefrontal cortex.

LIMITATIONS

This was a cross-sectional study. Because this is a multisite data set analysis, participant recruitment and image acquisition were performed in different centres. Most patients were taking medication, and treatment protocols differed across centres.

CONCLUSION

Our results provide evidence for structural network-level alterations in patients with OCD involving 2 frontosubcortical circuits of relevance for the disorder and indicate that structural covariance contributes to fully characterizing brain alterations in patients with psychiatric disorders.

Hippocampal-Prefrontal Interactions in Cognition, Behavior and Psychiatric Disease

The hippocampus and prefrontal cortex (PFC) have long been known to play a central role in various behavioral and cognitive functions. More recently, electrophysiological and functional imaging studies have begun to examine how interactions between the two structures contribute to behavior during various tasks. At the same time, it has become clear that hippocampal-prefrontal interactions are disrupted in psychiatric disease and may contribute to their pathophysiology. These impairments have most frequently been observed in schizophrenia, a disease that has long been associated with hippocampal and prefrontal dysfunction. Studies in animal models of the illness have also begun to relate disruptions in hippocampal-prefrontal interactions to the various risk factors and pathophysiological mechanisms of the illness. The goal of this review is to summarize what is known about the role of hippocampal-prefrontal interactions in normal brain function and compare how these interactions are disrupted in schizophrenia patients and animal models of the disease. Outstanding questions for future research on the role of hippocampal-prefrontal interactions in both healthy brain function and disease states are also discussed.

Imaging Brain Development: Benefiting from Individual Variability

Human brain development is a complex process that evolves from early childhood to young adulthood. Major advances in brain imaging are increasingly being used to characterize the developing brain. These advances have further helped to elucidate the dynamic maturational processes that lead to the emergence of complex cognitive abilities in both typical and atypical development. However, conventional approaches involve categorical group comparison models and tend to disregard the role of widespread interindividual variability in brain development. This review highlights how this variability can inform our understanding of developmental processes. The latest studies in the field of brain development are reviewed, with a particular focus on the role of individual variability and the consequent heterogeneity in brain structural and functional development. This review also highlights how such heterogeneity might be utilized to inform our understanding of complex neuropsychiatric disorders and recommends the use of more dimensional approaches to study brain development.

Task-based functional connectivity as an indicator of genetic liability to schizophrenia

Impaired functional connectivity has been hypothesized as a potential source of the cognitive deficits routinely observed in patients with schizophrenia. Additionally, these deficits may be manifestations of the genetic liability to schizophrenia and present in the non-psychotic first-degree relatives of that group. However, no study has examined task-based functional connectivity in schizophrenia relatives using independent component analysis (ICA). We employed group ICA to test the hypothesis that the unexpressed genetic liability to schizophrenia is reflected in the functional connectivity between brain regions during a task measuring context processing. We compared 20 schizophrenia patients and 32 patients' first-degree relatives to 22 controls demographically matched to the patients and 28 controls' relatives, respectively. The group ICA showed differential connectivity between patients and controls in a task-related network constituting right middle frontal gyrus (MFG) and right posterior parietal lobe. A network constituting left MFG and left posterior parietal, which was also related to the context processing task, did not differ between groups. These findings demonstrate that connectivity abnormalities associated with the genetic liability to schizophrenia are most strongly expressed in a right lateralized executive fronto-parietal network, and that these abnormalities are linked to context processing impairments.

The role of cell adhesion molecules in visual circuit formation: from neurite outgrowth to maps and synaptic specificity

The formation of visual circuitry is a multistep process that involves cell–cell interactions based on a range of molecular mechanisms. The correct implementation of individual events, including axon outgrowth and guidance, the formation of the topographic map, or the synaptic targeting of specific cellular subtypes, are prerequisites for a fully functional visual system that is able to appropriately process the information captured by the eyes. Cell adhesion molecules (CAMs) with their adhesive properties and their high functional diversity have been identified as key actors in several of these fundamental processes. Because of their growth‐promoting properties, CAMs play an important role in neuritogenesis. Furthermore, they are necessary to control additional neurite development, regulating dendritic spacing and axon pathfinding. Finally, trans‐synaptic interactions of CAMs ensure cell type‐specific connectivity as a basis for the establishment of circuits processing distinct visual features. Recent discoveries implicating CAMs in novel mechanisms have led to a better general understanding of neural circuit formation, but also revealed an increasing complexity of their function. This review aims at describing the different levels of action for CAMs to shape neural connectivity, with a special focus on the visual system. © 2015 Wiley Periodicals, Inc. Develop Neurobiol 75: 569–583, 2015

Functional connectivity with ventromedial prefrontal cortex reflects subjective value for social rewards

According to many studies, the ventromedial prefrontal cortex (VMPFC) encodes the subjective value of disparate rewards on a common scale. Yet, a host of other reward factors-likely represented outside of VMPFC-must be integrated to construct such signals for valuation. Using functional magnetic resonance imaging (fMRI), we tested whether the interactions between posterior VMPFC and functionally connected brain regions predict subjective value. During fMRI scanning, participants rated the attractiveness of unfamiliar faces. We found that activation in dorsal anterior cingulate cortex, anterior VMPFC and caudate increased with higher attractiveness ratings. Using data from a post-scan task in which participants spent money to view attractive faces, we quantified each individual's subjective value for attractiveness. We found that connectivity between posterior VMPFC and regions frequently modulated by social information-including the temporal-parietal junction (TPJ) and middle temporal gyrus-was correlated with individual differences in subjective value. Crucially, these additional regions explained unique variation in subjective value beyond that extracted from value regions alone. These findings indicate not only that posterior VMPFC interacts with additional brain regions during valuation, but also that these additional regions carry information employed to construct the subjective value for social reward.

Characteristics of canonical intrinsic connectivity networks across tasks and monozygotic twin pairs

Intrinsic connectivity networks (ICNs) are becoming more prominent in the analyses of in vivo brain activity as the field of neurometrics has revealed their importance for augmenting traditional cognitive neuroscience approaches. Consequently, tools that assess the coherence, or connectivity, and morphology of ICNs are being developed to support inferences and assumptions about the dynamics of the brain. Recently, we reported trait-like profiles of ICNs showing reliability over time and reproducibility across different contexts. This study further examined the trait-like and familial nature of ICNs by utilizing two divergent task paradigms in twins. The study aimed to identify stable network phenotypes that exhibited sensitivity to individual differences and external perturbations in task demands. Analogous ICNs were detected in each task and these ICNs showed consistency in morphology and intranetwork coherence across tasks, whereas the ICN timecourse dynamics showed sensitivity to task demands. Specifically, the timecourse of an arm/hand sensorimotor network showed the strongest correlation with the timeline of a hand imitation task, and the timecourse of a language-processing network showed the strongest temporal association with a verb generation task. The area V1/simple visual stimuli network exhibited the most consistency in morphology, coherence, and timecourse dynamics within and across tasks. Similarly, this network exhibited familiality in all three domains as well. Hence, this experiment is a proof of principle that the morphology and coherence of ICNs can be consistent both within and across tasks, that ICN timecourses can be differentially and meaningfully modulated by a task, and that these domains can exhibit familiality.

Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines

Imaging genetics is an emerging methodological field that combines genetic information with medical imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an imaging genetics study, it must be heritable: at least some proportion of its variance must be due to genetic influences. The Sequential Oligogenic Linkage Analysis Routines (SOLAR) imaging genetics software can estimate the heritability of a trait in complex pedigrees. We investigate the ability of SOLAR to accurately estimate heritability and common environmental effects on simulated imaging phenotypes in various family structures. We found that heritability is reliably estimated with small family-based studies of 40 to 80 individuals, though subtle differences remain between the family structures. In an imaging application analysis, we found that with 80 subjects in any of the family structures, estimated heritability of white matter fractional anisotropy was biased by <10% for every region of interest. Results from these studies can be used when investigators are evaluating power in planning genetic analyzes.

Structure and function of complex brain networks

An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a "rich club," centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed.

Functional neuroimaging and schizophrenia: a view towards effective connectivity modeling and polygenic risk

We review critical trends in imaging genetics as applied to schizophrenia research, and then discuss some future directions of the field. A plethora of imaging genetics studies have investigated the impact of genetic variation on brain function, since the paradigm of a neuroimaging intermediate phenotype for schizophrenia first emerged. It was initially posited that the effects of schizophrenia susceptibility genes would be more penetrant at the level of biologically based neuroimaging intermediate phenotypes than at the level of a complex and phenotypically heterogeneous psychiatric syndrome. The results of many studies support this assumption, most of which show single genetic variants to be associated with changes in activity of localized brain regions, as determined by select cognitive controlled tasks. From these basic studies, functional neuroimaging analysis of intermediate phenotypes has progressed to more complex and realistic models of brain dysfunction, incorporating models of functional and effective connectivity, including the modalities of psycho-physiological interaction, dynamic causal modeling, and graph theory metrics. The genetic association approaches applied to imaging genetics have also progressed to more sophisticated multivariate effects, including incorporation of two-way and three-way epistatic interactions, and most recently polygenic risk models. Imaging genetics is a unique and powerful strategy for understanding the neural mechanisms of genetic risk for complex CNS disorders at the human brain level.

ADHD pharmacogenetics across the life cycle: New findings and perspectives

Attention-deficit/hyperactivity disorder (ADHD) is a complex and heterogeneous disorder, affecting individuals across the life cycle. Although its etiology is not yet completely understood, genetics plays a substantial role. Pharmacological treatment is considered effective and safe for children and adults, but there is considerable inter-individual variability among patients regarding response to medication, required doses, and adverse events. We present here a systematic review of the literature on ADHD pharmacogenetics to provide a critical discussion of the existent findings, new approaches, limitations, and recommendations for future research. Our main findings are: first, the number of studies continues to grow, making ADHD one of the mental health areas with more pharmacogenetic studies. Second, there has been a focus shift on ADHD pharmacogenetic studies in the last years. There is an increasing number of studies assessing gene-gene and gene-environment interactions, using genome-wide association approaches, neuroimaging, and assessing pharmacokinetic properties. Third and most importantly, the heterogeneity in methodological strategies employed by different studies remains impressive. The question whether pharmacogenetics studies of ADHD will improve clinical management by shifting from trial-and-error approach to a pharmacological regimen that takes into account the individual variability remains unanswered. © 2014 Wiley Periodicals, Inc.

Role of FKBP5 in emotion processing: results on amygdala activity, connectivity and volume

Accumulating evidence suggests a role of FKBP5, a co-chaperone regulating the glucocorticoid receptor sensitivity, in the etiology of depression and anxiety disorders. Based on recent findings of altered amygdala activity following childhood adversity, the present study aimed at clarifying the impact of genetic variation in FKBP5 on threat-related neural activity and coupling as well as morphometric alterations in stress-sensitive brain systems. Functional magnetic resonance imaging during an emotional face-matching task was performed in 153 healthy young adults (66 males) from a high-risk community sample followed since birth. Voxel-based morphometry was applied to study structural alterations and DNA was genotyped for FKBP5 rs1360780. Childhood adversity was measured using retrospective self-report (Childhood Trauma Questionnaire) and by a standardized parent interview assessing childhood family adversity. Depression was assessed by the Beck Depression Inventory. There was a main effect of FKBP5 on the left amygdala, with T homozygotes showing the highest activity, largest volume and increased coupling with the left hippocampus and the orbitofrontal cortex (OFC). Moreover, amygdala-OFC coupling proved to be associated with depression in this genotype. In addition, our results support previous evidence of a gene-environment interaction on right amygdala activity with respect to retrospective assessment of childhood adversity, but clarify that this does not generalize to the prospective assessment. These findings indicated that activity in T homozygotes increased with the level of adversity, whereas the opposite pattern emerged in C homozygotes, with CT individuals being intermediate. The present results point to a functional involvement of FKBP5 in intermediate phenotypes associated with emotional processing, suggesting a possible mechanism for this gene in conferring susceptibility to stress-related disorders.

Use of magnetic resonance imaging in pharmacogenomics

Because of the large variation in the response to psychoactive medication, many studies have attempted to uncover genetic factors that determine response. While considerable knowledge exists on the large effects of genetic polymorphisms on pharmacokinetics and plasma concentrations of drugs, effects of the concentration at the target site and pharmacodynamic effects on brain functions in disease are much less known. This article reviews the role of magnetic resonance imaging (MRI) to visualize response to medication in brain behaviour circuits in vivo in humans and assess the influence of pharmacogenetic factors. Two types of studies have been used to characterize effects of medication and genetic variation. In task-related activation studies the focus is on changes in the activity of a neural circuit associated with a specific psychological process. The second type of study investigates resting state perfusion. These studies provide an assessment of vascular changes associated with bioavailability of drugs in the brain, but may also assess changes in neural activity after binding of centrally active agents. Task-related pharmacogenetic studies of cognitive function have characterized the effects in the prefrontal cortex of genetic polymorphisms of dopamine receptors (DRD2), metabolic enzymes (COMT) and in the post-synaptic signalling cascade under the administration of dopamine agonists and antagonists. In contrast, pharmacogenetic imaging with resting state perfusion is still in its infancy. However, the quantitative nature of perfusion imaging, its non-invasive character and its repeatability might be crucial assets in visualizing the effects of medication in vivo in man during therapy.

Genetics of borderline personality disorder: systematic review and proposal of an integrative model

Borderline personality disorder (BPD) is one of the most common mental disorders and is characterized by a pervasive pattern of emotional lability, impulsivity, interpersonal difficulties, identity disturbances, and disturbed cognition. Here, we performed a systematic review of the literature concerning the genetics of BPD, including familial and twin studies, association studies, and gene-environment interaction studies. Moreover, meta-analyses were performed when at least two case-control studies testing the same polymorphism were available. For each gene variant, a pooled odds ratio (OR) was calculated using fixed or random effects models. Familial and twin studies largely support the potential role of a genetic vulnerability at the root of BPD, with an estimated heritability of approximately 40%. Moreover, there is evidence for both gene-environment interactions and correlations. However, association studies for BPD are sparse, making it difficult to draw clear conclusions. According to our meta-analysis, no significant associations were found for the serotonin transporter gene, the tryptophan hydroxylase 1 gene, or the serotonin 1B receptor gene. We hypothesize that such a discrepancy (negative association studies but high heritability of the disorder) could be understandable through a paradigm shift, in which "plasticity" genes (rather than "vulnerability" genes) would be involved. Such a framework postulates a balance between positive and negative events, which interact with plasticity genes in the genesis of BPD.

Association of abstinence-induced alterations in working memory function and COMT genotype in smokers

RATIONALE

The common methionine (met) for valine (val) at codon 158 (val(158)met) polymorphism in the catechol-O-methyltransferase (COMT) gene has been associated with nicotine dependence, alterations in executive cognitive function, and abstinence-induced working memory deficits in smokers.

OBJECTIVES

We sought to replicate the association of the COMT val allele with abstinence-induced alterations in working memory-related activity in task-positive (executive control) and task-negative (default mode network) regions.

METHODS

Forty smokers (20 val/val and 20 met/met) performed an N-back task while undergoing blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) on two separate occasions: following 72 h of confirmed abstinence and during smoking as usual. An independent sample of 48 smokers who completed the identical N-back task during fMRI in smoking vs. abstinence for another study was used as a validation sample.

RESULTS

Contrary to expectations, genotype by session interactions on BOLD signal in executive control regions (dorsolateral prefrontal cortex and dorsal cingulate/medial prefrontal cortex) revealed significant abstinence-induced reductions in the met/met group, but not the val/val group. Results also revealed that val/val smokers may exhibit less suppression of activation in task-negative regions such as the posterior cingulate cortex during abstinence (vs. smoking). These patterns were confirmed in the validation sample and in the whole-brain analysis, though the regions differed from the a priori regions of interest (ROIs) (e.g., precuneus, insula).

CONCLUSIONS

The COMT val(158)met polymorphism was associated with abstinence-related working memory deficits in two independent samples of smokers. However, inconsistencies compared to prior findings and across methods (ROI vs. whole-brain analysis) highlight the challenges inherent in reproducing results of imaging genetic studies in addiction.

A comparison of cognitive functioning in acute schizophrenia and depression

OBJECTIVE

Even though cognitive deficits are well recognised in schizophrenia and depression, direct comparisons between the disorders are scarce in literature. This study aims to assess specificity and degree of cognitive deficits in inpatients with acute schizophrenia and unipolar major depression.

METHODS

A neuropsychological test battery was administered to 76 schizophrenic patients, 102 patients with unipolar major depression and 85 healthy controls (HCs), assessing verbal learning [Rey Auditory Verbal Learning Test (RAVLT)], processing speed (Trail Making Test), verbal fluency and visual memory (Wechsler Memory Scale-Revised test).

RESULTS

Both patient groups were significantly impaired compared with HCs with regard to all test outcomes. The schizophrenia group (SG) performed significantly worse in the Wechsler Memory Scale and verbal fluency than the depression group (DG). The DG reached significantly lower scores than the SG in the RAVLT delayed recall subtest. No significant group difference between SG and DG was found for the Trail Making Test and the RAVLT direct recall trails.

CONCLUSION

Our results indicate that cognitive impairment is present in both disorders. Schizophrenic patients performed worse than patients with unipolar depression in only two of the administered tests. Differences in cognitive performance between the groups are not as general as often assumed. Therefore, during the acute phase of illness, a diagnostic classification on the grounds of the patients' neurocognitive performance has to be done with caution.

Polymorphisms of the OXTR gene explain why sales professionals love to help customers

Polymorphisms of the OXTR gene affect people's social interaction styles in various social encounters: carriers of the OXTR GG, compared to the OXTR AA/AG in general, are more motivated to interact socially and detect social salience. We focus on sales professionals operating in knowledge intensive organizations. Study 1, with a sample of 141 sales people, shows that carriers of the OXTR GG allele, compared to the OXTR AA/AG allele, are more motivated to help customers than to manipulatively impose goods/services on them. Study 2, using genomic functional magnetic resonance imaging (fMRI) on a sample of 21 sales professionals processing facial pictures with different emotional valences, investigates key nuclei of social brain regions (SBRs). Compared to OXTR AA/AG carriers, OXTR GG carriers experience greater effective connectivity between SBRs of interest measured by Granger causality tests using univariate Haugh tests. In addition, the multivariate El-Himdi and Roy tests demonstrate that the amygdala, prefrontal cortex, and pars opercularis (inferior frontal gyrus) play key roles when processing emotional expressions. The bilateral amygdala and medial prefrontal cortex (mPFC) show significantly greater clout—influence on other brain regions—for GG allele carriers than non-carriers; likewise, the bilateral pars opercularis, left amygdala, and left mPFC are more receptive to activity in other brain regions among GG allele carriers than AG/AA allele carriers are. Thus, carriers of the OXTR GG allele are more sensitive to changes in emotional cues, enhancing social salience. To our knowledge, this is the first study on how insights from imaging genetics help understanding of the social motivation of people operating in a professional setting.

Neuroimaging in psychiatric pharmacogenetics research: the promise and pitfalls

The integration of research on neuroimaging and pharmacogenetics holds promise for improving treatment for neuropsychiatric conditions. Neuroimaging may provide a more sensitive early measure of treatment response in genetically defined patient groups, and could facilitate development of novel therapies based on an improved understanding of pathogenic mechanisms underlying pharmacogenetic associations. This review summarizes progress in efforts to incorporate neuroimaging into genetics and treatment research on major psychiatric disorders, such as schizophrenia, major depressive disorder, bipolar disorder, attention-deficit/hyperactivity disorder, and addiction. Methodological challenges include: performing genetic analyses in small study populations used in imaging studies; inclusion of patients with psychiatric comorbidities; and the extensive variability across studies in neuroimaging protocols, neurobehavioral task probes, and analytic strategies. Moreover, few studies use pharmacogenetic designs that permit testing of genotype × drug effects. As a result of these limitations, few findings have been fully replicated. Future studies that pre-screen participants for genetic variants selected a priori based on drug metabolism and targets have the greatest potential to advance the science and practice of psychiatric treatment.

Relation between variants in the neurotrophin receptor gene, NTRK3, and white matter integrity in healthy young adults

The NTRK3 gene (also known as TRKC) encodes a high affinity receptor for the neurotrophin 3'-nucleotidase (NT3), which is implicated in oligodendrocyte and myelin development. We previously found that white matter integrity in young adults is related to common variants in genes encoding neurotrophins and their receptors. This underscores the importance of neurotrophins for white matter development. NTRK3 variants are putative risk factors for schizophrenia, bipolar disorder, and obsessive-compulsive disorder hoarding, suggesting that some NTRK3 variants may affect the brain. To test this, we scanned 392 healthy adult twins and their siblings (mean age, 23.6 ± 2.2 years; range: 20-29 years) with 105-gradient 4-Tesla diffusion tensor imaging (DTI). We identified 18 single nucleotide polymorphisms (SNPs) in the NTRK3 gene that have been associated with neuropsychiatric disorders. We used a multi-SNP model, adjusting for family relatedness, age, and sex, to relate these variants to voxelwise fractional anisotropy (FA) - a DTI measure of white matter integrity. FA was optimally predicted (based on the highest false discovery rate critical p), by five SNPs (rs1017412, rs2114252, rs16941261, rs3784406, and rs7176429; overall FDR critical p=0.028). Gene effects were widespread and included the corpus callosum genu and inferior longitudinal fasciculus - regions implicated in several neuropsychiatric disorders and previously associated with other neurotrophin-related genetic variants in an overlapping sample of subjects. NTRK3 genetic variants, and neurotrophins more generally, may influence white matter integrity in brain regions implicated in neuropsychiatric disorders.

Networks of anatomical covariance

Functional imaging or diffusion-weighted imaging techniques are widely used to understand brain connectivity at the systems level and its relation to normal neurodevelopment, cognition or brain disorders. It is also possible to extract information about brain connectivity from the covariance of morphological metrics derived from anatomical MRI. These covariance patterns may arise from genetic influences on normal development and aging, from mutual trophic reinforcement as well as from experience-related plasticity. This review describes the basic methodological strategies, the biological basis of the observed covariance as well as applications in normal brain and brain disease before a final review of future prospects for the technique.

Association of rs1006737 in CACNA1C with alterations in prefrontal activation and fronto-hippocampal connectivity

BACKGROUND

Genome-wide association studies have identified the rs1006737 single nucleotide polymorphism (SNP) in the CACNA1C gene as a susceptibility locus for schizophrenia and bipolar disorder. On the neural systems level this association is explained by altered functioning of the dorsolateral prefrontal cortex (DLPFC) and the hippocampal formation (HF), brain regions also affected by mental illness. In the present study we investigated the association of rs1006737 genotype with prefrontal activation and fronto-hippocampal connectivity.

METHODS

We used functional magnetic resonance imaging to measure neural activation during an n-back working memory task in 94 healthy subjects. All subjects were genotyped for the SNP rs1006737. We tested associations of the rs1006737 genotype with changes in working-memory-related DLPFC activation and functional integration using a seed region functional connectivity approach.

RESULTS

Rs1006737 genotype was associated with altered right-hemispheric DLPFC activation. The homozygous A (risk) group showed decreased activation compared to G-allele carriers. Further, the functional connectivity analysis revealed a positive association of fronto-hippocampal connectivity with rs1006737 A alleles.

CONCLUSIONS

We did not replicate the previous findings of increased right DLPFC activation in CACNA1C rs1006737 A homozygotes. In fact, we found the opposite effect, thus questioning prefrontal inefficiency as rs1006737 genotype-related intermediate phenotype. On the other hand, our results indicate that alterations in the functional coupling between the prefrontal cortex and the medial temporal lobe could represent a neural system phenotype that is mediated by CACNA1C rs1006737 and other genetic susceptibility loci for schizophrenia and bipolar disorder.