Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets.

Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.

Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping.

Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.

Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.

The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder.

Subcortical volumetric abnormalities in bipolar disorder.

Considerable uncertainty exists about the defining brain changes associated with bipolar disorder (BD). Understanding and quantifying the sources of uncertainty can help generate novel clinical hypotheses about etiology and assist in the development of biomarkers for indexing disease progression and prognosis. Here we were interested in quantifying case-control differences in intracranial volume (ICV) and each of eight subcortical brain measures: nucleus accumbens, amygdala, caudate, hippocampus, globus pallidus, putamen, thalamus, lateral ventricles. In a large study of 1710 BD patients and 2594 healthy controls, we found consistent volumetric reductions in BD patients for mean hippocampus (Cohen's d=-0.232; P=3.50 × 10-7) and thalamus (d=-0.148; P=4.27 × 10-3) and enlarged lateral ventricles (d=-0.260; P=3.93 × 10-5) in patients. No significant effect of age at illness onset was detected. Stratifying patients based on clinical subtype (BD type I or type II) revealed that BDI patients had significantly larger lateral ventricles and smaller hippocampus and amygdala than controls. However, when comparing BDI and BDII patients directly, we did not detect any significant differences in brain volume. This likely represents similar etiology between BD subtype classifications. Exploratory analyses revealed significantly larger thalamic volumes in patients taking lithium compared with patients not taking lithium. We detected no significant differences between BDII patients and controls in the largest such comparison to date. Findings in this study should be interpreted with caution and with careful consideration of the limitations inherent to meta-analyzed neuroimaging comparisons.

Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium.

The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.

Resting-state brain function in schizophrenia and psychotic bipolar probands and their first-degree relatives.

BACKGROUND: Schizophrenia (SCZ) and psychotic bipolar disorder (PBD) share considerable overlap in clinical features, genetic risk factors and co-occurrence among relatives. The common and unique functional cerebral deficits in these disorders, and in unaffected relatives, remain to be identified. METHOD: A total of 59 healthy controls, 37 SCZ and 57 PBD probands and their unaffected first-degree relatives (38 and 28, respectively) were studied using resting-state functional magnetic resonance imaging (rfMRI). Regional cerebral function was evaluated by measuring the amplitude of low-frequency fluctuations (ALFF). Areas with ALFF alterations were used as seeds in whole-brain functional connectivity analysis. We then tested whether abnormalities identified in probands were present in unaffected relatives. RESULTS: SCZ and PBD probands both demonstrated regional hypoactivity in the orbital frontal cortex and cingulate gyrus, as well as abnormal connectivity within striatal-thalamo-cortical networks. SCZ probands showed greater and more widely distributed ALFF alterations including the thalamus and bilateral parahippocampal gyri. Increased parahippocampal ALFF was related to positive symptoms and cognitive deficit. PBD patients showed uniquely increased functional connectivity between the thalamus and bilateral insula. Only PBD relatives showed abnormal connectivity within striatal-thalamo-cortical networks seen in both proband groups. CONCLUSIONS: The present findings reveal a common pattern of deficits in frontostriatal circuitry across SCZ and PBD, and unique regional and functional connectivity abnormalities that distinguish them. The abnormal network connectivity in PBD relatives that was present in both proband groups may reflect genetic susceptibility associated with risk for psychosis, but within-family associations of this measure were not high.

Emotional scene processing in biotypes of psychosis.

Social-emotional deficits in psychosis may be indexed by deviations in emotional scene processing, but event-related potential (ERP) studies indicate such deviations may not map cleanly to diagnostic categories. Neurobiologically defined psychosis subgroups offer an alternative that may better capture neurophysiological correlates of social-emotional deficits. The current study investigates emotional scene-elicited ERPs in Biotypes of psychosis in a large (N = 622), well-characterized sample. Electroencephalography was recorded in healthy persons (N = 129), Biotype-1 (N = 195), Biotype-2 (N = 131), and Biotype-3 (N = 167) psychosis cases. ERPs were measured from posterior and centroparietal scalp locations. Neural responses to emotional scenes were compared between healthy and psychosis groups. Multivariate group discrimination analyses resulted in two composite variates that differentiated groups. The first variate displayed large differences between low-cognition (Biotype-1, Biotype-2) and intact-cognition groups (Biotype-3, healthy persons). The second indicated a small-to-moderate distinction of Biotypes-2 and -3 from Biotype-1 and healthy persons. Two multivariate correlations were identified indicating associations between 1) self-reported emotional experience and generalized cognition and 2) socio-occupational functioning and late-stage emotional processing. Psychosis Biotypes displayed emotional processing deficits not apparent in DSM psychosis subgroups. Future translational research may benefit from exploring emotional scene processing in such neurobiologically-defined psychosis groups.

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk.

Background: Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. Methods: We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. Results: Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. Conclusions: Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.

A selective review of simulated driving studies: Combining naturalistic and hybrid paradigms, analysis approaches, and future directions.

Naturalistic paradigms such as movie watching or simulated driving that mimic closely real-world complex activities are becoming more widely used in functional magnetic resonance imaging (fMRI) studies both because of their ability to robustly stimulate brain connectivity and the availability of analysis methods which are able to capitalize on connectivity within and among intrinsic brain networks identified both during a task and in resting fMRI data. In this paper we review over a decade of work from our group and others on the use of simulated driving paradigms to study both the healthy brain as well as the effects of acute alcohol administration on functional connectivity during such paradigms. We briefly review our initial work focused on the configuration of the driving simulator and the analysis strategies. We then describe in more detail several recent studies from our group including a hybrid study examining distracted driving and compare resulting data with those from a separate visual oddball task (Fig. 6). The analysis of these data was performed primarily using a combination of group independent component analysis (ICA) and the general linear model (GLM) and in the various studies we highlight novel findings which result from an analysis of either 1) within-network connectivity, 2) inter-network connectivity, also called functional network connectivity, or 3) the degree to which the modulation of the various intrinsic networks were associated with the alcohol administration and the task context. Despite the fact that the behavioral effects of alcohol intoxication are relatively well known, there is still much to discover on how acute alcohol exposure modulates brain function in a selective manner, associated with behavioral alterations. Through the above studies, we have learned more regarding the impact of acute alcohol intoxication on organization of the brain's intrinsic connectivity networks during performance of a complex, real-world cognitive operation. Lessons learned from the above studies have broader applicability to designing ecologically valid, complex, functional MRI cognitive paradigms and incorporating pharmacologic challenges into such studies. Overall, the use of hybrid driving studies is a particularly promising area of neuroscience investigation.

Abnormal prefrontal activity subserving attentional control of emotion in remitted depressed patients during a working memory task with emotional distracters.

BACKGROUND: Patients with major depressive disorder (MDD) show deficits in processing of facial emotions that persist beyond recovery and cessation of treatment. Abnormalities in neural areas supporting attentional control and emotion processing in remitted depressed (rMDD) patients suggests that there may be enduring, trait-like abnormalities in key neural circuits at the interface of cognition and emotion, but this issue has not been studied systematically. METHOD: Nineteen euthymic, medication-free rMDD patients (mean age 33.6 years; mean duration of illness 34 months) and 20 age- and gender-matched healthy controls (HC; mean age 35.8 years) performed the Emotional Face N-Back (EFNBACK) task, a working memory task with emotional distracter stimuli. We used blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to measure neural activity in the dorsolateral (DLPFC) and ventrolateral prefrontal cortex (VLPFC), orbitofrontal cortex (OFC), ventral striatum and amygdala, using a region of interest (ROI) approach in SPM2. RESULTS: rMDD patients exhibited significantly greater activity relative to HC in the left DLPFC [Brodmann area (BA) 9/46] in response to negative emotional distracters during high working memory load. By contrast, rMDD patients exhibited significantly lower activity in the right DLPFC and left VLPFC compared to HC in response to positive emotional distracters during high working memory load. These effects occurred during accurate task performance. CONCLUSIONS: Remitted depressed patients may continue to exhibit attentional biases toward negative emotional information, reflected by greater recruitment of prefrontal regions implicated in attentional control in the context of negative emotional information.

Positron emission tomography reveals elevated D2 dopamine receptors in drug-naive schizophrenics.

In postmortem studies of patients with schizophrenia, D2 dopamine receptors in the basal ganglia have been observed to be more numerous than in patients with no history of neurological or psychiatric disease. Because most patients with schizophrenia are treated with neuroleptic drugs that block D2 dopamine receptors in the caudate nucleus, it has been suggested that this increase in the number of receptors is a result of adaptation to these drugs rather than a biochemical abnormality intrinsic to schizophrenia. With positron emission tomography (PET), the D2 dopamine receptor density in the caudate nucleus of living human beings was measured in normal volunteers and in two groups of patients with schizophrenia--one group that had never been treated with neuroleptics and another group that had been treated with these drugs. D2 dopamine receptor densities in the caudate nucleus were higher in both groups of patients than in the normal volunteers. Thus, schizophrenia itself is associated with an increase in brain D2 dopamine receptor density.

Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements.

Principal component analysis (PCA) is often used to reduce the dimension of data before applying more sophisticated data analysis methods such as non-linear classification algorithms or independent component analysis. This practice is based on selecting components corresponding to the largest eigenvalues. If the ultimate goal is separation of data in two groups, then these set of components need not have the most discriminatory power. We measured the distance between two such populations using Mahalanobis distance and chose the eigenvectors to maximize it, a modified PCA method, which we call the discriminant PCA (DPCA). DPCA was applied to diffusion tensor-based fractional anisotropy images to distinguish age-matched schizophrenia subjects from healthy controls. The performance of the proposed method was evaluated by the one-leave-out method. We show that for this fractional anisotropy data set, the classification error with 60 components was close to the minimum error and that the Mahalanobis distance was twice as large with DPCA, than with PCA. Finally, by masking the discriminant function with the white matter tracts of the Johns Hopkins University atlas, we identified left superior longitudinal fasciculus as the tract which gave the least classification error. In addition, with six optimally chosen tracts the classification error was zero.

Hybrid ICA-Bayesian network approach reveals distinct effective connectivity differences in schizophrenia.

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal, frontal, and cerebellar regions during an auditory paradigm.

Deviation from expected cognitive ability across psychotic disorders.

Patients with schizophrenia show a deficit in cognitive ability compared to estimated premorbid and familial intellectual abilities. However, the degree to which this pattern holds across psychotic disorders and is familial is unclear. The present study examined deviation from expected cognitive level in schizophrenia, schizoaffective disorder, and psychotic bipolar disorder probands and their first-degree relatives. Using a norm-based regression approach, parental education and WRAT-IV Reading scores (both significant predictors of cognitive level in the healthy control group) were used to predict global neuropsychological function as measured by the composite score from the Brief Assessment of Cognition in Schizophrenia (BACS) test in probands and relatives. When compared to healthy control group, psychotic probands showed a significant gap between observed and predicted BACS composite scores and a greater likelihood of robust cognitive decline. This effect was not seen in unaffected relatives. While BACS and WRAT-IV Reading scores were themselves highly familial, the decline in cognitive function from expectation had lower estimates of familiality. Thus, illness-related factors such as epigenetic, treatment, or pathophysiological factors may be important causes of illness related decline in cognitive abilities across psychotic disorders. This is consistent with the markedly greater level of cognitive impairment seen in affected individuals compared to their unaffected family members.

Genome-wide association studies of smooth pursuit and antisaccade eye movements in psychotic disorders: findings from the B-SNIP study.

Eye movement deviations, particularly deficits of initial sensorimotor processing and sustained pursuit maintenance, and antisaccade inhibition errors, are established intermediate phenotypes for psychotic disorders. We here studied eye movement measures of 849 participants from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study (schizophrenia N=230, schizoaffective disorder N=155, psychotic bipolar disorder N=206 and healthy controls N=258) as quantitative phenotypes in relation to genetic data, while controlling for genetically derived ancestry measures, age and sex. A mixed-modeling genome-wide association studies approach was used including ~4.4 million genotypes (PsychChip and 1000 Genomes imputation). Across participants, sensorimotor processing at pursuit initiation was significantly associated with a single nucleotide polymorphism in IPO8 (12p11.21, P=8 × 10-11), whereas suggestive associations with sustained pursuit maintenance were identified with SNPs in SH3GL2 (9p22.2, P=3 × 10-8). In participants of predominantly African ancestry, sensorimotor processing was also significantly associated with SNPs in PCDH12 (5q31.3, P=1.6 × 10-10), and suggestive associations were observed with NRSN1 (6p22.3, P=5.4 × 10-8) and LMO7 (13q22.2, P=7.3x10-8), whereas antisaccade error rate was significantly associated with a non-coding region at chromosome 7 (P=6.5 × 10-9). Exploratory pathway analyses revealed associations with nervous system development and function for 40 top genes with sensorimotor processing and pursuit maintenance (P=4.9 × 10-2-9.8 × 10-4). Our findings suggest novel patterns of genetic variation relevant for brain systems subserving eye movement control known to be impaired in psychotic disorders. They include genes involved in nuclear trafficking and gene silencing (IPO8), fast axonal guidance and synaptic specificity (PCDH12), transduction of nerve signals (NRSN1), retinal degeneration (LMO7), synaptic glutamate release (SH3GL2), and broader nervous system development and function.

Schizophrenia, the heteromodal association neocortex and development: potential for a neurogenetic approach.

The heteromodal association neocortex is believed to be a major site of involvement in schizophrenia. This system includes the prefrontal cortex and portions of the superior temporal and inferior parietal cortices, which are linked in cognitive networks observing complex executive functions. The heteromodal cortex is highly elaborated in humans and is believed to continue to develop past birth. The neuropathology of schizophrenia is likely to be heterogeneous and appears to involve developmental abnormalities, with a prominent genetic component. However, the genes involved in the development of the neocortex, and particularly the heteromodal cortex, are not well understood. A candidate-gene approach to schizophrenia using techniques of differential expression might now be feasible and could illuminate the basic neurobiology of the heteromodal cortical network.

A pilot study on commonality and specificity of copy number variants in schizophrenia and bipolar disorder.

Schizophrenia (SZ) and bipolar disorder (BD) are known to share genetic risks. In this work, we conducted whole-genome scanning to identify cross-disorder and disorder-specific copy number variants (CNVs) for these two disorders. The Database of Genotypes and Phenotypes (dbGaP) data were used for discovery, deriving from 2416 SZ patients, 592 BD patients and 2393 controls of European Ancestry, as well as 998 SZ patients, 121 BD patients and 822 controls of African Ancestry. PennCNV and Birdsuite detected high-confidence CNVs that were aggregated into CNV regions (CNVRs) and compared with the database of genomic variants for confirmation. Then, large (size⩾500 kb) and small common CNVRs (size <500 kb, frequency⩾1%) were examined for their associations with SZ and BD. Particularly for the European Ancestry samples, the dbGaP findings were further evaluated in the Wellcome Trust Case Control Consortium (WTCCC) data set for replication. Previously implicated variants (1q21.1, 15q13.3, 16p11.2 and 22q11.21) were replicated. Some cross-disorder variants were noted to differentially affect SZ and BD, including CNVRs in chromosomal regions encoding immunoglobulins and T-cell receptors that were associated more with SZ, and the 10q11.21 small CNVR (GPRIN2) associated more with BD. Disorder-specific CNVRs were also found. The 22q11.21 CNVR (COMT) and small CNVRs in 11p15.4 (TRIM5) and 15q13.2 (ARHGAP11B and FAN1) appeared to be SZ-specific. CNVRs in 17q21.2, 9p21.3 and 9q21.13 might be BD-specific. Overall, our primary findings in individual disorders largely echo previous reports. In addition, the comparison between SZ and BD reveals both specific and common risk CNVs. Particularly for the latter, differential involvement is noted, motivating further comparative studies and quantitative models.

Unitary construct of generalized cognitive ability underlying BACS performance across psychotic disorders and in their first-degree relatives.

Despite robust evidence of neurocognitive dysfunction in psychotic patients, the degree of similarity in cognitive architecture across psychotic disorders and among their respective first-degree relatives is not well delineated. The present study examined the latent factor structure of the Brief Assessment of Cognition in Schizophrenia (BACS) neuropsychological battery. Analyses were conducted on 783 psychosis spectrum probands (schizophrenia, schizoaffective, psychotic bipolar), 887 of their first-degree relatives, and 396 non-psychiatric controls from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium. Exploratory factor analysis of BACS subtest scores indicated a single-factor solution that was similar across all groups and provided the best overall data fit in confirmatory analyses. Correlations between the standard BACS composite score and the sum of subscale scores weighted by their loadings on this unitary factor were very high in all groups (r≥.99). Thus, the BACS assesses a similar unitary cognitive construct in probands with different psychotic disorders, in their first-degree relatives, and in healthy controls, and this factor is well measured by the test's standard composite score.

Genome-wide significant loci for addiction and anxiety.

BACKGROUND: Psychiatric comorbidity is common among individuals with addictive disorders, with patients frequently suffering from anxiety disorders. While the genetic architecture of comorbid addictive and anxiety disorders remains unclear, elucidating the genes involved could provide important insights into the underlying etiology. METHODS: Here we examine a sample of 1284 Mexican-Americans from randomly selected extended pedigrees. Variance decomposition methods were used to examine the role of genetics in addiction phenotypes (lifetime history of alcohol dependence, drug dependence or chronic smoking) and various forms of clinically relevant anxiety. Genome-wide univariate and bivariate linkage scans were conducted to localize the chromosomal regions influencing these traits. RESULTS: Addiction phenotypes and anxiety were shown to be heritable and univariate genome-wide linkage scans revealed significant quantitative trait loci for drug dependence (14q13.2-q21.2, LOD=3.322) and a broad anxiety phenotype (12q24.32-q24.33, LOD=2.918). Significant positive genetic correlations were observed between anxiety and each of the addiction subtypes (ρg=0.550-0.655) and further investigation with bivariate linkage analyses identified significant pleiotropic signals for alcohol dependence-anxiety (9q33.1-q33.2, LOD=3.054) and drug dependence-anxiety (18p11.23-p11.22, LOD=3.425). CONCLUSIONS: This study confirms the shared genetic underpinnings of addiction and anxiety and identifies genomic loci involved in the etiology of these comorbid disorders. The linkage signal for anxiety on 12q24 spans the location of TMEM132D, an emerging gene of interest from previous GWAS of anxiety traits, whilst the bivariate linkage signal identified for anxiety-alcohol on 9q33 peak coincides with a region where rare CNVs have been associated with psychiatric disorders. Other signals identified implicate novel regions of the genome in addiction genetics.