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Schizophrenia is a psychiatric disorder with high heritability. Recent genome-wide association studies have provided a list of risk loci reliably derived from unprecedentedly large samples. However, further delineation of the diagnosis-associated susceptibility variants is needed to better characterize the genetic architecture given the disease's complex nature. In this sense, a data-driven approach might hold promise for identifying functionally related clusters of genetic variants that might not be captured by hypothesis-based models. In the current study, independent component analysis (ICA) was applied to the Psychiatric Genomics Consortium's schizophrenia-related single nucleotide polymorphisms (SNPs) in 104 schizophrenia patients and 142 healthy controls of European Ancestry. We found that, for 13 out of 16 extracted independent components, the associated loadings correlated highly (r>0.5) with the polygenic risk scores for SZ of the corresponding SNPs. These correlations were likely not inflated by the linkage disequilibrium structure (permutation p<0.001). In brief, we demonstrate an example of ICA analysis on SNP data yielding functionally meaningful clusters, which motivates further application of data-driven approaches as a complimentary tool for hypothesis-based methods to enrich our knowledge on the genetic basis of complex disorders. [ABSTRACT FROM AUTHOR]

Abstract: Many previous studies exploring cortical gray matter (GM) differences in schizophrenia have used “region of interest” (ROI) measurements to manually delineate GM volumes. Recently, some investigators have instead employed voxel-based morphometry (VBM), an automated whole-brain magnetic resonance image measurement technique. The purpose of the current study was to compare the above methods in calculating GM distributions in schizophrenia patients relative to matched controls. Using ROIs, Buchanan et al. (Buchanan, R.W., Francis, A., Arango, C., Miller, K., Lefkowitz, D.M., McMahon, R.P., Barta, P.E. and Pearlson, G.D., 2004. Morphometric assessment of the heteromodal association cortex in schizophrenia. Am J Psychiatry. 161 (2), 322–331.) found decreased dorsolateral prefrontal GM volume and altered symmetry of inferior parietal GM in schizophrenia patients. We hypothesized that VBM analyses of the same data would complement the ROI findings. As predicted, VBM analyses replicated results of less left inferior and right superior frontal cortical GM in schizophrenia. Additionally, VBM uncovered a significantly lower concentration of GM in the middle and superior temporal gyri, sought but not detected using ROIs, but did not replicate the parietal changes. The principal explanation for these differences may be the methodological differences between voxel-averaged, landmark-based ROI analyses and the single, voxel-by-voxel whole brain VBM measurements. Although VBM is rapid and fully automated, it is not a replacement for manual ROI-based analyses. Both methods provide different types of information and should thus be used in tandem. [Copyright &y& Elsevier]

Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N = 213 SZ and healthy control subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p = 6 × 10−3; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H 2 S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8 % of brain functional network connectivity from each component also showed significant differences between SZ and healthy controls with strong performance in classifying SZ from healthy controls, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity. [ABSTRACT FROM AUTHOR]

Over the last decade, there have been an increasing number of functional magnetic resonance imaging (fMRI) studies examining brain activity in schizophrenia (SZ) patients with persistent auditory verbal hallucinations (AVH) using either task-based or resting-state fMRI (rs-fMRI) paradigms. Such data have been conventionally collected and analyzed as distinct modalities, disregarding putative crossmodal interactions. Recently, it has become possible to incorporate two or more modalities in one comprehensive analysis to uncover hidden patterns of neural dysfunction not sufficiently captured by separate analysis. A novel multivariate fusion approach to multimodal data analysis, i.e., parallel independent component analysis (pICA), has been previously shown to be a powerful tool in this regard. We utilized three-way pICA to study covarying components among fractional amplitude of low-frequency fluctuations (fALFF) for rs-MRI and task-based activation computed from an alertness and a working memory (WM) paradigm of 15 SZ patients with AVH, 16 non-hallucinating SZ patients (nAVH), and 19 healthy controls (HC). The strongest connected triplet (false discovery rate (FDR)-corrected pairwise correlations) comprised a frontostriatal/temporal network (fALFF), a temporal/sensorimotor network (alertness task), and a frontoparietal network (WM task). Frontoparietal and frontostriatal/temporal network strength significantly differed between AVH patients and HC. Phenomenological features such as omnipotence and malevolence of AVH were associated with temporal/sensorimotor and frontoparietal network strength. The transmodal data confirm a complex interplay of neural systems subserving attentional processes and cognitive control interacting with speech and language processing networks. In addition, the data emphasize the importance of sensorimotor regions modulating specific symptom dimensions of AVH. [ABSTRACT FROM AUTHOR]

Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences. 455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives. Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms. These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum. [ABSTRACT FROM AUTHOR]

Schizophrenia is a complex disorder that affects perception, cognition, and emotion causing symptoms such as delusions, hallucinations, and suspiciousness. Schizophrenia is also associated with structural cortical abnormalities including lower gray matter (GM) concentration, GM volume, and cortical thickness relative to healthy control individuals. However, the association between GM measures and symptom dimensions in schizophrenia is still not well understood. Here, we applied parallel independent component analysis (pICA), a higher-order statistical approach that identifies covarying patterns within two (or more) data modalities simultaneously, to link covarying brain networks of GM concentration with covarying linear combinations of the positive and negative syndrome scale (PANSS). In a large sample of patients with schizophrenia (n = 337) the association between these two data modalities was investigated. The pICA revealed a distinct PANSS profile characterized by increased delusional symptoms, suspiciousness, hallucinations, and anxiety, that was associated with a pattern of lower GM concentration in inferior temporal gyri and fusiform gyri and higher GM concentration in the sensorimotor cortex. GM alterations replicate previous findings; additionally, applying a multivariate technique, we were able to map a very specific symptom profile onto these GM alterations extending our understanding of cortical abnormalities associated with schizophrenia. Techniques like parallel ICA can reveal linked patterns of alterations across different data modalities that can help to identify biologically-informed phenotypes which might help to improve future treatment targets. [ABSTRACT FROM AUTHOR]

Background: Psychiatric disorders are categorized using self-report and observational information rather than biological data. There is also considerable symptomatic overlap between different types of psychiatric disorders, which makes diagnostic categorization and multi-class classification challenging.Methods: In this work, we propose a unified framework for supervised classification and unsupervised clustering of psychotic disorders using brain imaging data. A new multi-scale recurrent neural network (MsRNN) model was developed and applied to fMRI time courses (TCs) for multi-class classification. The high-level representations of the original TCs were then submitted to a tSNE clustering model for visualizing the group differences between disorders. A leave-one-feature-out approach was used for disorder-related biomarker identification.Results: When studying fMRI from schizophrenia, psychotic bipolar disorder, schizoaffective disorder, and healthy individuals, the accuracy of a 4-class classification reached 46%, significantly above chance. The hippocampus, supplementary motor area and paracentral lobule were discovered as the most contributing regional TCs in the multi-class classification. Beyond this, visualization of the tSNE clustering suggested that the disease severity can be captured and schizoaffective disorder (SAD) may be separated into two subtypes. SAD cluster1 has significantly higher Positive And Negative Syndrome Scale (PANSS) scores than SAD cluster2 in PANSS negative2 (emotional withdrawal), general2 (anxiety), general3 (guilt feelings), general4 (tension).Conclusions: The proposed deep classification and clustering framework is not only able to identify psychiatric disorders with high accuracy, but also interpret the correlation between brain networks and specific psychiatric disorders, and reveal the relationship between them. This work provides a promising way to investigate a spectrum of similar disorders using neuroimaging-based measures. [ABSTRACT FROM AUTHOR]

Abstract: Background: Many studies have employed voxel-based morphometry (VBM) of MRI images as an automated method of investigating cortical gray matter differences in schizophrenia. However, results from these studies vary widely, likely due to different methodological or statistical approaches. Objective: To use VBM to investigate gray matter differences in schizophrenia in a sample significantly larger than any published to date, and to increase statistical power sufficiently to reveal differences missed in smaller analyses. Methods: Magnetic resonance whole brain images were acquired from four geographic sites, all using the same model 1.5T scanner and software version, and combined to form a sample of 200 patients with both first episode and chronic schizophrenia and 200 healthy controls, matched for age, gender and scanner location. Gray matter concentration was assessed and compared using optimized VBM. Results: Compared to healthy controls, schizophrenia patients showed significantly less gray matter concentration in multiple cortical and subcortical regions, some previously unreported. Overall, we found lower concentrations of gray matter in regions identified in prior studies, most of which reported only subsets of the affected areas. Conclusions: Gray matter differences in schizophrenia are most comprehensively elucidated using a large, diverse and representative sample. [Copyright &y& Elsevier]

Although visual processing impairments have been explored in schizophrenia (SZ), their underlying neurobiology of the visual processing impairments has not been widely studied. Also, while some research has hinted at differences in information transfer and flow in SZ, there are few investigations of the dynamics of functional connectivity within visual networks. In this study, we analyzed resting-state fMRI data of the visual sensory network (VSN) in 160 healthy control (HC) subjects and 151 SZ subjects. We estimated 9 independent components within the VSN. Then, we calculated the dynamic functional network connectivity (dFNC) using the Pearson correlation. Next, using k-means clustering, we partitioned the dFNCs into five distinct states, and then we calculated the portion of time each subject spent in each state, which we termed the occupancy rate (OCR). Using OCR, we compared HC with SZ subjects and investigated the link between OCR and visual learning in SZ subjects. Besides, we compared the VSN functional connectivity of SZ and HC subjects in each state. We found that this network is indeed highly dynamic. Each state represents a unique connectivity pattern of fluctuations in VSN FNC, and all states showed significant disruption in SZ. Overall, HC showed stronger connectivity within the VSN in states. SZ subjects spent more time in a state in which the connectivity between the middle temporal gyrus and other regions of VNS is highly negative. Besides, OCR in a state with strong positive connectivity between the middle temporal gyrus and other regions correlated significantly with visual learning scores in SZ. [ABSTRACT FROM AUTHOR]

This study assessed genetic contributions to six cognitive domains, identified by the MATRICS Cognitive Consensus Battery as relevant for schizophrenia, cognition-enhancing, clinical trials. Psychiatric Genomics Consortium Schizophrenia polygenic risk scores showed significant negative correlations with each cognitive domain. Genome-wide association analyses identified loci associated with attention/vigilance (rs830786 within HNF4G), verbal memory (rs67017972 near NDUFS4), and reasoning/problem solving (rs76872642 within HDAC9). Gene set analysis identified unique and shared genes across cognitive domains. These findings suggest involvement of common and unique mechanisms across cognitive domains and may contribute to the discovery of new therapeutic targets to treat cognitive deficits in schizophrenia. [ABSTRACT FROM AUTHOR]

New techniques to investigate functional network connectivity in resting state functional magnetic resonance imaging data have recently emerged. One novel approach, called meta-state analysis, goes beyond the mere cross-correlation of time courses of distinct brain areas and explores temporal dynamism in more detail, allowing for connectivity states to overlap in time and capturing global dynamic behavior. Previous studies have shown that patients with chronic schizophrenia exhibit reduced neural dynamism compared to healthy controls, but it is not known whether these alterations extend to earlier phases of the illness. In this study, we analyzed individuals at clinical high-risk (CHR, n = 53) for developing psychosis, patients in an early stage of schizophrenia (ESZ, n = 58), and healthy controls (HC, n = 70). ESZ individuals exhibit reduced neural dynamism across all domains compared to HC. CHR individuals also show reduced neural dynamism but only in 2 out of 4 domains investigated. Overall, meta-state analysis adds information about dynamic fluidity of functional connectivity. [ABSTRACT FROM AUTHOR]

Hallucinations characterize schizophrenia, with approximately 59% of patients reporting auditory hallucinations and 27% reporting visual hallucinations. Prior neuroimaging studies suggest that hallucinations are linked to disrupted communication across distributed (sensory, salience-monitoring and subcortical) networks. Yet, our understanding of the neurophysiological mechanisms that underlie auditory and visual hallucinations in schizophrenia remains limited. This study integrates two resting-state functional magnetic resonance imaging (fMRI) analysis methods - amplitudes of low-frequency fluctuations (ALFF) and functional network connectivity (FNC) - to explore the hypotheses that (1) abnormal FNC between salience and sensory (visual/auditory) networks underlies hallucinations in schizophrenia, and (2) disrupted hippocampal oscillations (as measured by hippocampal ALFF) beget changes in FNC linked to hallucinations. Our first hypothesis was supported by the finding that schizophrenia patients reporting hallucinations have higher FNC between the salience network and an associative auditory network relative to healthy controls. Hippocampal ALFF was negatively associated with FNC between primary auditory cortex and the salience network in healthy subjects, but was positively associated with FNC between these networks in patients reporting hallucinations. These findings provide indirect support favoring our second hypothesis. We suggest future studies integrate fMRI with electroencephalogram (EEG) and/or magnetoencephalogram (MEG) methods to directly probe the temporal relation between altered hippocampal oscillations and changes in cross-network functional communication. [ABSTRACT FROM AUTHOR]

Both schizophrenia (SZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. The significant genomic risk loci are of great importance but with no guarantee of known functional impact and they cannot totally explain the genetic inheritance. In this study we present regional enrichment analyses across the genome, aiming to strike a balance between individual risk loci and integrated regional effects. Chromosomes were partitioned into 2 million base-pair regions (indicated by an underscore sign in the cytogenetic bands) on which enrichment tests are performed. In the discovery phase, we leverage the Psychiatric Genomics Consortium SZ and BD initial association test results for European Ancestry (EA) population and dbGAP SNP data for African Ancestry (AA) population. 78 and 48 regions show significantly enriched associations with SZ and BD respectively in the EA population, and nine are in common including MHC, 3p21.1, 7p22.3_2, 2q32.3_2, 8q24.3_4, and 19q13.33_1. The most unique SZ associated region is 1p21.3_3, while the most unique BD associated region is 6q25.2_1. For the AA population fewer regions are discovered with only 10% overlapping with that of EA population. A replication test using Wellcome Trust Case Control Consortium data for EA population verified 9% of the SZ enriched regions and 40% of the BD enriched regions. In summary, we showed that regional enrichment analyses produce reliable genetic association profiles using about one tenth of samples compared to single base-pair genome wide association approach. The identified association regions will be useful for further genetic functional studies. [ABSTRACT FROM AUTHOR]

Default mode network (DMN) has been reported altered in schizophrenia (SZ) using static connectivity analysis. However, the studies on dynamic characteristics of DMN in SZ are still limited. In this work, we compare dynamic connectivity within DMN between 82 healthy controls (HC) and 82 SZ patients using resting-state fMRI. Firstly, dynamic DMN was computed using a sliding time window method for each subject. Then, the overall connectivity strengths were compared between two groups. Furthermore, we estimated functional connectivity states using K-means clustering, and then investigated group differences with respect to the connectivity strengths in states, the dwell time in each state, and the transition times between states. Finally, graph metrics of time-varying connectivity patterns and connectivity states were assessed. Results suggest that measured by the overall connectivity, HC showed stronger inter-subsystem interaction than patients. Compared to HC, patients spent more time in the states with nodes sparsely connected. For each state, SZ patients presented relatively weaker connectivity strengths mainly in inter-subsystem. Patients also exhibited lower values in averaged node strength, clustering coefficient, global efficiency, and local efficiency than HC. In summary, our findings indicate that SZ show impaired interaction among DMN subsystems, with a reduced central role for posterior cingulate cortex (PCC) and anterior medial prefrontal cortex (aMPFC) hubs as well as weaker interaction between dorsal medial prefrontal cortex (dMPFC) subsystem and medial temporal lobe (MTL) subsystem. For SZ, decreased integration of DMN may be associated with impaired ability in making self-other distinctions and coordinating present mental states with episodic decisions about future. [ABSTRACT FROM AUTHOR]

Both methylation and brain volume patterns hold important biological information for the development and prognosis of schizophrenia (SZ). A combined study to probe the association between them provides a new perspective to understanding SZ. Genomic methylation of peripheral blood and regional brain volumes derived from magnetic resonance imaging were analyzed using parallel independent component analyses in this study. Nine methylation components and five brain volumetric components were extracted for 94 SZ patients and 106 healthy controls. After controlling for age, sex, race, and substance use, a component comprised primarily of bilateral cerebellar volumes was significantly correlated to a methylation component from 14 CpG sites in 13 genes. Both patients and healthy controls demonstrated similar associations, but patients had significantly smaller cerebellar volumes and dysmethylation in the associated epigenetic component compared to controls. The 13 genes are enriched in cellular growth and proliferation with some genes involved in neuronal growth and cerebellum development (GATA4, ADRA1D, EPHA3, and KCNK10), and these genes are prominently associated with neurological and psychological disorders. Such findings suggest that the methylation pattern of the genes coding for cellular growth may influence the cerebellar development through regulating gene expression, and the alteration in the methylation of these genes in SZ patients may contribute to the cerebellar volume reduction observed in patients. [ABSTRACT FROM AUTHOR]

Background Individuals with schizophrenia demonstrate deficits in context processing. These deficits can be characterized by examining the influence of auditory context on ERP responses to rare target tones. Previous studies demonstrate that target ERP deficits in schizophrenia depend on the number of non-targets that precede the target ERP. Our goal was to extend these findings by examining whether patients with schizophrenia demonstrate a reduced sensitivity to subtle differences in the auditory context preceding rare target stimuli, as quantified by Itti and Baldi's Bayesian prediction error model. Methods Cortical responses to auditory oddball tones were measured within 59 individuals with schizophrenia (SZ) and 59 controls (HC). Individual trial amplitudes were estimated by conducting group ICA on the EEG time series and analyzing the reconstructed individual temporal sources. We quantified the auditory context of target tones using the Bayesian prediction error model and determined whether ERP amplitudes to tones were sensitive to this measure of context, or the number of preceding non-targets directly, within HC and SZ. Results Individuals with schizophrenia show a significant reduction in ERP response amplitudes to targets approximately 244–412 ms following target onsets. Individual amplitudes within this window showed significantly greater sensitivity to the modeled prediction error within the controls than in individuals with schizophrenia. These differences approached significance when examining differences in amplitudes as a function of the number of preceding non-targets. Conclusions These findings further clarify differences in HC and SZ with regard to their attentional and perceptual sensitivity to subtle environmental regularities. [ABSTRACT FROM AUTHOR]

Abstract: Background: Cognitive deficits are prominent in schizophrenia and represent promising endophenotypes for genetic research. Methods: The current study investigated the importance of two conceptually distinct genetic aggregates, one based on copy number variations (uncommon deletion burden), and one based on single nucleotide polymorphisms identified in recent risk studies (genetic risk score). The impact of these genetic factors, and their interaction, was examined on cognitive endophenotypes defined by principal component analysis (PCA) in a multi-center sample of 50 patients with schizophrenia and 86 controls. PCA was used to identify three different types of executive function (EF: planning, fluency, and inhibition), and in separate analyses, a measure general cognitive ability (GCA). Results: Cognitive deficits were prominent among individuals with schizophrenia, but no group differences were evident for either genetic factor. Among patients the deletion burden measures predicted cognitive deficits across the three EF components and GCA. Further, an interaction was noted between the two genetic factors for both EF and GCA and the observed patterns of interaction suggested antagonistic epistasis. In general, the set of genetic interactions examined predicted a substantial portion of variance in these cognitive endophenotypes. Limitations: Though adequately powered, our sample size is small for a genetic study. Conclusions: These results draw attention to genetic interactions and the possibility that genetic influences on cognition differ in patients and controls. [Copyright &y& Elsevier]

Abstract: Background: While the functional disconnectivity hypothesis of schizophrenia has received considerable attention, fewer studies have investigated the contribution of genotype to structural connectivity between brain regions either in schizophrenia patients or in healthy controls. In this study, we obtained diffusion tensor imaging (DTI) data and genome-wide single nucleotide polymorphism (SNP) data from 74 cases and 87 age- and gender-matched controls. Methods: We used independent component analysis (ICA) to analyze fractional anisotropy (FA) values and correlated FA values with 121 SNPs in genes associated with myelination and/or schizophrenia risk. Results: Using ICA, we identified 6 maximally independent components in which the majority of the voxels corresponded to known white matter (WM) tracts. Among these WM-enriched components, two had FA values that were significantly decreased in patients. In addition, we examined the relationship between FA values and genotype and found that a SNP located in the intronic region of the metabotropic glutamate receptor 3 gene, GRM3, shows a significant correlation with FA values in a component containing tracts from the cortico–cerebellar–thalamic–cortical circuit of patients but not controls. Conclusions: Our findings strengthen the evidence for an association between GRM3 genotype and schizophrenia and suggest a role for glutamate neurotransmission in the establishment and maintenance of myelinated fibers. [Copyright &y& Elsevier]

Abstract: Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder. [Copyright &y& Elsevier]

Abstract: Altered brain connectivity has emerged as a central feature of schizophrenia. Low frequency oscillations and connectivity strength (CS) of resting state brain networks are altered in patients with schizophrenia (SZs). However, the relationship between these two measures has not yet been studied. Such work may be helpful in understanding the so-called “rich club” organization (i.e. high-CS nodes are more densely connected among themselves than are nodes of a lower CS in the human brain) in healthy controls (HCs) and SZs. Here we present a study of HCs and SZs examining low frequency oscillations and CS by first decomposing resting state fMRI (R-fMRI) data into independent components (ICs) using group independent component analysis (ICA) and computing the low frequency power ratio (LFPR) of each ICA time course. Weighted brain graphs consisting of ICs were built based on correlations between ICA time courses. Positive CS and negative CS of each node in the brain graphs were then examined. The correlations between LFPR and CSs as well as “rich club” coefficients of group mean brain graphs were assessed. Results demonstrate that the LFPR of some ICs were lower in SZs compared to HCs. In addition, LFPR was correlated with positive CS in HCs, but to a lesser extent in SZs. HCs showed higher normalized rich club parameter than SZs. The findings provide new insight into disordered intrinsic brain graphs in schizophrenia. [Copyright &y& Elsevier]

Abstract: Background: Dysconnectivity between brain regions is thought to underlie the cognitive abnormalities that characterise schizophrenia (SZ). Consistent with this notion functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) studies in SZ have reliably provided evidence of abnormalities in functional integration and in white matter connectivity. Yet little is known about how alterations at the functional level related to abnormalities in anatomical connectivity. Methods: We obtained fMRI data during the 2-back working memory task from 25 patients with SZ and 19 healthy controls matched for age, sex and IQ. DTI data were also acquired in the same session. In addition to conventional unimodal analyses we extracted “features” [contrast maps for fMRI and fractional anisotropy (FA) for DTI] that were subjected to joint independent component analysis (JICA) in order to examine interactions between fMRI and DTI data sources. Results: Conventional unimodal analyses revealed both functional and structural deficits in patients with SZ. The JICA identified regions of joint, multimodal brain sources that differed in patients and controls. The fMRI source implicated regions within the anterior cingulate and ventrolateral prefrontal cortex and in the cuneus where patients showed relative hypoactivation and within the frontopolar cortex where patients showed relative hyperactivation. The DTI source localised reduced FA in patients in the splenium and posterior cingulum. Conclusions: This study promotes our understanding of structure–function relationships in SZ by characterising linked functional and white matter changes that contribute to working memory dysfunction in this disorder. [Copyright &y& Elsevier]

Abstract: Background: Working memory studies in schizophrenia (SZ), using functional magnetic resonance imaging (fMRI) and univariate analyses, have led to observations of hypo- or hyperactivation of discrete cortical regions and subsequent interpretations (e.g. neural inefficiencies). We employed a data-driven, multivariate analysis to identify the patterns of brain–behavior relationships in SZ during working memory. Methods: fMRI scans were collected from 13 SZ and 18 healthy control (HC) participants performing a modified Sternberg item recognition paradigm with three memory loads. We applied partial least squares analysis (PLS) to assess brain activation during the task both alone and with behavioral measures (accuracy and response time, RT) as covariates. Results: While the HC primary pattern was not affected by increasing load demands, SZ participants showed an exaggerated change in the Blood Oxygenation Level Dependent (BOLD) signal from the low to moderate memory load conditions and subsequent decrease in the greatest memory load, in frontal, motor, parietal and subcortical areas. With behavioral covariates, the separate groups identified distinct brain–behavior relationships and circuits. Increased activation of the middle temporal gyrus was associated with greater accuracy and faster RT only in SZ. Conclusions: The inverted U-shaped curves in the SZ BOLD signal in the same areas that show flat activation in the HC data indicate widespread neural inefficiency in working memory in SZ. While both groups performed the task with similar levels of accuracy, participants with schizophrenia show a compensatory network of different sub-regions of the prefrontal cortex, parietal lobule, and the temporal gyri in this working memory task. [Copyright &y& Elsevier]

Abstract: Identifying intermediate phenotypes of genetically complex psychiatric illnesses such as schizophrenia is important. First-degree relatives of persons with schizophrenia have increased genetic risk for the disorder and tend to show deficits on working memory (WM) tasks. An open question is the relationship between such behavioral endophenotypes and the corresponding brain activation patterns revealed during functional imaging. We measured task performance during a Sternberg WM task and used functional magnetic resonance imaging (fMRI) to assess whether 23 non-affected first-degree relatives showed altered performance and functional activation compared to 43 matched healthy controls. We predicted that a significant proportion of unaffected first-degree relatives would show either aberrant task performance and/or abnormal related fMRI blood oxygen level dependent (BOLD) patterns. While task performance in the relatives was not different than that of controls they were significantly slower in responding to probes., Schizophrenia relatives displayed reduced activation, most markedly in bilateral dorsolateral/ventrolateral (DLPFC/VLPFC) prefrontal and posterior parietal cortex when encoding stimuli and in bilateral DLPFC and parietal areas during response selection. Additionally, fMRI differences in both conditions were modulated by load, with a parametric increase in between-group differences with load in several key regions during encoding and an opposite effect during response selection. [Copyright &y& Elsevier]

Background: Many studies have employed voxel-based morphometry (VBM) of MRI images as an automated method of investigating cortical gray matter differences in schizophrenia. However, results from these studies vary widely, likely due to different methodological or statistical approaches.Objective: To use VBM to investigate gray matter differences in schizophrenia in a sample significantly larger than any published to date, and to increase statistical power sufficiently to reveal differences missed in smaller analyses.Methods: Magnetic resonance whole brain images were acquired from four geographic sites, all using the same model 1.5T scanner and software version, and combined to form a sample of 200 patients with both first episode and chronic schizophrenia and 200 healthy controls, matched for age, gender and scanner location. Gray matter concentration was assessed and compared using optimized VBM.Results: Compared to healthy controls, schizophrenia patients showed significantly less gray matter concentration in multiple cortical and subcortical regions, some previously unreported. Overall, we found lower concentrations of gray matter in regions identified in prior studies, most of which reported only subsets of the affected areas.Conclusions: Gray matter differences in schizophrenia are most comprehensively elucidated using a large, diverse and representative sample. [ABSTRACT FROM AUTHOR]