Convergent approaches for defining functional imaging endophenotypes in schizophrenia

DNAJC13 influences responses of the extended reward system to conditioned stimuli: a genome-wide association study

Reward system dysfunction is implicated in the pathogenesis of major psychiatric disorders. We conducted a genome-wide association study (GWAS) to identify genes that influence activation strength of brain regions within the extended reward system in humans. A homogeneous sample of 214 participants was genotyped and underwent functional magnetic resonance imaging (fMRI). All subjects performed the 'desire-reason dilemma' (DRD) paradigm allowing systematic investigation of systems-level mechanisms of reward processing in humans. As a main finding, we identified the single nucleotide variant rs113408797 in the DnaJ Heat Shock Protein Family Member C13 gene [DNAJC13], alias Receptor-Mediated Endocytosis 8 [RME-8], that was associated with the activation strength of the ventral tegmental area (VTA; p = 2.50E-07) and the nucleus accumbens (NAcc; p = 5.31E-05) in response to conditioned reward stimuli. Moreover, haplotype analysis assessing the information across the entire DNAJC13 locus demonstrated an impact of a five-marker haplotype on VTA activation (p = 3.21E-07), which further corroborates a link between this gene and reward processing. The present findings provide first direct empirical evidence that genetic variation of DNAJC13 influences neural responses within the extended reward system to conditioned stimuli. Further studies are required to investigate the role of this gene in the pathogenesis and pathophysiology of neuropsychiatric disorders.

Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity

Patients with schizophrenia (SCZ), as well as their unaffected siblings (SIB), show functional connectivity (FC) alterations during performance of tasks involving attention. As compared with SCZ, these alterations are present in SIB to a lesser extent and are more pronounced during high cognitive demand, thus possibly representing one of the pathways in which familial risk is translated into the SCZ phenotype. Our aim is to measure the separability of SCZ and SIB from healthy controls (HC) using attentional control-dependent FC patterns, and to test to which extent these patterns span a continuum of neurofunctional alterations between HC and SCZ. 65 SCZ with 65 age and gender-matched HC and 39 SIB with 39 matched HC underwent the Variable Attentional Control (VAC) task. Load-dependent connectivity matrices were generated according to correct responses in each VAC load. Classification performances of high, intermediate and low VAC load FC on HC-SCZ and HC-SIB cohorts were tested through machine learning techniques within a repeated nested cross-validation framework. HC-SCZ classification models were applied to the HC-SIB cohort, and vice-versa. A high load-related decreased FC pattern discriminated between HC and SCZ with 66.9% accuracy and with 57.7% accuracy between HC and SIB. A high load-related increased FC network separated SIB from HC (69.6% accuracy), but not SCZ from HC (48.5% accuracy). Our findings revealed signatures of attentional FC abnormalities shared by SCZ and SIB individuals. We also found evidence for potential, SIB-specific FC signature, which may point to compensatory neurofunctional mechanisms in persons at familial risk for schizophrenia.

A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.

Applications of Resting State Functional MR Imaging to Neuropsychiatric Diseases

Resting state studies in neuropsychiatric disorders have already provided much useful information, but the field is regarded as being at a relatively preliminary stage and subject to several design issues that set limits on the overall utility.

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.

Functional dysconnectivity of corticostriatal circuitry and differential response to methylphenidate in youth with attention-deficit/hyperactivity disorder

BACKGROUND

Brain frontostriatal circuits have been implicated in the pathophysiology of attention-deficit/hyperactivity disorder (ADHD). However, effects of methylphenidate on circuit-level functional connectivity are as yet unclear. The aim of the present study was to comprehensively investigate the functional connectivity of major striatal subregions in children with ADHD, including subanalyses directed at mapping cognitive and treatment response characteristics.

METHODS

Using a comprehensive seeding strategy, we examined resting-state functional connectivity of dorsal and ventral subdivisions of the caudate nucleus and putamen in children and adolescents with ADHD and in age- and sex-matched healthy controls.

RESULTS

We enrolled 83 patients with ADHD and 22 controls in our study. Patients showed significantly reduced dorsal caudate functional connectivity with the superior and middle prefrontal cortices as well as reduced dorsal putamen connectivity with the parahippocampal cortex. These connectivity measures were correlated in opposite directions in patients and controls with attentional performance, as assessed using the Continuous Performance Test. Patients showing a good response to methylphenidate had significantly reduced ventral caudate/nucleus accumbens connectivity with the inferior frontal cortices compared with poor responders.

LIMITATIONS

Possible confounding effects of age-related functional connectivity change were not excluded owing to the wide age range of participants.

CONCLUSION

We observed a region-specific effect of methylphenidate on resting-state functional connectivity, suggesting the pretreatment level of ventral frontostriatal functional connectivity as a possible methylphenidate response biomarker of ADHD.

Imaging the where and when of tic generation and resting state networks in adult Tourette patients

Introduction: Tourette syndrome (TS) is a neuropsychiatric disorder with the core phenomenon of tics, whose origin and temporal pattern are unclear. We investigated the When and Where of tic generation and resting state networks (RSNs) via functional magnetic resonance imaging (fMRI).

Methods: Tic-related activity and the underlying RSNs in adult TS were studied within one fMRI session. Participants were instructed to lie in the scanner and to let tics occur freely. Tic onset times, as determined by video-observance were used as regressors and added to preceding time-bins of 1 s duration each to detect prior activation. RSN were identified by independent component analysis (ICA) and correlated to disease severity by the means of dual regression.

Results: Two seconds before a tic, the supplementary motor area (SMA), ventral primary motor cortex, primary sensorimotor cortex and parietal operculum exhibited activation; 1 s before a tic, the anterior cingulate, putamen, insula, amygdala, cerebellum and the extrastriatal-visual cortex exhibited activation; with tic-onset, the thalamus, central operculum, primary motor and somatosensory cortices exhibited activation. Analysis of resting state data resulted in 21 components including the so-called default-mode network. Network strength in those regions in SMA of two premotor ICA maps that were also active prior to tic occurrence, correlated significantly with disease severity according to the Yale Global Tic Severity Scale (YGTTS) scores.

Discussion: We demonstrate that the temporal pattern of tic generation follows the cortico-striato-thalamo-cortical circuit, and that cortical structures precede subcortical activation. The analysis of spontaneous fluctuations highlights the role of cortical premotor structures. Our study corroborates the notion of TS as a network disorder in which abnormal RSN activity might contribute to the generation of tics in SMA.

Brain networks in schizophrenia

Schizophrenia--a severe psychiatric condition characterized by hallucinations, delusions, loss of initiative and cognitive function--is hypothesized to result from abnormal anatomical neural connectivity and a consequent decoupling of the brain's integrative thought processes. The rise of in vivo neuroimaging techniques has refueled the formulation of dysconnectivity hypotheses, linking schizophrenia to abnormal structural and functional connectivity in the brain at both microscopic and macroscopic levels. Over the past few years, advances in high-field structural and functional neuroimaging techniques have made it increasingly feasible to reconstruct comprehensive maps of the macroscopic neural wiring system of the human brain, know as the connectome. In parallel, advances in network science and graph theory have improved our ability to study the spatial and topological organizational layout of such neural connectivity maps in detail. Combined, the field of neural connectomics has created a novel platform that provides a deeper understanding of the overall organization of brain wiring, its relation to healthy brain function and human cognition, and conversely, how brain disorders such as schizophrenia arise from abnormal brain network wiring and dynamics. In this review we discuss recent findings of connectomic studies in schizophrenia that examine how the disorder relates to disruptions of brain connectivity.

State-related functional integration and functional segregation brain networks in schizophrenia

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.

Brain connectivity networks in schizophrenia underlying resting state functional magnetic resonance imaging

Schizophrenia (SZ) is a severe neuropsychiatric disorder. A leading hypothesis is that SZ is a brain dysconnection syndrome, involving abnormal interactions between widespread brain networks. Resting state functional magnetic resonance imaging (R-fMRI) is a powerful tool to explore the dysconnectivity of brain networks in SZ and other disorders. Seed-based functional connectivity analysis, spatial independent component analysis (ICA), and graph theory-based analysis are popular methods to quantify brain network connectivity in R-fMRI data. Widespread network dysconnectivity in SZ has been observed using both seed-based analysis and ICA, although most seed-based studies report decreased connectivity while ICA studies report both increases and decreases. Importantly, most of the findings from both techniques are also associated with typical symptoms of the illness. Disrupted topological properties and altered modular community structure of brain system in SZ have been shown using graph theory-based analysis. Overall, the resting-state findings regarding brain networks deficits have advanced our understanding of the underlying pathology of SZ. In this article, we review aberrant brain connectivity networks in SZ measured in R-fMRI by the above approaches, and discuss future challenges.

Genetic influences of resting state fMRI activity in language-related brain regions in healthy controls and schizophrenia patients: a pilot study

Individuals with schizophrenia show a broad range of language impairments, similar to those observed in reading disability (RD). Genetic linkage and association studies of RD have identified a number of candidate RD-genes that are associated with neuronal migration. Some individuals with schizophrenia also show evidence of impaired cortical neuronal migration. We have previously linked RD-related genes with gray matter distributions in healthy controls and schizophrenia. The aim of the current study was to extend these structural findings and to examine links between putative RD-genes and functional connectivity of language-related regions in healthy controls (n = 27) and schizophrenia (n = 28). Parallel independent component analysis (parallel-ICA) was used to examine the relationship between language-related regions extracted from resting-state fMRI and 16 single nucleotide polymorphisms (SNPs) spanning 5 RD-related genes. Parallel-ICA identified four significant fMRI-SNP relationships. A Left Broca-Superior/Inferior Parietal network was related to two KIAA0319 SNPs in controls but not in schizophrenia. For both diagnostic groups, a Broca-Medial Parietal network was related to two DCDC2 SNPs, while a Left Wernicke-Fronto-Occipital network was related to two KIAA0319 SNPs. A Bilateral Wernicke-Fronto-Parietal network was related to one KIAA0319 SNP only in controls. Thus, RD-genes influence functional connectivity in language-related regions, but no RD-gene uniquely affected network function in schizophrenia as compared to controls. This is in contrast with our previous study where RD-genes affected gray matter distribution in some structural networks in schizophrenia but not in controls. Thus these RD-genes may exert a more important influence on structure rather than function of language-related networks in schizophrenia.

Classification of schizophrenia patients based on resting-state functional network connectivity

There is a growing interest in automatic classification of mental disorders based on neuroimaging data. Small training data sets (subjects) and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. Several linear and non-linear classification methods were trained using a training dataset and evaluate with a separate testing dataset. Results show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors (KNNs) which is very promising. We compare and report detailed results of each classifier as well as statistical analysis and evaluation of each single feature. To our knowledge our effects represent the first use of resting-state functional network connectivity (FNC) features to classify schizophrenia.

Altered fronto-limbic activity in children and adolescents with familial high risk for schizophrenia

Early symptoms of schizophrenia tend to emerge during adolescence, hich is a critical period for development of executive and emotional processing. While individuals with familial high risk (FHR) for schizophrenia may show cognitive and emotional changes, the neural mechanisms underlying the development of these changes remain unclear. The goal of this study was to identify functional differences in fronto-striato-limbic regions in children with FHR. Functional magnetic resonance imaging (MRI) data were collected from 21 children with a first-degree family member with schizophrenia and 21 controls without FHR. Participants performed an emotional oddball task requiring both selective attention and suppression of task-irrelevant emotional information. During selective attention, the group with FHR showed enhanced activation in the inferior frontal gyrus and caudate, with decreases in middle frontal gyrus and insular activation. The FHR group also showed greater age-related recruitment of anterior cingulate, temporal and occipital cortical areas during selective attention. During emotional processing, the FHR group showed decreased anterior cingulate activation, with decreased age-related recruitment of inferior frontal, parietal and occipital areas. The results suggest that FHR for schizophrenia may be associated with abnormal hyperactivation and hypoactivation of the neural circuitry engaged during executive and emotional processing and with age-related changes in neural recruitment during adolescence.

Neuroimaging studies in schizophrenia: an overview of research from Asia

Neuroimaging studies in schizophrenia help clarify the neural substrates underlying the pathogenesis of this neuropsychiatric disorder. Contemporary brain imaging in schizophrenia is predominated by magnetic resonance imaging (MRI)-based research approaches. This review focuses on the various imaging studies from India and their relevance to the understanding of brain abnormalities in schizophrenia. The existing studies are predominantly comprised of structural MRI reports involving region-of-interest and voxel-based morphometry approaches, magnetic resonance spectroscopy and single-photon emission computed tomography/positron emission tomography (SPECT/PET) studies. Most of these studies are significant in that they have evaluated antipsychotic-naïve schizophrenia patients--a relatively difficult population to obtain in contemporary research. Findings of these studies offer robust support to the existence of significant brain abnormalities at very early stages of the disorder. In addition, theoretically relevant relationships between these brain abnormalities and developmental aberrations suggest possible neurodevelopmental basis for these brain deficits.

Patients with Schizophrenia Fail to Up-Regulate Task-Positive and Down-Regulate Task-Negative Brain Networks: An fMRI Study Using an ICA Analysis Approach

Recent research suggests that the cerebral correlates of cognitive deficits in schizophrenia are nested in the activity of widespread, inter-regional networks rather than being restricted to any specific brain location. One of the networks that have received focus lately is the default mode network. Parts of this network have been reported as hyper-activated in schizophrenia patients (SZ) during rest and during task performance compared to healthy controls (HC), although other parts have been found to be hypo-activated. In contrast to this network, task-positive networks have been reported as hypo-activated compared in SZ during task performance. However, the results are mixed, with, e.g., the dorsolateral prefrontal cortex showing both hyper- and hypo-activation in SZ. In this study we were interested in signal increase and decrease differences between a group of SZ and HC in cortical networks, assuming that the regulatory dynamics of alternating task-positive and task-negative neuronal processes are aberrant in SZ. We compared 31 SZ to age- and gender-matched HC, and used fMRI and independent component analysis (ICA) in order to identify relevant networks. We selected the independent components (ICs) with the largest signal intensity increases (STG, insula, supplementary motor cortex, anterior cingulate cortex, and MTG) and decreases (fusiform gyri, occipital lobe, PFC, cingulate, precuneus, and angular gyrus) in response to a dichotic auditory cognitive task. These ICs were then tested for group differences. Our findings showed deficient up-regulation of the executive network and a corresponding deficit in the down-regulation of the anterior default mode, or effort network during task performance in SZ when compared with HC. These findings may indicate a deficit in the dynamics of alternating task-dependent and task-independent neuronal processes in SZ. The results may cast new light on the mechanisms underlying cognitive deficits in schizophrenia, and may be of relevance for diagnostics and new treatments.

Differences in resting-state functional magnetic resonance imaging functional network connectivity between schizophrenia and psychotic bipolar probands and their unaffected first-degree relatives

BACKGROUND

Schizophrenia and bipolar disorder share overlapping symptoms and genetic etiology. Functional brain dysconnectivity is seen in both disorders.

METHODS

We compared 70 schizophrenia and 64 psychotic bipolar probands, their respective unaffected first-degree relatives (n = 70, and n = 52), and 118 healthy subjects, all group age-, gender-, and ethnicity-matched. We used functional network connectivity analysis to measure differential connectivity among 16 functional magnetic resonance imaging resting state networks. First, we examined connectivity differences between probands and control subjects. Next, we probed these dysfunctional connections in relatives for potential endophenotypes. Network connectivity was then correlated with Positive and Negative Syndrome Scale (PANSS) scores to reveal clinical relationships.

RESULTS

Three different network pairs were differentially connected in probands (false-discovery rate corrected q < .05) involving five individual resting-state networks: (A) fronto/occipital, (B) anterior default mode/prefrontal, (C) meso/paralimbic, (D) fronto-temporal/paralimbic, and (E) sensory-motor. One abnormal pair was unique to schizophrenia, (C-E), one unique to bipolar, (C-D), and one (A-B) was shared. Two of these three combinations (A-B, C-E) were also abnormal in bipolar relatives but none was normal in schizophrenia relatives (nonsignificant trend for C-E). The paralimbic circuit (C-D), which uniquely distinguished bipolar probands, contained multiple mood-relevant regions. Network relationship C-D correlated significantly with PANSS negative scores in bipolar probands, and A-B with PANSS positive and general scores in schizophrenia.

CONCLUSIONS

Schizophrenia and psychotic bipolar probands share several abnormal resting state network connections, but there are also unique neural network underpinnings between disorders. We identified specific connections that might also be candidate psychosis endophenotypes.

Convergent evidence from multimodal imaging reveals amygdala abnormalities in schizophrenic patients and their first-degree relatives

BACKGROUND

Shared neuropathological features between schizophrenic patients and their first-degree relatives have potential as indicators of genetic vulnerability to schizophrenia. We sought to explore genetic influences on brain morphology and function in schizophrenic patients and their relatives.

METHODS

Using a multimodal imaging strategy, we studied 33 schizophrenic patients, 55 of their unaffected parents, 30 healthy controls for patients, and 29 healthy controls for parents with voxel-based morphometry of structural MRI scans and functional connectivity analysis of resting-state functional MRI data.

RESULTS

Schizophrenic patients showed widespread gray matter reductions in the bilateral frontal cortices, bilateral insulae, bilateral occipital cortices, left amygdala and right thalamus, whereas their parents showed more localized reductions in the left amygdala, left thalamus and right orbitofrontal cortex. Patients and their parents shared gray matter loss in the left amygdala. Further investigation of the resting-state functional connectivity of the amygdala in the patients showed abnormal functional connectivity with the bilateral orbitofrontal cortices, bilateral precunei, bilateral dorsolateral frontal cortices and right insula. Their parents showed slightly less, but similar changes in the pattern in the amygdala connectivity. Co-occurrences of abnormal connectivity of the left amygdala with the left orbitofrontal cortex, right dorsolateral frontal cortex and right precuneus were observed in schizophrenic patients and their parents.

CONCLUSIONS

Our findings suggest a potential genetic influence on structural and functional abnormalities of the amygdala in schizophrenia. Such information could help future efforts to identify the endophenotypes that characterize the complex disorder of schizophrenia.

Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study

Aberrant topological properties of small-world human brain networks in patients with schizophrenia (SZ) have been documented in previous neuroimaging studies. Aberrant functional network connectivity (FNC, temporal relationships among independent component time courses) has also been found in SZ by a previous resting state functional magnetic resonance imaging (fMRI) study. However, no study has yet determined if topological properties of FNC are also altered in SZ. In this study, small-world network metrics of FNC during the resting state were examined in both healthy controls (HCs) and SZ subjects. FMRI data were obtained from 19 HCs and 19 SZ. Brain images were decomposed into independent components (ICs) by group independent component analysis (ICA). FNC maps were constructed via a partial correlation analysis of ICA time courses. A set of undirected graphs were built by thresholding the FNC maps and the small-world network metrics of these maps were evaluated. Our results demonstrated significantly altered topological properties of FNC in SZ relative to controls. In addition, topological measures of many ICs involving frontal, parietal, occipital and cerebellar areas were altered in SZ relative to controls. Specifically, topological measures of whole network and specific components in SZ were correlated with scores on the negative symptom scale of the Positive and Negative Symptom Scale (PANSS). These findings suggest that aberrant architecture of small-world brain topology in SZ consists of ICA temporally coherent brain networks.

Putative structural neuroimaging endophenotypes in schizophrenia: a comprehensive review of the current evidence

The genetic contribution to schizophrenia etiopathogenesis is underscored by the fact that the best predictor of developing schizophrenia is having an affected first-degree relative, which increases lifetime risk by tenfold, as well as the observation that when both parents are affected, the risk of schizophrenia increases to approximately 50%, compared with 1% in the general population. The search to elucidate the complex genetic architecture of schizophrenia has employed various approaches, including twin and family studies to examine co-aggregation of brain abnormalities, studies on genetic linkage and studies using genome-wide association to identify genetic variations associated with schizophrenia. ‘Endophenotypes’, or ‘intermediate phenotypes’, are potentially narrower constructs of genetic risk. Hypothetically, they are intermediate in the pathway between genetic variation and clinical phenotypes and can supposedly be implemented to assist in the identification of genetic diathesis for schizophrenia and, possibly, in redefining clinical phenomenology.

Beyond the dopamine receptor: novel therapeutic targets for treating schizophrenia

All current drugs approved to treat schizophrenia appear to exert their antipsychotic effects through blocking the dopamine D2 receptor. Recent meta-analyses and comparative efficacy studies indicate marginal differences in efficacy of newer atypical antipsychotics and the older drugs, and little effects on negative and cognitive symptoms. This review integrates findings from postmortem, imaging, and drug-challenge studies to elucidate a corticolimbic “pathologic circuit” in schizophrenia that may be particularly relevant to the negative symptoms and cognitive impairments of schizophrenia. Potential sites for pharmacologic intervention targeting glutatatergic, GABAergic, and cholinergic neurotransmission to treat these symptoms of schizophrenia are discussed.

Qualifying brain functional MRI parameters as endophenotypes in schizophrenia

Although the genetic contribution to schizophrenia pathogenesis has been well established, with an approximate heritability of 81%, the endeavor to elucidate the complex genetic architecture of schizophrenia has met limited success. ‘Endophenotypes’, or ‘intermediate phenotypes’, are more restricted constructs of genetic risk than the clinical manifestations hitherto employed by molecular geneticists. They are, putatively, intermediate in the pathophysiological pathway between genetic variation and clinical phenomenology and can possibly be used to assist in the elucidation of genetic diathesis for schizophrenia. In this article, we present the current evidence that supports functional MRI parameters as promising candidate endophenotypes in schizophrenia.

Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients

Autism spectrum disorders (ASDs) are characterized by deficits in social and communication processes. Recent data suggest that altered functional connectivity (FC), i.e. synchronous brain activity, might contribute to these deficits. Of specific interest is the FC integrity of the default mode network (DMN), a network active during passive resting states and cognitive processes related to social deficits seen in ASD, e.g. Theory of Mind. We investigated the role of altered FC of default mode sub-networks (DM-SNs) in 16 patients with high-functioning ASD compared to 16 matched healthy controls of short resting fMRI scans using independent component analysis (ICA). ICA is a multivariate data-driven approach that identifies temporally coherent networks, providing a natural measure of FC. Results show that compared to controls, patients showed decreased FC between the precuneus and medial prefrontal cortex/anterior cingulate cortex, DMN core areas, and other DM-SNs areas. FC magnitude in these regions inversely correlated with the severity of patients' social and communication deficits as measured by the Autism Diagnostic Observational Schedule and the Social Responsiveness Scale. Importantly, supplemental analyses suggest that these results were independent of treatment status. These results support the hypothesis that DM-SNs under-connectivity contributes to the core deficits seen in ASD. Moreover, these data provide further support for the use of data-driven analysis with resting-state data for illuminating neural systems that differ between groups. This approach seems especially well suited for populations where compliance with and performance of active tasks might be a challenge, as it requires minimal cooperation.