Overall brain connectivity maps show cortico-subcortical abnormalities in schizophrenia

Functional and structural effects of repetitive transcranial magnetic stimulation (rTMS) for the treatment of auditory verbal hallucinations in schizophrenia: A systematic review

BACKGROUND

Auditory verbal hallucinations (AVH) are a disabling symptom for people with schizophrenia (SCZ), and do not always respond to antipsychotics. Repetitive transcranial magnetic stimulation (rTMS) has shown efficacy for medication-refractory AVH, though the underlying neural mechanisms by which rTMS produces these effects remain unclear. This systematic review evaluated the structural and functional impact of rTMS for AVH in SCZ, and its association with clinical outcomes.

METHODS

A systematic search was conducted in Medline, PsychINFO, and PubMed using terms for four key concepts: AVH, SCZ, rTMS, neuroimaging. Using PRISMA guidelines, 18 studies were identified that collected neuroimaging data of an rTMS intervention for AVH in SCZ. Risk of bias assessments was conducted.

RESULTS

Low frequency (<5 Hz) rTMS targeting left hemispheric language processing regions may normalize brain abnormalities in AVH patients at structural, functional, electrophysiological, and topological levels, with concurrent symptom improvement. Amelioration of aberrant neural activity in frontotemporal networks associated with speech and auditory processing was commonly observed, as well as in cerebellar and emotion regulation regions. Neuroimaging analyses identified neural substrates with direct correlations to post-rTMS AVH severity, propounding their use as therapeutic targets.

DISCUSSION

Combined rTMS-neuroimaging highlights the multidimensional alterations of rTMS on brain activity and structure in treatment-resistant AVH, which may be used to develop more efficacious therapies. Larger randomized, sham-controlled studies are needed. Future studies should explore alternate stimulation targets, investigate the neural effects of high-frequency rTMS and evaluate long-term neuroimaging outcomes.

Brain subnetworks most sensitive to alterations of functional connectivity in Schizophrenia: a data-driven approach

Functional connectivity (FC) of the brain changes in various brain disorders. Its complexity, however, makes it difficult to obtain a systematic understanding of these alterations, especially when they are found individually and through hypothesis-based methods. It would be easier if the variety of brain connectivity alterations is extracted through data-driven approaches and expressed as variation modules (subnetworks). In the present study, we modified a blind approach to determine inter-group brain variations at the network level and applied it specifically to schizophrenia (SZ) disorder. The analysis is based on the application of independent component analysis (ICA) over the subject's dimension of the FC matrices, obtained from resting-state functional magnetic resonance imaging (rs-fMRI). The dataset included 27 SZ people and 27 completely matched healthy controls (HC). This hypothesis-free approach led to the finding of three brain subnetworks significantly discriminating SZ from HC. The area associated with these subnetworks mostly covers regions in visual, ventral attention, and somatomotor areas, which are in line with previous studies. Moreover, from the graph perspective, significant differences were observed between SZ and HC for these subnetworks, while there was no significant difference when the same parameters (path length, network strength, global/local efficiency, and clustering coefficient) across the same limited data were calculated for the whole brain network. The increased sensitivity of those subnetworks to SZ-induced alterations of connectivity suggested whether an individual scoring method based on their connectivity values can be applied to classify subjects. A simple scoring classifier was then suggested based on two of these subnetworks and resulted in acceptable sensitivity and specificity with an area under the ROC curve of 77.5%. The third subnetwork was found to be a less specific building block (module) for describing SZ alterations. It projected a wider range of inter-individual variations and, therefore, had a lower chance to be considered as a SZ biomarker. These findings confirmed that investigating brain variations from a modular viewpoint can help to find subnetworks that are more sensitive to SZ-induced alterations. Altogether, our study results illustrated the developed method's ability to systematically find brain alterations caused by SZ disorder from a network perspective.

Dorsal striatial hypoconnectivity predicts antipsychotic medication treatment response in first-episode psychosis and unmedicated patients with schizophrenia

INTRODUCTION

The dorsal striatum, comprised of the caudate and putamen, is implicated in the pathophysiology of psychosis spectrum disorders. Given the high concentration of dopamine receptors in the striatum, striatal dopamine imbalance is a likely cause in cortico-striatal dysconnectivity. There is great interest in understanding the relationship between striatal abnormalities in psychosis and antipsychotic treatment response, but few studies have considered differential involvement of the caudate and putamen. This study's goals were twofold. First, identify patterns of dorsal striatal dysconnectivity for the caudate and putamen separately in patients with a psychosis spectrum disorder; second, determine if these dysconnectivity patterns were predictive of treatment response.

METHODS

Using resting state functional connectivity, we evaluated dorsal striatal connectivity using separate bilateral caudate and putamen seed regions in two cohorts of subjects: a cohort of 71 medication-naïve first episode psychosis patients and a cohort of 42 unmedicated patients with schizophrenia (along with matched controls). Patient and control connectivity maps were contrasted for each cohort. After receiving 6 weeks of risperidone treatment, patients' clinical response was calculated. We used regression analyses to determine the relationship between baseline dysconnectivity and treatment response.

RESULTS

This dysconnectivity was also predictive of treatment response in both cohorts.

DISCUSSION

These findings suggest that the caudate may be more of a driving factor than the putamen in early cortico-striatal dysconnectivity.

Multimodel Order Independent Component Analysis: A Data-Driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales

Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods: We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. Results: In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions: In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single-model order analysis. This work expands upon and adds to the relatively new literature on resting functional magnetic resonance imaging-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model

Functional connectivity (FC) between brain region has been widely studied and linked with cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation of fMRI blood oxygen level-dependent (BOLD) signals between two brain regions. Although FC has been effective to understand brain organization, it cannot reveal the direction of interactions. Many directed acyclic graph (DAG) based methods have been applied to study the directed interactions but their performance was limited by the small sample size while high dimensionality of the available data. By enforcing group regularization and utilizing samples from both case and control groups, we propose a joint DAG model to estimate the directed FC. We first demonstrate that the proposed model is efficient and accurate through a series of simulation studies. We then apply it to the case-control study of schizophrenia (SZ) with data collected from the MIND Clinical Imaging Consortium (MCIC). We have successfully identified decreased functional integration, disrupted hub structures and characteristic edges (CtEs) in SZ patients. Those findings have been confirmed by previous studies with some identified to be potential markers for SZ patients. A comparison of the results between the directed FC and undirected FC showed substantial differences in the selected features. In addition, we used the identified features based on directed FC for the classification of SZ patients and achieved better accuracy than using undirected FC or raw features, demonstrating the advantage of using directed FC for brain network analysis.

Abnormal functional connectivity of the striatum in first-episode drug-naive early-onset Schizophrenia

Abnormal brain network connectivity is strongly implicated in the pathogenesis of schizophrenia. The striatum, consisting of the caudate and putamen, is the major treatment target for antipsychotics, the primary treatments for schizophrenia; however, there are few studies on the functional connectivity (FC) of striatum in drug-naive early-onset schizophrenia (EOS) patients. We examined the FC values of the caudate nucleus and putamen with whole brain by resting-state functional magnetic resonance imaging (RS-fMRI) and the associations with indices of clinical severity. Patients demonstrated abnormal FC between subregions of the putamen and both the visual network (left middle occipital gyrus) and default mode network (bilateral anterior cingulate, left superior frontal, and right middle frontal gyri). Furthermore, FC between dorsorostral putamen and left superior frontal gyrus correlated with both positive symptom subscore and total score on the Positive and Negative Syndrome Scale (PANSS). These findings demonstrate abnormal FC between the striatum and other brain areas even in the early stages of schizophrenia, supporting neurodevelopmental disruption in disease etiology and expression.

Altered Dynamic Functional Connectivity of Cuneus in Schizophrenia Patients: A Resting-State fMRI Study

Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients’ social lives. As a result, accurate diagnosis of SZ has attracted researchers’ interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients’ and 74 healthy controls’ (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease.

Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate

A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative results revealed shared and human-specific RSFC of different striatal clusters reinforcing the complex organization and function of the striatum. In addition, we provided a testable hypothesis that abnormalities in a region with human-specific connectivity, i.e., dorsal caudate, might be associated with neuropsychiatric disorders.

16p11 Duplication Disrupts Hippocampal-Orbitofrontal-Amygdala Connectivity, Revealing a Neural Circuit Endophenotype for Schizophrenia

Chromosome 16p11.2 duplications dramatically increase risk for schizophrenia, but the mechanisms remain largely unknown. Here, we show that mice with an equivalent genetic mutation (16p11.2 duplication mice) exhibit impaired hippocampal-orbitofrontal and hippocampal-amygdala functional connectivity. Expression of schizophrenia-relevant GABAergic cell markers (parvalbumin and calbindin) is selectively decreased in orbitofrontal cortex, while somatostatin expression is decreased in lateral amygdala. When 16p11.2 duplication mice are tested in cognitive tasks dependent on hippocampal-orbitofrontal connectivity, performance is impaired in an 8-arm maze "N-back" working memory task and in a touchscreen continuous performance task. Consistent with hippocampal-amygdala dysconnectivity, deficits in ethologically relevant social behaviors are also observed. Overall, the cellular/molecular, brain network, and behavioral alterations markedly mirror those observed in schizophrenia patients. Moreover, the data suggest that 16p11.2 duplications selectively impact hippocampal-amygdaloid-orbitofrontal circuitry, supporting emerging ideas that dysfunction in this network is a core element of schizophrenia and defining a neural circuit endophenotype for the disease.

Simultaneously decreased temporal variability and enhanced variability-strength coupling of emotional network connectivities are related to positive symptoms in patients with schizophrenia

We hypothesize that decreased temporal variability of emotional network connectivities, corresponding to a continual state of hyperactivity, may play a role in mediating symptoms in schizophrenia. Resting-state magnetic resonance data were collected from 64 subjects, including 21 positive symptom profile schizophrenia patients (PSZ group), 19 negative symptom profile schizophrenia patients (NSZ group), and 24 healthy controls. The emotional brain network was defined based on the coordinates obtained from multi-level kernel density analysis. The temporal variability of intra-network functional connectivities (FCs) was calculated by constructing networks from blood oxygen level-dependent signals at successive, non-overlapping time windows, and was compared between groups. The results showed that the mean FC-variability of the whole emotional network (P = 0.021), and the FC-variabilities in the bilateral anterior insula (both, P < 0.001) were significantly decreased in the PSZ group compared with the control and NSZ groups. Abnormally enhanced negative coupling between variability and FC strength (V-S coupling) was observed in the PSZ group (P = 0.027). In summary, this study found a relation between the positive symptoms of schizophrenia and decreased variability of emotional network connectivities. These findings may help us better understand the neurobiological effect of the time-varying properties of the brain network in schizophrenia patients, and the underlying relation to the generation of psychosis.

Addressing indirect frequency coupling via partial generalized coherence

Distinguishing between direct and indirect frequency coupling is an important aspect of functional connectivity analyses because this distinction can determine if two brain regions are directly connected. Although partial coherence quantifies partial frequency coupling in the linear Gaussian case, we introduce a general framework that can address even the nonlinear and non-Gaussian case. Our technique, partial generalized coherence (PGC), expands prior work by allowing pairwise frequency coupling analyses to be conditioned on other processes, enabling model-free partial frequency coupling results. By taking advantage of recent advances in conditional mutual information estimation, we are able to implement our technique in a way that scales well with dimensionality, making it possible to condition on many processes and produce a partial frequency coupling graph. We analyzed both linear Gaussian and nonlinear simulated networks. We then performed PGC analysis of calcium recordings from mouse olfactory bulb glomeruli under anesthesia and quantified the dominant influence of breathing-related activity on the pairwise relationships between glomeruli for breathing-related frequencies. Overall, we introduce a technique capable of eliminating indirect frequency coupling in a model-free way, empowering future research to correct for potentially misleading frequency interactions in functional connectivity analyses.

Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia

The human brain is a dynamic system that incorporates the evolution of local activities and the reconfiguration of brain interactions. Reoccurring brain patterns, regarded as "brain states", have revealed new insights into the pathophysiology of brain disorders, particularly schizophrenia. However, previous studies only focus on the dynamics of either brain activity or connectivity, ignoring the temporal co-evolution between them. In this work, we propose to capture dynamic brain states with covarying activity-connectivity and probe schizophrenia-related brain abnormalities. We find that the state-based activity and connectivity show high correspondence, where strong and antagonistic connectivity is accompanied with strong low-frequency fluctuations across the whole brain while weak and sparse connectivity co-occurs with weak low-frequency fluctuations. In addition, graphical analysis shows that connectivity network efficiency is associated with the fluctuation of brain activities and such associations are different across brain states. Compared with healthy controls, schizophrenia patients spend more time in weakly-connected and -activated brain states but less time in strongly-connected and -activated brain states. schizophrenia patients also show lower efficiency in thalamic regions within the "strong" states. Interestingly, the atypical fractional occupancy of one brain state is correlated with individual attention performance. Our findings are replicated in another independent dataset and validated using different brain parcellation schemes. These converging results suggest that the brain spontaneously reconfigures with covarying activity and connectivity and such co-evolutionary property might provide meaningful information on the mechanism of brain disorders which cannot be observed by investigating either of them alone.

Dynamic Causal Modelling suggests impaired effective connectivity in patients with schizophrenia spectrum disorders during gesture-speech integration

Integrating visual and auditory information during gesture-speech integration (GSI) is important for successful social communication, which is often impaired in schizophrenia. Several studies suggested the posterior superior temporal sulcus (pSTS) to be a relevant multisensory integration site. However, intact STS activation patterns were often reported in patients. Thus, here we used Dynamic Causal Modelling (DCM) to analyze whether information processing in schizophrenia spectrum disorders (SSD) is impaired during GSI on network level. We investigated GSI in three different samples. First, we replicated a recently published connectivity model for GSI in a healthy subject group (n = 19). Second, we investigated differences between patients with SSD and a matched healthy control group (n = 17 each). Participants were presented videos of an actor performing intrinsically meaningful gestures accompanied by spoken sentences in German or Russian, or just telling a German sentence without gestures. Across all groups, fMRI analyses revealed similar activation patterns, and DCM analyses resulted in the same winning model for GSI. This finding directly replicates previous results. However, patients revealed significantly reduced connectivity in the verbal pathway (from left middle temporal gyrus (MTG) to left STS). The clinical significance of this connection is supported by its correlations with the severity of concretism and a subscale of negative symptoms (SANS). Our model confirms the importance of the pSTS as integration site during audio-visual integration. Patients showed generally intact connectivity during GSI, but revealed impaired information transfer via the verbal pathway. This might be the basis of interpersonal communication problems in patients with SSD.

Altered intra- and inter-hemispheric functional dysconnectivity in schizophrenia

Despite convergent evidence suggesting that schizophrenia is a disorder of brain dysconnectivity, it remains unclear whether intra- or inter-hemispheric deficits or their combination underlie the dysconnection. This study examined the source of the functional dysconnection in schizophrenia. Resting-state fMRI was performed in 66 patients with schizophrenia and 73 matched healthy controls. Functional brain networks were constructed for each participant and further partitioned into intra- and inter-hemispheric connections. We examined how schizophrenia altered the intra-hemispheric topological properties and the inter-hemispheric nodal strength. Although several subcortical and cingulate regions exhibited hemispheric-independent aberrations of regional efficiency, the optimal small-world properties in the hemispheric networks and their lateralization were preserved in patients. A significant deficit in the inter-hemispheric connectivity was revealed in most of the hub regions, leading to an inter-hemispheric hypo-connectivity pattern in patients. These abnormal intra- and inter-hemispheric network organizations were associated with the clinical features of schizophrenia. The patients in the present study received different medications. These findings provide new insights into the nature of dysconnectivity in schizophrenia, highlighting the dissociable processes between the preserved intra-hemispheric network topology and altered inter-hemispheric functional connectivity.

Cognition- and circuit-based dysfunction in a mouse model of 22q11.2 microdeletion syndrome: effects of stress

Genetic microdeletion at the 22q11 locus is associated with very high risk for schizophrenia. The 22q11.2 microdeletion (Df(h22q11)/+) mouse model shows cognitive deficits observed in this disorder, some of which can be linked to dysfunction of the prefrontal cortex (PFC). We used behavioral (n = 10 per genotype), electrophysiological (n = 7 per genotype per group), and neuroanatomical (n = 5 per genotype) techniques to investigate schizophrenia-related pathology of Df(h22q11)/+ mice, which showed a significant decrease in the total number of parvalbumin positive interneurons in the medial PFC. The Df(h22q11)/+ mice when tested on PFC-dependent behavioral tasks, including gambling tasks, perform significantly worse than control animals while exhibiting normal behavior on hippocampus-dependent tasks. They also show a significant decrease in hippocampus-medial Prefrontal cortex (H-PFC) synaptic plasticity (long-term potentiation, LTP). Acute platform stress almost abolished H-PFC LTP in both wild-type and Df(h22q11)/+ mice. H-PFC LTP was restored to prestress levels by clozapine (3 mg/kg i.p.) in stressed Df(h22q11)/+ mice, but the restoration of stress-induced LTP, while significant, was similar between wild-type and Df(h22q11)/+ mice. A medial PFC dysfunction may underlie the negative and cognitive symptoms in human 22q11 deletion carriers, and these results are relevant to the current debate on the utility of clozapine in such subjects.

Decreased regional homogeneity and increased functional connectivity of default network correlated with neurocognitive deficits in subjects with genetic high-risk for schizophrenia: A resting-state fMRI study

The complex symptoms of schizophrenia (SCZ) have been associated with dysfunction of the default mode network (DMN). Subjects at genetic high risk (GHR) for SCZ exhibit similar but milder brain abnormalities. This study aimed to investigate functional alterations of DMN from the local to the whole and their relationships with cognitive deficits in GHR subjects. 42 GHR subjects and 38 matched healthy controls (HC) were studied by resting-state functional magnetic resonance imaging (rs-fMRI). Regional homogeneity (ReHo) analysis was performed to measure the local brain function of the DMN, derived by the group independent component analysis, and areas with aberrant ReHo were used as seeds in functional connectivity (FC). Compared with the HC group, the GHR group exhibited significantly decreased ReHo and increased FC in the fronto-limbic-striatal system within the DMN. Furthermore, a significant negative correlation was found between decreased ReHo in the right superior frontal gyrus and the delayed recall in GHR subjects. Our findings revealed decreased local function and hyper-connectivity in the fronto-limbic-striatal system of the DMN in GHR subjects, which is associated with cognitive deficits. This may improve our understanding of the neurophysiological endophenotypes of SCZ and the neural substrate underlying the cognitive deficits of the disease.

Functional fractionation of default mode network in first episode schizophrenia

A disruption in the connectivity between brain regions may underlie the core pathology in schizophrenia. One of the most consistent observations in human functional imaging is a network of brain regions referred to as the default network (DMN) that contains core subsystem, the dorsomedial prefrontal cortex (dMPFC) subsystem and the medial temporal lobe (MTL) subsystem, with differential contributions. The goal of this study was to examine abnormalities of different DMN subsystems in first episode schizophrenia and associations between these abnormalities and individual psychopathology. We recruited 203 patients and 131 healthy controls. A seed-based resting-state functional connectivity (RSFC) analysis on the 2D surface was conducted. Individual DMN functional connectivity matrices were then obtained by calculating spatial correlations between pairs of RSFC maps, characterizing the functional fractionation of the DMN. Patients showed patterns similar to controls but markedly reduced strength of DMN fractionation, with the degree centrality of the MTL subsystem significantly reduced, including the posterior inferior parietal lobule (pIPL), parahippocampal cortex (PHC) and lateral temporal cortex (LTC). Patients also exhibited hypo-connectivity within the MTL subsystem and between the MTL and dMPFC subsystems. Clinical symptoms were negatively correlated with degree centrality of LTC, pIPL and PHC in patients. Hyper-fractionation of different DMN components implied that communication and coordination throughout the dissociated components of the DMN are functionally over-segregated in schizophrenia. The associations between the hyper-fractionation with clinical symptoms suggest a role of the high fractionation in the DMN in the abnormal neuropathology observed in schizophrenia.

Abnormalities in hubs location and nodes centrality predict cognitive slowing and increased performance variability in first-episode schizophrenia patients

Introducing the Minimum Spanning Tree (MST) algorithms to neural networks science eliminated the problem of arbitrary setting of the threshold for connectivity strength. Despite these advantages, MST has been rarely used to study network abnormalities in schizophrenia. An MST graph mapping a network structure is its simplification, therefore, it is important to verify whether the reconfigured network is significantly related to the behavioural dimensions of the clinical picture of schizophrenia. 35 first-episode schizophrenia patients and 35 matched healthy controls underwent an assessment of information processing speed, cognitive inter-trial variability modelled with ex-Gaussian distributional analysis of reaction times and resting-state EEG recordings to obtain frequency-specific functional connectivity matrices from which MST graphs were computed. The patients’ network had a more random structure and star-like arrangement with overloaded hubs positioned more posteriorly than it was in the case of the control group. Deficient processing speed in the group of patients was predicted by increased maximal betweenness centrality in beta and gamma bands, while decreased consistency in cognitive processing was predicted by the betweenness centrality of posterior nodes in the gamma band, together with duration of illness. The betweenness centrality of posterior nodes in the gamma band was also significantly correlated with positive psychotic symptoms in the clinical group.

Hybrid Functional Brain Network With First-Order and Second-Order Information for Computer-Aided Diagnosis of Schizophrenia

Brain functional connectivity network (BFCN) analysis has been widely used in the diagnosis of mental disorders, such as schizophrenia. In BFCN methods, brain network construction is one of the core tasks due to its great influence on the diagnosis result. Most of the existing BFCN construction methods only consider the first-order relationship existing in each pair of brain regions and ignore the useful high-order information, including multi-region correlation in the whole brain. Some early schizophrenia patients have subtle changes in brain function networks, which cannot be detected in conventional BFCN construction methods. It is well-known that the high-order method is usually more sensitive to the subtle changes in signal than the low-order method. To exploit high-order information among brain regions, we define the triplet correlation among three brain regions, and derive the second-order brain network based on the connectivity difference and ordinal information in each triplet. For making full use of the complementary information in different brain networks, we proposed a hybrid approach to fuse the first- and second-order brain networks. The proposed method is applied to identify the biomarkers of schizophrenia. The experimental results on six schizophrenia datasets (totally including 439 patients and 426 controls) show that the proposed method outperforms the existing brain network methods in the diagnosis of schizophrenia.

Alterations in resting-state gamma activity in patients with schizophrenia: a high-density EEG study

Alterations of EEG gamma activity in schizophrenia have been reported during sensory and cognitive tasks, but it remains unclear whether changes are present in resting state. Our aim was to examine whether changes occur in resting state, and to delineate those brain regions where gamma activity is altered. Furthermore, we wanted to identify the associations between changes in gamma activity and psychopathological characteristics. We studied gamma activity (30-48 Hz) in 60 patients with schizophrenia and 76 healthy controls. EEGs were acquired in resting state with closed eyes using a high-density, 256-channel EEG-system. The two groups were compared in absolute power measures in the gamma frequency range. Compared to controls, in patients with schizophrenia the absolute power was significantly elevated (false discovery rate corrected p < 0.05). The alterations clustered into fronto-central and posterior brain regions, and were positively associated with the severity of psychopathology, measured by the PANSS. Changes in gamma activity can lead to disturbed coordination of large-scale brain networks. Thus, the increased gamma activity in certain brain regions that we found may result in disturbances in temporal coordination of task-free/resting-state networks in schizophrenia. Positive association of increased gamma power with psychopathology suggests that altered gamma activity provides a contribution to symptom presentation.

Fronto-subcortical functional connectivity in patients with schizophrenia and bipolar disorder during a verbal fluency task

OBJECTIVES

Impairments in language production are common of schizophrenia (SZ) and bipolar disorder (BD). Identifying distinct functional connectivity (FC) patterns in SZ and BD may provide biomarkers for their diagnoses.

METHODS

Forty-nine participants (15 SZ, 14 BD and 20 healthy controls (HC)) underwent a verbal fluency task consisting of mentally generating verbs in French, alternated with periods of silence. Functional network allowed identifying activation clusters: the medio-frontal cluster (MFC), the left subcortical cluster (LSCC) and the left fronto-lateral cluster (LFLC). FC was calculated between the average blood oxygen level-dependent signal time series in each cluster. Analyses of covariance were performed to test group differences on FC among the three paired-seed regions.

RESULTS

SZ presented a significant reduced FC compared to HC within two paired-seed regions between the LFLC and the LSCC and between the MFC and the LSCC while BD were not significantly different from HC. SZ compared to BD exhibited a reduced FC within one paired-seed region between the MFC and the LSCC. There was no group effect between the MFC and the LFLC.

CONCLUSIONS

A specific medio-prefronto-striato-thalamic functional dysconnectivity may be implicated in the pathophysiology of schizophrenia. This reduced fronto-subcortical FC could be a functional brain biomarker of schizophrenia.

Default mode network connectivity is associated with long-term clinical outcome in patients with schizophrenia

This study investigated whether resting-state functional connectivity is associated with long-term clinical outcomes of patients with schizophrenia. Resting-state brain images were obtained from 79 outpatients with schizophrenia and 30 healthy controls (HC), using a 3 T-MRI scanner. All patients were 20-50 years old with >3 years' duration of illness and appeared clinically stable. We assessed their psychopathology using the 18-item Brief Psychiatric Rating Scale (BPRS-18) and divided them into "good," "moderate," and "poor" outcome (SZ-GO, SZ-MO, and SZ-PO) groups depending on BPRS-18 total score. We obtained individual functional connectivity maps between a seed region of the bilateral posterior cingulate cortex (PCC) and all other brain regions and compared the functional connectivity of the default mode network (DMN) among the HC and 3 schizophrenia outcome groups, with a voxel-wise threshold of P < .001 within a cluster-extent threshold of 114 voxels. Additionally, we assessed correlations between functional connectivity and BPRS-18 scores. The SZ-MO and SZ-PO groups showed decreased functional connectivity between PCC and right ventromedial prefrontal cortex (vmPFC), left middle cingulate cortex, and left frontopolar cortex (FPC) compared to the SZ-GO and HC groups. DMN connectivity in the right vmPFC and left FPC negatively correlated with subscale scores of the BPRS-18, except the negative symptoms subscale. In this study, poorer clinical outcomes in patients with schizophrenia were associated with decreased DMN connectivity. In particular, the decreased functional connectivity might be related to the severity of positive and mood symptoms rather than negative symptoms.

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder

Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophrenia. However, similar changes have also been found in patients with major depressive disorder, prompting the question of specific triple network signatures for the two disorders. In this study, we proposed Supervised Convex Nonnegative Matrix Factorization (SCNMF) to extract distributed multi-modal brain patterns. These patterns distinguish schizophrenia and major depressive disorder in a latent low-dimensional space of the triple brain network. Specifically, 21 patients of schizophrenia and 25 patients of major depressive disorder were assessed by T1-weighted, diffusion-weighted, and resting-state functional MRIs. Individual structural and functional connectivity networks, based on pre-defined regions of the triple network were constructed, respectively. Afterwards, SCNMF was employed to extract the discriminative patterns. Experiments indicate that SCNMF allows extracting the low-rank discriminative patterns between the two disorders, achieving a classification accuracy of 82.6% based on the extracted functional and structural abnormalities with support vector machine. Experimental results show the specific brain patterns for schizophrenia and major depressive disorder that are multi-modal, complex, and distributed in the triple network. Parts of the prefrontal cortex including superior frontal gyri showed variation between patients with schizophrenia and major depression due to structural properties. In terms of functional properties, the middle cingulate cortex, inferior parietal lobule, and cingulate cortex were the most discriminative regions.

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Specific changes in SZP and MDD are complex but subtle, and distributed in triple networks.

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Low-rank network signatures on multi-modal data well separate SZP and MDD.

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Group-specific latent disrupted patterns are uncovered with SCNMF.

Dysconnectivity of Multiple Brain Networks in Schizophrenia: A Meta-Analysis of Resting-State Functional Connectivity

Background: Seed-based studies on resting-state functional connectivity (rsFC) in schizophrenia have shown disrupted connectivity involving a number of brain networks; however, the results have been controversial. Methods: We conducted a meta-analysis based on independent component analysis (ICA) brain templates to evaluate dysconnectivity within resting-state brain networks in patients with schizophrenia. Seventy-six rsFC studies from 70 publications with 2,588 schizophrenia patients and 2,567 healthy controls (HCs) were included in the present meta-analysis. The locations and activation effects of significant intergroup comparisons were extracted and classified based on the ICA templates. Then, multilevel kernel density analysis was used to integrate the results and control bias. Results: Compared with HCs, significant hypoconnectivities were observed between the seed regions and the areas in the auditory network (left insula), core network (right superior temporal cortex), default mode network (right medial prefrontal cortex, and left precuneus and anterior cingulate cortices), self-referential network (right superior temporal cortex), and somatomotor network (right precentral gyrus) in schizophrenia patients. No hyperconnectivity between the seed regions and any other areas within the networks was detected in patients, compared with the connectivity in HCs. Conclusions: Decreased rsFC within the self-referential network and default mode network might play fundamental roles in the malfunction of information processing, while the core network might act as a dysfunctional hub of regulation. Our meta-analysis is consistent with diffuse hypoconnectivities as a dysregulated brain network model of schizophrenia.

Altered Functional Connectivity of the Default Mode Network in Patients With Schizo-obsessive Comorbidity: A Comparison Between Schizophrenia and Obsessive-compulsive Disorder

Clinical and neuroimaging data support the idea that schizo-obsessive comorbidity (SOC), similar to obsessive-compulsive disorder (OCD) and schizophrenia (SCZ), may be a distinct brain disorder. In this study, we examined the strength of resting-state functional connectivity (rsFC) between 19 subregions of the default mode network (DMN) and whole brain voxels in 22 patients with SOC features, 20 patients with SCZ alone, 22 patients with OCD, and 22 healthy controls (HC). The main results demonstrated that patients with SOC exhibited the highest rsFC strength within subregions of the DMN and the lowest rsFC strength between the DMN and subregions of the salience network (SN) compared with the other 3 groups. In addition, compared with HCs, all 3 patient groups exhibited increased rsFC between subregions of the DMN and the executive control network (ECN). The SOC and SCZ group both exhibited increased rsFC between subregions of the DMN and the middle temporal gyrus, but the OCD group exhibited decreased rsFC between them. These findings highlight a specific alteration in functional connectivity in the DMN in patients with SOC, and provide new insights into the dysfunctional brain organization of different mental disorders.

Comparison of Resting-State Brain Activation Detected by BOLD, Blood Volume and Blood Flow

Resting-state brain activity has been widely investigated using blood oxygenation level dependent (BOLD) contrast techniques. However, BOLD signal changes reflect a combination of the effects of cerebral blood flow (CBF), cerebral blood volume (CBV), as well as the cerebral metabolic rate of oxygen (CMRO₂). In this study, resting-state brain activation was detected and compared using the following techniques: (a) BOLD, using a gradient-echo echo planar imaging (GE-EPI) sequence; (b) CBV-weighted signal, acquired using gradient and spin echo (GRASE) based vascular space occupancy (VASO); and (c) CBF, using pseudo-continuous arterial spin labeling (pCASL). Reliable brain networks were detected using VASO and ASL, including sensorimotor, auditory, primary visual, higher visual, default mode, salience and left/right executive control networks. Differences between the resting-state activation detected with ASL, VASO and BOLD could potentially be due to the different temporal signal-to-noise ratio (tSNR) and the short post-labeling delay (PLD) in ASL, along with differences in the spin-echo readout of VASO. It is also possible that the dynamics of spontaneous fluctuations in BOLD, CBV and CBF could differ due to biological reasons, according to their location within the brain.

Possible Biological Mechanisms Linking Mental Health and Heat-A Contemplative Review

This review provides examples of possible biological mechanisms that could, at least partly, explain the existing epidemiological evidence of heatwave-related exacerbation of mental disease morbidity. The author reviews the complicated central processes involved in the challenge of maintaining a stable body temperature in hot environments, and the maladaptive effects of certain psychiatric medicines on thermoregulation. In addition, the author discusses some alternative mechanisms, such as interrupted functional brain connectivity and the effect of disrupted sleep, which may further increase the vulnerability of mental health patients during heatwaves.

Sleep apnea: Altered brain connectivity underlying a working-memory challenge

Obstructive sleep apnea (OSA) is characterized by the frequent presence of neuro-cognitive impairment. Recent studies associate cognitive dysfunction with altered resting-state brain connectivity between key nodes of the executive and default-mode networks, two anti-correlated functional networks whose strength of activation increases or decreases with cognitive activity, respectively. To date no study has investigated a relationship between cognitive impairment in OSA and brain connectivity during an active working-memory challenge. We thus investigated the effect of OSA on working-memory performance and underlying brain connectivity.

OSA patients and matched healthy controls underwent functional magnetic resonance imaging (fMRI) scanning while performing a 2-back working-memory task. Standard fMRI analyses highlighted the brain regions activated at increasing levels of working-memory load, which were used as seeds in connectivity analyses. The latter were based on a multiregional Psycho-Physiological-Interaction (PPI) approach, to unveil group differences in effective connectivity underlying working-memory performance.

Compared with controls, in OSA patients normal working-memory performance reflected in: a) reduced interhemispheric effective connectivity between the frontal “executive” nodes of the working-memory network, and b) increased right-hemispheric connectivity among regions mediating the “salience-based” switch from the default resting-state mode to the effortful cognitive activity associated with the executive network. The strength of such connections was correlated, at increasing task-demands, with executive (Stroop test) and memory (Digit Span test) performance in neuro-cognitive evaluations.

The analysis of effective connectivity changes during a working-memory challenge provides a complementary window, compared with resting-state studies, on the mechanisms supporting preserved performance despite functional and structural brain modifications in OSA.

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Sleep apnea (OSA) is frequently characterized by neuro-cognitive impairment.

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We addressed brain connectivity underlying working-memory (WM) in OSA.

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Normal WM reflected in reduced interhemispheric connectivity in the executive network.

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Normal WM reflected in increased connectivity between salience and default networks.

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Brain connectivity highlights compensatory mechanisms supporting performance in OSA.

Increased delayed reward during intertemporal decision-making in schizophrenic patients and their unaffected siblings

Intertemporal choices are decisions with consequences in multiple time periods and constitute a significant part of social cognition. The shared neuropathological characteristics of patients with schizophrenia and their siblings might express intermediate phenotypes in behavior that could be used to further characterize the illness. Schizophrenic patients, unaffected siblings, and healthy controls underwent a computerized version of the "Intertemporal Choice Task". All participants could choose between sooner-smaller (SS) and later-larger (LL) options in now-trials and in not-now-trials. Subjects also underwent a battery of cognitive neuropsychological assessment. Our results indicated that schizophrenic patients and unaffected siblings both had a tendency to choose LL options in now-trials or not-now-trials compared to healthy controls. Schizophrenic patients had significantly lower scores in several cognitive tasks, including MoCA, attention, executive functions, and information processing when compared with the other two groups. Moreover, within the schizophrenic patient group, significant correlations were found between intertemporal decision-making performance and executive function. The present study showed that both schizophrenic patients and unaffected siblings preferred to choose larger-delayed rewards during intertemporal decision-making, which may result from frontal-striatal and frontal-parietal network dysfunction. Their intertemporal decision-making performance was associated with executive function performance.

Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity

Schizophrenia is a complex mental disorder with disorganized communication among large-scale brain networks, as demonstrated by impaired resting-state functional connectivity (rsFC). Individual rsFC studies, however, vary greatly in their methods and findings. We searched for consistent patterns of network dysfunction in schizophrenia by using a coordinate-based meta-analysis. Fifty-six seed-based voxel-wise rsFC datasets from 52 publications (2115 patients and 2297 healthy controls) were included in this meta-analysis. Then, coordinates of seed regions of interest (ROI) and between-group effects were extracted and coded. Seed ROIs were categorized into seed networks by their location within an a priori template. Multilevel kernel density analysis was used to identify brain networks in which schizophrenia was linked to hyper-connectivity or hypo-connectivity with each a priori network. Our results showed that schizophrenia was characterized by hypo-connectivity within the default network (DN, self-related thought), affective network (AN, emotion processing), ventral attention network (VAN, processing of salience), thalamus network (TN, gating information) and somatosensory network (SS, involved in sensory and auditory perception). Additionally, hypo-connectivity between the VAN and TN, VAN and DN, VAN and frontoparietal network (FN, external goal-directed regulation), FN and TN, and FN and DN were found in schizophrenia. Finally, the only instance of hyper-connectivity in schizophrenia was observed between the AN and VAN. Our meta-analysis motivates an empirical foundation for a disconnected large-scale brain networks model of schizophrenia in which the salience processing network (VAN) plays the core role, and its imbalanced communication with other functional networks may underlie the core difficulty of patients to differentiate self-representation (inner world) and environmental salience processing (outside world).

Avolition-Apathy and White Matter Connectivity in Schizophrenia: Reduced Fractional Anisotropy Between Amygdala and Insular Cortex

The avolition/apathy domain of negative symptoms includes motivation- and pleasure-related impairments. In people with schizophrenia, structural and functional abnormalities were reported in key regions within the motivational reward system, including ventral-tegmental area (VTA), striatum (especially at the level of the nucleus accumbens, NAcc), orbitofrontal cortex (OFC), as well as amygdala (Amy) and insular cortex (IC). However, the association of the reported abnormalities with avoliton-apathy is still controversial. In the present study, we investigated white matter connectivity patterns within these regions, using a probabilistic analysis of diffusion tensor imaging (DTI) data, in male subjects with schizophrenia. Thirty-five male subjects with schizophrenia (SCZ) and 17 male healthy controls (HC) matched for age, underwent DTI. SCZ were evaluated using the Schedule for Deficit Syndrome (SDS), the Positive and Negative Syndrome Scale (PANSS), and the MATRICS Consensus Cognitive Battery (MCCB). Probabilistic tractography was applied to investigate pathways connecting the Amy and the NAcc with the OFC and IC. Reduced fractional anisotropy (FA) was observed in left Amy-ventral anterior IC connections, in SCZ compared with controls. This abnormality was negatively correlated with avolition/apathy but not with expressive deficit scores. SCZ showed also a reduced connectivity index between right NAcc and medial OFC, as compared with controls. Finally, the left NAcc-dorsal anterior IC connectivity index was negatively correlated with working memory scores. Our results indicate that only the avolition/apathy domain of negative symptoms is related to abnormal connectivity in the motivation-related circuits. The findings also demonstrate that distinct alterations underlie cognitive impairment and avolition/apathy.

Decreased functional connectivity in schizophrenia: The relationship between social functioning, social cognition and graph theoretical network measures

Schizophrenia is a complex disorder in which abnormalities in brain connectivity and social functioning play a central role. The aim of this study is to explore small-world network properties, and understand their relationship with social functioning and social cognition in the context of schizophrenia, by testing functional connectivity differences in network properties and its relation to clinical behavioral measures. Resting-state fMRI time series data were acquired from 23 patients diagnosed with schizophrenia and 23 healthy volunteers. The results revealed that patients with schizophrenia show significantly decreased connectivity between a range of brain regions, particularly involving connections among the right orbitofrontal cortex, bilateral putamen and left amygdala. Furthermore, topological properties of functional brain networks in patients with schizophrenia were characterized by reduced path length compared to healthy controls; however, no significant difference was found for clustering coefficient, local efficiency or global efficiency. Additionally, we found that nodal efficiency of the amygdala and the putamen were significantly correlated with the independence-performance subscale of social functioning scale (SFC), and Reading the Mind in the Eyes test; however, the correlations do not survive correction for multiple comparison. The current results help to clarify the relationship between social functioning deficits and topological brain measures in schizophrenia.

Impaired cortico-limbic functional connectivity in schizophrenia patients during emotion processing

Functional dysconnection is increasingly recognized as a core pathological feature in schizophrenia. Aberrant interactions between regions of the cortico-limbic circuit may underpin the abnormal emotional processing associated with this illness. We used a functional magnetic resonance imaging (fMRI) paradigm designed to dissociate the various components of the cortico-limbic circuit (i.e. a ventral automatic circuit that is intertwined with a dorsal cognitive circuit), in order to explore bottom-up appraisal as well as top-down control during emotion processing. In schizophrenia patients compared to healthy controls, bottom-up processes were associated with reduced interaction between the amygdala and both the anterior cingulate cortex (ACC) and the dorsolateral prefrontal cortex (DLPFC). Contrariwise, top-down control processes led to stronger connectivity between the ventral affective and the dorsal cognitive circuits, i.e. heightened interactions between the ventral ACC and the DLPFC as well as between dorsal and ventral ACC. These findings offer a comprehensive view of the cortico-limbic dysfunction in schizophrenia. They confirm previous results of impaired propagation of information between the amygdala and the prefrontal cortex and suggest a defective functional segregation in the dorsal cognitive part of the cortico-limbic circuit.

Impaired cortico-limbic functional connectivity in schizophrenia patients during emotion processing

Functional dysconnection is increasingly recognized as a core pathological feature in schizophrenia. Aberrant interactions between regions of the cortico-limbic circuit may underpin the abnormal emotional processing associated with this illness. We used a functional magnetic resonance imaging paradigm designed to dissociate the various components of the cortico-limbic circuit (i.e. a ventral automatic circuit that is intertwined with a dorsal cognitive circuit), to explore bottom-up appraisal as well as top-down control during emotion processing. In schizophrenia patients compared with healthy controls, bottom-up processes were associated with reduced interaction between the amygdala and both the anterior cingulate cortex (ACC) and the dorsolateral prefrontal cortex. Contrariwise, top-down control processes led to stronger connectivity between the ventral affective and the dorsal cognitive circuits, i.e. heightened interactions between the ventral ACC and the dorsolateral prefrontal cortex as well as between dorsal and ventral ACC. These findings offer a comprehensive view of the cortico-limbic dysfunction in schizophrenia. They confirm previous results of impaired propagation of information between the amygdala and the prefrontal cortex and suggest a defective functional segregation in the dorsal cognitive part of the cortico-limbic circuit.

Transdiagnostic differences in the resting-state functional connectivity of the prefrontal cortex in depression and schizophrenia

BACKGROUND

Depression and schizophrenia are two of the most serious psychiatric disorders. They share similar symptoms but the pathology-specific commonalities and differences remain unknown. This study was conducted to acquire a full picture of the functional alterations in schizophrenia and depression patients.

METHODS

The resting-state fMRI data from 20 patients with schizophrenia, 20 patients with depression and 20 healthy control subjects were collected. A data-driven approach that included local functional connectivity density (FCD) analysis combined with multivariate pattern analysis (MVPA) was used to compare the three groups.

RESULTS

Based on the results of the MVPA, the local FCD value in the orbitofrontal cortex (OFC) can differentiate depression patients from schizophrenia patients. The patients with depression had a higher local FCD value in the medial and anterior parts of the OFC than the subjects in the other two groups, which suggested altered abstract and reward reinforces processing in depression patients. Subsequent functional connectivity analysis indicated that the connection in the prefrontal cortex was significantly lower in people with schizophrenia compared to people with depression and healthy controls.

LIMITATION

The systematically different medications for schizophrenia and depression may have different effects on functional connectivity.

CONCLUSIONS

These results suggested that the resting-state functional connectivity pattern in the prefrontal cortex may be a transdiagnostic difference between depression and schizophrenia patients.

Fronto-temporal connectivity predicts cognitive empathy deficits and experiential negative symptoms in schizophrenia

Impaired cognitive empathy is a core social cognitive deficit in schizophrenia associated with negative symptoms and social functioning. Cognitive empathy and negative symptoms have also been linked to medial prefrontal and temporal brain networks. While shared behavioral and neural underpinnings are suspected for cognitive empathy and negative symptoms, research is needed to test these hypotheses. In two studies, we evaluated whether resting-state functional connectivity between data-driven networks, or components (referred to as, inter-component connectivity), predicted cognitive empathy and experiential and expressive negative symptoms in schizophrenia subjects. Study 1: We examined associations between cognitive empathy and medial prefrontal and temporal inter-component connectivity at rest using a group-matched schizophrenia and control sample. We then assessed whether inter-component connectivity metrics associated with cognitive empathy were also related to negative symptoms. Study 2: We sought to replicate the connectivity-symptom associations observed in Study 1 using an independent schizophrenia sample. Study 1 results revealed that while the groups did not differ in average inter-component connectivity, a medial-fronto-temporal metric and an orbito-fronto-temporal metric were related to cognitive empathy. Moreover, the medial-fronto-temporal metric was associated with experiential negative symptoms in both schizophrenia samples. These findings support recent models that link social cognition and negative symptoms in schizophrenia. Hum Brain Mapp 38:1111-1124, 2017. © 2016 Wiley Periodicals, Inc.

Patients with schizophrenia show aberrant patterns of basal ganglia activation: Evidence from ALE meta-analysis

The diverse circuits and functional contributions of the basal ganglia, coupled with known differences in dopaminergic function in patients with schizophrenia, suggest they may be an important contributor to the etiology of the hallmark symptoms and cognitive dysfunction experienced by these patients. Using activation-likelihood-estimation meta-analysis of functional imaging research, we investigated differences in activation patterns in the basal ganglia in patients with schizophrenia, relative to healthy controls across task domains. This analysis included 42 functional neuroimaging studies, representing a variety of behavioral domains that have been linked to basal ganglia function in prior work. We provide important new information about the functional activation patterns and functional topography of the basal ganglia for different task domains in healthy controls. Crucially however, we demonstrate that across task domains, patients with schizophrenia show markedly decreased activation in the basal ganglia relative to healthy controls. Our results provide further support for basal ganglia dysfunction in patients with schizophrenia, and the broad dysfunction across task domains may contribute to the symptoms and cognitive deficits associated with schizophrenia.

Association of familial risk for schizophrenia with thalamic and medial prefrontal functional connectivity during attentional control

Anomalies in behavioral correlates of attentional processing and related brain activity are crucial correlates of schizophrenia and associated with familial risk for this brain disorder. However, it is not clear how brain functional connectivity during attentional processes is key for schizophrenia and linked with trait vs. state related variables. To address this issue, we investigated patterns of functional connections during attentional control in healthy siblings of patients with schizophrenia, who share with probands genetic features but not variables related to the state of the disorder. 356 controls, 55 patients with schizophrenia on stable treatment with antipsychotics and 40 healthy siblings of patients with this brain disorder underwent the Variable Attentional Control (VAC) task during fMRI. Independent Component Analysis (ICA) is allowed to identify independent components (IC) of BOLD signal recorded during task performance. Results indicated reduced connectivity strength in patients with schizophrenia as well as in their healthy siblings in left thalamus within an attentional control component and greater connectivity in right medial prefrontal cortex (PFC) within the so-called Default Mode Network (DMN) compared to healthy individuals. These results suggest a relationship between familial risk for schizophrenia and brain functional networks during attentional control, such that this biological phenotype may be considered a useful intermediate phenotype in order to link genes effects to aspects of the pathophysiology of this brain disorder.

Structural and functional brain changes in delusional disorder

BACKGROUND

Delusional disorder has been the subject of very little investigation using brain imaging.

AIMS

To examine potential structural and/or functional brain abnormalities in this disorder.

METHOD

We used structural imaging (voxel-based morphometry, VBM) and functional imaging (during performance of the n-back task and whole-brain resting connectivity analysis) to examine 22 patients meeting DSM-IV criteria for delusional disorder and 44 matched healthy controls.

RESULTS

The patients showed grey matter reductions in the medial frontal/anterior cingulate cortex and bilateral insula on unmodulated (but not on modulated) VBM analysis, failure of de-activation in the medial frontal/anterior cingulate cortex during performance of the n-back task, and decreased resting-state connectivity in the bilateral insula.

CONCLUSIONS

The findings provide evidence of brain abnormality in the medial frontal/anterior cingulate cortex and insula in delusional disorder. A role for the former region in the pathogenesis of delusions is consistent with several other lines of evidence.

Converging Medial Frontal Resting State and Diffusion-Based Abnormalities in Borderline Personality Disorder

BACKGROUND

The psychological profile of patients with borderline personality disorder (BPD), with impulsivity and emotional dysregulation as core symptoms, has guided the search for abnormalities in specific brain areas such as the hippocampal-amygdala complex and the frontomedial cortex. However, whole-brain imaging studies so far have delivered highly heterogeneous results involving different brain locations.

METHODS

Functional resting-state and diffusion magnetic resonance imaging data were acquired in patients with BPD and in an equal number of matched control subjects (n = 60 for resting and n = 43 for diffusion). While mean diffusivity and fractional anisotropy brain images were generated from diffusion data, amplitude of low-frequency fluctuations and global brain connectivity images were used for the first time to evaluate BPD-related brain abnormalities from resting functional acquisitions.

RESULTS

Whole-brain analyses using a p = .05 corrected threshold showed a convergence of alterations in BPD patients in genual and perigenual structures, with frontal white matter fractional anisotropy abnormalities partially encircling areas of increased mean diffusivity and global brain connectivity. Additionally, a cluster of enlarged amplitude of low-frequency fluctuations (high resting activity) was found involving part of the left hippocampus and amygdala. In turn, this cluster showed increased resting functional connectivity with the anterior cingulate.

CONCLUSIONS

With a multimodal approach and without using a priori selected regions, we prove that structural and functional abnormality in BPD involves both temporolimbic and frontomedial structures as well as their connectivity. These structures have been previously related to behavioral and clinical symptoms in patients with BPD.

Abnormal Connectional Fingerprint in Schizophrenia: A Novel Network Analysis of Diffusion Tensor Imaging Data

The graph theoretical analysis of structural magnetic resonance imaging (MRI) data has received a great deal of interest in recent years to characterize the organizational principles of brain networks and their alterations in psychiatric disorders, such as schizophrenia. However, the characterization of networks in clinical populations can be challenging, since the comparison of connectivity between groups is influenced by several factors, such as the overall number of connections and the structural abnormalities of the seed regions. To overcome these limitations, the current study employed the whole-brain analysis of connectional fingerprints in diffusion tensor imaging data obtained at 3 T of chronic schizophrenia patients (n = 16) and healthy, age-matched control participants (n = 17). Probabilistic tractography was performed to quantify the connectivity of 110 brain areas. The connectional fingerprint of a brain area represents the set of relative connection probabilities to all its target areas and is, hence, less affected by overall white and gray matter changes than absolute connectivity measures. After detecting brain regions with abnormal connectional fingerprints through similarity measures, we tested each of its relative connection probability between groups. We found altered connectional fingerprints in schizophrenia patients consistent with a dysconnectivity syndrome. While the medial frontal gyrus showed only reduced connectivity, the connectional fingerprints of the inferior frontal gyrus and the putamen mainly contained relatively increased connection probabilities to areas in the frontal, limbic, and subcortical areas. These findings are in line with previous studies that reported abnormalities in striatal-frontal circuits in the pathophysiology of schizophrenia, highlighting the potential utility of connectional fingerprints for the analysis of anatomical networks in the disorder.

Analysis of the Complexity Measures in the EEG of Schizophrenia Patients

Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients' clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2 min was partitioned into identical epochs of 20 s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel-Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics' brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics' brain.

Increased co-expression of genes harboring the damaging de novo mutations in Chinese schizophrenic patients during prenatal development

Schizophrenia is a heritable, heterogeneous common psychiatric disorder. In this study, we evaluated the hypothesis that de novo variants (DNVs) contribute to the pathogenesis of schizophrenia. We performed exome sequencing in Chinese patients (N = 45) with schizophrenia and their unaffected parents (N = 90). Forty genes were found to contain DNVs. These genes had enriched transcriptional co-expression profile in prenatal frontal cortex (Bonferroni corrected p < 9.1 × 10⁻³), and in prenatal temporal and parietal regions (Bonferroni corrected p < 0.03). Also, four prenatal anatomical subregions (VCF, MFC, OFC and ITC) have shown significant enrichment of connectedness in co-expression networks. Moreover, four genes (LRP1, MACF1, DICER1 and ABCA2) harboring the damaging de novo mutations are strongly prioritized as susceptibility genes by multiple evidences. Our findings in Chinese schizophrenic patients indicate the pathogenic role of DNVs, supporting the hypothesis that schizophrenia is a neurodevelopmental disease.

Accurate classification of schizophrenia patients based on novel resting-state fMRI features

There is a growing interest in automatic classification of mental disorders such as schizophrenia based on neuroimaging data. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state fMRI data has not been used much to evaluate 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. In this study, we extract two types of features from resting-state fMRI data: functional network connectivity features that capture internetwork connectivity patterns and autoconnectivity features capturing temporal connectivity of each brain network. Autoconnectivity is a novel concept we have recently proposed. We used minimum redundancy maximum relevancy to select features. Classification results using support vector machine shows that combining these two types of features can improve the classification on a large resting fMRI dataset consisting of 195 patients with schizophrenia and 175 healthy controls. We achieved the accuracy of 85% which is very promising.

Aberrant functional connectivity of resting state networks associated with trait anxiety

Trait anxiety, a personality dimension, has been characterized by functional consequences such as increased distractibility, attentional bias in favor of threat-related information and hyper-responsive amygdala. However, literature on the association between resting state brain functional connectivity, as studied using resting state functional magnetic resonance imaging (rs-fMRI), and reported anxiety levels in the sub-clinical population is limited. In the present study, we employed rs-fMRI to investigate the possible alterations in the functional integrity of Resting State Networks (RSNs) associated with trait anxiety of the healthy subjects (15 high anxious and 14 low anxious). The rs-fMRI data was analyzed using independent component analysis and a dual regression approach that was applied on 12 RSNs that were identified using FSL. High anxious subjects showed significantly reduced functional connectivity in regions of the default mode network (posterior cingulate gyrus, middle and superior temporal gyrus, planum polare, supramarginal gyrus, temporal pole, angular gyrus and lateral occipital gyrus) which has been suggested to be involved in episodic memory, theory of mind, self-evaluation, and introspection, and perceptual systems including medial visual network, auditory network and another network involving temporal, parieto-occipital and frontal regions. Reduction in resting state connectivity in regions of the perceptual networks might underlie the perceptual, attentional and working memory deficits associated with trait anxiety. To our knowledge, this is the first study to relate trait anxiety to resting state connectivity using independent component analysis.

Evidence of a dissociation pattern in default mode subnetwork functional connectivity in schizophrenia

The default mode network (DMN) is suggested to play a pivotal role in schizophrenia; however, the dissociation pattern of functional connectivity of DMN subsystems remains uncharacterized in this disease. In this study, resting-state fMRI data were acquired from 55 schizophrenic patients and 53 matched healthy controls. DMN connectivity was estimated from time courses of independent components. The lateral DMN exhibited decreased connectivity with the unimodal sensorimotor cortex but increased connectivity with the heteromodal association areas in schizophrenics. The increased connectivity between the lateral DMN and right control network was significantly correlated with negative and anergia factor scores in the schizophrenic patients. The anterior and posterior DMNs exhibited increased and decreased connectivity with the right control and lateral visual networks, respectively, in schizophrenics. The altered DMN connectivity may underlie the hallucinations, delusions, thought disturbances, and negative symptoms involved in schizophrenia. Furthermore, DMN connectivity patterns could be used to differentiate patients from controls with 76.9% accuracy. These findings may shed new light on the distinct role of DMN subsystems in schizophrenia, thereby furthering our understanding of the pathophysiology of schizophrenia. Elucidating key disease-related DMN subsystems is critical for identifying treatment targets and aiding in the clinical diagnosis and development of treatment strategies.

Increased short-range and long-range functional connectivity in first-episode, medication-naive schizophrenia at rest

OBJECTIVE

Schizophrenia is conceived as a disconnection syndrome and anatomical distance may affect functional connectivity (FC) in schizophrenia patients. However, whether and how anatomical distance affects FC remains unclear in first-episode, medication-naive schizophrenia at rest.

METHODS

Forty-nine schizophrenia patients and 50 age-, sex-, and education-matched healthy controls underwent resting-state functional magnetic resonance imaging scanning. Regional FC strength was computed for each voxel in the brain, which was further divided into short-range and long-range FC strength.

RESULTS

The patients exhibited increased short-range positive FC strength in the left superior medial frontal gyrus, and increased long-range positive FC strength in the right angular gyrus and bilateral posterior cingulate cortex (PCC)/precuneus compared with the controls. Further seed-based FC analysis showed that the left superior medial frontal gyrus had increased short-range FC with the right inferior frontal gyrus, while the right angular gyrus and bilateral PCC/precuneus had increased long-range FC with the prefrontal gyrus. No significant correlation was observed between abnormal FC strength and clinical variables in the patient group.

CONCLUSIONS

The findings reveal a pattern of increased anatomical distance affecting FC in the patients, with the results of increased short-range positive FC strength in the anterior default-mode network (DMN) and increased long-range positive FC strength in the posterior DMN in schizophrenia, and highlight the importance of the DMN in the neurobiology of schizophrenia.

Cholinergic and serotonergic modulations differentially affect large-scale functional networks in the mouse brain

Resting-state functional MRI (rsfMRI) is a widely implemented technique used to investigate large-scale topology in the human brain during health and disease. Studies in mice provide additional advantages, including the possibility to flexibly modulate the brain by pharmacological or genetic manipulations in combination with high-throughput functional connectivity (FC) investigations. Pharmacological modulations that target specific neurotransmitter systems, partly mimicking the effect of pathological events, could allow discriminating the effect of specific systems on functional network disruptions. The current study investigated the effect of cholinergic and serotonergic antagonists on large-scale brain networks in mice. The cholinergic system is involved in cognitive functions and is impaired in, e.g., Alzheimer's disease, while the serotonergic system is involved in emotional and introspective functions and is impaired in, e.g., Alzheimer's disease, depression and autism. Specific interest goes to the default-mode-network (DMN), which is studied extensively in humans and is affected in many neurological disorders. The results show that both cholinergic and serotonergic antagonists impaired the mouse DMN-like network similarly, except that cholinergic modulation additionally affected the retrosplenial cortex. This suggests that both neurotransmitter systems are involved in maintaining integrity of FC within the DMN-like network in mice. Cholinergic and serotonergic modulations also affected other functional networks, however, serotonergic modulation impaired the frontal and thalamus networks more extensively. In conclusion, this study demonstrates the utility of pharmacological rsfMRI in animal models to provide insights into the role of specific neurotransmitter systems on functional networks in neurological disorders.