Characterizing the connectome in schizophrenia with diffusion spectrum imaging

Age Trajectories of the Structural Connectome in Child and Adolescent Offspring of Individuals With Bipolar Disorder or Schizophrenia

Background

Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated.

Methods

A total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity.

Results

Offspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder.

Conclusions

The findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.

Genetic architecture of the structural connectome

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia

Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.

Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses

Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal "attacks" (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.

Increased cortical structural covariance correlates with anhedonia in schizophrenia

Anhedonia is a common symptom in schizophrenia and is closely related to poor functional outcomes. Several lines of evidence reveal that the orbitofrontal cortex plays an important role in anhedonia. In the present study, we aimed to investigate abnormalities in structural covariance within the orbitofrontal subregions, and to further study their role in anticipatory and consummatory anhedonia in schizophrenia. T1 images of 35 schizophrenia patients and 45 healthy controls were obtained. The cortical thickness of 68 cerebral regions parcellated by the Desikan-Killiany (DK) atlas was calculated. The structural covariance within the orbitofrontal subregions was calculated in both schizophrenia and healthy control groups. Stepwise linear regression was performed to examine the relationship between structural covariance and anhedonia in schizophrenia patients. Patients with schizophrenia exhibited higher structural covariance between the left and right medial orbitofrontal thickness, the left lateral orbitofrontal thickness and left pars orbitalis thickness compared to healthy controls (p < 0.05, FDR corrected). This results imply that the increased structural covariance in orbitofrontal thickness may be involved in the process of developing anhedonia in schizophrenia. The result indicated that the increased structural covariance between the left and right medial orbitofrontal thickness might be a protective factor for anticipatory pleasure (B' = 0.420, p = 0.012).

Abnormal Structural Network Communication Reflects Cognitive Deficits in Schizophrenia

Schizophrenia has long been thought to be a disconnection syndrome and several previous studies have reported widespread abnormalities in white matter tracts in individuals with schizophrenia. Furthermore, reductions in structural connectivity may also impair communication between anatomically unconnected pairs of brain regions, potentially impacting global signal traffic in the brain. Therefore, we used different communication models to examine direct and indirect structural connections (polysynaptic) communication in large-scale brain networks in schizophrenia. Diffusion-weighted magnetic resonance imaging scans were acquired from 62 patients diagnosed with schizophrenia and 35 controls. In this study, we used five network communication models including, shortest paths, navigation, diffusion, search information and communicability to examine polysynaptic communication in large-scale brain networks in schizophrenia. We showed less efficient communication between spatially widespread brain regions particulary encompassing cortico-subcortical basal ganglia network in schizophrenia group relative to controls. Then, we also examined whether reduced communication efficiency was related to clinical symptoms in schizophrenia group. Among different measures of communication efficiency, only navigation efficiency was associated with global cognitive impairment across multiple cognitive domains including verbal learning, processing speed, executive functions and working memory, in individuals with schizophrenia. We did not find any association between communication efficiency measures and positive or negative symptoms within the schizophrenia group. Our findings are important for improving our mechanistic understanding of neurobiological process underlying cognitive symptoms in schizophrenia.

The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics

The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.

Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p &lt; 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p &lt; 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

Structural covariance networks in schizophrenia: A systematic review Part II

BACKGROUND

Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia.

METHODS

A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria.

RESULTS

A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness.

CONCLUSIONS

Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.

The relation of integrated psychological therapy to resting state functional brain connectivity networks in patients with schizophrenia

Functional brain dysconnectivity measured with resting state functional magnetic resonance imaging (rsfMRI) has been linked to cognitive impairment in schizophrenia. This study investigated the effects on functional brain connectivity of Integrated Psychological Therapy (IPT), a cognitive behavioral oriented group intervention program, in 31 patients with schizophrenia. Patients received IPT or an equal intensity non-specific psychological treatment in a non-randomized design. Evidence of improvement in executive and social functions, psychopathology and overall level of functioning was observed after treatment completion at six months only in the IPT treatment group and was partially sustained at one-year follow up. Independent Component Analysis and Isometric Mapping (ISOMAP), a non-linear manifold learning algorithm, were used to construct functional connectivity networks from the rsfMRI data. Functional brain dysconnectivity was observed in patients compared to a group of 17 healthy controls, both globally and specifically including the default mode (DMN) and frontoparietal network (FPN). DMN and FPN connectivity were reversed towards healthy control patterns only in the IPT treatment group and these effects were sustained at follow up for DMN but not FPN. These data suggest the use of rsfMRI as a biomarker for accessing and monitoring the therapeutic effects of cognitive remediation therapy in schizophrenia.

White matter structural connectivity as a biomarker for detecting juvenile myoclonic epilepsy by transferred deep convolutional neural networks with varying transfer rates

Objective. By detecting abnormal white matter changes, diffusion magnetic resonance imaging (MRI) contributes to the detection of juvenile myoclonic epilepsy (JME). In addition, deep learning has greatly improved the detection performance of various brain disorders. However, there is almost no previous study effectively detecting JME by a deep learning approach with diffusion MRI.Approach. In this study, the white matter structural connectivity was generated by tracking the white matter fibers in detail based on Q-ball imaging and neurite orientation dispersion and density imaging. Four advanced deep convolutional neural networks (CNNs) were deployed by using the transfer learning approach, in which the transfer rate searching strategy was proposed to achieve the best detection performance.Main results. Our results showed: (a) Compared to normal control, the white matter' neurite density of JME was significantly decreased. The most significantly abnormal fiber tracts between the two groups were found to be cortico-cortical connection tracts. (b) The proposed transfer rate searching approach contributed to find each CNN's best performance, in which the best JME detection accuracy of 92.2% was achieved by using the Inception_resnet_v2 network with a 16% transfer rate.Significance. The results revealed: (a) Through detection of the abnormal white matter changes, the white matter structural connectivity can be used as a useful biomarker for detecting JME, which helps to characterize the pathophysiology of epilepsy. (b) The proposed transfer rate, as a new hyperparameter, promotes the CNNs transfer learning performance in detecting JME.

Rich-club structure contributes to individual variance of reading skills via feeder connections in children with reading disabilities

The present work considers how connectome-wide differences in brain organization might distinguish good and poor readers. The connectome comprises a ‘rich-club’ organization in which a small number of hub regions play a focal role in assisting global communication across the whole brain. Prior work indicates that this rich-club structure is associated with typical and impaired cognitive function although no work so far has examined how this relates to skilled reading or its disorders. Here we investigated the rich-club structure of brain’s white matter connectome and its relationship to reading subskills in 64 children with and without reading disabilities. Among three types of white matter connections, the strength of feeder connections that connect hub and non-hub nodes was significantly correlated with word reading efficiency and phonemic decoding. Phonemic decoding was also positively correlated with connectivity between connectome-wide hubs and nodes within the left-hemisphere reading network, as well as the local efficiency of the reading network. Exploratory analyses also identified sex differences indicating these effects were stronger in girls. This work highlights the independent roles of connectome-wide structure and the more narrowly-defined reading network in understanding the neural bases of skilled and impaired reading in children.

ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia

We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan-Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme (consisting of an outer and an inner loop of "Leave one out" cross-validation (LOOCV)). The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus.

Divergent Alterations of Structural-Functional Connectivity Couplings in First-episode and Chronic Schizophrenia Patients

Emerging evidence suggests that the coupling relating the structural connectivity (SC) of the brain to its functional connectivity (FC) exhibits remarkable changes during development, normal aging, and diseases. Although altered structural-functional connectivity couplings (SC-FC couplings) have been previously reported in schizophrenia patients, the alterations in SC-FC couplings of different illness stages of schizophrenia (SZ) remain largely unknown. In this study, we collected structural and resting-state functional MRI data from 73 normal controls (NCs), 61 first-episode (FeSZ) and 78 chronic (CSZ) schizophrenia patients. Positive and negative syndrome scale (PANSS) scores were assessed for all patients. Structural and functional brain networks were constructed using gray matter volume (GMV) and resting-state magnetic resonance imaging (rs-fMRI) time series measurements. At the connectivity level, the CSZ patients showed significantly increased SC-FC coupling strength compared with the FeSZ patients. At the node strength level, significant decreased SC-FC coupling strength was observed in the FeSZ patients compared to that of the NCs, and the coupling strength was positively correlated with negative PANSS scores. These results demonstrated divergent alterations of SC-FC couplings in FeSZ and CSZ patients. Our findings provide new insight into the neuropathological mechanisms underlying the developmental course of SZ.

Functional brain networks in the schizophrenia spectrum and bipolar disorder with psychosis

Psychotic experiences have been proposed to lie on a spectrum, ranging from subclinical experiences to treatment-resistant schizophrenia. We aimed to characterize functional connectivity and brain network characteristics in relation to the schizophrenia spectrum and bipolar disorder with psychosis to disentangle neural correlates to psychosis. Additionally, we studied antipsychotic medication and lithium effects on network characteristics. We analyzed functional connectivity strength and network topology in 487 resting-state functional MRI scans of individuals with schizophrenia spectrum disorder (SCZ), bipolar disorder with a history of psychotic experiences (BD), treatment-naïve subclinical psychosis (SCP), and healthy controls (HC). Since differences in connectivity strength may confound group comparisons of brain network topology, we analyzed characteristics of the minimum spanning tree (MST), a relatively unbiased backbone of the network. SCZ and SCP subjects had a lower connectivity strength than BD and HC individuals but showed no differences in network topology. In contrast, BD patients showed a less integrated network topology but no disturbances in connectivity strength. No differences in outcome measures were found between SCP and SCZ, or between BD patients that used antipsychotic medication or lithium and those that did not. We conclude that functional networks in patients prone to psychosis have different signatures for chronic SCZ patients and SCP compared to euthymic BD patients, with a limited role for medication. Connectivity strength effects may have confounded previous studies, as no functional network alterations were found in SCZ after strict correction for connectivity strength.

Stable biomarker identification for predicting schizophrenia in the human connectome

Schizophrenia, as a psychiatric disorder, has recognized brain alterations both at the structural and at the functional magnetic resonance imaging level. The developing field of connectomics has attracted much attention as it allows researchers to take advantage of powerful tools of network analysis in order to study structural and functional connectivity abnormalities in schizophrenia. Many methods have been proposed to identify biomarkers in schizophrenia, focusing mainly on improving the classification performance or performing statistical comparisons between groups. However, the stability of biomarkers selection has been for long overlooked in the connectomics field. In this study, we follow a machine learning approach where the identification of biomarkers is addressed as a feature selection problem for a classification task. We perform a recursive feature elimination and support vector machines (RFE-SVM) approach to identify the most meaningful biomarkers from the structural, functional, and multi-modal connectomes of healthy controls and patients. Furthermore, the stability of the retrieved biomarkers is assessed across different subsamplings of the dataset, allowing us to identify the affected core of the pathology. Considering our technique altogether, it demonstrates a principled way to achieve both accurate and stable biomarkers while highlighting the importance of multi-modal approaches to brain pathology as they tend to reveal complementary information.

Diagnosing schizophrenia with network analysis and a machine learning method

OBJECTIVE

Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research.

METHODS

We investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls.

RESULTS

In classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns.

CONCLUSION

In conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia.

Combined Parietal-Insular-Striatal Cortex Stroke with New-Onset Hallucinations: Supporting the Salience Network Model of Schizophrenia

Brain imaging studies have identified multiple neuronal networks and circuits in the brain with altered functioning in patients with schizophrenia. These include the hippocampo-cerebello-cortical circuit, the prefrontal-thalamic-cerebellar circuit, functional integration in the bilateral caudate nucleus, and the salience network consisting of the insular cortex, parietal anterior cingulate cortex, and striatum, as well as limbic structures. Attributing psychotic symptoms to any of these networks in schizophrenia is confounded by the disruption of these networks in schizophrenic patients. Such attribution can be done with isolated dysfunction in any of these networks with concurrent psychotic symptoms. We present the case of a patient who presents with new-onset hallucinations and a stroke in brain regions similar to the salience network (insular cortex, parietal cortex, and striatum). The implication of these findings in isolating psychotic symptoms of the salience network is discussed.

Evolutionary modifications in human brain connectivity associated with schizophrenia

The genetic basis and human-specific character of schizophrenia has led to the hypothesis that human brain evolution may have played a role in the development of the disorder. We examined schizophrenia-related changes in brain connectivity in the context of evolutionary changes in human brain wiring by comparing in vivo neuroimaging data from humans and chimpanzees, one of our closest living evolutionary relatives and a species with which we share a very recent common ancestor. We contrasted the connectome layout between the chimpanzee and human brain and compared differences with the pattern of schizophrenia-related changes in brain connectivity as observed in patients. We show evidence of evolutionary modifications of human brain connectivity to significantly overlap with the cortical pattern of schizophrenia-related dysconnectivity (P < 0.001, permutation testing). We validated these effects in three additional, independent schizophrenia datasets. We further assessed the specificity of effects by examining brain dysconnectivity patterns in seven other psychiatric and neurological brain disorders (including, among others, major depressive disorder and obsessive-compulsive disorder, arguably characterized by behavioural symptoms that are less specific to humans), which showed no such associations with modifications of human brain connectivity. Comparisons of brain connectivity across humans, chimpanzee and macaques further suggest that features of connectivity that evolved in the human lineage showed the strongest association to the disorder, that is, brain circuits potentially related to human evolutionary specializations. Taken together, our findings suggest that human-specific features of connectome organization may be enriched for changes in brain connectivity related to schizophrenia. Modifications in human brain connectivity in service of higher order brain functions may have potentially also rendered the brain vulnerable to brain dysfunction.

N-Acetyl-Cysteine Supplementation Improves Functional Connectivity Within the Cingulate Cortex in Early Psychosis: A Pilot Study

BACKGROUND

There is increasing evidence that redox dysregulation, which can lead to oxidative stress and eventually to impairment of oligodendrocytes and parvalbumin interneurons, may underlie brain connectivity alterations in schizophrenia. Accordingly, we previously reported that levels of brain antioxidant glutathione in the medial prefrontal cortex were positively correlated with increased functional connectivity along the cingulum bundle in healthy controls but not in early psychosis patients. In a recent randomized controlled trial, we observed that 6-month supplementation with a glutathione precursor, N-acetyl-cysteine, increased brain glutathione levels and improved symptomatic expression and processing speed.

METHODS

We investigated the effect of N-acetyl-cysteine supplementation on the functional connectivity between regions of the cingulate cortex, which have been linked to positive symptoms and processing speed decline. In this pilot study, we compared structural connectivity and resting-state functional connectivity between early psychosis patients treated with 6-month N-acetyl-cysteine (n = 9) or placebo (n = 11) supplementation with sex- and age-matched healthy control subjects (n = 74).

RESULTS

We observed that 6-month N-acetyl-cysteine supplementation increases functional connectivity along the cingulum and more precisely between the caudal anterior part and the isthmus of the cingulate cortex. These functional changes can be partially explained by an increase of centrality of these regions in the functional brain network.

CONCLUSIONS

N-acetyl-cysteine supplementation has a positive effect on functional connectivity within the cingulate cortex in early psychosis patients. To our knowledge, this is the first study suggesting that increased brain glutathione levels via N-acetyl-cysteine supplementation may improve brain functional connectivity.

Structural connectivity in schizophrenia and bipolar disorder: Effects of chronicity and antipsychotic treatment

Previous studies based on graph theory parameters applied to diffusion tensor imaging support an alteration of the global properties of structural connectivity network in schizophrenia. However, the specificity of this alteration and its possible relation with chronicity and treatment have received small attention. We have assessed small-world (SW) and connectivity strength indexes of the structural network built using fractional anisotropy values of the white matter tracts connecting 84 cortical and subcortical regions in 25 chronic and 18 first episode (FE) schizophrenia and 24 bipolar patients and 28 healthy controls. Chronic schizophrenia and bipolar patients showed significantly smaller SW and connectivity strength indexes in comparison with controls and FE patients. SW reduction was driven by increased averaged path-length (PL) values. Illness duration but not treatment doses were negatively associated with connectivity strength, SW and PL in patients. Bipolar patients exposed to antipsychotics did not differ in SW or connectivity strength from bipolar patients without such an exposure. Executive functions and social cognition were related to SW index in the schizophrenia group. Our results support a role for chronicity but not treatment in structural network alterations in major psychoses, which may not differ between schizophrenia and bipolar disorder, and may hamper cognition.

Visual and Action-control Expressway Associated with Efficient Information Transmission in Elite Athletes

Effective information transmission for open skill performance requires fine-scale coordination of distributed networks of brain regions linked by white matter tracts. However, how patterns of connectivity in these anatomical pathways may improve global efficiency remains unclear. In this study, we hypothesized that the feeder edges in visual and motor systems have the potential to become "expressways" that increase the efficiency of information communication across brain networks of open skill experts. Thirty elite athletes and thirty novice subjects were recruited to participate in visual tracking and motor imagery tasks. We collected structural imaging data from these subjects, and then resolved structural neural networks using deterministic tractography to identify streamlines connecting cortical and subcortical brain regions of each participant. We observed that superior skill performance in elite athletes was associated with increased information transmission efficiency in feeder edges distributed between orbitofrontal and basal ganglia modules, as well as among temporal, occipital, and limbic system modules. These findings suggest that there is an expressway linking visual and action-control system of skill experts that enables more efficient interactions of peripheral and central information in support of effective performance of an open skill.

Impaired Effective Connectivity During a Cerebellar-Mediated Sensorimotor Synchronization Task in Schizophrenia

Prominent conceptual models characterize schizophrenia as a dysconnectivity syndrome, with recent research focusing on the contributions of the cerebellum in this framework. The present study examined the role of the cerebellum and its effective connectivity to the cerebrum during sensorimotor synchronization in schizophrenia. Specifically, the role of the cerebellum in temporally coordinating cerebral motor activity was examined through path analysis. Thirty-one individuals diagnosed with schizophrenia and 40 healthy controls completed a finger-tapping fMRI task including tone-paced synchronization and self-paced continuation tapping at a 500 ms intertap interval (ITI). Behavioral data revealed shorter and more variable ITIs during self-paced continuation, greater clock (vs motor) variance, and greater force of tapping in the schizophrenia group. In a whole-brain analysis, groups showed robust activation of the cerebellum during self-paced continuation but not during tone-paced synchronization. However, effective connectivity analysis revealed decreased connectivity in individuals with schizophrenia between the cerebellum and primary motor cortex but increased connectivity between cerebellum and thalamus during self-paced continuation compared with healthy controls. These findings in schizophrenia indicate diminished temporal coordination of cerebral motor activity by cerebellum during the continuation tapping portion of sensorimotor synchronization. Taken together with the behavioral finding of greater temporal variability in schizophrenia, these effective connectivity results are consistent with structural and temporal models of dysconnectivity in the disorder.

Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging

Early in the course of psychosis, alterations in brain connectivity accompany the emergence of psychiatric symptoms and cognitive impairments, including processing speed. The clinical-staging model is a refined form of diagnosis that places the patient along a continuum of illness conditions, which allows stage-specific interventions with the potential of improving patient care and outcome. This cross-sectional study investigates brain connectivity features that characterize the clinical stages following a first psychotic episode. Structural brain networks were derived from diffusion-weighted MRI for 71 early-psychosis patients and 76 healthy controls. Patients were classified into stage II (first-episode), IIIa (incomplete remission), IIIb (one relapse), and IIIc (two or more relapses), according to the course of the illness until the time of scanning. Brain connectivity measures and diffusion parameters (fractional anisotropy, apparent diffusion coefficient) were investigated using general linear models and sparse linear discriminant analysis (sLDA), studying distinct subgroups of patients who were at specific stages of early psychosis. We found that brain connectivity impairments were more severe in clinical stages following the first-psychosis episode (stages IIIa, IIIb, IIIc) than in first-episode psychosis (stage II) patients. These alterations were spatially diffuse but converged on a set of vulnerable regions, whose inter-connectivity selectively correlated with processing speed in patients and controls. The sLDA suggested that relapsing-remitting (stages IIIb, IIIc) and non-remitting (stage IIIa) patients are characterized by distinct dysconnectivity profiles. Our results indicate that neuroimaging markers of brain dysconnectivity in early psychosis may reflect the heterogeneity of the illness and provide a connectomics signature of the clinical-staging model.

Disrupted rich club organization and structural brain connectome in unmedicated bipolar disorder

BACKGROUND

Bipolar disorder (BD) has been associated with altered brain structural and functional connectivity. However, little is known regarding alterations of the structural brain connectome in BD. The present study aimed to use diffusion-tensor imaging (DTI) and graph theory approaches to investigate the rich club organization and white matter structural connectome in BD.

METHODS

Forty-two patients with unmedicated BD depression and 59 age-, sex- and handedness-matched healthy control participants underwent DTI. The whole-brain structural connectome was constructed by a deterministic fiber tracking approach. Graph theory analysis was used to examine the group-specific global and nodal topological properties, and rich club organizations, and then nonparametric permutation tests were used for group comparisons of network parameters.

RESULTS

Compared with healthy control participants, the patients with BD showed abnormal global properties, including increased characteristic path length, and decreased global efficiency and local efficiency. Locally, the patients with BD showed abnormal nodal parameters (nodal strength, nodal efficiency, and nodal betweenness) predominantly in the parietal, orbitofrontal, occipital, and cerebellar regions. Moreover, the patients with BD showed decreased rich club and feeder connectivity density.

CONCLUSIONS

Our results may reflect the disrupted white matter topological organization in the whole-brain, and abnormal regional connectivity supporting cognitive and affective functioning in depressed BD, which, in part, be due to impaired rich club connectivity.

Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study

BACKGROUND

In patients with schizophrenia, distributed abnormalities are observed in grey matter volume. A recent hypothesis posits that these distributed changes are indicative of a plastic reorganisation process occurring in response to a functional defect in neuronal information transmission. We investigated the structural covariance across various brain regions in early-stage schizophrenia to determine if indeed the observed patterns of volumetric loss conform to a coordinated pattern of structural reorganisation.

METHODS

Structural magnetic resonance imaging scans were obtained from 40 healthy adults and 41 age, gender and parental socioeconomic status matched patients with schizophrenia. Volumes of grey matter tissue were estimated at the regional level across 90 atlas-based parcellations. Group-level structural covariance was studied using a graph theoretical framework.

RESULTS

Patients had distributed reduction in grey matter volume, with high degree of localised covariance (clustering) compared with controls. Patients with schizophrenia had reduced centrality of anterior cingulate and insula but increased centrality of the fusiform cortex, compared with controls. Simulating targeted removal of highly central nodes resulted in significant loss of the overall covariance patterns in patients compared with controls.

CONCLUSION

Regional volumetric deficits in schizophrenia are not a result of random, mutually independent processes. Our observations support the occurrence of a spatially interconnected reorganisation with the systematic de-escalation of conventional 'hub' regions. This raises the question of whether the morphological architecture in schizophrenia is primed for compensatory functions, albeit with a high risk of inefficiency.

Altered structural and functional brain network overall organization predict human intertemporal decision-making

Intertemporal decision-making is naturally ubiquitous to us: individuals always make a decision with different consequences occurring at different moments. These choices are invariably involved in life-changing outcomes regarding marriage, education, fertility, long-term well-being, and even public policy. Previous studies have clearly uncovered the neurobiological mechanism of the intertemporal decision in the schemes of regional location or sub-network. However, it still remains unclear how to characterize intertemporal behavior with multimodal whole-brain network metrics to date. Here, we combined diffusion tensor image and resting-state functional connectivity MRI technology, in conjunction with graph-theoretical analysis, to explore the link between topological properties of integrated structural and functional whole-brain networks and intertemporal decision-making. Graph-theoretical analysis illustrated that the participants with steep discounting rates exhibited the decreased global topological organizations including small-world and rich-club regimes in both functional and structural connectivity networks, and reflected the dreadful local topological dynamics in the modularity of functional connectome. Furthermore, in the cross-modalities configuration, the same relationship was predominantly observed for the coupling of structural-functional connectivity as well. Above topological metrics are commonly indicative of the communication pattern of simultaneous global and local parallel information processing, and it thus reshapes our accounts on intertemporal decision-making from functional regional/sub-network scheme to multimodal brain overall organization.

Advances in microstructural diffusion neuroimaging for psychiatric disorders

Understanding the neuropathological underpinnings of mental disorders such as schizophrenia, major depression, and bipolar disorder is an essential step towards the development of targeted treatments. Diffusion MRI studies utilizing the diffusion tensor imaging (DTI) model have been extremely successful to date in identifying microstructural brain abnormalities in individuals suffering from mental illness, especially in regions of white matter, although identified abnormalities have been biologically non-specific. Building on DTI's success, in recent years more advanced diffusion MRI methods have been developed and applied to the study of psychiatric populations, with the aim of offering increased sensitivity to subtle neurological abnormalities, as well as improved specificity to candidate pathologies such as demyelination and neuroinflammation. These advanced methods, however, usually come at the cost of prolonged imaging sequences or reduced signal to noise, and they are more difficult to evaluate compared with the more simplified approach taken by the now common DTI model. To date, a limited number of advanced diffusion MRI methods have been employed to study schizophrenia, major depression and bipolar disorder populations. In this review we survey these studies, compare findings across diverse methods, discuss the main benefits and limitations of the different methods, and assess the extent to which the application of more advanced diffusion imaging approaches has led to novel and transformative information with regards to our ability to better understand the etiology and pathology of mental disorders.

Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders

Background

Brain connectome research based on graph theoretical analysis shows that small-world topological properties play an important role in the structural and functional alterations observed in patients with psychiatric disorders. However, the reported global topological alterations in small-world properties are controversial, are not consistently conceptualized according to agreed-upon criteria, and are not critically examined for consistent alterations in patients with each major psychiatric disorder.

Methods

Based on a comprehensive PubMed search, we systematically reviewed studies using noninvasive neuroimaging data and graph theoretical approaches for 6 major psychiatric disorders: schizophrenia, major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), obsessive–compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). Here, we describe the main patterns of altered small-world properties and then systematically review the evidence for these alterations in the structural and functional connectome in patients with these disorders.

Results

We selected 40 studies of schizophrenia, 33 studies of MDD, 5 studies of ADHD, 5 studies of BD, 7 studies of OCD and 5 studies of PTSD. The following 4 patterns of altered small-world properties are defined from theperspectives of segregation and integration: "regularization," "randomization," "stronger small-worldization" and "weaker small-worldization." Although more differences than similarities are noted in patients with these disorders, a prominent trend is the structural regularization versus functional randomization in patients with schizophrenia.

Limitations

Differences in demographic and clinical characteristics, preprocessing steps and analytical methods can produce contradictory results, increasing the difficulty of integrating results across different studies.

Conclusion

Four psychoradiological patterns of altered small-world properties are proposed. The analysis of altered smallworld properties may provide novel insights into the pathophysiological mechanisms underlying psychiatric disorders from a connectomic perspective. In future connectome studies, the global network measures of both segregation and integration should be calculated to fully evaluate altered small-world properties in patients with a particular disease.

Deterioration in Global Organization of Structural Brain Networks in Schizophrenia: A Diffusion MRI Tractography Study

Schizophrenia is a heterogenous neuropsychiatric disorder with varying degrees of altered connectivity in a wide range of brain areas. Network analysis using graph theory allows researchers to integrate and quantify relationships between widespread changes in a network system. This study examined the organization of brain structural networks by applying diffusion MRI, probabilistic tractography, and network analysis to 48 schizophrenia patients and 24 healthy controls. T1-weighted MR images obtained from all participants were parcellated into 87 regions of interests (ROIs) according to a prior anatomical template and registered to diffusion-weighted images (DWI) of the same subjects. Probabilistic tractography was performed to obtain sets of white matter tracts between any two ROIs and determine the connection probabilities between them. Connectivity matrices were constructed using these estimated connectivity probabilities, and several network properties related to network effectiveness were calculated. Global efficiency, local efficiency, clustering coefficient, and mean connectivity strength were significantly lower in schizophrenia patients (p = 0.042, p = 0.011, p = 0.013, p = 0.046). Mean betweenness centrality was significantly higher in schizophrenia (p = 0.041). Comparisons of node wise properties showed trends toward differences in several brain regions. Nodal local efficiency was consistently lower in the basal ganglia, frontal, temporal, cingulate, diencephalon, and precuneus regions in the schizophrenia group. Inter-group differences in nodal degree and nodal betweenness centrality varied by region and showed inconsistent results. Robustness was not significantly different between the study groups. Significant positive correlations were found between t-score of color trails test part-1 and local efficiency and mean connectivity strength in the patient group. The findings of this study suggest that schizophrenia results in deterioration of the global network organization of the brain and reduced ability for information processing.

Cortical Dysconnectivity Measured by Structural Covariance Is Associated With the Presence of Psychotic Symptoms in 22q11.2 Deletion Syndrome

BACKGROUND

22q11.2 deletion syndrome (22q11DS) is the third-largest known genetic risk factor for the development of psychosis. Dysconnectivity has consistently been implicated in the physiopathology of psychosis. Structural covariance of cortical morphology is a method of exploring connectivity among brain regions that to date has not been employed in 22q11DS.

METHODS

In the present study we employed structural covariance of cortical thickness to explore connectivity alterations in a group of 108 patients with 22q11DS compared with 96 control subjects. We subsequently divided patients into two subgroups of 31 subjects each according to the presence of attenuated psychotic symptoms. FreeSurfer software was used to obtain the mean cortical thickness in 148 brain regions from T1-weighted 3T images. For each population we reconstructed a brain graph using Pearson correlation between the average thickness of each couple of brain regions, which we characterized in terms of mean correlation strength and in terms of network architecture using graph theory.

RESULTS

Patients with 22q11DS presented increased mean correlation strength, but there was no difference in global architecture compared with control subjects. However, symptomatic patients presented increased mean correlation strength coupled with increased segregation and decreased integration compared with both control subjects and nonsymptomatic patients. They also presented increased centrality for a cluster of anterior cingulate and dorsomedial prefrontal regions.

CONCLUSIONS

These results confirm the importance of cortical dysconnectivity in the physiopathology of psychosis. Moreover they support the significance of aberrant anterior cingulate connectivity.

Multimodal Connectomics in Psychiatry: Bridging Scales From Micro to Macro

The human brain is a highly complex system, with a large variety of microscale cellular morphologies and macroscale global properties. Working at multiple scales, it forms an efficient system for processing and integration of multimodal information. Studies have repeatedly demonstrated strong associations between modalities of both microscales and macroscales of brain organization. These consistent observations point toward potential common organization principles where regions with a microscale architecture supportive of a larger computational load have more and stronger connections in the brain network on the macroscale. Conversely, disruptions observed on one organizational scale could modulate the other. First neuropsychiatric micro-macro comparisons in, among other conditions, Alzheimer's disease and schizophrenia, have, for example, shown overlapping alterations across both scales. We give an overview of recent findings on associations between microscale and macroscale organization observed in the healthy brain, followed by a summary of microscale and macroscale findings reported in the context of brain disorders. We conclude with suggestions for future multiscale connectome comparisons linking multiple scales and modalities of organization and suggest how such comparisons could contribute to a more complete fundamental understanding of brain organization and associated disease-related alterations.

Relations between structural and EEG-based graph metrics in healthy controls and schizophrenia patients

Our aim was to assess structural and functional networks in schizophrenia patients; and the possible prediction of the latter based on the former. The possible dependence of functional network properties on structural alterations has not been analyzed in schizophrenia. We applied averaged path-length (PL), clustering coefficient, and density (D) measurements to data from diffusion magnetic resonance and electroencephalography in 39 schizophrenia patients and 79 controls. Functional data were collected for the global and theta frequency bands during an odd-ball task, prior to stimulus delivery and at the corresponding processing window. Connectivity matrices were constructed from tractography and registered cortical segmentations (structural) and phase-locking values (functional). Both groups showed a significant electroencephalographic task-related modulation (change between prestimulus and response windows) in the global and theta bands. Patients showed larger structural PL and prestimulus density in the global and theta bands, and lower PL task-related modulation in the theta band. Structural network values predicted prestimulus global band values in controls and global band task-related modulation in patients. Abnormal functional values found in patients (prestimulus density in the global and theta bands and task-related modulation in the theta band) were not predicted by structural data in this group. Structural and functional network abnormalities respectively predicted cognitive performance and positive symptoms in patients. Taken together, the alterations in the structural and functional theta networks in the patients and the lack of significant relations between these alterations, suggest that these types of network abnormalities exist in different groups of schizophrenia patients.

Investigation of superior longitudinal fasciculus fiber complexity in recent onset psychosis

BACKGROUND

Standard diffusion tensor imaging measures (e.g., fractional anisotropy; FA) are difficult to interpret in brain regions with crossing white-matter (WM) fibers. Diffusion spectrum imaging (DSI) can be used to resolve fiber crossing, but has been difficult to implement in studies of patients with psychosis given long scan times.

METHODS

We used four fold accelerated compressed sensing to accelerate DSI acquisition to investigate the superior longitudinal fasciculus (SLF) in 27 (20M/7F) patients with recent onset psychosis and 23 (11M/12F) healthy volunteers. Dependent measures included the number of crossing fiber directions, multi directional anisotropy (MDA), which is a measure sensitive to the anisotropy of the underlying water diffusion in regions of crossing fibers, generalized FA (GFA) computed from the orientation distribution function, FA and tract volume.

RESULTS

Patients demonstrated a greater number of crossing WM fibers, lower MDA, GFA and FA in the left SLF compared to healthy volunteers. Patients also demonstrated a reversal in the normal (R>L) asymmetry of crossing fiber directions in the SLF and a lack of normal (L>R) asymmetry in MDA, GFA and FA compared to healthy volunteers. Lower GFA correlated significantly (p<0.05) with worse overall neuropsychological functioning; posthoc tests revealed significant effects with verbal functioning and processing speed.

CONCLUSIONS

Our findings provide the first in vivo evidence for abnormal crossing fibers within the SLF among individuals with psychosis and their functional correlates. A reversal in the normal pattern of WM asymmetry of crossing fibers in patients may be consistent with an aberrant neurodevelopmental process.

Pulsed focused ultrasound changes nerve conduction of earthworm giant axonal fibers

This study examined the effects of pulsed focused ultrasound (FUS) in disrupting nerve conduction. FUS operating at a 210 kHz fundamental frequency was administered to the medial and lateral giant axonal nerve fibers of earthworms in a burst of pulses (1 ms tone burst duration, 20 Hz pulse repetition frequency). The magnitude and latencies of the nerve potentials induced by electrical stimulation were measured under three experimental conditions - (I) no sonication, (II) sonication at 600 mW/cm spatial-peak temporal-average intensity (Ispta), and (III) sonication at 200 mW/cm Ispta. The sonication at 600 mW/cm temporarily decreased the magnitude of the action potential peak (~16%), whereas the baseline peak level was quickly restored in postsonication sessions. Sonication administered at a lower intensity (i.e. 200 mW/cm) did not alter the peak magnitude. The sonication did not alter the nerve conduction velocity. The acoustic intensities used in the experiment did not increase the temperature of the sonicated tissue. The results indicate that axonal neurotransmission can be disrupted temporarily by the application of pulsed FUS, suggesting its potential utility in modulating the functional connectivity established by white matter tracts in the brain.

Probabilistic thresholding of functional connectomes: Application to schizophrenia

Functional connectomes are commonly analysed as sparse graphs, constructed by thresholding cross-correlations between regional neurophysiological signals. Thresholding generally retains the strongest edges (correlations), either by retaining edges surpassing a given absolute weight, or by constraining the edge density. The latter (more widely used) method risks inclusion of false positive edges at high edge densities and exclusion of true positive edges at low edge densities. Here we apply new wavelet-based methods, which enable construction of probabilistically-thresholded graphs controlled for type I error, to a dataset of resting-state fMRI scans of 56 patients with schizophrenia and 71 healthy controls. By thresholding connectomes to fixed edge-specific P value, we found that functional connectomes of patients with schizophrenia were more dysconnected than those of healthy controls, exhibiting a lower edge density and a higher number of (dis)connected components. Furthermore, many participants' connectomes could not be built up to the fixed edge densities commonly studied in the literature (∼5-30%), while controlling for type I error. Additionally, we showed that the topological randomisation previously reported in the schizophrenia literature is likely attributable to "non-significant" edges added when thresholding connectomes to fixed density based on correlation. Finally, by explicitly comparing connectomes thresholded by increasing P value and decreasing correlation, we showed that probabilistically thresholded connectomes show decreased randomness and increased consistency across participants. Our results have implications for future analysis of functional connectivity using graph theory, especially within datasets exhibiting heterogenous distributions of edge weights (correlations), between groups or across participants.

Affected Anatomical Rich Club and Structural-Functional Coupling in Young Offspring of Schizophrenia and Bipolar Disorder Patients

BACKGROUND

Emerging evidence suggests disruptions in the wiring organization of the brain's network in schizophrenia (SZ) and bipolar disorder (BD). As the importance of genetic predisposition has been firmly established in these illnesses, children (offspring) of patients constitute an at-risk population. This study examines connectome organization in children at familial high risk for psychosis.

METHODS

Diffusion-weighted magnetic resonance imaging scans were collected from 127 nonpsychotic offspring 8 to 18 years of age (average age = 13.5 years) of a parent diagnosed with SZ (SZ offspring; n = 28) or BD (BD offspring; N = 60) and community control subjects (n = 39). Resting-state functional magnetic resonance imaging scans were available for 82 subjects. Anatomical and functional brain networks were reconstructed and examined using graph theoretical analysis.

RESULTS

SZ offspring were found to show connectivity deficits of the brain's central rich club (RC) system relative to both control subjects and BD offspring. The disruption in anatomical RC connectivity in SZ offspring was associated with increased modularity of the functional connectome. In addition, increased coupling between structural and functional connectivity of long-distance connections was observed in both SZ offspring and BD offspring.

CONCLUSIONS

This study shows lower levels of anatomical RC connectivity in nonpsychotic young offspring of SZ patients. This finding suggests that the brain's anatomical RC system is affected in at-risk youths, reflecting a connectome signature of familial risk for psychotic illness. Moreover, finding no RC deficits in offspring of BD patients suggest a differential effect of genetic predisposition for SZ versus BD on the developmental formation of the connectome.

Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia

Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network (p_FDR  < 0.05). All metrics across networks were significantly associated with intelligence (p_FDR  < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia (r = -0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp 38:5919-5930, 2017. © 2017 Wiley Periodicals, Inc.

Structural Network Disorganization in Subjects at Clinical High Risk for Psychosis

Previous network studies in chronic schizophrenia patients revealed impaired structural organization of the brain's rich-club members, a set of highly interconnected hub regions that play an important integrative role for global brain communication. Moreover, impaired rich-club connectivity has also been found in unaffected siblings of schizophrenia patients, suggesting that abnormal rich-club connectivity is related to familiar, possibly reflecting genetic, vulnerability for schizophrenia. However, no study has yet investigated whether structural rich-club organization is also impaired in individuals with a clinical risk syndrome for psychosis. Diffusion tensor imaging and probabilistic tractography was used to construct structural whole-brain networks in 24 healthy controls and 24 subjects with an at-risk mental state (ARMS). Graph theory was applied to quantify the structural rich-club organization and global network properties. ARMS subjects revealed a significantly altered structural rich-club organization compared with the control group. The disruption of rich-club organization was associated with the severity of negative psychotic symptoms and led to an elevated level of modularity in ARMS subjects. This study shows that abnormal structural rich-club organization is already evident in clinical high-risk subjects for psychosis and further demonstrates the impact of rich-club disorganization on global network communication. Together with previous evidence in chronic schizophrenia patients and unaffected siblings, our findings suggest that abnormal structural rich-club organization may reflect an endophenotypic marker of psychosis.

Connectomic correlates of response to treatment in first-episode psychosis

Connectomic approaches using diffusion tensor imaging have contributed to our understanding of brain changes in psychosis, and could provide further insights into the neural mechanisms underlying response to antipsychotic treatment. We here studied the brain network organization in patients at their first episode of psychosis, evaluating whether connectome-based descriptions of brain networks predict response to treatment, and whether they change after treatment. Seventy-six patients with a first episode of psychosis and 74 healthy controls were included. Thirty-three patients were classified as responders after 12 weeks of antipsychotic treatment. Baseline brain structural networks were built using whole-brain diffusion tensor imaging tractography, and analysed using graph analysis and network-based statistics to explore baseline characteristics of patients who subsequently responded to treatment. A subgroup of 43 patients was rescanned at the 12-week follow-up, to study connectomic changes over time in relation to treatment response. At baseline, those subjects who subsequently responded to treatment, compared to those that did not, showed higher global efficiency in their structural connectomes, a network configuration that theoretically facilitates the flow of information. We did not find specific connectomic changes related to treatment response after 12 weeks of treatment. Our data suggest that patients who have an efficiently-wired connectome at first onset of psychosis show a better subsequent response to antipsychotics. However, response is not accompanied by specific structural changes over time detectable with this method.

Increased density of DISC1-immunoreactive oligodendroglial cells in fronto-parietal white matter of patients with paranoid schizophrenia

Profound white matter abnormalities have repeatedly been described in schizophrenia, which involve the altered expression of numerous oligodendrocyte-associated genes. Transcripts of the disrupted-in-schizophrenia 1 (DISC1) gene, a key susceptibility factor in schizophrenia, have recently been shown to be expressed by oligodendroglial cells and to negatively regulate oligodendrocyte differentiation and maturation. To learn more about the putative role(s) of oligodendroglia-associated DISC1 in schizophrenia, we analyzed the density of DISC1-immunoreactive oligodendrocytes in the fronto-parietal white matter in postmortem brains of patients with schizophrenia. Compared with controls (N = 12) and cases with undifferentiated/residual schizophrenia (N = 6), there was a significantly increased density of DISC1-expressing glial cells in paranoid schizophrenia (N = 12), which unlikely resulted from neuroleptic treatment. Pathophysiologically, over-expression of DISC1 protein(s) in white matter oligodendrocytes might add to the reduced levels of two myelin markers, 2',3'-cyclic-nucleotide 3'-phosphodiesterase and myelin basic protein in schizophrenia. Moreover, it might significantly contribute to cell cycle abnormalities as well as to deficits in oligodendroglial cell differentiation and maturation found in schizophrenia.

Organizational topology of brain and its relationship to ADHD in adolescents with d-transposition of the great arteries

OBJECTIVE

Little is currently known about the impact of congenital heart disease (CHD) on the organization of large-scale brain networks in relation to neurobehavioral outcome. We investigated whether CHD might impact ADHD symptoms via changes in brain structural network topology in a cohort of adolescents with d-transposition of the great arteries (d-TGA) repaired with the arterial switch operation in early infancy and referent subjects. We also explored whether these effects might be modified by apolipoprotein E (APOE) genotype, as the APOE ε2 allele has been associated with worse neurodevelopmental outcomes after repair of d-TGA in infancy.

METHODS

We applied graph analysis techniques to diffusion tensor imaging (DTI) data obtained from 47 d-TGA adolescents and 29 healthy referents to construct measures of structural topology at the global and regional levels. We developed statistical mediation models revealing the respective contributions of d-TGA, APOE genotype, and structural network topology on ADHD outcome as measured by the Connors ADHD/DSM-IV Scales (CADS).

RESULTS

Changes in overall network connectivity, integration, and segregation mediated worse ADHD outcomes in d-TGA patients compared to healthy referents; these changes were predominantly in the left and right intrahemispheric regional subnetworks. Exploratory analysis revealed that network topology also mediated detrimental effects of the APOE ε4 allele but improved neurobehavioral outcomes for the APOE ε2 allele.

CONCLUSION

Our results suggest that disruption of organization of large-scale networks may contribute to neurobehavioral dysfunction in adolescents with CHD and that this effect may interact with APOE genotype.

Impaired fornix-hippocampus integrity is linked to peripheral glutathione peroxidase in early psychosis

Several lines of evidence implicate the fornix-hippocampus circuit in schizophrenia. In early-phase psychosis, this circuit has not been extensively investigated and the underlying mechanisms affecting the circuit are unknown. The hippocampus and fornix are vulnerable to oxidative stress at peripuberty in a glutathione (GSH)-deficient animal model. The purposes of the current study were to assess the integrity of the fornix-hippocampus circuit in early-psychosis patients (EP), and to study its relationship with peripheral redox markers. Diffusion spectrum imaging and T1-weighted magnetic resonance imaging (MRI) were used to assess the fornix and hippocampus in 42 EP patients compared with 42 gender- and age-matched healthy controls. Generalized fractional anisotropy (gFA) and volumetric properties were used to measure fornix and hippocampal integrity, respectively. Correlation analysis was used to quantify the relationship of gFA in the fornix and hippocampal volume, with blood GSH levels and glutathione peroxidase (GPx) activity. Patients compared with controls exhibited lower gFA in the fornix as well as smaller volume in the hippocampus. In EP, but not in controls, smaller hippocampal volume was associated with high GPx activity. Disruption of the fornix-hippocampus circuit is already present in the early stages of psychosis. Higher blood GPx activity is associated with smaller hippocampal volume, which may support a role of oxidative stress in disease mechanisms.

Aberrant Functional Whole-Brain Network Architecture in Patients With Schizophrenia: A Meta-analysis

Findings from multiple lines of research provide evidence of aberrant functional brain connectivity in schizophrenia. By using graph-analytical measures, recent studies indicate that patients with schizophrenia exhibit changes in the organizational principles of whole-brain networks and that these changes relate to cognitive symptoms. However, there has not been a systematic investigation of functional brain network changes in schizophrenia to test the consistency of these changes across multiple studies. A comprehensive literature search was conducted to identify all available functional graph-analytical studies in patients with schizophrenia. Effect size measures were derived from each study and entered in a random-effects meta-analytical model. All models were tested for effects of potential moderator variables as well as for the presence of publication bias. The results of a total of n = 13 functional neuroimaging studies indicated that brain networks in patients with schizophrenia exhibit significant decreases in measures of local organization (g = -0.56, P = .02) and significant decreases in small-worldness (g = -0.65, P = .01) whereas global short communication paths seemed to be preserved (g = 0.26, P = .32). There was no evidence for a publication bias or moderator effects. The present meta- analysis demonstrates significant changes in whole brain network architecture associated with schizophrenia across studies.

Diffusion Imaging of White Matter In Schizophrenia: Progress and Future Directions

Diffusion tensor imaging (DTI) is a powerful tool for the in-vivo assessment of white matter microstructure. The application of DTI methodologies to the study of schizophrenia has supported and advanced the hypothesis of schizophrenia as a disorder of disrupted connectivity. In the context of impaired structural connectivity, the extended time frame of white matter development may offer unique opportunities for treatment that can capitalize on the neural flexibility that is still present in the period leading up to and after disease onset. Therefore, it is important to gain a clear understanding of white matter deficits and how they may emerge and change across the illness. However, while there is broad consistency in the findings of white matter deficits in patients with schizophrenia, there is also a great deal of variability in specific findings across studies. In this review, the aim is to move beyond summarizing case-control analyses, to consider the many factors that may impact DTI measures, to explain variability of findings, and to explore future directions for the field. The topics explored include ways to parse DTI patterns associated with different disease subtypes, ways in which novel and established treatments might interact with or enhance white matter, ways of dissociating developmental change from the disease process itself, and understanding the role of emerging analytic methodologies.

Long-range dysconnectivity in frontal and midline structures is associated to psychosis in 22q11.2 deletion syndrome

Patients affected by 22q11.2 deletion syndrome (22q11DS) present a characteristic cognitive and psychiatric profile and have a genetic predisposition to develop schizophrenia. Although brain morphological alterations have been shown in the syndrome, they do not entirely account for the complex clinical picture of the patients with 22q11DS and for their high risk of psychotic symptoms. Since Friston proposed the "disconnection hypothesis" in 1998, schizophrenia is commonly considered as a disorder of brain connectivity. In this study, we review existing evidence pointing to altered brain structural and functional connectivity in 22q11DS, with a specific focus on the role of dysconnectivity in the emergence of psychotic symptoms. We show that widespread alterations of structural and functional connectivity have been described in association with 22q11DS. Moreover, alterations involving long-range association tracts as well as midline structures, such as the corpus callosum and the cingulate gyrus, have been associated with psychotic symptoms in this population. These results suggest common mechanisms for schizophrenia in syndromic and non-syndromic populations. Future directions for investigations are also discussed.

Brain network characterization of high-risk preterm-born school-age children

Higher risk for long-term cognitive and behavioral impairments is one of the hallmarks of extreme prematurity (EP) and pregnancy-associated fetal adverse conditions such as intrauterine growth restriction (IUGR). While neurodevelopmental delay and abnormal brain function occur in the absence of overt brain lesions, these conditions have been recently associated with changes in microstructural brain development. Recent imaging studies indicate changes in brain connectivity, in particular involving the white matter fibers belonging to the cortico-basal ganglia-thalamic loop. Furthermore, EP and IUGR have been related to altered brain network architecture in childhood, with reduced network global capacity, global efficiency and average nodal strength. In this study, we used a connectome analysis to characterize the structural brain networks of these children, with a special focus on their topological organization. On one hand, we confirm the reduced averaged network node degree and strength due to EP and IUGR. On the other, the decomposition of the brain networks in an optimal set of clusters remained substantially different among groups, talking in favor of a different network community structure. However, and despite the different community structure, the brain networks of these high-risk school-age children maintained the typical small-world, rich-club and modularity characteristics in all cases. Thus, our results suggest that brain reorganizes after EP and IUGR, prioritizing a tight modular structure, to maintain the small-world, rich-club and modularity characteristics. By themselves, both extreme prematurity and IUGR bear a similar risk for neurocognitive and behavioral impairment, and the here defined modular network alterations confirm similar structural changes both by IUGR and EP at school age compared to control. Interestingly, the combination of both conditions (IUGR + EP) does not result in a worse outcome. In such cases, the alteration in network topology appears mainly driven by the effect of extreme prematurity, suggesting that these brain network alterations present at school age have their origin in a common critical period, both for intrauterine and extrauterine adverse conditions.