Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging

Altered Functional Connectivity of Striatum Based on the Integrated Connectivity Model in First-Episode Schizophrenia

Background: The human striatum is a heterogeneous structure involved in diverse functional domains that related to distinct striatum subregions. Striatal dysfunction was thought to be a fundamental element in schizophrenia. However, the connectivity pattern of striatum solely based on functional or structural characteristics leads to inconsistent findings in healthy adult and also schizophrenia. This study aims to develop an integrated striatal model and reveal the altered functional connectivity pattern of the striatum in schizophrenia. Methods: Two data-driven approaches, task-dependent meta-analytic connectivity modeling (MACM) and task-independent resting-state functional connectivity (RSFC), were used for seven anatomical connectivity-based striatum subregions to provide an integrated striatal model. Then, RSFC analyses of seven striatal subregions were applied to 45 first-episode schizophrenia (FES) and 27 healthy controls to examine the difference, based on the integrated model, of functional connectivity pattern of striatal subregions. Results: MACM and RSFC results showed that striatum subregions were associated with discrete cortical regions and involved in distinct cognitive processes. Besides, RSFC results overlapped with MACM findings but showed broader distributions. Importantly, significantly reduced functional connectivity was identified between limbic subregion and thalamus, medial prefrontal cortex, anterior cingulate cortex, and insula and also between executive subregions and thalamus, supplementary motor area, and insula in FES. Conclusions: Combing functional and structural connectivity information, this study provides the integrated model of corticostriatal subcircuits and confirms the abnormal functional connectivity of limbic and executive striatum subregions with different networks and thalamus, supporting the important role of the corticostriatal-thalamic loop in the pathophysiology of schizophrenia.

Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis

Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding-window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting-state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting-state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting-state dynamics are not trivially attributable to sampling variability and head motion.

An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity

Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer's disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction. Additionally, human uncertainties arise during interpretation due to the complexity in understanding the data. While existing FCN visual analytics tools have begun to mitigate the human ambiguities, reducing the impact of data limitations is an open problem. In this paper, we propose a novel visual analytics framework with three linked, purpose-designed components to evoke deeper interpretation of the fMRI data: (i) an enhanced FCN abstraction; (ii) a temporal signal viewer; and (iii) the anatomical context. Each component has been specifically designed with novel visual cues and interaction to expose the impact of uncertainties on the data. We augment this with two methods designed for comparing subjects, by using a small multiples and a marker approach. We demonstrate the enhancements enabled by our framework on three case studies of common research scenarios, using clinical schizophrenia data, which highlight the value in interpreting fMRI FCN data with an awareness of the uncertainties. Finally, we discuss our framework in the context of fMRI visual analytics and the extensibility of our approach.

Theory of Mind Skills Are Related to Resting-State Frontolimbic Connectivity in Schizophrenia

Patients with schizophrenia (SCH) often demonstrate impairment in social-cognitive functions as well as disturbances in large-scale network connectivity. The ventromedial prefrontal cortex (vmPFC) is a core region of the default mode network, with projections to limbic structures. It plays an important role in social and emotional decision-making. We investigated whether resting-state functional connectivity (FC) relates to the cognitive and affective domains of theory of mind (ToM). Twenty-three SCH patients and 19 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging scanning. vmPFC seed connectivity was correlated with behavioral measures assessing ToM domains. SCH performed less well than HCs in both ToM task domains. An analysis of the resting-state FC revealed that SCH had reduced connectivity from the vmPFC to the subcallosal cortex, right amygdala, and right hippocampus as a function of behavioral scores in both ToM domains. Within-group analyses indicated that in HCs, the performance in ToM was positively associated with frontoamygdalar resting-state connectivity, whereas in SCH, the performance in ToM was negatively associated with the frontosubcallosal connectivity. Differences in the pattern of the resting-state frontolimbic connectivity and its associations with performance in ToM tasks between the two study groups might represent a different setup for processing social information in patients with SCH.

Association between schizophrenia polygenic risk and neural correlates of emotion perception

The neural correlates of emotion perception have been shown to be significantly altered in schizophrenia (SCZ) patients as well as their healthy relatives, possibly reflecting genetic susceptibility to the disease. The aim of the study was to investigate the association between SCZ polygenic risk and brain activity whilst testing perception of multisensory, dynamic emotional stimuli. We created SCZ polygenic risk scores (PRS) for a sample of twenty-eight healthy individuals. The PRS was based on data from the Psychiatric Genomics Consortium and was used as a regressor score in the neuroimaging analysis. The results of a multivariate brain-behaviour analysis show that higher SCZ PRS are related to increased activity in brain regions critical for emotion during the perception of threatening (angry) emotions. These results suggest that individuals with higher SCZ PRS over-activate the neural correlates underlying emotion during perception of threat, perhaps due to an increased experience of fear or neural inefficiency in emotion-regulation areas. Moreover, over-recruitment of emotion regulation regions might function as a compensation to maintain normal emotion regulation during threat perception. If replicated in larger studies, these findings may have important implications for understanding the neurophysiological biomarkers relevant in SCZ.

Remodeling of Sensorimotor Brain Connectivity in Gpr88-Deficient Mice

Recent studies have demonstrated that orchestrated gene activity and expression support synchronous activity of brain networks. However, there is a paucity of information on the consequences of single gene function on overall brain functional organization and connectivity and how this translates at the behavioral level. In this study, we combined mouse mutagenesis with functional and structural magnetic resonance imaging (MRI) to determine whether targeted inactivation of a single gene would modify whole-brain connectivity in live animals. The targeted gene encodes GPR88 (G protein-coupled receptor 88), an orphan G protein-coupled receptor enriched in the striatum and previously linked to behavioral traits relevant to neuropsychiatric disorders. Connectivity analysis of Gpr88-deficient mice revealed extensive remodeling of intracortical and cortico-subcortical networks. Most prominent modifications were observed at the level of retrosplenial cortex connectivity, central to the default mode network (DMN) whose alteration is considered a hallmark of many psychiatric conditions. Next, somatosensory and motor cortical networks were most affected. These modifications directly relate to sensorimotor gating deficiency reported in mutant animals and also likely underlie their hyperactivity phenotype. Finally, we identified alterations within hippocampal and dorsal striatum functional connectivity, most relevant to a specific learning deficit that we previously reported in Gpr88^-/- animals. In addition, amygdala connectivity with cortex and striatum was weakened, perhaps underlying the risk-taking behavior of these animals. This is the first evidence demonstrating that GPR88 activity shapes the mouse brain functional and structural connectome. The concordance between connectivity alterations and behavior deficits observed in Gpr88-deficient mice suggests a role for GPR88 in brain communication.

Hippocampal Network Modularity Is Associated With Relational Memory Dysfunction in Schizophrenia

BACKGROUND

Functional dysconnectivity has been proposed as a major pathophysiological mechanism for cognitive dysfunction in schizophrenia. The hippocampus is a focal point of dysconnectivity in schizophrenia, with decreased hippocampal functional connectivity contributing to the marked memory deficits observed in patients. Normal memory function relies on the interaction of complex corticohippocampal networks. However, only recent technological advances have enabled the large-scale exploration of functional networks with accuracy and precision.

METHODS

We investigated the modularity of hippocampal resting-state functional networks in a sample of 45 patients with schizophrenia spectrum disorders and 38 healthy control subjects. Modularity was calculated for two distinct functional networks: a core hippocampal-medial temporal lobe cortex network and an extended hippocampal-cortical network. As hippocampal function differs along its longitudinal axis, follow-up analyses examined anterior and posterior networks separately. To explore effects of resting network function on behavior, we tested associations between modularity and relational memory ability. Age, sex, handedness, and parental education were similar between groups.

RESULTS

Network modularity was lower in schizophrenia patients, especially in the posterior hippocampal network. Schizophrenia patients also showed markedly lower relational memory ability compared with control subjects. We found a distinct brain-behavior relationship in schizophrenia that differed from control subjects by network and anterior/posterior division-while relational memory in control subjects was associated with anterior hippocampal-cortical modularity, schizophrenia patients showed an association with posterior hippocampal-medial temporal lobe cortex network modularity.

CONCLUSIONS

Our findings support a model of abnormal resting-state corticohippocampal network coherence in schizophrenia, which may contribute to relational memory deficits.

Aberrant patterns of local and long-range functional connectivity densities in schizophrenia

Schizophrenia is a disorder of brain dysconnectivity, and both the connection strength and connection number are disrupted in patients with schizophrenia. The functional connectivity density (FCD) can reflect alterations in the connection number. Alterations in the global FCD (gFCD) in schizophrenia were previously demonstrated; however, alterations in two other indices of the pathological characteristics of the brain, local FCD (lFCD) and long-range FCD (lrFCD), have not been revealed. To investigate lFCD and lrFCD alterations in patients with schizophrenia, 95 patients and 93 matched healthy controls were examined using structural and resting-state functional magnetic resonance imaging scanning. lFCD and lrFCD were measured using FCD mapping, and differences were identified using a two-sample t-test in a voxel-wise manner, with age and gender considered to increase variability. Multiple comparisons were performed using a false discovery rate method with a corrected threshold of P<0.05. Our analysis showed that lFCD was primarily decreased in the postcentral gyrus, right calcarine sulcus, and inferior occipital gyrus lobule, but increased in the bilateral subcortical regions. The differences in lFCD were more pronounced and complicated than those in lrFCD. In summary, in contrast with previous studies that focused on the connection strength, our findings, from the perspective of connection number, indicate that schizophrenia is a disorder of brain dysconnectivity, particularly affecting the local functional connectivity network, and support the hypothesis that schizophrenia is associated with a widespread cortical functional connectivity/activity deficit, with hyper- and/or hypo-connectivity/activity coexisting in some cortical or subcortical regions.

Disrupted modular organization of primary sensory brain areas in schizophrenia

Abnormal brain resting-state functional connectivity has been consistently observed in patients affected by schizophrenia (SCZ) using functional MRI and other neuroimaging techniques. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the organization of brain connectivity networks. A few studies have shown altered distribution of connectivity within and between functional modules in SCZ patients, an indication of imbalanced functional segregation ad integration. However, no major alterations of modular organization have been reported in patients, and unambiguous identification of the neural substrates affected remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe resolution limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This resolution limit is likely to have hampered the ability to resolve differences between patients and controls in previous studies. Here, we apply Surprise, a novel resolution limit-free approach, to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients and in matched healthy controls. Leveraging these important methodological advances we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings support the hypothesis that cognitive dysfunction in SCZ may involve deficits occurring already at early stages of sensory processing.

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.

Multiple functional networks modeling for autism spectrum disorder diagnosis

Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way. In this article, we propose a novel framework to model multiple networks of rsfMRI with data-driven approaches. Specifically, we construct large-scale functional networks with hierarchical clustering and find discriminative connectivity patterns between ASD and normal controls (NC). We then learn features and classifiers for each cluster through discriminative restricted Boltzmann machines (DRBMs). In the testing phase, each DRBM determines whether a test sample is ASD or NC, based on which we make a final decision with a majority voting strategy. We assess the diagnostic performance of the proposed method using public datasets and describe the effectiveness of our method by comparing it to competing methods. We also rigorously analyze FCs learned by DRBMs on each cluster and discover dominant FCs that play a major role in discriminating between ASD and NC. Hum Brain Mapp 38:5804-5821, 2017. © 2017 Wiley Periodicals, Inc.

Altered language network in benign childhood epilepsy patients with spikes from non-dominant side: A resting-state fMRI study

Benign childhood epilepsy with centrotemporal spikes (BECTS) is one of the most common childhood epilepsy syndromes, and language deficits associated with BECTS have become a hot topic. This study investigated alterations of the language network in BECTS children with spikes from the non-dominant side in comparison with healthy controls. Twenty-three children with BECTS and 20 age-matched healthy controls were enrolled. Region of interest -based whole brain functional connectivity analysis was used to identify the potential differences in the functional connectivity of the Broca's area between the two groups. Increased positive functional connectivity within the Broca's region was detected mainly at the left superior frontal gyrus (Brodmann area 8), bilateral insula, and anterior and posterior cingulate in the BECTS group. No regions showed significantly decreased connection in the BECTS patients compared to the controls. This study suggested alterations in the language network that was related with the Broca's area in children with BECTS from the non-dominant side. Further studies with longitudinal assessments from the perceptive of functional neuroimaging are needed to illustrate the dynamic course of language development and corresponding neuroimaging evidence.

Altered Brain Network Connectivity as a Potential Endophenotype of Schizophrenia

Abnormal functional brain connectivity could be considered an endophenotype of psychosis in schizophrenia. Identifying candidate endophenotypes may serve as a tool for elucidating its biological and neural mechanisms. The present study investigated the similarities and differences of features of brain network connectivity between patients and their first-degree relatives. Independent component analysis was conducted on imaging data collected from 34 healthy controls, 33 schizophrenia patients, and 30 unaffected first-degree relatives. The correlation between functional connectivity with neurocognitive performance and clinical symptoms were calculated. Abnormalities of between-network connectivity largely overlapped in patients and first-degree relatives, but the extent of such abnormalities was relatively minor in relatives. Negative connectivity between language networks and executive control networks was impaired in schizophrenia patients and their first-degree relatives, and this decreased connectivity was correlated with performance in language processing. Similar impairments were found in high-visual network and executive network coupling, and this decreased connection was correlated with the severity of positive symptoms in patients. The results indicated that abnormal functional connectivity within and between perceptual systems (i.e., high-visual and language) and executive control networks was related to the generic risk of schizophrenia, which makes it a potential endophenotype for schizophrenia.

A joint time-frequency analysis of resting-state functional connectivity reveals novel patterns of connectivity shared between or unique to schizophrenia patients and healthy controls

Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivity aspects of diseases such as schizophrenia and bipolar. Methods that are developed to measure functional connectivity are based on the underlying hypotheses regarding the actual nature of RS-connectivity including evidence of temporally dynamic versus static RS-connectivity and evidence of frequency-specific versus hemodynamically-driven connectivity over a wide frequency range. This study is derived by these observations of variation of RS-connectivity in temporal and frequency domains and evaluates such characteristics of RS-connectivity in clinical population and jointly in temporal and frequency domains (the spectro-temporal domain). We base this study on the hypothesis that by studying functional connectivity of schizophrenia patients and comparing it to the one of healthy controls in the spectro-temporal domain we might be able to make new observations regarding the differences and similarities between diseased and healthy brain connectivity and such observations could be obscured by studies which investigate such characteristics separately. Interestingly, our results include, but are not limited to, a spectrally localized (mostly mid-range frequencies) modular dynamic connectivity pattern in which sensory motor networks are anti-correlated with visual, auditory and sub-cortical networks in schizophrenia, as well as evidence of lagged dependence between default-mode and sensory networks in schizophrenia. These observations are unique to the proposed augmented domain of connectivity analysis. We conclude this study by arguing not only resting-state connectivity has structured spectro-temporal variability, but also that studying properties of connectivity in this joint domain reveals distinctive group-based differences and similarities between clinical and healthy populations.

Functional brain networks in treatment-resistant schizophrenia

INTRODUCTION

Up to 20% of individuals with schizophrenia show minimal or no response to medication and are considered to have 'treatment-resistant' schizophrenia (TRS). Unlike early and established schizophrenia, few studies have investigated resting-state functional connectivity (rs-FC) in TRS. Here, we test for disruptions in FC and altered efficiency of functional brain networks in a well-characterized cohort of TRS patients.

METHODS

Resting-state functional magnetic resonance imaging was used to investigate functional brain networks in 42 TRS participants prescribed clozapine (30 males, mean age=41.3(10)) and 42 healthy controls (24 males, mean age=38.4(10)). Graph analysis was used to characterize between-group differences in local and global efficiency of functional brain network organization as well as the strength of FC.

RESULTS

Global brain FC was reduced in TRS patients (p=0.0001). Relative to controls, 3.4% of all functional connections showed reduced strength in TRS (p<0.001), predominantly involving fronto-temporal, fronto-occipital and temporo-occipital connections. Global efficiency was reduced in TRS (p=0.0015), whereas local efficiency was increased (p=0.0042).

CONCLUSIONS

TRS is associated with widespread reductions in rs-FC and altered network topology. Increased local functional network efficiency coupled with decreased global efficiency suggests that hub-to-hub connections are preferentially affected in TRS. These findings further our understanding of the neurobiological impairments in TRS.

Reduced intrinsic visual cortical connectivity is associated with impaired perceptual closure in schizophrenia

Sensory perceptual processing deficits, such as impaired visual object identification and perceptual closure, have been reported in schizophrenia. These perceptual impairments may be associated with neural deficits in visual association areas, including lateral occipital cortex and inferior temporal areas. However, it remains unknown if such deficits can be found in the intrinsic architecture of the visual system.

In the current study, we measured perceptual closure performance and resting-state functional connectivity using functional magnetic resonance imaging (FMRI) in 16 schizophrenia patients and 16 matched healthy controls. We estimated intrinsic functional connectivity using self-organized grouping spatial ICA, which clusters component maps in the subject space according to spatial similarity. Patients performed worse than controls in the perceptual closure task. This impaired closure performance of patients was correlated with increased severity of psychotic symptoms. We also found that intrinsic connectivity of the visual processing system was diminished in patients compared to controls. Lower perceptual closure performance was correlated to lower visual cortical intrinsic connectivity overall.

We suggest that schizophrenia is associated with impaired intrinsic connectivity of the visual system, and that it is a potential mechanism leading to impaired visual object perception. These findings contribute to increasing evidence for impairments of higher visual functions in schizophrenia.

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We found reduced visual resting-state network connectivity in schizophrenia.

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Reduced connectivity correlated with impaired perceptual closure performance

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Schizophrenia is associated with impaired intrinsic connectivity of the visual system.

Default mode functional connectivity is associated with social functioning in schizophrenia

Individuals with schizophrenia display notable deficits in social functioning. Research indicates that neural connectivity within the default mode network (DMN) is related to social cognition and social functioning in healthy and clinical populations. However, the association between DMN connectivity, social cognition, and social functioning has not been studied in schizophrenia. For the present study, the authors used resting-state neuroimaging data to evaluate connectivity between the main DMN hubs (i.e., the medial prefrontal cortex [mPFC] and the posterior cingulate cortex-anterior precuneus [PPC]) in individuals with schizophrenia (n = 28) and controls (n = 32). The authors also examined whether DMN connectivity was associated with social functioning via social attainment (measured by the Specific Levels of Functioning Scale) and social competence (measured by the Social Skills Performance Assessment), and if social cognition mediates the association between DMN connectivity and these measures of social functioning. Results revealed that DMN connectivity did not differ between individuals with schizophrenia and controls. However, connectivity between the mPFC and PCC hubs was significantly associated with social competence and social attainment in individuals with schizophrenia but not in controls as reflected by a significant group-by-connectivity interaction. Social cognition did not mediate the association between DMN connectivity and social functioning in individuals with schizophrenia. The findings suggest that fronto-parietal DMN connectivity in particular may be differentially associated with social functioning in schizophrenia and controls. As a result, DMN connectivity may be used as a neuroimaging marker to monitor treatment response or as a potential target for interventions that aim to enhance social functioning in schizophrenia. (PsycINFO Database Record

Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations

Major psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), autism (AUT), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SZ), are highly heritable and polygenic. Evidence suggests that these five disorders have both shared and distinct genetic risks and neural connectivity abnormalities. To measure aggregate genetic risks, the polygenic risk score (PGRS) was computed. Two independent general populations (N = 360 and N = 323) were separately examined to investigate whether the cross-disorder PGRS and PGRS for a specific disorder were associated with individual variability in functional connectivity. Consistent altered functional connectivity was found with the bilateral insula: for the left supplementary motor area and the left superior temporal gyrus with the cross-disorder PGRS, for the left insula and right middle and superior temporal lobe associated with the PGRS for autism, for the bilateral midbrain, posterior cingulate, cuneus, and precuneus associated with the PGRS for BD, and for the left angular gyrus and the left dorsolateral prefrontal cortex associated with the PGRS for schizophrenia. No significant functional connectivity was found associated with the PGRS for ADHD and MDD. Our findings indicated that genetic effects on the cross-disorder and disorder-specific neural connectivity of common genetic risk loci are detectable in the general population. Our findings also indicated that polygenic risk contributes to the main neurobiological phenotypes of psychiatric disorders and that identifying cross-disorder and specific functional connectivity related to polygenic risks may elucidate the neural pathways for these disorders.

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Altered cross-disorder functional connectivity related to PGRSs is detected.

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Altered disorder-specific functional connectivity related to PGRSs is detected.

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Altered functional connectivity related to PGRSs is involved in brain networks.

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Polygenic risk contributes to neurobiological phenotypes of psychiatric disorders.

Dopamine, fronto-striato-thalamic circuits and risk for psychosis

A series of parallel, integrated circuits link distinct regions of prefrontal cortex with specific nuclei of the striatum and thalamus. Dysfunction of these fronto-striato-thalamic systems is thought to play a major role in the pathogenesis of psychosis. In this review, we examine evidence from human and animal investigations that dysfunction of a specific dorsal fronto-striato-thalamic circuit, linking the dorsolateral prefrontal cortex, dorsal (associative) striatum, and mediodorsal nucleus of the thalamus, is apparent across different stages of psychosis, including prior to the onset of a first episode, suggesting that it represents a candidate risk biomarker. We consider how abnormalities at distinct points in the circuit may give rise to the pattern of findings seen in patient populations, and how these changes relate to disruptions in dopamine, glutamate and GABA signaling.

Altered brain connectivity in patients with schizophrenia is consistent across cognitive contexts

BACKGROUND

Schizophrenia has been defined as a dysconnection syndrome characterized by aberrant functional brain connectivity. Using task-based fMRI, we assessed to what extent the nature of the cognitive context may further modulate abnormal functional brain connectivity.

METHODS

We analyzed data matched for motion in patients with schizophrenia and healthy controls who performed 3 different tasks. Tasks 1 and 2 both involved emotional processing and only slighlty differed (incidental encoding v. memory recognition), whereas task 3 was a much different mental rotation task. We conducted a connectome-wide general linear model analysis aimed at identifying context-dependent and independent functional brain connectivity alterations in patients with schizophrenia.

RESULTS

After matching for motion, we included 30 patients with schizophrenia and 30 healthy controls in our study. Abnormal connectivity in patients with schizophrenia followed similar patterns regardless of the degree of similarity between cognitive tasks. Decreased connectivity was most notable in the medial prefrontal cortex, the anterior and posterior cingulate, the temporal lobe, the lobule IX of the cerebellum and the premotor cortex.

LIMITATIONS

A more circumscribed yet significant context-dependent effect might be detected with larger sample sizes or cognitive domains other than emotional and visuomotor processing.

CONCLUSION

The context-independence of functional brain dysconnectivity in patients with schizophrenia provides a good justification for pooling data from multiple experiments in order to identify connectivity biomarkers of this mental illness.

Risperidone Effects on Brain Dynamic Connectivity-A Prospective Resting-State fMRI Study in Schizophrenia

Resting-state functional connectivity studies in schizophrenia evaluating average connectivity over the entire experiment have reported aberrant network integration, but findings are variable. Examining time-varying (dynamic) functional connectivity may help explain some inconsistencies. We assessed dynamic network connectivity using resting-state functional MRI in patients with schizophrenia, while unmedicated (n = 34), after 1 week (n = 29) and 6 weeks of treatment with risperidone (n = 24), as well as matched controls at baseline (n = 35) and after 6 weeks (n = 19). After identifying 41 independent components (ICs) comprising resting-state networks, sliding window analysis was performed on IC timecourses using an optimal window size validated with linear support vector machines. Windowed correlation matrices were then clustered into three discrete connectivity states (a relatively sparsely connected state, a relatively abundantly connected state, and an intermediately connected state). In unmedicated patients, static connectivity was increased between five pairs of ICs and decreased between two pairs of ICs when compared to controls, dynamic connectivity showed increased connectivity between the thalamus and somatomotor network in one of the three states. State statistics indicated that, in comparison to controls, unmedicated patients had shorter mean dwell times and fraction of time spent in the sparsely connected state, and longer dwell times and fraction of time spent in the intermediately connected state. Risperidone appeared to normalize mean dwell times after 6 weeks, but not fraction of time. Results suggest that static connectivity abnormalities in schizophrenia may partly be related to altered brain network temporal dynamics rather than consistent dysconnectivity within and between functional networks and demonstrate the importance of implementing complementary data analysis techniques.

Resting-State Magnetoencephalography Reveals Different Patterns of Aberrant Functional Connectivity in Combat-Related Mild Traumatic Brain Injury

Blast mild traumatic brain injury (mTBI) is a leading cause of sustained impairment in military service members and veterans. However, the mechanism of persistent disability is not fully understood. The present study investigated disturbances in brain functioning in mTBI participants using a source-imaging-based approach to analyze functional connectivity (FC) from resting-state magnetoencephalography (rs-MEG). Study participants included 26 active-duty service members or veterans who had blast mTBI with persistent post-concussive symptoms, and 22 healthy control active-duty service members or veterans. The source time courses from regions of interest (ROIs) were used to compute ROI to whole-brain (ROI-global) FC for different frequency bands using two different measures: 1) time-lagged cross-correlation and 2) phase-lock synchrony. Compared with the controls, blast mTBI participants showed increased ROI-global FC in beta, gamma, and low-frequency bands, but not in the alpha band. Sources of abnormally increased FC included the: 1) prefrontal cortex (right ventromedial prefrontal cortex [vmPFC], right rostral anterior cingulate cortex [rACC]), and left ventrolateral and dorsolateral prefrontal cortex; 2) medial temporal lobe (bilateral parahippocampus, hippocampus, and amygdala); and 3) right putamen and cerebellum. In contrast, the blast mTBI group also showed decreased FC of the right frontal pole. Group differences were highly consistent across the two different FC measures. FC of the left ventrolateral prefrontal cortex correlated with executive functioning and processing speed in mTBI participants. Altogether, our findings of increased and decreased regionalpatterns of FC suggest that disturbances in intrinsic brain connectivity may be the result of multiple mechanisms, and are associated with cognitive sequelae of the injury.

Reorganization of brain connectivity in obesity

Global brain connectivity (GBC) identifies regions of the brain, termed "hubs," which are densely connected and metabolically costly, and have a wide influence on brain function. Since obesity is associated with central and peripheral metabolic dysfunction we sought to determine if GBC is altered in obesity. Two independent fMRI data sets were subjected to GBC analyses. The first data set was acquired while participants (n = 15 healthy weight and 15 obese) tasted milkshake and the second with participants at rest (n = 33 healthy weight and 28 obese). In the resting state and during milkshake consumption GBC is consistently decreased in the ventromedial and ventrolateral prefrontal cortex, insula and caudate nucleus, and increased in brain regions belonging to the dorsal attention network including premotor areas, superior parietal lobule, and visual cortex. During milkshake consumption, but not at rest, additional decreases in GBC are observed in feeding-related circuitry including the insula, amygdala, anterior hippocampus, hypothalamus, midbrain, brainstem and somatomotor cortex. Additionally, GBC differences were not accounted for by age. These results demonstrate that obesity is associated with decreased GBC in prefrontal and feeding circuits and increased GBC in the dorsal attention network. We therefore conclude that global brain organization is altered in obesity to favor networks important for external orientation over those monitoring homeostatic state and guiding feeding decisions. Furthermore, since prefrontal decreases are also observed at rest in obese individuals future work should evaluate whether these changes are associated with neurocognitive impairments frequently observed in obesity and diabetes. Hum Brain Mapp 38:1403-1420, 2017. © 2016 Wiley Periodicals, Inc.

Disrupted salience network functional connectivity and white-matter microstructure in persons at risk for psychosis: findings from the LYRIKS study

BACKGROUND

Salience network (SN) dysconnectivity has been hypothesized to contribute to schizophrenia. Nevertheless, little is known about the functional and structural dysconnectivity of SN in subjects at risk for psychosis. We hypothesized that SN functional and structural connectivity would be disrupted in subjects with At-Risk Mental State (ARMS) and would be associated with symptom severity and disease progression.

METHOD

We examined 87 ARMS and 37 healthy participants using both resting-state functional magnetic resonance imaging and diffusion tensor imaging. Group differences in SN functional and structural connectivity were examined using a seed-based approach and tract-based spatial statistics. Subject-level functional connectivity measures and diffusion indices of disrupted regions were correlated with CAARMS scores and compared between ARMS with and without transition to psychosis.

RESULTS

ARMS subjects exhibited reduced functional connectivity between the left ventral anterior insula and other SN regions. Reduced fractional anisotropy (FA) and axial diffusivity were also found along white-matter tracts in close proximity to regions of disrupted functional connectivity, including frontal-striatal-thalamic circuits and the cingulum. FA measures extracted from these disrupted white-matter regions correlated with individual symptom severity in the ARMS group. Furthermore, functional connectivity between the bilateral insula and FA at the forceps minor were further reduced in subjects who transitioned to psychosis after 2 years.

CONCLUSIONS

Our findings support the insular dysconnectivity of the proximal SN hypothesis in the early stages of psychosis. Further developed, the combined structural and functional SN assays may inform the prognosis of persons at-risk for psychosis.

The dysconnection hypothesis (2016)

Twenty years have passed since the dysconnection hypothesis was first proposed (Friston and Frith, 1995; Weinberger, 1993). In that time, neuroscience has witnessed tremendous advances: we now live in a world of non-invasive neuroanatomy, computational neuroimaging and the Bayesian brain. The genomics era has come and gone. Connectomics and large-scale neuroinformatics initiatives are emerging everywhere. So where is the dysconnection hypothesis now? This article considers how the notion of schizophrenia as a dysconnection syndrome has developed - and how it has been enriched by recent advances in clinical neuroscience. In particular, we examine the dysconnection hypothesis in the context of (i) theoretical neurobiology and computational psychiatry; (ii) the empirical insights afforded by neuroimaging and associated connectomics - and (iii) how bottom-up (molecular biology and genetics) and top-down (systems biology) perspectives are converging on the mechanisms and nature of dysconnections in schizophrenia.

Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction

Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24)—each essential parameters in wavelet-based methods—on the estimated values of graph metrics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of graph metrics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.

Implicit Timing as the Missing Link between Neurobiological and Self Disorders in Schizophrenia?

Disorders of consciousness and the self are at the forefront of schizophrenia symptomatology. Patients are impaired in feeling themselves as the authors of their thoughts and actions. In addition, their flow of consciousness is disrupted, and thought fragmentation has been suggested to be involved in the patients' difficulties in feeling as being one unique, unchanging self across time. Both impairments are related to self disorders, and both have been investigated at the experimental level. Here we review evidence that both mechanisms of motor control and the temporal structure of signal processing are impaired in schizophrenia patients. Based on this review, we propose that the sequencing of action and perception plays a key role in the patients' impairments. Furthermore, the millisecond time scale of the disorders, as well as the impaired sequencing, highlights the cooperation between brain networks including the cerebellum, as proposed by Andreasen (1999). We examine this possibility in the light of recent knowledge on the anatomical and physiological properties of the cerebellum, its role in timing, and its involvement in known physiological impairments in patients with schizophrenia, e.g., resting states and brain dynamics. A disruption in communication between networks involving the cerebellum, related to known impairments in dopamine, glutamate and GABA transmission, may help to better explain why patients experience reduced attunement with the external world and possibly with themselves.

Distinct disruptions of resting-state functional brain networks in familial and sporadic schizophrenia

Clinical and brain structural differences have been reported between patients with familial and sporadic schizophrenia; however, little is known about the brain functional differences between the two subtypes of schizophrenia. Twenty-six patients with familial schizophrenia (PFS), 26 patients with sporadic schizophrenia (PSS) and 26 healthy controls (HC) underwent a resting-state functional magnetic resonance imaging. The whole-brain functional network was constructed and analyzed using graph theoretical approaches. Topological properties (including global, nodal and edge measures) were compared among the three groups. We found that PFS, PSS and HC exhibited common small-world architecture of the functional brain networks. However, at a global level, only PFS showed significantly lower normalized clustering coefficient, small-worldness, and local efficiency, indicating a randomization shift of their brain networks. At a regional level, PFS and PSS disrupted different neural circuits, consisting of abnormal nodes (increased or decreased nodal centrality) and edges (decreased functional connectivity strength), which were widely distributed throughout the entire brain. Furthermore, some of these altered network measures were significantly correlated with severity of psychotic symptoms. These results suggest that familial and sporadic schizophrenia had segregated disruptions in the topological organization of the intrinsic functional brain network, which may be due to different etiological contributions.

Enhanced spontaneous functional connectivity of the superior temporal gyrus in early deafness

Early auditory deprivation may drive the auditory cortex into cross-modal processing of non-auditory sensory information. In a recent study, we had shown that early deaf subjects exhibited increased activation in the superior temporal gyrus (STG) bilaterally during visual spatial working memory; however, the changes in the organization of the STG related spontaneous functional network, and their cognitive relevance are still unknown. To clarify this issue, we applied resting state functional magnetic resonance imaging on 42 early deafness (ED) and 40 hearing controls (HC). We also acquired the visual spatial and numerical n-back working memory (WM) information in these subjects. Compared with hearing subjects, the ED exhibited faster reaction time of visual WM tasks in both spatial and numerical domains. Furthermore, ED subjects exhibited significantly increased functional connectivity between the STG (especially of the right hemisphere) and bilateral anterior insula and dorsal anterior cingulated cortex. Finally, the functional connectivity of STG could predict visual spatial WM performance, even after controlling for numerical WM performance. Our findings suggest that early auditory deprivation can strengthen the spontaneous functional connectivity of STG, which may contribute to the cross-modal involvement of this region in visual working memory.

Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis

Research on an early detection of Mild Cognitive Impairment (MCI), a prodromal stage of Alzheimer's Disease (AD), with resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been of great interest for the last decade. Witnessed by recent studies, functional connectivity is a useful concept in extracting brain network features and finding biomarkers for brain disease diagnosis. However, it still remains challenging for the estimation of functional connectivity from rs-fMRI due to the inevitable high dimensional problem. In order to tackle this problem, we utilize a group sparse representation along with a structural equation model. Unlike the conventional group sparse representation method that does not explicitly consider class-label information, which can help enhance the diagnostic performance, in this paper, we propose a novel supervised discriminative group sparse representation method by penalizing a large within-class variance and a small between-class variance of connectivity coefficients. Thanks to the newly devised penalization terms, we can learn connectivity coefficients that are similar within the same class and distinct between classes, thus helping enhance the diagnostic accuracy. The proposed method also allows the learned common network structure to preserve the network specific and label-related characteristics. In our experiments on the rs-fMRI data of 37 subjects (12 MCI; 25 healthy normal control) with a cross-validation technique, we demonstrated the validity and effectiveness of the proposed method, showing the diagnostic accuracy of 89.19 % and the sensitivity of 0.9167.

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.

Hippocampal-Prefrontal Interactions in Cognition, Behavior and Psychiatric Disease

The hippocampus and prefrontal cortex (PFC) have long been known to play a central role in various behavioral and cognitive functions. More recently, electrophysiological and functional imaging studies have begun to examine how interactions between the two structures contribute to behavior during various tasks. At the same time, it has become clear that hippocampal-prefrontal interactions are disrupted in psychiatric disease and may contribute to their pathophysiology. These impairments have most frequently been observed in schizophrenia, a disease that has long been associated with hippocampal and prefrontal dysfunction. Studies in animal models of the illness have also begun to relate disruptions in hippocampal-prefrontal interactions to the various risk factors and pathophysiological mechanisms of the illness. The goal of this review is to summarize what is known about the role of hippocampal-prefrontal interactions in normal brain function and compare how these interactions are disrupted in schizophrenia patients and animal models of the disease. Outstanding questions for future research on the role of hippocampal-prefrontal interactions in both healthy brain function and disease states are also discussed.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach.

Inefficient Preparatory fMRI-BOLD Network Activations Predict Working Memory Dysfunctions in Patients with Schizophrenia

Patients with schizophrenia show abnormal dynamics and structure of temporally -coherent networks (TCNs) assessed using fMRI, which undergo adaptive shifts in preparation for a cognitively demanding task. During working memory (WM) tasks, patients with schizophrenia show persistent deficits in TCNs as well as EEG indices of WM. Studying their temporal relationship during WM tasks might provide novel insights into WM performance deficits seen in schizophrenia. Simultaneous EEG-fMRI data were acquired during the performance of a verbal Sternberg WM task with two load levels (load 2 and load 5) in 17 patients with schizophrenia and 17 matched healthy controls. Using covariance mapping, we investigated the relationship of the activity in the TCNs before the memoranda were encoded and EEG spectral power during the retention interval. We assessed four TCNs - default mode network (DMN), dorsal attention network (dAN), left and right working memory networks (WMNs) - and three EEG bands - theta, alpha, and beta. In healthy controls, there was a load-dependent inverse relation between DMN and frontal midline theta power and an anti-correlation between DMN and dAN. Both effects were not significantly detectable in patients. In addition, healthy controls showed a left-lateralized load-dependent recruitment of the WMNs. Activation of the WMNs was bilateral in patients, suggesting more resources were recruited for successful performance on the WM task. Our findings support the notion of schizophrenia patients showing deviations in their neurophysiological responses before the retention of relevant information in a verbal WM task. Thus, treatment strategies as neurofeedback -targeting prestates could be beneficial as task performance relies on the preparatory state of the brain.

Cognitive Enhancement Therapy Improves Resting-State Functional Connectivity in Early Course Schizophrenia

OBJECTIVE

Cognitive remediation is emerging as an effective psychosocial intervention for addressing untreated cognitive and functional impairments in persons with schizophrenia, and might achieve its benefits through neuroplastic changes in brain connectivity. This study seeks to examine the effects of cognitive enhancement therapy (CET) on fronto-temporal brain connectivity in a randomized controlled trial with individuals in the early course of schizophrenia.

METHOD

Stabilized, early course outpatients with schizophrenia or schizoaffective disorder (N = 41) were randomly assigned to CET (n = 25) or an active enriched supportive therapy (EST) control (n = 16) and treated for 2 years. Functional MRI data were collected annually, and pseudo resting-state functional connectivity analysis was used to examine differential changes in fronto-temporal connectivity between those treated with CET compared with EST.

RESULTS

Individuals receiving CET evidenced significantly less functional connectivity loss between the resting-state network and the left dorsolateral prefrontal cortex as well as significantly increased connectivity with the right insular cortex compared to EST (all corrected p < .01). These neural networks are involved in emotion processing and problem-solving. Increased connectivity with the right insula significantly mediated CET effects on improved emotion perception (z' = -1.96, p = .021), and increased connectivity with the left dorsolateral prefrontal cortex mediated CET-related improvements in emotion regulation (z' = -1.71, p = .052).

CONCLUSIONS

These findings provide preliminary evidence that CET, a psychosocial cognitive remediation intervention, may enhance connectivity between frontal and temporal brain regions implicated in problem-solving and emotion processing in service of cognitive enhancement in schizophrenia.

Effect of rs1344706 in the ZNF804A gene on the connectivity between the hippocampal formation and posterior cingulate cortex

ZNF804A is one of the most promising candidate genes for schizophrenia. Previous fMRI studies have repeatedly shown an association between SNP rs1344706 in this gene and the functional connectivity from the right dorsolateral prefrontal cortex (rDLPFC) to the left hippocampal formation (lHF) during the N-back task. However, the rDLPFC-lHF functional connectivity included several subconnections and it is not known whether rs1344706 plays the same role in these subconnections. This study addressed that question using both fMRI and DTI data of 87 subjects. First, we replicated the association between rs1344706 and the rDLPFC-lHF functional connectivity using our fMRI data from the N-back task. Second, we reconstructed fiber connections between rDLPFC and lHF using our DTI data, which included three subconnections: from lHF to posterior cingulate cortex (PCC), from PCC to anterior cingulated cortex (ACC), and from ACC to rDLPFC. We found that only the lHF-PCC tract showed significantly lower fractional anisotropy (FA) in risk allele homozygotes. Finally, we analyzed the fMRI data (from the N-back task and the resting state). Both consistently showed relatively lower lHF-PCC functional connectivity in risk allele homozygotes. Taken together, the disconnectivity of the lHF-PCC tract seems to be a plausible intermediate phenotype that links rs1344706 and schizophrenia.

Schizophrenia symptoms and brain network efficiency: A resting-state fMRI study

Schizophrenia is a condition marked by a disrupted brain functional network. In schizophrenia, the brain network is characterized by reduced distributed information processing efficiency; however, the correlation between information processing efficiency and the symptomatology of schizophrenia remains unclear. Few studies have examined path length efficiencies in schizophrenia. In this study, we examined small-world network metrics computed from resting state functional magnetic resonance imaging data collected from 49 patients with schizophrenia and 28 healthy people. We calculated brain network efficiency using graph theoretical analysis of the networks of brain areas, as defined by the Automated Anatomical Labeling parcellation scheme, and investigated efficiency correlations by using the 5-factor model of psychopathology, which considers the various domains of schizophrenic symptoms and might also consider discrete pathogenetic processes. The global efficiency of the resting schizophrenic brains was lower than that of the healthy controls, but local efficiency did not differ between the groups. The severity of psychopathology, negative symptoms, and depression and anxiety symptoms were correlated with global efficiency in schizophrenic brains. The severity of psychopathology was correlated with increased network efficiency from short-range connections, but not networks from long-range connections. Our findings indicate that schizophrenic psychopathology is correlated with brain network information processing efficiency.

Variability of structurally constrained and unconstrained functional connectivity in schizophrenia

Spatial variation in connectivity is an integral aspect of the brain's architecture. In the absence of this variability, the brain may act as a single homogenous entity without regional specialization. In this study, we investigate the variability in functional links categorized on the basis of the presence of direct structural paths (primary) or indirect paths mediated by one (secondary) or more (tertiary) brain regions ascertained by diffusion tensor imaging. We quantified the variability in functional connectivity using an unbiased estimate of unpredictability (functional connectivity entropy) in a neuropsychiatric disorder where structure-function relationship is considered to be abnormal; 34 patients with schizophrenia and 32 healthy controls underwent DTI and resting state functional MRI scans. Less than one-third (27.4% in patients, 27.85% in controls) of functional links between brain regions were regarded as direct primary links on the basis of DTI tractography, while the rest were secondary or tertiary. The most significant changes in the distribution of functional connectivity in schizophrenia occur in indirect tertiary paths with no direct axonal linkage in both early (P=0.0002, d=1.46) and late (P=1×10(-17), d=4.66) stages of schizophrenia, and are not altered by the severity of symptoms, suggesting that this is an invariant feature of this illness. Unlike those with early stage illness, patients with chronic illness show some additional reduction in the distribution of connectivity among functional links that have direct structural paths (P=0.08, d=0.44). Our findings address a critical gap in the literature linking structure and function in schizophrenia, and demonstrate for the first time that the abnormal state of functional connectivity preferentially affects structurally unconstrained links in schizophrenia. It also raises the question of a continuum of dysconnectivity ranging from less direct (structurally unconstrained) to more direct (structurally constrained) brain pathways underlying the progressive clinical staging and persistence of schizophrenia.

Nodal centrality of functional network in the differentiation of schizophrenia

A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.

Dysfunctional resting-state connectivities of brain regions with structural deficits in drug-naive first-episode schizophrenia adolescents

OBJECTIVE

Individuals with adolescent-onset schizophrenia (AOS) are a subgroup of patients who present clinical symptoms between 13 and 18years of age. Little is known about neurodevelopmental abnormalities in this patient population. The present study was to examine possible resting-state dysfunctional connectivity of brain regions with altered gray matter volume in AOS.

METHODS

Gray matter volume was investigated by voxel-based morphometry (VBM) analysis. Resting-state functional connectivity analysis was used to examine the correlations between regions with structural deficits and the remaining regions.

RESULTS

Thirty-seven first-episode schizophrenia adolescents and 30 healthy controls were enrolled. Compared to the controls, the patients showed significantly decreased gray matter volumes in the right superior temporal gyrus (STG) and middle temporal gyrus (MTG) (ps<0.05). With the right STG as seed, significantly reduced connectivities were found within the frontal-temporal networks in the patient group (ps<0.05). With the right MTG as seed, the patient group showed significantly reduced connectivities in the default-mode networks and visual networks (ps<0.05). Compared to significant correlations in the controls (p=0.02), the patients had no observed correlations between functional connectivity of the right STG and gray matter volume of this region. Significant positive correlations were found between functional connectivity of the right STG with the left middle frontal gyrus and the Positive and Negative Syndrome Scale total scores (p=0.048) after controlling the confounding variables.

CONCLUSIONS

These findings show dysfunctional resting-state connectivities of the right STG and MTG with decreased gray matter volume in adolescents with AOS, suggesting that neurodevelopmental abnormalities may be present in AOS.

Converging models of schizophrenia--Network alterations of prefrontal cortex underlying cognitive impairments

The prefrontal cortex (PFC) and its connections with other brain areas are crucial for cognitive function. Cognitive impairments are one of the core symptoms associated with schizophrenia, and manifest even before the onset of the disorder. Altered neural networks involving PFC contribute to cognitive impairments in schizophrenia. Both genetic and environmental risk factors affect the development of the local circuitry within PFC as well as development of broader brain networks, and make the system vulnerable to further insults during adolescence, leading to the onset of the disorder in young adulthood. Since spared cognitive functions correlate with functional outcome and prognosis, a better understanding of the mechanisms underlying cognitive impairments will have important implications for novel therapeutics for schizophrenia focusing on cognitive functions. Multidisciplinary approaches, from basic neuroscience to clinical studies, are required to link molecules, circuitry, networks, and behavioral phenotypes. Close interactions among such fields by sharing a common language on connectomes, behavioral readouts, and other concepts are crucial for this goal.

Association of cerebrospinal fluid tap-related oscillatory activity and shunt outcome in idiopathic normal-pressure hydrocephalus

BACKGROUND

Idiopathic normal-pressure hydrocephalus (iNPH) is a neuropsychiatric syndrome characterized by the clinical triad of gait disturbance, urinary dysfunction, and cognitive impairment. The aim of the present study was to find specific EEG patterns associated with shunt response in iNPH.

METHODS

Twenty five iNPH patients (10 shunt responders and 15 non-responders) were enrolled in this study. We performed current source density (CSD) analysis in several frequency bands (delta: 2-4 Hz, theta: 4-8 Hz, alpha: 8-13 Hz, beta: 13-30 Hz, gamma: 30-60 Hz) using exact Low Resolution Brain Electromagnetic Tomography (eLORETA). CSD distribution was compared between shunt responders and non-responders for each frequency band before and after CSF tap test.

RESULTS

Shunt responders showed increased gamma CSD in the left temporal cortex before CSF tapping relative to non-responders. However, after CSF tapping, shunt response was associated with significantly higher CSDs in several frequency bands, specifically theta, alpha, beta and gamma, involving mainly the frontal and temporal areas. Using eLORETA analysis, we were able to identify cortical oscillatory activity before and after CSF tap test related to clinical recovery due to shunt operation in iNPH.

CONCLUSION

Our findings support and extend the results of previous studies examining the effects of CSF tap test and shunt operation in patients with iNPH, possibly indicating electrophysiological features of shunt response in this disease. These findings warrant future studies to use EEG for prediction of shunt response in iNPH.

Mapping pathological changes in brain structure by combining T1- and T2-weighted MR imaging data

Introduction

A workflow based on the ratio between standardized T1-weighted (T1-w) and T2-weighted (T2-w) MR images has been proposed as a new tool to study brain structure. This approach was previously used to map structural properties in the healthy brain. Here, we evaluate whether the T1-w/T2-w approach can support the assessment of structural impairments in the diseased brain. We use schizophrenia data to demonstrate the potential clinical utility of the technique.

Methods

We analyzed T1-w and T2-w images of 36 schizophrenic patients and 35 age-matched controls. These were collected for the Function Biomedical Informatics Research Network (fBIRN) collaborative project, which had an IRB approval and followed the HIPAA guidelines. We computed T1-w/T2-w images for each individual and compared intensities in schizophrenic and control groups on a voxel-wise basis, as well as in regions of interest (ROIs).

Results

Our results revealed that the T1-w/T2-w image permits to discriminate brain regions showing group-level differences between patients and controls with greater accuracy than conventional T1-w and T2-w images. Both the ROIs and the voxel-wise analysis showed globally reduced gray and white matter values in patients compared to controls. Significantly reduced values were found in regions such as insula, primary auditory cortex, hippocampus, inferior longitudinal fasciculus, and inferior fronto-occipital fasciculus.

Conclusion

Our findings were consistent with previous meta-analyses in schizophrenia corroborating the hypothesis of a potential “disconnection” syndrome in conjunction with structural alterations in local gray matter regions. Overall, our study suggested that the T1-w/T2-w technique permits to reliably map structural differences between the brains of patients and healthy individuals.

Abnormal resting state FMRI activity predicts processing speed deficits in first-episode psychosis

Little is known regarding the neuropsychological significance of resting state functional magnetic resonance imaging (rs-fMRI) activity early in the course of psychosis. Moreover, no studies have used different approaches for analysis of rs-fMRI activity and examined gray matter thickness in the same cohort. In this study, 41 patients experiencing a first-episode of psychosis (including N=17 who were antipsychotic drug-naive at the time of scanning) and 41 individually age- and sex-matched healthy volunteers completed rs-fMRI and structural MRI exams and neuropsychological assessments. We computed correlation matrices for 266 regions-of-interest across the brain to assess global connectivity. In addition, independent component analysis (ICA) was used to assess group differences in the expression of rs-fMRI activity within 20 predefined publicly available templates. Patients demonstrated lower overall rs-fMRI global connectivity compared with healthy volunteers without associated group differences in gray matter thickness assessed within the same regions-of-interest used in this analysis. Similarly, ICA revealed worse rs-fMRI expression scores across all 20 networks in patients compared with healthy volunteers, with posthoc analyses revealing significant (p<0.05; corrected) abnormalities within the caudate nucleus and planum temporale. Worse processing speed correlated significantly with overall lower global connectivity using the region-of-interest approach and lower expression scores within the planum temporale using ICA. Our findings implicate dysfunction in rs-fMRI activity in first-episode psychosis prior to extensive antipsychotic treatment using different analytic approaches (in the absence of concomitant gray matter structural differences) that predict processing speed.

Three approaches to investigating functional compromise to the default mode network after traumatic axonal injury

The default mode network (DMN) is a reliably elicited functional neural network with potential clinical implications. Its discriminant and prognostic utility following traumatic axonal injury (TAI) have not been previously investigated. The present study used three approaches to analyze DMN functional connectedness, including a whole-brain analysis [A1], network-specific analysis [A2], and between-node (edge) analysis [A3]. The purpose was to identify the utility of each method in distinguishing between healthy and brain-injured individuals, and determine whether observed differences have clinical significance. Resting-state fMRI was acquired from 25 patients with TAI and 17 healthy controls. Patients were scanned 6-11 months post-injury, and functional and neurocognitive outcomes were assessed the same day. Using all three approaches, TAI subjects revealed significantly weaker functional connectivity (FC) than controls, and binary logistic regressions demonstrated all three approaches have discriminant value. Clinical outcomes were not correlated with FC using any approach. Results suggest that compromise to the functional connectedness of the DMN after TAI can be identified using resting-state FC; however, the degree of functional compromise to this network, as measured in this study, may not have clinical implications in chronic TAI.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.