Anterior cingulate cortex-related connectivity in first-episode schizophrenia: a spectral dynamic causal modeling study with functional magnetic resonance imaging

Dysregulation of myelination-related genes in schizophrenia

Schizophrenic individuals display disrupted myelination patterns, altered oligodendrocyte distribution, and abnormal oligodendrocyte morphology. Schizophrenia is linked with dysregulation of a variety of genes involved in oligodendrocyte function and myelin production. Single-nucleotide polymorphisms (SNPs) and rare mutations in myelination-related genes are observed in certain schizophrenic populations, representing potential genetic risk factors. Downregulation of myelination-related RNAs and proteins, particularly in frontal and limbic regions, is consistently associated with the disorder across multiple studies. These findings support the notion that disruptions in myelination may contribute to the cognitive and behavioral impairments experienced in schizophrenia, although further evidence of causation is needed.

Astrocyte-secreted neurocan controls inhibitory synapse formation and function

Astrocytes strongly promote the formation and maturation of synapses by secreted proteins. Several astrocyte-secreted synaptogenic proteins controlling excitatory synapse development were identified; however, those that induce inhibitory synaptogenesis remain elusive. Here, we identify neurocan as an astrocyte-secreted inhibitory synaptogenic protein. After secretion from astrocytes, neurocan is cleaved into N- and C-terminal fragments. We found that these fragments have distinct localizations in the extracellular matrix. The neurocan C-terminal fragment localizes to synapses and controls cortical inhibitory synapse formation and function. Neurocan knockout mice lacking the whole protein or only its C-terminal synaptogenic domain have reduced inhibitory synapse numbers and function. Through super-resolution microscopy, in vivo proximity labeling by secreted TurboID, and astrocyte-specific rescue approaches, we discovered that the synaptogenic domain of neurocan localizes to somatostatin-positive inhibitory synapses and strongly regulates their formation. Together, our results unveil a mechanism through which astrocytes control circuit-specific inhibitory synapse development in the mammalian brain.

Functional connectivity and glutamate levels of the medial prefrontal cortex in schizotypy are related to sensory amplification in a probabilistic reasoning task

The circular inference (CI) computational model assumes a corruption of sensory data by prior information and vice versa, leading at the extremes to 'see what we expect' (through prior amplification) and/or to 'expect what we see' (through sensory amplification). Although a CI mechanism has been reported in a schizophrenia population, it has not been investigated in individuals experiencing psychosis-like experiences, such as people with high schizotypy traits. Furthermore, the neurobiological basis of CI, such as the link between hierarchical amplifications, excitatory neurotransmission, and resting state functional connectivity (RSFC), remains untested. The participants included in the present study consisted of a subsample of those recruited in a study previously published by our group, Kozhuharova et al. (2021b). We included 36 participants with High (n=18) and Low (n=18) levels of schizotypy who completed a probabilistic reasoning task (the Fisher task) for which individual confidence levels were obtained and fitted to the CI model. Participants also underwent a 1H-Magnetic Resonance Spectroscopy (MRS) scan to measure medial prefrontal cortex (mPFC) glutamate metabolite levels, and a functional Magnetic Resonance Imaging (fMRI) scan to measure RSFC of the medial prefrontal cortex (mPFC). People with high levels of schizotypy exhibited changes in CI parameters, altered cortical excitatory neurotransmission and RSFC that were all associated with sensory amplification. Our findings capture a multimodal signature of CI that is observable in people early in the psychosis spectrum.

Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.

Astrocyte-Secreted Neurocan Controls Inhibitory Synapse Formation and Function

Astrocytes strongly promote the formation and maturation of synapses by secreted proteins. To date, several astrocyte-secreted synaptogenic proteins controlling different stages of excitatory synapse development have been identified. However, the identities of astrocytic signals that induce inhibitory synapse formation remain elusive. Here, through a combination of in vitro and in vivo experiments, we identified Neurocan as an astrocyte-secreted inhibitory synaptogenic protein. Neurocan is a chondroitin sulfate proteoglycan that is best known as a protein localized to the perineuronal nets. However, Neurocan is cleaved into two after secretion from astrocytes. We found that the resulting N- and C-terminal fragments have distinct localizations in the extracellular matrix. While the N-terminal fragment remains associated with perineuronal nets, the Neurocan C-terminal fragment localizes to synapses and specifically controls cortical inhibitory synapse formation and function. Neurocan knockout mice lacking the whole protein or only its C-terminal synaptogenic region have reduced inhibitory synapse numbers and function. Through super-resolution microscopy and in vivo proximity labeling by secreted TurboID, we discovered that the synaptogenic domain of Neurocan localizes to somatostatin-positive inhibitory synapses and strongly regulates their formation. Together, our results unveil a mechanism through which astrocytes control circuit-specific inhibitory synapse development in the mammalian brain.

Baseline white matter function predicts short-term treatment response in first-episode schizophrenia

BACKGROUND AND PURPOSE

The detection and characterization of functional activities in the gray matter of schizophrenia (SZ) have been widely explored. However, the relationship between resting-state functional signals in the white matter of first-episode SZ and short-term treatment response remains unclear.

METHODS

Thirty-six patients with first-episode SZ and 44 matched healthy controls were recruited in this study. Patients were classified as nonresponders and responders based on response to antipsychotic medication during a single hospitalization. The fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) of white matter were calculated. The relationships between functional changes and clinical features were analyzed. In addition, voxel-based morphometry was performed to analyze the white matter volume.

RESULTS

One-way analysis of variance showed significant differences of fALFF and ReHo in the left posterior thalamic radiation and left cingulum (hippocampus) in the patient group, and the areas were regarded as seeds. The FC was calculated between seeds and other white matter networks. Compared with responders, nonresponders showed significantly increased FC between the left cingulum (hippocampus) and left posterior thalamic radiation, splenium of corpus callosum, and left tapetum, and were associated with the changes of clinical assessment. However, there was no difference in white matter volume between groups.

CONCLUSION

Our work provides a novel insight that psycho-neuroimaging-based white matter function holds promise for influencing the clinical diagnosis and treatment of SZ.

Functional dysconnectivity of anterior cingulate subregions in schizophrenia and psychotic and nonpsychotic bipolar disorder

Aberrant resting-state functional connectivity (FC) of anterior cingulate cortex (ACC) has been implicated in the pathophysiology of schizophrenia and bipolar disorder (BP). This study investigated the subregional FC of ACC across schizophrenia and psychotic (PBP) and nonpsychotic BP (NPBP) and the relationship between brain functional alterations and clinical manifestations. A total of 174 first-episode medication-naive patients with schizophrenia (FES), 80 patients with PBP, 77 patients with NPBP and 173 demographically matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. Brain-wide FC of ACC subregions was computed for each individual, and compared between the groups. General intelligence was evaluated using the short version of the Wechsler Adult Intelligence Scale. Relationships between FC and various clinical and cognitive variables were estimated using the skipped correlation. The FES, PBP and NPBP groups showed differing connectivity patterns in the left caudal, dorsal and perigenual ACC. Transdiagnostic dysconnectivity was found in the subregional ACC associated with cortical, limbic, striatal and cerebellar regions. Disorder-specific dysconnectivity in FES was identified between the left perigenual ACC and bilateral orbitofrontal cortex, and the left caudal ACC coupling with the default mode network (DMN) and visual processing region was correlated with psychotic symptoms. In the PBP group, FC between the left dorsal ACC and the right caudate was correlated with psychotic symptoms, and FC connected with the DMN was associated with affective symptoms. The current findings confirmed that subregional ACC dysconnectivity could be a key transdiagnostic feature and associated with differing clinical symptomology across schizophrenia and PBP.

Resting-State Brain Activity Dysfunctions in Schizophrenia and Their Associations with Negative Symptom Domains: An fMRI Study

The aim of the present study was to examine the neurobiological correlates of the two negative symptom domains of schizophrenia, the Motivational Deficit domain (including avolition, anhedonia, and asociality) and the Expressive Deficit domain (including blunted affect and alogia), focusing on brain areas that are most commonly found to be associated with negative symptoms in previous literature. Resting-state (rs) fMRI data were analyzed in 62 subjects affected by schizophrenia (SZs) and 46 healthy controls (HCs). The SZs, compared to the HCs, showed higher rs brain activity in the right inferior parietal lobule and the right temporoparietal junction, and lower rs brain activity in the right dorsolateral prefrontal cortex, the bilateral anterior dorsal cingulate cortex, and the ventral and dorsal caudate. Furthermore, in the SZs, the rs brain activity in the left orbitofrontal cortex correlated with negative symptoms (r = -0.436, p = 0.006), in particular with the Motivational Deficit domain (r = -0.424, p = 0.002), even after controlling for confounding factors. The left ventral caudate correlated with negative symptoms (r = -0.407, p = 0.003), especially with the Expressive Deficit domain (r = -0.401, p = 0.003); however, these results seemed to be affected by confounding factors. In line with the literature, our results demonstrated that the two negative symptom domains might be underpinned by different neurobiological mechanisms.

Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia

BACKGROUND AND HYPOTHESIS

Disrupted control of brain state transitions may contribute to the diverse dysfunctions of cognition, emotion, and behavior that are fundamental to schizophrenia. Control theory provides the rationale for evaluating brain state transitions from a controllability perspective, which may help reveal the brain mechanism for clinical features such as cognitive control deficits associated with schizophrenia. We hypothesized that brain controllability would be altered in patients with schizophrenia, and that controllability of brain networks would be related to clinical symptomatology.

STUDY DESIGN

Controllability measurements of functional brain networks, including average controllability and modal controllability, were calculated and compared between 125 first-episode never-treated patients with schizophrenia and 133 healthy controls (HCs). Associations between controllability metrics and clinical symptoms were evaluated using sparse canonical correlation analysis.

STUDY RESULTS

Compared to HCs, patients showed significantly increased average controllability (PFDR = .023) and decreased modal controllability (PFDR = .023) in dorsal anterior cingulate cortex (dACC). General psychopathology symptoms and positive symptoms were positively correlated with average controllability in regions of default mode network and negatively associated with average controllability in regions of sensorimotor, dorsal attention, and frontoparietal networks.

CONCLUSIONS

Our findings suggest that altered controllability of functional activity in dACC may play a critical role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations. The demonstration of associations of functional controllability with psychosis symptoms suggests that the identified alterations in average controllability of brain function may contribute to the severity of acute psychotic illness in schizophrenia.

Neurobiological Features of Posttraumatic Stress Disorder (PTSD) and Their Role in Understanding Adaptive Behavior and Stress Resilience

Posttraumatic stress disorder (PTSD) has been impacting the functioning of a large number of people in military activities and victims of violence for many generations. However, investments in research aiming to understand the neurobiological aspects of the disorder started relatively late, around the last third of the 20th century. The development of neuroimaging methods has greatly supported further understanding of the structural and functional changes in the re-organization processes of brains with PTSD. This helps to better explain the severity and evolution of behavioral symptoms, and opens the possibilities for identifying individual preexisting structural characteristics that could increase symptom severity and the risk of development. Here, we review the advances in neuroanatomical research on these adaptations in PTSD and discuss how those modifications in prefrontal and anterior cingulate circuitry impact the severity and development of the disorder, detaching the research from an amygdalocentric perspective. In addition, we investigate existing and contradictory evidence regarding the preexisting neurobiological features found mostly in twin studies and voxel-based morphometry (VBM) reports.

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

Effective Connectivity of the Hippocampus Can Differentiate Patients with Schizophrenia from Healthy Controls: A Spectral DCM Approach

We applied spectral dynamic causal modelling (Friston et al. in Neuroimage 94:396–407. 10.1016/j.neuroimage.2013.12.009, 2014) to analyze the effective connectivity differences between the nodes of three resting state networks (i.e. default mode network, salience network and dorsal attention network) in a dataset of 31 male healthy controls (HC) and 25 male patients with a diagnosis of schizophrenia (SZ). Patients showed increased directed connectivity from the left hippocampus (LHC) to the: dorsal anterior cingulate cortex (DACC), right anterior insula (RAI), left frontal eye fields and the bilateral inferior parietal sulcus (LIPS & RIPS), as well as increased connectivity from the right hippocampus (RHC) to the: bilateral anterior insula (LAI & RAI), right frontal eye fields and RIPS. In SZ, negative symptoms predicted the connectivity strengths from the LHC to: the DACC, the left inferior parietal sulcus (LIPAR) and the RHC, while positive symptoms predicted the connectivity strengths from the LHC to the LIPAR and from the RHC to the LHC. These results reinforce the crucial role of hippocampus dysconnectivity in SZ pathology and its potential as a biomarker of disease severity.

Fronto-thalamic structural and effective connectivity and delusions in schizophrenia: a combined DTI/DCM study

BACKGROUND

Schizophrenia (SZ) is a complex disorder characterized by a range of behavioral and cognitive symptoms as well as structural and functional alterations in multiple cortical and subcortical structures. SZ is associated with reduced functional network connectivity involving core regions such as the anterior cingulate cortex (ACC) and the thalamus. However, little is known whether effective coupling, the directed influence of one structure over the other, is altered during rest in the ACC-thalamus network.

METHODS

We collected resting-state fMRI and diffusion-weighted MRI data from 18 patients and 20 healthy controls. We analyzed fronto-thalamic effective connectivity using dynamic causal modeling for cross-spectral densities in a network consisting of the ACC and the left and right medio-dorsal thalamic regions. We studied structural connectivity using fractional anisotropy (FA).

RESULTS

We found decreased coupling strength from the right thalamus to the ACC and from the right thalamus to the left thalamus, as well as increased inhibitory intrinsic connectivity in the right thalamus in patients relative to controls. ACC-to-left thalamus coupling strength correlated with the Positive and Negative Syndrome Scale (PANSS) total positive syndrome score and with delusion score. Whole-brain structural analysis revealed several tracts with reduced FA in patients, with a maximum decrease in white matter tracts containing fronto-thalamic and cingulo-thalamic fibers.

CONCLUSIONS

We found altered effective and structural connectivity within the ACC-thalamus network in SZ. Our results indicate that ACC-thalamus network activity at rest is characterized by reduced thalamus-to-ACC coupling. We suggest that positive symptoms may arise as a consequence of compensatory measures to imbalanced fronto-thalamic coupling.

Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.

Triple network hypothesis-related disrupted connections in schizophrenia: A spectral dynamic causal modeling analysis with functional magnetic resonance imaging

OBJECTIVE

The symptom-related neurobiology characteristic of schizophrenia in the brain from a network perspective is still poorly understood, leading to a lack of potential biologically-based markers and difficulty identifying therapeutic targets. We aim to test the dysregulated cross-network interactions among the Salience Network (SN), Central Executive Network (CEN) and Default Mode Network (DMN) and how they contributed to different symptoms in schizophrenia patients.

METHODS

We examined network interactions among the SN, CEN and DMN in 76 patients with schizophrenia vs. 80 well-matched controls using dynamic causal modeling (DCM). We further analyzed the relation between network dynamics and Positive and Negative Syndrome Scale (PANSS).

RESULTS

We observed that the DMN, CEN and SN across healthy controls and schizophrenia patients showed several similarities within or between-network pattern in the resting state. Comparing schizophrenia to controls, SN-centered cross-network interactions were most significantly reduced. Crucially, the strength of connections from CEN subnetwork 1 to DMN subnetwork 1 was positively correlated with the Positive Score of PANSS. The connection from the DMN subnetwork 2 to CEN subnetwork 2 was negatively correlated with the Negative Score of PANSS.

CONCLUSIONS

Our study provides strong evidence for the dysregulation among SN, CEN and DMN in a triple-network perspective in schizophrenia. The connection between DMN and CEN could be clinically-relevant neurobiological signature of schizophrenia symptoms. Our study indicated that the description of brain triple network hypothesis could be a novel and possible bio-marker for schizophrenia.

Advanced Signal Processing Methods for Characterization of Schizophrenia

OBJECTIVE

Schizophrenia is a severe mental disorder associated with nerobiological deficits. Auditory oddball P300 have been found to be one of the most consistent markers of schizophrenia. The goal of this study is to find quantitative features that can objectively distinguish patients with schizophrenia (SCZs) from healthy controls (HCs) based on their recorded auditory odd-ball P300 electroencephalogram (EEG) data.

METHODS

Using EEG dataset, we develop a machine learning (ML) algorithm to distinguish 57 SCZs from 66 HCs. The proposed ML algorithm has three steps. In the first step, a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on EEG signals to extract source waveforms from 30 specified brain regions. In the second step, a method for estimating effective connectivity, referred to as symbolic transfer entropy (STE), is applied to the source waveforms. In the third step the ML algorithm is applied to the STE connectivity matrix to determine whether a set of features can be found that successfully discriminate SCZ from HC.

RESULTS

The findings revealed that the SCZs have significantly higher effective connectivity compared to HCs and the selected STE features could achieve an accuracy of 92.68%, with a sensitivity of 92.98% and specificity of 92.42%.

CONCLUSION

The findings imply that the extracted features are from the regions that are mainly affected by SCZ and can be used to distinguish SCZs from HCs.

SIGNIFICANCE

The proposed ML algorithm may prove to be a promising tool for the clinical diagnosis of schizophrenia.

Abnormalities of effective connectivity and white matter microstructure in the triple network in patients with schizophrenia

Disorganized communication among large-scale brain networks, especially in the salience network, default mode network and central executive network, have been consistently reported in schizophrenia (SZ) patients. However, abnormal patterns of the effective connectivity and abnormalities in the white matter of these networks remains unclear in patients with SZ. Fifty-six SZ patients and fifty-five healthy controls were enrolled in the present study and underwent resting state functional magnetic resonance and diffusion tensor imaging. Twelve main nodes within the triple networks were defined by independent components analysis. Effective connectivity between these main nodes was computed using Granger causality analysis. Voxel-based analysis of the diffusion tensor imaging data was conducted to explore white matter changes. The SZ patients showed abnormal effective connectivity between the anterior cingulate cortex and the dorsolateral prefrontal cortex. The abnormal white matter showed decreased fractional anisotropy localized in the bilateral anterior corona radiate and left superior long fasciculus in patients with SZ. These findings shed light on the importance of the triple network in the pathogenesis of SZ, which may facilitate the understanding of SZ.

Neuropsychiatric aspects of Parkinson disease psychopharmacology: Insights from circuit dynamics

Parkinson disease (PD) is a neurodegenerative disorder with a complex pathophysiology characterized by the progressive loss of dopaminergic neurons within the substantia nigra. Persons with PD experience several motoric and neuropsychiatric symptoms. Neuropsychiatric features of PD include depression, anxiety, psychosis, impulse control disorders, and apathy. In this chapter, we will utilize the National Institutes of Mental Health Research Domain Criteria (RDoC) to frame and integrate observations from two prevailing disease constructions: neurotransmitter anomalies and circuit physiology. When there is available evidence, we posit how unified translational observations may have clinical relevance and postulate importance outside of PD. Finally, we review the limited evidence available for pharmacologic management of these symptoms.

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.

Using resting-state DMN effective connectivity to characterize the neurofunctional architecture of empathy

Neuroimaging studies in social neuroscience have largely relied on functional connectivity (FC) methods to characterize the functional integration between different brain regions. However, these methods have limited utility in social-cognitive studies that aim to understand the directed information flow among brain areas that underlies complex psychological processes. In this study we combined functional and effective connectivity approaches to characterize the functional integration within the Default Mode Network (DMN) and its role in self-perceived empathy. Forty-two participants underwent a resting state fMRI scan and completed a questionnaire of dyadic empathy. Independent Component Analysis (ICA) showed that higher empathy scores were associated with an increased contribution of the medial prefrontal cortex (mPFC) to the DMN spatial mode. Dynamic causal modelling (DCM) combined with Canonical Variance Analysis (CVA) revealed that this association was mediated indirectly by the posterior cingulate cortex (PCC) via the right inferior parietal lobule (IPL). More specifically, in participants with higher scores in empathy, the PCC had a greater effect on bilateral IPL and the right IPL had a greater influence on mPFC. These results highlight the importance of using analytic approaches that address directed and hierarchical connectivity within networks, when studying complex psychological phenomena, such as empathy.

Prediction of early response to overall treatment for schizophrenia: A functional magnetic resonance imaging study

INTRODUCTION

Treatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response.

OBJECTS

We aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia.

METHODS

Amplitude of low-frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFF_ratio , was evaluated based on diagnostic tests and clinical correlates.

RESULTS

In the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non-responders. ALFF_ratio of responders before treatment was significantly higher than that of non-responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFF_ratio to distinguish responders from non-responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset.

CONCLUSIONS

Baseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho-neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia.

Altered Effective Connectivity in the Default Network of the Brains of First-Episode, Drug-Naïve Schizophrenia Patients With Auditory Verbal Hallucinations

Although the default mode network (DMN) is known to be abnormal in schizophrenia (SZ) patients with auditory verbal hallucinations (AVHs), it is still unclear whether AVHs that occur in SZ are associated with certain information flow in the DMN. This study collected resting-state functional magnetic resonance imaging data from 28 first-episode, drug-naïve SZ patients with AVHs, 20 SZ patients without AVHs, and 38 healthy controls. We used Granger causality analysis (GCA) to examine effective connectivity (EC) of two hub regions [posterior cingulate cortex (PCC) and anteromedial prefrontal cortex (aMPFC)] within the DMN. We used two-sample t-tests to compare the difference in EC between the two patient groups, and used Spearman correlation analysis to characterize the relationship between imaging findings and clinical assessments. The GCA revealed that, compared with the non-AVHs group, EC decreased from aMPFC to left inferior temporal gyrus (ITG) and from PCC to left cerebellum posterior lobe, ITG, and right middle frontal gyrus in SZ patients with AVHs. We also found significant correlations between clinical assessments and mean strengths of connectivity from aMPFC to left ITG and from PCC to left ITG. Moreover, receiver operating characteristic analysis revealed that the above-mentioned effective connectivities had a diagnostic value for distinguishing SZ patients with AVHs from non-AVHs patients. These findings suggest that AVHs in SZ patients may be associated with the aberrant information flows of the DMN, and the left ITG may probably serve as a potential biomarker for the neural mechanisms underlying AVHs in SZ patients.

Glutamatergic Deficits in Schizophrenia - Biomarkers and Pharmacological Interventions within the Ketamine Model

Background:

The observation that N-methyl-D-aspartate glutamate receptor (NMDAR) antagonists such as ketamine transiently induce schizophrenia-like positive, negative and cognitive symptoms has led to a paradigm shift from dopaminergic to glutamatergic dysfunction in pharmacological models of schizophrenia. NMDAR hypofunction can explain many schizophrenia symptoms directly due to excitatory-to-inhibitory (E/I) imbalance, but also dopaminergic dysfunction itself. However, so far no new drug targeting the NMDAR has been successfully approved. In the search for possible biomarkers it is interesting that ketamine-induced psychopathological changes in healthy participants were accompanied by altered electro-(EEG), magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) signals.

Methods:

We systematically searched PubMed/Medline and Web of Knowledge databases (January 2006 to July 2017) to identify EEG/MEG and fMRI studies of the ketamine model of schizophrenia with human subjects. The search strategy identified 209 citations of which 46 articles met specified eligibility criteria.

Results:

In EEG/MEG studies, ketamine induced changes of event-related potentials, such as the P300 potential and the mismatch negativity, similar to alterations observed in schizophrenia patients. In fMRI studies, alterations of activation were observed in different brain regions, most prominently within the anterior cingulate cortex and limbic structures as well as task-relevant brain regions. These alterations were accompanied by changes in functional connectivity, indicating a balance shift of the underlying brain networks. Pharmacological treatments did alter ketamine-induced changes in EEG/MEG and fMRI studies to different extents.

Conclusion:

This review highlights the potential applicability of the ketamine model for schizophrenia drug development by offering the possibility to assess the effect of pharmacological agents on schizophrenia-like symptoms and to find relevant neurophysiological and neuroimaging biomarkers.

The Time Course of Dorsal and Rostral-Ventral Anterior Cingulate Cortex Activity in the Emotional Stroop Experiment Reveals Valence and Arousal Aberrant Modulation in Patients with Schizophrenia

This paper aims to investigate the temporal dynamics within the dorsal anterior cingulate cortex (dACC) and the rostral-ventral (rv) ACC during the interaction of emotional valence and arousal with cognitive control in patients with Schizophrenia (SZ). Although cognitive deficits in SZ are highly relevant and emotional disturbances are common, the temporal relationship of brain regions involved in the interaction of emotional and cognitive processing in SZ is yet to be determined. To address this issue, the reaction time (RT), event-related potential (ERP) and temporal dynamics of the dACC and rvACC activity were compared between SZ subjects and healthy controls (HC), using a modified emotional Stroop experiment (with factors namely congruence, arousal and valence). EEG was recorded with 64 channels and source localisation was performed using the sLORETA software package. We observed slower initial increase and lower peaks of time course activity within the dACC and rvACC in the SZ group. In this particular group, the dACC activity during late negativity was negatively correlated with a significantly higher RT in the high arousal conflict condition. In contrast to HC subjects, at the N450 window, there was no significant valence (ERP and rvACC ROI) modulation effect in the SZ subjects. Using high density EEG and source localisation, it was possible to distinguish various disturbances within the dACC and rvACC in patients with SZ, during emotion–cognition processing.

Resting-state functional brain networks in first-episode psychosis: A 12-month follow-up study

INTRODUCTION

Schizophrenia is increasingly conceived as a disorder of brain network connectivity and organization. However, reports of network abnormalities during the early illness stage of psychosis are mixed. This study adopted a data-driven whole-brain approach to investigate functional connectivity and network architecture in a first-episode psychosis cohort relative to healthy controls and whether functional network properties changed abnormally over a 12-month period in first-episode psychosis.

METHODS

Resting-state functional connectivity was performed at two time points. At baseline, 29 first-episode psychosis individuals and 30 healthy controls were assessed, and at 12 months, 14 first-episode psychosis individuals and 20 healthy controls completed follow-up. Whole-brain resting-state functional connectivity networks were mapped for each individual and analyzed using graph theory to investigate whether network abnormalities associated with first-episode psychosis were evident and whether functional network properties changed abnormally over 12 months relative to controls.

RESULTS

This study found no evidence of abnormal resting-state functional connectivity or topology in first-episode psychosis individuals relative to healthy controls at baseline or at 12-months follow-up. Furthermore, longitudinal changes in network properties over a 12-month period did not significantly differ between first-episode psychosis individuals and healthy control. Network measures did not significantly correlate with symptomatology, duration of illness or antipsychotic medication.

CONCLUSIONS

This is the first study to show unaffected resting-state functional connectivity and topology in the early psychosis stage of illness. In light of previous literature, this suggests that a subgroup of first-episode psychosis individuals who have a neurotypical resting-state functional connectivity and topology may exist. Our preliminary longitudinal analyses indicate that there also does not appear to be deterioration in these network properties over a 12-month period. Future research in a larger sample is necessary to confirm our longitudinal findings.

Disease Definition for Schizophrenia by Functional Connectivity Using Radiomics Strategy

Specific biomarker reflecting neurobiological substrates of schizophrenia (SZ) is required for its diagnosis and treatment selection of SZ. Evidence from neuroimaging has implicated disrupted functional connectivity in the pathophysiology. We aimed to develop and validate a method of disease definition for SZ by resting-state functional connectivity using radiomics strategy. This study included 2 data sets collected with different scanners. A total of 108 first-episode SZ patients and 121 healthy controls (HCs) participated in the current study, among which 80% patients and HCs (n = 183) and 20% (n = 46) were selected for training and testing in intra-data set validation and 1 of the 2 data sets was selected for training and the other for testing in inter-data set validation, respectively. Functional connectivity was calculated for both groups, features were selected by Least Absolute Shrinkage and Selection Operator (LASSO) method, and the clinical utility of its features and the generalizability of effects across samples were assessed using machine learning by training and validating multivariate classifiers in the independent samples. We found that the accuracy of intra-data set training was 87.09% for diagnosing SZ patients by applying functional connectivity features, with a validation in the independent replication data set (accuracy = 82.61%). The inter-data set validation further confirmed the disease definition by functional connectivity features (accuracy = 83.15% for training and 80.07% for testing). Our findings demonstrate a valid radiomics approach by functional connectivity to diagnose SZ, which is helpful to facilitate objective SZ individualized diagnosis using quantitative and specific functional connectivity biomarker.

Do patients with different psychiatric disorders show altered social decision-making? A systematic review of ultimatum game experiments in clinical populations

BACKGROUND

Impairments in social functioning are a common feature of psychiatric disorders. Game paradigms pose a unique way for studying how people make decisions in interpersonal contexts. In the last decade, researchers have started to use these paradigms to study social decision-making in patients with psychiatric disorders.

PURPOSE

The aim of this systematic literature review is to summarise the currently available evidence on the behaviour of patients with psychiatric disorders in the commonly used Ultimatum Game (UG).

METHOD

A systematic literature search including MEDLINE, PsycINFO, PSYNDEXplus Tests, PSYNDEXPLUS Literature, EBM Reviews-Cochrane Central Register of Controlled Trials, Embase and PASCAL was performed via the Ovid interface.

RESULTS

We found evidence for alterations in UG behaviour for patients with frontotemporal dementia, schizophrenia, affective disorders, alcohol, cocaine, heroin and 3,4-methylenedioxymethamphetamine consumption, alcohol dependence, anxiety disorders, borderline personality disorder, autism, Tourette syndrome and oppositional defiant disorder.

CONCLUSION

There is some evidence that different psychiatric disorders might go along with alterations in social decision-making. However, in general, data are currently limited and studies are hard to compare due to differences in methodologies.

Sulforaphane Augments Glutathione and Influences Brain Metabolites in Human Subjects: A Clinical Pilot Study

Schizophrenia and other neuropsychiatric disorders await mechanism-associated interventions. Excess oxidative stress is increasingly appreciated to participate in the pathophysiology of brain disorders, and decreases in the major antioxidant, glutathione (GSH), have been reported in multiple studies. Technical cautions regarding the estimation of oxidative stress-related changes in the brain via imaging techniques have led investigators to explore peripheral GSH as a possible pathological signature of oxidative stress-associated brain changes. In a preclinical model of GSH deficiency, we found a correlation between whole brain and peripheral GSH levels. We found that the naturally occurring isothiocyanate sulforaphane increased blood GSH levels in healthy human subjects following 7 days of daily oral administration. In parallel, we explored the potential influence of sulforaphane on brain GSH levels in the anterior cingulate cortex, hippocampus, and thalamus via 7-T magnetic resonance spectroscopy. A significant positive correlation between blood and thalamic GSH post- and pre-sulforaphane treatment ratios was observed, in addition to a consistent increase in brain GSH levels in response to treatment. This clinical pilot study suggests the value of exploring relationships between peripheral GSH and clinical/neuropsychological measures, as well as the influences sulforaphane has on functional measures that are altered in neuropsychiatric disorders.

Identifying first-episode drug naïve patients with schizophrenia with or without auditory verbal hallucinations using whole-brain functional connectivity: A pattern analysis study

Many studies have focused on patients with schizophrenia with or without auditory verbal hallucinations (AVHs), but due to the complexity of schizophrenia, biologically based diagnosis of patients with schizophrenia remains unsolved. The objectives of this study are to classify between first-episode drug-naïve patients with schizophrenia and healthy controls, and to classify between patients with and without AVHs. Resting state fMRI data from 41 patients with schizophrenia (22 with and 19 without AVHs) and 23 normal controls (NC) were included to compute functional connectivity between brain regions. Classifiers based on support vector machine (SVM) were developed to classify patients with schizophrenia from NC, as well as between the two subgroups of patients. The classification accuracy was evaluated with a leave-one-out cross-validation (LOOCV) strategy. The accuracy in discriminating both subgroups of patients from NC was 81.3%, with 92.0% (sensitivity) and 65.2% (specificity) for the patients and NC, respectively. The classification accuracy in discriminating patients with and without AVHs was 75.6%, with 77.3% (sensitivity) and 73.9% (specificity) for patients with and without AVHs, respectively. The results suggest that functional connectivity provided good discriminative power not only for identifying patients with schizophrenia among NC, but also in discriminating patients with schizophrenia with and without AVHs.

Verbal learning and hippocampal dysfunction in schizophrenia: A meta-analysis

This meta-analysis summarizes research examining whether deficits in verbal learning are related to bilateral hippocampal volume reductions in patients with or at risk for schizophrenia and in healthy controls. 17 studies with 755 patients with schizophrenia (SCZ), 232 Genetic High Risk (GHR) subjects and 914 healthy controls (HC) were included. Pooled correlation coefficients were calculated between hemisphere (left, right or total) and type of recall (immediate or delayed) for each diagnostic group individually (SCZ, GHR and HC). In SCZ, left and right hippocampal volume positively correlated with immediate (r=0.256, 0.230) and delayed (r=0.132, 0.231) verbal recall. There was also a correlation between total hippocampal volume and delayed recall (r=0.233). None of these correlations were significant in healthy controls. There was however, a positive correlation between left hippocampal volume and immediate recall in the GHR group (r=0.356). The results suggest that hippocampal volume affects immediate and delayed verbal learning capacity in schizophrenia and provides further evidence of hippocampal dysfunction in the pathophysiology of schizophrenia.

From Shortage to Surge: A Developmental Switch in Hippocampal-Prefrontal Coupling in a Gene-Environment Model of Neuropsychiatric Disorders

Cognitive deficits represent a major burden of neuropsychiatric disorders and result in part from abnormal communication within hippocampal–prefrontal circuits. While it has been hypothesized that this network dysfunction arises during development, long before the first clinical symptoms, experimental evidence is still missing. Here, we show that pre-juvenile mice mimicking genetic and environmental risk factors of disease (dual-hit GE mice) have poorer recognition memory that correlates with augmented coupling by synchrony and stronger directed interactions between prefrontal cortex and hippocampus. The network dysfunction emerges already during neonatal development, yet it initially consists in a diminished hippocampal theta drive and consequently, a weaker and disorganized entrainment of local prefrontal circuits in discontinuous oscillatory activity in dual-hit GE mice when compared with controls. Thus, impaired maturation of functional communication within hippocampal–prefrontal networks switching from hypo- to hyper-coupling may represent a mechanism underlying the pathophysiology of cognitive deficits in neuropsychiatric disorders.

Aberrant perfusion and its connectivity within default mode network of first-episode drug-naïve schizophrenia patients and their unaffected first-degree relatives

Neural substrates behind schizophrenia (SZ) and its heritability mediated by brain function are largely unknown. Cerebral blood flow (CBF), as a biomarker of activation in the brain, reflects the neuronal metabolism, and is promisingly used to detect cerebral alteration thereby shedding light on the features of individuals at high genetic risk. We performed a cross-sectional functional magnetic resonance imaging (MRI) study enrolling 45 first-episode drug-naïve patients with SZ, 32 unaffected first-degree relatives of these patients, and 51 healthy controls (HCs). We examined CBF, CBF connectivity, and CBF topological properties. SZ patients showed increased CBF in the left medial superior frontal gyrus and right precuneus compared with HCs, and decreased CBF in the left middle temporal gyrus compared with their relatives. Furthermore, unaffected relatives revealed higher level of CBF pronounced in regions within default mode network (DMN). Both SZ patients and their relatives exhibited dysconnectivity patterns. Notably, as for the network properties, unaffected relatives were with an intermediate level between SZ patients and HCs in the local efficiency and global efficiency. Our findings demonstrate the aberrant CBF of areas within DMN and the CBF connectivity pattern might be a familial feature in the brain of first-episode SZ patients and their relatives.

Functional dysconnectivity of the limbic loop of frontostriatal circuits in first-episode, treatment-naive schizophrenia

Frontostriatal circuits dysfunction has been implicated in the etiology and psychopathology of patients with schizophrenia (SZ). However, few studies have investigated SZ-related functional connectivity (FC) alterations in discrete frontostriatal circuits and their relationship with pathopsychology in first-episode schizophrenia (FESZ). The goal of this study was to identify dysfunctions in discrete frontostriatal circuits that are associated with key features of FESZ. To this end, a case-control, cross-sectional study was conducted, wherein resting-state (RS) functional magnetic resonance (fMRI) data were collected from 37 treatment-naïve FESZ patients and 29 healthy control (HC) subjects. Seed-based FC analyses were performed by placing six bilateral pairs of seeds within a priori defined subdivisions of the striatum. We observed significantly decreased FC for the FESZ group relative to the HC group [p < .05, family-wise error (FWE)-corrected] in the limbic loop, but not in the sensorimotor or associative loops, of frontostriatal circuitry. Moreover, bilaterally decreased inferior ventral striatum/nucleus accumbens (VSi)-dorsal anterior cingulate cortex (dACC) FC within the limbic loop correlated inversely with overall FESZ symptom severity and the disorganization factor score of PANSS. These findings provide new insight into the role of frontostriatal limbic loop hypoconnectivity in early-stage schizophrenia pathology and suggest potential novel therapeutic targets.

Nicotinic modulation of salience network connectivity and centrality in schizophrenia

Although functional abnormalities of the salience network are associated with schizophrenia, the acute effects of nicotine on its function and network dynamics during the resting state in patients are poorly understood. In this study, the effects of a 7 mg nicotine patch (vs. placebo) on salience network connectivity were examined in 17 patients with schizophrenia and 19 healthy subjects. We hypothesized abnormal connectivity between the salience network and other major networks (e.g. executive network) in patients under placebo administration and amelioration of this difference after nicotine. We also examined effects of nicotine on betweenness centrality (a measure of the influence of a region on information transfer throughout the brain) and local efficiency (a measure of local information transfer) of the network. A hybrid independent component analysis (ICA)/seed-based connectivity approach was implemented in which the salience network was extracted by ICA and cortical network peaks (anterior cingulate cortex (ACC), left and right insula) were used as seeds for whole-brain seed-to-voxel connectivity analysis. Significant drug X diagnosis interactions were observed between the ACC seed and superior parietal lobule and ventrolateral prefrontal cortex. A significant interaction effect was also observed between the left insula seed and middle cingulate cortex. During placebo conditions, abnormal connectivity predicted negative symptom severity and lower global functioning in patients. A significant drug X diagnosis interaction was also observed for betweenness centrality of the ACC. These results suggest that nicotine may target abnormalities in functional connectivity between salience and executive network areas in schizophrenia as well as affect the ability of the salience network to act as an integrator of global signaling in the disorder.

Changes in Neuronal Oscillations Accompany the Loss of Hippocampal LTP that Occurs in an Animal Model of Psychosis

The first-episode of psychosis is followed by a transient time-window of ca. 60 days during which therapeutic interventions have a higher likelihood of being effective than interventions that are started with a greater latency. This suggests that, in the immediate time-period after first-episode psychosis, functional changes occur in the brain that render it increasingly resistant to intervention. The precise mechanistic nature of these changes is unclear, but at the cognitive level, sensory and hippocampus-based dysfunctions become increasingly manifest. In an animal model of first-episode psychosis that comprises acute treatment of rats with the irreversible N-methyl-D-aspartate receptor (NMDAR)-antagonist, MK801, acute but also chronic deficits in long-term potentiation (LTP) and spatial memory occur. Neuronal oscillations, especially in the form of information transfer through θ and γ frequency oscillations are an intrinsic component of normal information processing in the hippocampus. Changes in θ-γ coupling and power are known to accompany deficits in hippocampal plasticity. Here, we examined whether changes in δ, θ, α, β and γ oscillations, or θ-γ coupling accompany the chronic loss of LTP that is observed in the MK801-animal model of psychosis. One and 4 weeks after acute systemic treatment of adult rats with MK801, a potent loss of hippocampal in vivo LTP was evident compared to vehicle-treated controls. Overall, the typical pattern of θ-γ oscillations that are characteristic for the successful induction of LTP was altered. In particular, θ-power was lower and an uncoupling of θ-γ oscillations was evident in MK801-treated rats. The alterations in network oscillations that accompany LTP deficits in this animal model may comprise a mechanism through which disturbances in sensory information processing and hippocampal function occur in psychosis. These data suggest that the hippocampus is likely to comprise a very early locus of functional change after instigation of a first-episode psychosis-like state in rodents.

Abnormal Effective Connectivity in the Brain is Involved in Auditory Verbal Hallucinations in Schizophrenia

Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations (AVHs) in schizophrenia (SZ) remains unclear. In this study, we used stochastic dynamic causal modeling (sDCM) to quantify connections among the left dorsolateral prefrontal cortex (inner speech monitoring), auditory cortex (auditory processing), hippocampus (memory retrieval), thalamus (information filtering), and Broca's area (language production) in 17 first-episode drug-naïve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging. Finally, we performed receiver operating characteristic (ROC) analysis and correlation analysis between image measures and symptoms. sDCM revealed an increased sensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca's area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVH-specific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.

Dynamic Causal Modeling of Hippocampal Links within the Human Default Mode Network: Lateralization and Computational Stability of Effective Connections

The purpose of this paper was to study causal relationships between left and right hippocampal regions (LHIP and RHIP, respectively) within the default mode network (DMN) as represented by its key structures: the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and the inferior parietal cortex of left (LIPC) and right (RIPC) hemispheres. Furthermore, we were interested in testing the stability of the connectivity patterns when adding or deleting regions of interest. The functional magnetic resonance imaging (fMRI) data from a group of 30 healthy right-handed subjects in the resting state were collected and a connectivity analysis was performed. To model the effective connectivity, we used the spectral Dynamic Causal Modeling (DCM). Three DCM analyses were completed. Two of them modeled interaction between five nodes that included four DMN key structures in addition to either LHIP or RHIP. The last DCM analysis modeled interactions between four nodes whereby one of the main DMN structures, PCC, was excluded from the analysis. The results of all DCM analyses indicated a high level of stability in the computational method: those parts of the winning models that included the key DMN structures demonstrated causal relations known from recent research. However, we discovered new results as well. First of all, we found a pronounced asymmetry in LHIP and RHIP connections. LHIP demonstrated a high involvement of DMN activity with preponderant information outflow to all other DMN regions. Causal interactions of LHIP were bidirectional only in the case of LIPC. On the contrary, RHIP was primarily affected by inputs from LIPC, RIPC, and LHIP without influencing these or other DMN key structures. For the first time, an inhibitory link was found from MPFC to LIPC, which may indicate the subjects’ effort to maintain a resting state. Functional connectivity data echoed these results, though they also showed links not reflected in the patterns of effective connectivity. We suggest that such lateralized architecture of hippocampal connections may be related to lateralization phenomena in verbal and spatial domains documented in human neurophysiology, neuropsychology, and neurolinguistics.

Anterior Cingulate Cortico-Hippocampal Dysconnectivity in Unaffected Relatives of Schizophrenia Patients: A Stochastic Dynamic Causal Modeling Study

Familial risk plays a significant role in the etiology of schizophrenia (SZ). Many studies using neuroimaging have demonstrated structural and functional alterations in relatives of SZ patients, with significant results found in diverse brain regions involving the anterior cingulate cortex (ACC), caudate, dorsolateral prefrontal cortex (DLPFC), and hippocampus. This study investigated whether unaffected relatives of first episode SZ differ from healthy controls (HCs) in effective connectivity measures among these regions. Forty-six unaffected first-degree relatives of first episode SZ patients-according to the DSM-IV-were studied. Fifty HCs were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI). We used stochastic dynamic causal modeling (sDCM) to estimate the directed connections between the left ACC, right ACC, left caudate, right caudate, left DLPFC, left hippocampus, and right hippocampus. We used Bayesian parameter averaging (BPA) to characterize the differences. The BPA results showed hyperconnectivity from the left ACC to right hippocampus and hypoconnectivity from the right ACC to right hippocampus in SZ relatives compared to HCs. The pattern of anterior cingulate cortico-hippocampal connectivity in SZ relatives may be a familial feature of SZ risk, appearing to reflect familial susceptibility for SZ.

Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools

The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website.

Medial Prefrontal and Anterior Insular Connectivity in Early Schizophrenia and Major Depressive Disorder: A Resting Functional MRI Evaluation of Large-Scale Brain Network Models

Anomalies in the medial prefrontal cortex, anterior insulae, and large-scale brain networks associated with them have been proposed to underlie the pathophysiology of schizophrenia and major depressive disorder (MDD). In this study, we examined the connectivity of the medial prefrontal cortices and anterior insulae in 24 healthy controls, 24 patients with schizophrenia, and 24 patients with MDD early in illness with seed-based resting state functional magnetic resonance imaging analysis using Statistical Probability Mapping. As hypothesized, reduced connectivity was found between the medial prefrontal cortex and the dorsal anterior cingulate cortex and other nodes associated with directed effort in patients with schizophrenia compared to controls while patients with MDD had reduced connectivity between the medial prefrontal cortex and ventral prefrontal emotional encoding regions compared to controls. Reduced connectivity was found between the anterior insulae and the medial prefrontal cortex in schizophrenia compared to controls, but contrary to some models emotion processing regions failed to demonstrate increased connectivity with the medial prefrontal cortex in MDD compared to controls. Although, not statistically significant after correction for multiple comparisons, patients with schizophrenia tended to demonstrate decreased connectivity between basal ganglia-thalamocortical regions and the medial prefrontal cortex compared to patients with MDD, which might be expected as these regions effect action. Results were interpreted to support anomalies in nodes associated with directed effort in schizophrenia and nodes associated with emotional encoding network in MDD compared to healthy controls.

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

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