Functional brain networks in treatment-resistant schizophrenia

Aberrant connectivity in the hippocampus, bilateral insula and temporal poles precedes treatment resistance in first-episode psychosis: a prospective resting-state functional magnetic resonance imaging study with connectivity concordance mapping

Functional connectivity resting-state functional magnetic resonance imaging has been proposed to predict antipsychotic treatment response in schizophrenia. However, only a few prospective studies have examined baseline resting-state functional magnetic resonance imaging data in drug-naïve first-episode schizophrenia patients with regard to subsequent treatment response. Data-driven approaches to conceptualize and measure functional connectivity patterns vary broadly, and model-free, voxel-wise, whole-brain analysis techniques are scarce. Here, we apply such a method, called connectivity concordance mapping to resting-state functional magnetic resonance imaging data acquired from an Asian sample (n = 60) with first-episode psychosis, prior to pharmaceutical treatment. Using a longitudinal design, 12 months after the resting-state functional magnetic resonance imaging, we measured and classified patients into two groups based on psychometric testing: treatment responsive and treatment resistant. Next, we compared the two groups' connectivity concordance maps that were derived from the resting-state functional magnetic resonance imaging data at baseline. We have identified consistently higher functional connectivity in the treatment-resistant group in a network including the left hippocampus, bilateral insula and temporal poles. These data-driven novel findings can help researchers to consider new regions of interest and facilitate biomarker development in order to identify treatment-resistant schizophrenia patients early, in advance of treatment and at the time of their first psychotic episode.

Serum S100B protein and white matter changes in schizophrenia before and after medication

Schizophrenia patients have abnormalities in white matter (WM) integrity in brain regions. S100B has been shown to be a marker protein for glial cells. The atypical antipsychotics have neuroprotective effects on the brain. It is not clear whether antipsychotics can induce S100B changes and improve symptoms by protecting oligodendrocytes. To investigate WM and S100B changes and associations and determine the effect of quetiapine on WM and S100B in schizophrenia patients, we determined serum S100B levels with solid phase immunochromatography and fractional anisotropy（FA）values of 36 patients and 40 healthy controls. Patients exhibited significantly higher serum concentrations of S100B and decreased FA values in left postcentral，right superior frontal，right thalamus, and left inferior occipital gyrus, while higher in right temporal cortex WM compared with healthy controls. Following treatment with quetiapine, patients had decreased S100B and higher FA values in right cerebellum，right superior frontal，right thalamus, and left parietal cortex，and decreased FA values in right temporal cortex WM compared with pre-treatment values. Furthermore, S100B were negatively correlated with PANSS positive scores and positively correlated with FA values in the left postcentral cortex. In addition，the percentage change in FA values in the right temporal cortex was positively correlated with the percentage change in the S100B, percentage reduction in PANSS scores, and percentage reduction in PANSS-positive scores. Our findings demonstrated abnormalities in S100B and WM microstructure in patients with schizophrenia. These abnormalities may be partly reversed by quetiapine treatment.

Effect of intracranial electrical stimulation on dynamic functional connectivity in medically refractory epilepsy

Objective

The objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators.

Methods

The time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests.

Results

DFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset.

Conclusion

Our findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy.

Altered functional connectivity in anterior cingulate cortex subregions in treatment-resistant schizophrenia patients

BACKGROUND

The anterior cingulate cortex (ACC) plays a key role in motor control, attention, and cognitive control. It is well established that schizophrenia is associated with impaired functional connectivity (FC) of the ACC pathway. So far, however, there has been little discussion about the ACC subregions function in patients with treatment-resistant schizophrenia (TRS).

AIM

This study aims to characterize resting-state functional connectivity (rs-FC) profiles of ACC subregions in patients with TRS. The association between these FC and clinical symptoms, neurocognitive function, and grey matter volume (GMV) was studied as well.

METHODS

A total of 81 patients with schizophrenia (40 patients with TRS = 40, 41 patients with non-treatment-resistant schizophrenia (NTRS)) and 39 age- and gender-matched healthy controls (HC) were enrolled, and underwent structural magnetic resonance imaging (MRI), resting-state functional MRI (rs-fMRI), clinical evaluation. The ACC subregions, including subgenual ACC (sgACC), pregenual ACC (pgACC), and dorsal ACC (dACC), were selected as seed regions from the automated anatomical labelling atlas 3 (AAL3). The GMV of the ACC subregions were calculated and seed-based FC maps for all ACC subregions were generated and compared between the TRS and NTRS, HC group. Additionally, correlations between altered FC and clinical symptoms, GMV, and neurocognitive functions in the TRS patients were explored.

RESULT

Compared with HC, increased FC was observed in TRS and NTRS groups between bilateral sgACC and left cuneus, right cuneus, and left lingual gyrus, while decreased FC was found between bilateral dACC and thalamic. Additionally, compared with NTRS, the TRS group showed increased FC between bilateral dACC and right cuneus and decreased FC between bilateral dACC and thalamic. The TRS group showed decreased GMV in all ACC subregions than the HC group, and there is no significant difference between the TRS group and the NTRS group.

CONCLUSION

The findings in this study suggest that disrupted FC of subregional ACC has the potential as a marker for TRS. The dysconnectivity of bilateral dACC- right cuneus and bilateral dACC-thalamus, are likely to be the unique FC profiles of TRS. These findings further our understanding of the neurobiological impairments in TRS.

Resting-state functional MRI in treatment-resistant schizophrenia

BACKGROUND

Abnormalities in brain regions involved in the pathophysiology of schizophrenia (SCZ) may present insight into individual clinical symptoms. Specifically, functional connectivity irregularities may provide potential biomarkers for treatment response or treatment resistance, as such changes can occur before any structural changes are visible. We reviewed resting-state functional magnetic resonance imaging (rs-fMRI) findings from the last decade to provide an overview of the current knowledge on brain functional connectivity abnormalities and their associations to symptoms in treatment-resistant schizophrenia (TRS) and ultra-treatment-resistant schizophrenia (UTRS) and to look for support for the dysconnection hypothesis.

METHODS

PubMed database was searched for articles published in the last 10 years applying rs-fMRI in TRS patients, i.e., who had not responded to at least two adequate treatment trials with different antipsychotic drugs.

RESULTS

Eighteen articles were selected for this review involving 648 participants (TRS and control cohorts). The studies showed frontal hypoconnectivity before the initiation of treatment with CLZ or riluzole, an increase in frontal connectivity after riluzole treatment, fronto-temporal hypoconnectivity that may be specific for non-responders, widespread abnormal connectivity during mixed treatments, and ECT-induced effects on the limbic system.

CONCLUSION

Probably due to the heterogeneity in the patient cohorts concerning antipsychotic treatment and other clinical variables (e.g., treatment response, lifetime antipsychotic drug exposure, duration of illness, treatment adherence), widespread abnormalities in connectivity were noted. However, irregularities in frontal brain regions, especially in the prefrontal cortex, were noted which are consistent with previous SCZ literature and the dysconnectivity hypothesis. There were major limitations, as most studies did not differentiate between TRS and UTRS (i.e., CLZ-resistant schizophrenia) and investigated heterogeneous cohorts treated with mixed treatments (with or without CLZ). This is critical as in different subtypes of the disorder an interplay between dopaminergic and glutamatergic pathways involving frontal, striatal, and hippocampal brain regions in separate ways is likely. Better definitions of TRS and UTRS are necessary in future longitudinal studies to correctly differentiate brain regions underlying the pathophysiology of SCZ, which could serve as potential functional biomarkers for treatment resistance.

Graph-Theory-Based Degree Centrality Combined with Machine Learning Algorithms Can Predict Response to Treatment with Antipsychotic Medications in Patients with First-Episode Schizophrenia

Objectives

Schizophrenia (SCZ) is associated with disrupted functional brain connectivity, and antipsychotic medications are the primary and most commonly used treatment for schizophrenia. However, not all patients respond to antipsychotic medications.

Methods

The study is aimed at investigating whether the graph-theory-based degree centrality (DC), derived from resting-state functional MRI (rs-fMRI), can predict the treatment outcomes. rs-fMRI data from 38 SCZ patients were collected and compared with findings from 38 age- and gender-matched healthy controls (HCs). The patients were treated with antipsychotic medications for 16 weeks before undergoing a second rs-fMRI scan. DC data were processed using DPABI and SPM12 software.

Results

SCZ patients at baseline showed increased DC in the frontal and temporal gyrus, anterior cingulate cortex, and precuneus and reduced DC in bilateral subcortical gray matter structures. However, those abnormalities showed a clear renormalization after antipsychotic medication treatments. Support vector machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.2% (sensitivity 78.9%, specificity 89.5%, and area under the receiver operating characteristic curve (AUC) 0.925) for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the bilateral putamen, left inferior frontal gyrus, left middle occipital cortex, bilateral middle frontal gyrus, left cerebellum, left medial frontal gyrus, left inferior temporal gyrus, and left angular. Furthermore, the DC change within the bilateral putamen is negatively correlated with the symptom improvements after treatment.

Conclusions

Our study confirmed that graph-theory-based measures, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of antipsychotic medications.

Treatment-resistant schizophrenia: How far have we traveled?

Treatment-resistant schizophrenia is a lack of adequate response to antipsychotic medications resulting in incomplete functional and social recovery from the illness. Different definitions have been proposed for clinical practice and research work. Antipsychotics that are used in the management of schizophrenia mainly act on multiple dopaminergic pathways which are implicated in the development of symptoms of schizophrenia. Newer antipsychotics also are implicated to affect the serotonergic pathways. Clozapine is the only evidence-based treatment available for the management of treatment-resistant cases. Neurobiologically, there is a considerable overlap between treatment-resistant and treatment-responsive cases. The factors that are implicated in the evolution of treatment resistance are still not conclusive. These make the management of such patients a challenge. However, certain peculiarities of treatment-resistant schizophrenia have been identified which can guide us in the early identification and precise treatment of the treatment-resistant cases.

Changes in corticostriatal connectivity and striatal tissue iron associated with efficacy of clozapine for treatment‑resistant schizophrenia

RATIONALE

Though numerous studies demonstrate the superiority of clozapine (CLZ) for treatment of persistent psychotic symptoms that are characteristic of treatment-refractory schizophrenia (TRS), what remains unknown are the neural and molecular mechanisms underlying CLZ's efficacy. Recent work implicates increased corticostriatal functional connectivity as a marker of response to non-CLZ, dopamine (DA) D2-receptor blocking antipsychotic drugs. However, it is undetermined whether this connectivity finding also relates to CLZ's unique efficacy, or if response to CLZ is associated with changes in striatal DA functioning.

OBJECTIVE

In a cohort of 22 individuals with TRS, we examined response to CLZ in relation to the following: (1) change in corticostriatal functional connectivity; and (2) change in a magnetic resonance-based measure of striatal tissue iron (R2'), which demonstrates utility as a proxy measure for elements of DA functioning.

METHODS

Participants underwent scanning while starting CLZ and after 12 weeks of CLZ treatment. We used both cortical and striatal regions of interest to examine changes in corticostriatal interactions and striatal R2' in relation to CLZ response (% reduction of psychotic symptoms).

RESULTS

We first found that response to CLZ was associated with an increase in corticostriatal connectivity between the dorsal caudate and regions of the frontoparietal network (P < 0.05, corrected). Secondly, we observed no significant changes in striatal R2' across CLZ treatment.

CONCLUSION

Overall, these results indicate that changes in corticostriatal networks without gross shifts in striatal DA functioning underlies CLZ response. Our results provide novel mechanistic insight into response to CLZ treatment.

Dopaminergic dysfunction and excitatory/inhibitory imbalance in treatment-resistant schizophrenia and novel neuromodulatory treatment

Antipsychotic drugs are the mainstay in the treatment of schizophrenia. However, one-third of patients do not show adequate improvement in positive symptoms with non-clozapine antipsychotics. Additionally, approximately half of them show poor response to clozapine, electroconvulsive therapy, or other augmentation strategies. However, the development of novel treatment for these conditions is difficult due to the complex and heterogenous pathophysiology of treatment-resistant schizophrenia (TRS). Therefore, this review provides key findings, potential treatments, and a roadmap for future research in this area. First, we review the neurobiological pathophysiology of TRS, particularly the dopaminergic, glutamatergic, and GABAergic pathways. Next, the limitations of existing and promising treatments are presented. Specifically, this article focuses on the therapeutic potential of neuromodulation, including electroconvulsive therapy, repetitive transcranial magnetic stimulation, transcranial direct current stimulation, and deep brain stimulation. Finally, we propose multivariate analyses that integrate various perspectives of the pathogenesis, such as dopaminergic dysfunction and excitatory/inhibitory imbalance, thereby elucidating the heterogeneity of TRS that could not be obtained by conventional statistics. These analyses can in turn lead to a precision medicine approach with closed-loop neuromodulation targeting the detected pathophysiology of TRS.

Functional reconfiguration of cerebellum-cerebral neural loop in schizophrenia following electroconvulsive therapy

Recent evidence highlights the role of the cerebellum-cerebral loop in the pathophysiology of schizophrenia (SZ). Electroconvulsive therapy (ECT) is clinically applied to augment the effect of antipsychotic drugs. The study aims to address whether the cerebellum-cerebral loop is involved in the mechanisms of ECT's augmentation effect. Forty-two SZ patients and 23 healthy controls (HC) were recruited and scanned using resting-state functional MRI (rs-fMRI). Twenty-one patients received modified ECT plus antipsychotics (MSZ group), and 21 patients took antipsychotics only (DSZ group). All patients were re-scanned four weeks later. Brain functional network was constructed according to the graph theory. The sub-network exhibited longitudinal changes after ECT or medications were constructed. For the MSZ group, a sub-network involving default-mode network and cerebellum showed significant longitudinal changes. For the DSZ group, a different sub-network involving the thalamus, frontal and occipital cortex was found to be altered in the follow-up scan. In addition, the changing FC of the left cerebellar crus2 region was correlated with the changing scores of the psychotic symptoms only in the MSZ group but not in the DSZ group. In conclusion, the cerebral-cerebellum loop is possibly involved in the antipsychotic mechanisms of ECT for schizophrenia.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis

Previous computational models have related spontaneous resting-state brain activity with local excitatory–inhibitory balance in neuronal populations. However, how underlying neurotransmitter kinetics associated with E–I balance govern resting-state spontaneous brain dynamics remains unknown. Understanding the mechanisms by virtue of which fluctuations in neurotransmitter concentrations, a hallmark of a variety of clinical conditions, relate to functional brain activity is of critical importance. We propose a multiscale dynamic mean field (MDMF) model—a system of coupled differential equations for capturing the synaptic gating dynamics in excitatory and inhibitory neural populations as a function of neurotransmitter kinetics. Individual brain regions are modeled as population of MDMF and are connected by realistic connection topologies estimated from diffusion tensor imaging data. First, MDMF successfully predicts resting-state functional connectivity. Second, our results show that optimal range of glutamate and GABA neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level-dependent signals. Third, for predictive validity the network measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) from existing healthy and pathological brain network studies could be captured by simulated functional connectivity from an MDMF model.

How changes in neurotransmitter kinetics impact the organization of large-scale neurocognitive networks is an open question in neuroscience. Here, we propose a multiscale dynamic mean field (MDMF) model that incorporates biophysically realistic kinetic parameters of receptor binding in a dynamic mean field model and captures brain dynamics from the “whole brain.” MDMF could reliably reproduce the resting-state brain functional connectivity patterns. Further employing graph theoretic methods, MDMF could qualitatively explain the idiosyncrasies of network integration and segregation measures reported by previous clinical studies.

Aberrant triple-network connectivity patterns discriminate biotypes of first-episode medication-naive schizophrenia in two large independent cohorts

Schizophrenia is a complex disorder associated with aberrant brain functional connectivity. This study aims to demonstrate the relation of heterogeneous symptomatology in this disorder to distinct brain connectivity patterns within the triple-network model. The study sample comprised 300 first-episode antipsychotic-naive patients with schizophrenia (FES) and 301 healthy controls (HCs). At baseline, resting-state functional magnetic resonance imaging data were captured for each participant, and concomitant neurocognitive functions were evaluated outside the scanner. Clinical information of 49 FES in the discovery dataset were reevaluated at a 6-week follow-up. Differential features between FES and HCs were selected from triple-network connectivity profiles. Cutting-edge unsupervised machine learning algorithms were used to define patient subtypes. Clinical and cognitive variables were compared between patient subgroups. Two FES subgroups with differing triple-network connectivity profiles were identified in the discovery dataset and confirmed in an independent hold-out cohort. One patient subgroup appearing to have more severe clinical symptoms was distinguished by salience network (SN)-centered hypoconnectivity, which was associated with greater impairments in sustained attention. The other subgroup exhibited hyperconnectivity and manifested greater deficits in cognitive flexibility. The SN-centered hypoconnectivity subgroup had more persistent negative symptoms at the 6-week follow-up than the hyperconnectivity subgroup. The present study illustrates that clinically relevant cognitive subtypes of schizophrenia may be associated with distinct differences in connectivity in the triple-network model. This categorization may foster further analysis of the effects of therapy on these network connectivity patterns, which may help to guide therapeutic choices to effectively reach personalized treatment goals.

Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity

At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.

Progressive reconfiguration of resting-state brain networks as psychosis develops: Preliminary results from the North American Prodrome Longitudinal Study (NAPLS) consortium

Mounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P < 0.05). The change rates of network efficiency and network diversity were significantly correlated (P = 0.003), suggesting these changes may reflect shared neural mechanisms. In addition, change rates of global efficiency and node diversity were significantly correlated with change rate of cortical thinning in the prefrontal cortex in converters (P < 0.03) and could be predicted by visuospatial memory scores at baseline (P < 0.04). These results provide preliminary evidence for longitudinal reconfiguration of resting-state brain networks during psychosis development and suggest that decreased network efficiency, reflecting an increase in path length between nodes, and increased network diversity, reflecting a decrease in the consistency of functional network organization, may be implicated in the progression to full psychosis.

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.

Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia

Previous studies have reported that schizophrenia (SZ) patients showed selective reinforcement learning deficits and abnormal feedback-related event-related potential (ERP) components. However, how the brain networks and their topological properties evolve over time during transient feedback-related cognition processing in SZ patients has not been investigated so far. In this paper, using publicly available feedback-related ERP data which were recorded from SZ patients and healthy controls (HC) when they performed a reinforcement learning task, we carried out an event-related network analysis where topology of brain functional networks was characterized with some graph measures including clustering coefficient (C), global efficiency (E_global) and local efficiency (E_local) on a millisecond timescale. Our results showed that the brain functional networks displayed rapid rearrangements of topological properties during transient feedback-related cognition process for both two groups. More importantly, we found that SZ patients exhibited significantly reduced theta-band (time window of 170-350 ms after stimuli onset) brain functional connectivity strength, E_global, E_local and C in response to negative feedback stimuli compared to HC group. The network based statistic (NBS) analysis detected one significantly decreased theta-band subnetwork in SZ patients mainly involving in frontal-occipital and temporal-occipital connections compared to HC group. In addition, clozapine treatment seemed to greatly reduce theta-band power and topological measures of brain networks in SZ patients. Finally, the theta-band power, graph measures and functional connectivity were extracted to train a support vector machine classifier for classification of HC from SZ, or Cloz + SZ or Cloz- SZ, and a relatively good classification accuracy of 84.48%, 89.47% and 78.26% was obtained, respectively. The above results suggested a less optimal organization of theta-band brain network in SZ patients, and studying the topological parameters of brain networks evolve over time during transient feedback-related processing could be useful for understanding the pathophysiologic mechanisms underlying reinforcement learning deficits in SZ patients.

Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks

The sliding-window-based dynamic functional connectivity networks (D-FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) are effective methods for diagnosing various neurological diseases, including autism spectrum disorder (ASD). However, traditional D-FCNs are low-order networks based on pairwise correlation between brain regions, thus overlooking high-level interactions across multiple regions of interest (ROIs). Moreover, D-FCNs suffer from the temporal mismatching issue, i.e., subnetworks in the same temporal window do not have temporal correspondence across different subjects. To address the above problems, we first construct a novel high-order D-FCNs based on the principle of “correlation’s correlation” to further explore the higher level and more complex interaction relationships among multiple ROIs. Furthermore, we propose to use a central-moment method to extract temporal-invariance properties contained in either low- or high-order D-FCNs. Finally, we design and train an ensemble classifier by fusing the features extracted from conventional FCN, low-order D-FCNs, and high-order D-FCNs for the diagnosis of ASD and normal control subjects. Our method achieved the best ASD classification accuracy (83%), and our results revealed the features extracted from different networks fingerprinting the autistic brain at different connectional levels.

Clinical Course, Neurobiology and Therapeutic Approaches to Treatment Resistant Schizophrenia. Toward an Integrated View

Despite considerable psychotherapeutic advancement since the discovery of chlorpromazine, almost one third of patients with schizophrenia remain resistant to dopamine-blocking antipsychotics, and continue to be exposed to unwanted and often disabling side effects, but little if any clinical benefit. Even clozapine, the superior antipsychotic treatment, is ineffective in approximately half of these patients. Thus treatment resistant schizophrenia (TRS), continues to present a major therapeutic challenge to psychiatry. The main impediment to finding novel treatments is the lack of understanding of precise molecular mechanisms leading to TRS. Not only has the neurobiology been enigmatic for decades, but accurate and early detection of patients who are at risk of not responding to dopaminergic blockade remains elusive. Fortunately, recent work has started to unravel some of the neurobiological mechanisms underlying treatment resistance, providing long awaited answers, at least to some extent. Here we focus on the scientific advances in the field, from the clinical course of TRS to neurobiology and available treatment options. We specifically emphasize emerging evidence from TRS imaging and genetic literature that implicates dysregulation in several neurotransmitters, particularly dopamine and glutamate, and in addition genetic and neural alterations that concertedly may lead to the formation of TRS. Finally, we integrate available findings into a putative model of TRS, which may provide a platform for future studies in a bid to open the avenues for subsequent development of effective therapeutics.

Resting-state functional connectivity in treatment response and resistance in schizophrenia: A systematic review

BACKGROUND

Treatment-resistant schizophrenia (TRS) and treatment-responsive schizophrenia may exhibit distinct pathophysiology. Several functional magnetic resonance imaging (fMRI) studies have used resting-state functional connectivity analyses (rs-FC) in TRS patients to identify markers of treatment resistance. However, to date, existing findings have not been systematically evaluated.

METHODS

A systematic literature search using Embase, MEDLINE, PsycINFO, ProQuest, PUBMED, and Scopus was performed. The query sought fMRI articles investigating rs-FC in treatment response or resistance in patients with schizophrenia. Only studies that examined treatment response, operationalized as the explicit categorization of patients by their response to antipsychotic medication, were considered eligible. Pairwise comparisons between patient groups and controls were extracted from each study.

RESULTS

The search query identified 159 records. Ten studies met inclusion criteria. Five studies examined not TRS (NTRS), and 8 studies examined TRS. Differences in rs-FC analysis methodology precluded direct comparisons between studies. However, disruptions in areas involved in visual and auditory information processing were implicated in both patients with TRS and NTRS. Changes in connectivity with sensorimotor network areas tended to appear in the context of TRS but not NTRS. Moreover, there was some indication that this connectivity could be affected by clozapine.

CONCLUSIONS

Functional connectivity may provide clinically meaningful biomarkers of treatment response and resistance in schizophrenia. Studies generally identified similar areas of disruption, though methodological differences largely precluded direct comparison between disruption effects. Implementing data sharing as standard practice will allow future reviews and meta-analyses to identify rs-FC correlates of TRS.

Functional neuroimaging in treatment resistant schizophrenia: A systematic review

Despite the availability of several drugs, about 30% of patients with schizophrenia still fail to respond properly to a course of appropriate antipsychotic treatment. Functional neuroimaging studies have shown widespread patterns of altered activation and functional connectivity in treatment-resistant schizophrenia (TRS). The aim of the present study was to examine the available functional magnetic resonance imaging studies investigating TRS and to identify common patterns of altered brain function that could predict the lack of response to antipsychotic treatment in this disorder. Alterations of activation and functional connectivity in fronto-temporal, cortico-striatal, default mode network and salience networks, and of their interplay, were associated with TRS. Our findings support the notion that large-scale network alterations present in schizophrenia lie in a continuum within treatment response with the most severe dysfunction in TRS. Few studies with small sample size and without adequate control group limit the generalizability of current literature. Future controlled longitudinal studies are needed to identify neuroimaging biomarkers of pharmacotherapy response to inform individual treatment selection and facilitate early clinical response.

Treatment-Resistant Schizophrenia: Genetic and Neuroimaging Correlates

Schizophrenia is a severe neuropsychiatric disorder that affects approximately 0.5–1% of the population. Response to antipsychotic therapy is highly variable, and it is not currently possible to predict those patients who will or will not respond to antipsychotic medication. Furthermore, a high percentage of patients, approximately 30%, are classified as treatment-resistant (treatment-resistant schizophrenia; TRS). TRS is defined as a non-response to at least two trials of antipsychotic medication of adequate dose and duration. These patients are usually treated with clozapine, the only evidence-based pharmacotherapy for TRS. However, clozapine is associated with severe adverse events. For these reasons, there is an increasing interest to identify better targets for drug development of new compounds and to establish better biomarkers for existing medications. The ability of antipsychotics to improve psychotic symptoms is dependent on their antagonist and reverse agonist activities at different neuroreceptors, and some genetic association studies of TRS have focused on different pharmacodynamic factors. Some genetic studies have shown an association between antipsychotic response or TRS and neurodevelopment candidate genes, antipsychotic mechanisms of action (such as dopaminergic, serotonergic, GABAergic, and glutamatergic) or pharmacokinetic factors (i.e., differences in the cytochrome families). Moreover, there is a growing body of literature on the structural and functional neuroimaging research into TRS. Neuroimaging studies can help to uncover the underlying neurobiological reasons for such resistance and identify resistant patients earlier. Studies examining the neuropharmacological mechanisms of antipsychotics, including clozapine, can help to improve our knowledge of their action on the central nervous system, with further implications for the discovery of biomarkers and the development of new treatments. The identification of the underlying mechanisms of TRS is a major challenge for developing personalized medicine in the psychiatric field for schizophrenia treatment. The main goal of precision medicine is to use genetic and brain-imaging information to improve the safety, effectiveness, and health outcomes of patients via more efficiently targeted risk stratification, prevention, and tailored medication and treatment management approaches. The aim of this review is to summarize the state of art of pharmacogenetic, pharmacogenomic and neuroimaging studies in TRS.

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

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

A Pilot Study on Daily Intensive ECT over 8 Days Improved Positive and Negative Symptoms and General Psychopathology of Patients with Treatment-Resistant Schizophrenia up to 4 Weeks after Treatment

OBJECTIVE

For patients with treatment-resistant schizophrenia (TRS) who do not respond to drug therapy, electroconvulsive therapy (ECT) is often employed as an additional treatment. The aims of the present study were to investigate to what extent an 8-day daily ECT treatment might reduce symptoms of schizophrenia among patients with TRS both in the short term (end of the treatment) and medium term, that is 4 and 12 weeks after the treatment.

METHODS

Fourteen patients with TRS based on DSM-5 criteria took part in the present study. ECT consisted of daily sessions for 8 consecutive days. At baseline, at the end of the intervention, and 4 and 12 weeks after study completion, trained psychiatrists assessed the patients' disease severity (positive and negative symptoms; psychopathology) and cognitive functions.

RESULTS

Disease symptoms (positive and negative symptoms; psychopathology) became reduced from baseline to the end of the intervention and to 4 weeks after treatment. Twelve weeks after the intervention symptoms again increased. Cognitive functions decreased from baseline to the end of the study and 4 weeks after treatment. However, by 12 weeks after the intervention, cognitive functions had returned to baseline levels.

CONCLUSION

The pattern of results suggests that an intensive 8-day daily course of ECT reduced psychiatric symptoms (positive and negative symptoms, psychopathology) in both the short and medium term among patients with TRS. The increase in symptoms between 4 and 12 weeks following intervention suggests that booster sessions of ECT could be beneficial.

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

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

Neural substrate of unrelenting negative symptoms in schizophrenia: a longitudinal resting-state fMRI study

Developing a mechanistic insight into the specific brain processes that underpin improvement in negative symptoms can help us design novel chemical and physical treatments against these unrelenting symptoms. The aim of the present study is to explore the longitudinal changes in the brain's regional functional efficiency that accompany improvement in negative symptoms seen in first-episode patients with schizophrenia when treated with antipsychotic for 1 year. Forty-seven first-episode patients with schizophrenia were scanned at a drug-naive baseline state and followed up for 1 year to identify negative symptom responders (Rn) and non-responders (NRn). Fractional amplitude of low-frequency fluctuations (fALFF) and Granger analysis of effective connectivity (EC) were used to examine the different patterns of regional function and connectivity between Rn and NRn during the 1 year follow-up. Increase of fALFF in the left superior temporal gyrus (STG) and increase of EC from the left STG to the dorsolateral prefrontal cortex (DLPFC) was found in Rn compared to NRn. We further validated that the identified changes in fALFF/EC of STG occur specifically in relation to negative symptoms only (i.e., not pseudo-specific in relation to positive, extrapyramidal or depressive symptoms), and occur irrespective of arbitrary clinical categorization of treatment response. An increase in fALFF in the precuneus and the inferior parietal lobule, and a decrease in EC from the left STG to the occipital cortex, were also found at the 1 year follow-up irrespective of improvement in negative symptoms. Interventions that improve the functional efficiency of left STG and its prefrontal connectivity may show efficacy in alleviating negative symptoms in first-episode 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.

Risk and resilience brain networks in treatment-resistant schizophrenia

BACKGROUND

Genes, molecules and neural circuits that are associated with, or confer risk to developing schizophrenia have been studied and mapped. It is hypothesized that certain neural systems may counterbalance familial risk of schizophrenia, and thus confer resilience to developing the disorder. This study sought to identify resting-state functional brain connectivity (rs-FC) representing putative risk or resilience endophenotypes in schizophrenia.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 42 individuals with treatment resistant schizophrenia (TRS), 16 unaffected first-degree family members (UFM) and 42 healthy controls. Whole-brain rs-FC networks were mapped for each individual and analysed graph theoretically to identify network markers associated with schizophrenia risk or resilience.

RESULTS

The ~900 functional connections showing between-group differences were operationalized as conferring: i) resilience, ii) risk, or iii) precipitating risk and/or illness effects. Approximately 95% of connections belonged to the latter two categories, with substantially fewer connections associated with resilience. Schizophrenia risk primarily involved reduced frontal and occipital rs-FC, with patients showing additional reduced frontal and temporal rs-FC. Functional brain networks were characterized by greater local efficiency in UFM, compared to TRS and controls.

CONCLUSIONS

TRS and UFM share frontal and occipital rs-FC deficits, representing a 'risk' endophenotype. Additional reductions in frontal and temporal rs-FC appear to be associated with risk that precipitates psychosis in vulnerable individuals, or may be due to other illness-related effects, such as medication. Functional brain networks are more topologically resilient in UFM compared to TRS, which may protect UFM from psychosis onset despite familial liability.

Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment

Functional connectivity (FC) network has been becoming an increasingly useful tool for understanding the cerebral working mechanism and mining sensitive biomarkers for neural/mental disease diagnosis. Currently, Pearson's Correlation (PC) is the simplest and most commonly used scheme in FC estimation. Despite its empirical effectiveness, PC only encodes the low-order (i.e., second-order) statistics by calculating the pairwise correlations between network nodes (brain regions), which fails to capture the high-order information involved in FC (e.g., the correlations among different edges in a network). To address this issue, we propose a novel FC estimation method based on Matrix Variate Normal Distribution (MVND), which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. Specifically, we first generate a set of BOLD subseries by the sliding window scheme, and for each subseries we construct a temporal FC network by PC. Then, we employ the constructed FC networks as samples to estimate the final low- and high-order FC networks by maximizing the likelihood of MVND. To illustrate the effectiveness of the proposed method, we conduct experiments to identify subjects with Mild Cognitive Impairment (MCI) from Normal Controls (NCs). Experimental results show that the fusion of low- and high-order FCs can generally help to improve the final classification performance, even though the high-order FC may contain less discriminative information than its low-order counterpart. Importantly, the proposed method for simultaneous estimation of low- and high-order FCs can achieve better classification performance than the two baseline methods, i.e., the original PC method and a recent high-order FC estimation method.

Distinguishing Between Treatment-Resistant and Non-Treatment-Resistant Schizophrenia Using Regional Homogeneity

Background: Patients with treatment-resistant schizophrenia (TRS) and non-treatment-resistant schizophrenia (NTRS) respond to antipsychotic drugs differently. Previous studies demonstrated that patients with TRS or NTRS exhibited abnormal neural activity in different brain regions. Accordingly, in the present study, we tested the hypothesis that a regional homogeneity (ReHo) approach could be used to distinguish between patients with TRS and NTRS. Methods: A total of 17 patients with TRS, 17 patients with NTRS, and 29 healthy controls (HCs) matched in sex, age, and education levels were recruited to undergo resting-state functional magnetic resonance imaging (RS-fMRI). ReHo was used to process the data. ANCOVA followed by post-hoc t-tests, receiver operating characteristic curves (ROC), and correlation analyses were applied for the data analysis. Results: ANCOVA analysis revealed widespread differences in ReHo among the three groups in the occipital, frontal, temporal, and parietal lobes. ROC results indicated that the optimal sensitivity and specificity of the ReHo values in the left postcentral gyrus, left inferior frontal gyrus/triangular part, and right fusiform could differentiate TRS from NTRS, TRS from HCs, and NTRS from HCs were 94.12 and 82.35%, 100 and 86.21%, and 82.35 and 93.10%, respectively. No correlation was found between abnormal ReHo and clinical symptoms in patients with TRS or NTRS. Conclusions: TRS and NTRS shared most brain regions with abnormal neural activity. Abnormal ReHo values in certain brain regions might be applied to differentiate TRS from NTRS, TRS from HC, and NTRS from HC with high sensitivity and specificity.

Multidimensional Connectomics and Treatment-Resistant Schizophrenia: Linking Phenotypic Circuits to Targeted Therapeutics

Schizophrenia is a very complex syndrome that involves widespread brain multi-dysconnectivity. Neural circuits within specific brain regions and their links to corresponding regions are abnormal in the illness. Theoretical models of dysconnectivity and the investigation of connectomics and brain network organization have been examined in schizophrenia since the early nineteenth century. In more recent years, advancements have been achieved with the development of neuroimaging tools that have provided further clues to the structural and functional organization of the brain and global neural networks in the illness. Neural circuitry that extends across prefrontal, temporal and parietal areas of the cortex as well as limbic and other subcortical brain regions is disrupted in schizophrenia. As a result, many patients have a poor response to antipsychotic treatment and treatment failure is common. Treatment resistance that is specific to positive, negative, and cognitive domains of the illness may be related to distinct circuit phenotypes unique to treatment-refractory disease. Currently, there are no customized neural circuit-specific and targeted therapies that address this neural dysconnectivity. Investigation of targeted therapeutics that addresses particular areas of substantial regional dysconnectivity is an intriguing approach to precision medicine in schizophrenia. This review examines current findings of system and circuit-level brain dysconnectivity in treatment-resistant schizophrenia based on neuroimaging studies. Within a connectome context, on-off circuit connectivity synonymous with excitatory and inhibitory neuronal pathways is discussed. Mechanistic cellular, neurochemical and molecular studies are included with specific emphasis given to cell pathology and synaptic communication in glutamatergic and GABAergic systems. In this review we attempt to deconstruct how augmenting treatments may be applied within a circuit context to improve circuit integration and treatment response. Clinical studies that have used a variety of glutamate receptor and GABA interneuron modulators, nitric oxide-based therapies and a variety of other strategies as augmenting treatments with antipsychotic drugs are included. This review supports the idea that the methodical mapping of system-level networks to both on (excitatory) and off (inhibitory) cellular circuits specific to treatment-resistant disease may be a logical and productive approach in directing future research toward the advancement of targeted pharmacotherapeutics in schizophrenia.