Treatment with olanzapine is associated with modulation of the default mode network in patients with Schizophrenia

Effects of long-term antipsychotic medication on brain instability in first-episode schizophrenia patients: a resting-state fMRI study

Early initiation of antipsychotic treatment plays a crucial role in the management of first-episode schizophrenia (FES) patients, significantly improving their prognosis. However, limited attention has been given to the long-term effects of antipsychotic drug therapy on FES patients. In this research, we examined the changes in abnormal brain regions among FES patients undergoing long-term treatment using a dynamic perspective. A total of 98 participants were included in the data analysis, comprising 48 FES patients, 50 healthy controls, 22 patients completed a follow-up period of more than 6 months with qualified data. We processed resting-state fMRI data to calculate coefficient of variation of fractional amplitude of low-frequency fluctuations (CVfALFF), which reflects the brain regional activity stability. Data analysis was performed at baseline and after long-term treatment. We observed that compared with HCs, patients at baseline showed an elevated CVfALFF in the supramarginal gyrus (SMG), parahippocampal gyrus (PHG), caudate, orbital part of inferior frontal gyrus (IOG), insula, and inferior frontal gyrus (IFG). After long-term treatment, the instability in SMG, PHG, caudate, IOG, insula and inferior IFG have ameliorated. Additionally, there was a positive correlation between the decrease in dfALFF in the SMG and the reduction in the SANS total score following long-term treatment. In conclusion, FES patients exhibit unstable regional activity in widespread brain regions at baseline, which can be ameliorated with long-term treatment. Moreover, the extent of amelioration in SMG instability is associated with the amelioration of negative symptoms.

Functional network interactions in patients with schizophrenia with persistent auditory verbal hallucinations: A multimodal MRI fusion approach using three-way pICA

Over the last decade, there have been an increasing number of functional magnetic resonance imaging (fMRI) studies examining brain activity in schizophrenia (SZ) patients with persistent auditory verbal hallucinations (AVH) using either task-based or resting-state fMRI (rs-fMRI) paradigms. Such data have been conventionally collected and analyzed as distinct modalities, disregarding putative crossmodal interactions. Recently, it has become possible to incorporate two or more modalities in one comprehensive analysis to uncover hidden patterns of neural dysfunction not sufficiently captured by separate analysis. A novel multivariate fusion approach to multimodal data analysis, i.e., parallel independent component analysis (pICA), has been previously shown to be a powerful tool in this regard. We utilized three-way pICA to study covarying components among fractional amplitude of low-frequency fluctuations (fALFF) for rs-MRI and task-based activation computed from an alertness and a working memory (WM) paradigm of 15 SZ patients with AVH, 16 non-hallucinating SZ patients (nAVH), and 19 healthy controls (HC). The strongest connected triplet (false discovery rate (FDR)-corrected pairwise correlations) comprised a frontostriatal/temporal network (fALFF), a temporal/sensorimotor network (alertness task), and a frontoparietal network (WM task). Frontoparietal and frontostriatal/temporal network strength significantly differed between AVH patients and HC. Phenomenological features such as omnipotence and malevolence of AVH were associated with temporal/sensorimotor and frontoparietal network strength. The transmodal data confirm a complex interplay of neural systems subserving attentional processes and cognitive control interacting with speech and language processing networks. In addition, the data emphasize the importance of sensorimotor regions modulating specific symptom dimensions of AVH.

Evaluation of nasal delivery systems of olanzapine by desorption electrospray ionization mass spectrometry imaging

Nose-to-brain delivery presents an attractive administration route for neuroactive drugs that suffer from compromised bioavailability or fail to pass the blood-brain barrier. However, the conventional gauge of effectiveness for intranasal delivery platforms primarily involves detecting the presence of the administered drug within the brain, with little insight into its precise localization within brain structures. This may undermine the therapeutic efficacy of drugs and hinder the design of systems that target specific brain regions. In this study, we designed two intranasal delivery systems for the antipsychotic drug, olanzapine, and evaluated its distribution in the rat brain following intranasal administration. The first evaluated system was an olanzapine-loaded microemulsion and the second one was nanoparticulate aqueous dispersion of olanzapine. Both systems exhibited characteristics that render them compatible for intranasal administration, and successfully delivered olanzapine to the brain. We further employed an ambient mass spectrometry imaging method, called desorption electrospray ionization mass spectrometry imaging, to visualize the signal intensity of olanzapine in different brain regions following the intranasal administration of these two systems. Substantial variations in the distribution patterns of olanzapine across various brain structures were revealed, potentially highlighting the importance of mass spectrometry imaging in designing and evaluating intranasal drug delivery platforms.

Multi feature fusion network for schizophrenia classification and abnormal brain network recognition

Schizophrenia classification and abnormal brain network recognition have an important research significance. Researchers have proposed many classification methods based on machine learning and deep learning. However, fewer studies utilized the advantages of complementary information from multi feature to learn the best representation of schizophrenia. In this study, we proposed a multi-feature fusion network (MFFN) using functional network connectivity (FNC) and time courses (TC) to distinguish schizophrenia patients from healthy controls. DNN backbone was adopted to learn the feature map of functional network connectivity, C-RNNAM backbone was designed to learn the feature map of time courses, and Deep SHAP was applied to obtain the most discriminative brain networks. We proved the effectiveness of this proposed model using the combining two public datasets and evaluated this model quantitatively using the evaluation indexes. The results showed that the functional network connectivity generated by independent component analysis has advantage in schizophrenia classification by comparing static and dynamic functional connections. This method obtained the best classification accuracy (ACC=87.30%, SPE=89.28%, SEN=85.71%, F1 =88.23%, and AUC=0.9081), and it demonstrated the superiority of this proposed model by comparing state-of-the-art methods. Ablation experiment also demonstrated that multi feature fusion and attention module can improve classification accuracy. The most discriminative brain networks showed that default mode network and visual network of schizophrenia patients have aberrant connections in brain networks. In conclusion, this method can identify schizophrenia effectively and visualize the abnormal brain network, and it has important clinical application value.

A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations

Previous studies suggest that there may be important links between functional connectivity, disease mechanisms underpinning the Dementia with Lewy Body (DLB) and the key clinical symptoms, but the exact relationship remains unclear. We performed a systematic literature review to address this gap by summarising the research findings while critically considering the impact of methodological differences on findings. The main methodological choices of fMRI articles included data-driven, seed-based or regions of interest approaches, or their combinations. Most studies focused on examining large-scale resting-state networks, which revealed a consistent decrease in connectivity and some associations with non-cognitive symptoms. Although the inter-network connectivity showed mixed results, the main finding is consistent with theories positing disconnection between visual and attentional areas of the brain implicated in the aetiology of psychotic symptoms in the DLB. The primary methodological choice of EEG studies was implementing the phase lag index and using graph theory. The EEG studies revealed a consistent decrease in connectivity on alpha and beta frequency bands. While the overall trend of findings showed decreased connectivity, more subtle changes in the directionality of connectivity were observed when using a hypothesis-driven approach. Problems with cognition were also linked with greater functional connectivity disturbances. In summary, connectivity measures can capture brain disturbances in the DLB and remain crucial in uncovering the causal relationship between the networks' disorganisation and underlying mechanisms resulting in psychotic, motor, and cognitive symptoms of the DLB.

Resting-state functional magnetic resonance imaging alterations in borderline personality disorder: A systematic review

BACKGROUND

Borderline personality disorder (BPD) is a severe psychiatric disorder characterized by emotion dysregulation, impulsivity, and interpersonal disturbances. Several structural and functional neuroimaging abnormalities have been described in BPD. In particular, resting-state functional magnetic resonance imaging (rs-fMRI) studies have recently suggested various connectivity alterations within and between large-scale brain networks in BPD. This review aimed at providing an updated summary of the evidence reported by the available rs-fMRI studies in BPD individuals.

METHODS

A search on PubMed, Scopus, and Web of Science was performed to identify rs-fMRI alterations in BPD. A total of 15 studies met our inclusion criteria.

RESULTS

Overall, aberrant resting-state functional connectivity (rs-FC) within and between default mode network (DMN), salience network (SN), and central executive network (CEN) were observed in BPD compared to healthy controls, as well as selective functional impairments in bilateral amygdala, anterior and posterior cingulate cortex, hippocampus, and prefrontal cortex.

LIMITATIONS

The observational design, small sample size, prevalence of females, high rates of concurrent comorbidities and medications, and heterogeneity across imaging methodologies limit the generalizability of the results.

CONCLUSIONS

The identification of altered patterns of rs-FC within and between selective brain networks, including DMN, SN, and CEN, could further our knowledge of the clinical symptoms of BPD, and therefore, future studies with multimodal methodologies and longitudinal designs are warranted to further explore the neural correlates of this disorder.

Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling

The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG.

Increased Functional Connectivity Involving the Parahippocampal Gyrus in Patients with Schizophrenia during Theory of Mind Processing: A Psychophysiological Interaction Study

BACKGROUND

Theory of Mind (ToM) is an ability to infer the mental state of others, which plays an important role during social events. Previous studies have shown that ToM deficits exist frequently in schizophrenia, which may result from abnormal activity in brain regions related to sociality. However, the interactions between brain regions during ToM processing in schizophrenia are still unclear. Therefore, in this study, we investigated functional connectivity during ToM processing in patients with schizophrenia, using functional magnetic resonance imaging (fMRI).

METHODS

A total of 36 patients with schizophrenia and 33 healthy controls were recruited to complete a ToM task from the Human Connectome Project (HCP) during fMRI scanning. Psychophysiological interaction (PPI) analysis was applied to explore functional connectivity.

RESULTS

Patients with schizophrenia were less accurate than healthy controls in judging social stimuli from non-social stimuli (Z = 2.31, p = 0.021), and displayed increased activity in the right inferior frontal gyrus and increased functional connectivity between the bilateral middle temporal gyrus and the ipsilateral parahippocampal gyrus during ToM processing (AlphaSim corrected p < 0.05).

CONCLUSIONS

Here, we showed that the brain regions related to sociality interact more with the parahippocampal gyrus in patients with schizophrenia during ToM processing, which may reflect a possible compensatory pathway of ToM deficits in schizophrenia. Our study provides a new idea for ToM deficits in schizophrenia, which could be helpful to better understand social cognition of schizophrenia.

Recurrence quantification analysis of periodic dynamics in the default mode network in first-episode drug-naïve schizophrenia

Abnormal functional connectivity (FC) within the default model network (DMN) in schizophrenia has been frequently reported in previous studies. However, traditional FC analysis was mostly linear correlations based, with the information on nonlinear or temporally lagged brain signals largely overlooked. Fifty-five first-episode drug-naïve schizophrenia (FES) patients and 53 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging scanning. The DMN was extracted using independent component analysis. Recurrence quantification analysis was used to measure the duration, predictability, and complexity of the periodic processes of the nonlinear DMN time series. The Mann‒Whitney U test was conducted to compare these features between FES patients and HCs. The support vector machine was applied to discriminate FES from HCs based on these features. Determinism, which means predictability of periodic process activity, between the ventromedial prefrontal cortex (vMPFC) and posterior cingulate and between the vMPFC and precuneus, was significantly decreased in FES compared with HCs. Determinism between the vMPFC and precuneus was positively correlated with category fluency scores in FES. The classifier achieved 77% accuracy. Our results suggest that synchronized periodicity among DMN brain regions is dysregulated in FES, and the periodicity in BOLD signals may be a promising indicator of brain functional connectivity.

Online Left-Hemispheric In-Phase Frontoparietal Theta tACS Modulates Theta-Band EEG Source-Based Large-Scale Functional Network Connectivity in Patients with Schizophrenia: A Randomized, Double-Blind, Sham-Controlled Clinical Trial

EEG studies indicated that schizophrenia patients had increased resting-state theta-band functional connectivity, which was associated with negative symptoms. We recently published the first study showing that theta (6 Hz) transcranial alternating current stimulation (tACS) over left prefrontal and parietal cortices during a working memory task for accentuating frontoparietal theta-band synchronization (in-phase theta-tACS) reduced negative symptoms in schizophrenia patients. Here, we hypothesized that in-phase theta-tACS can modulate theta-band large-scale networks connectivity in schizophrenia patients. In this randomized, double-blind, sham-controlled trial, patients received twice-daily, 2 mA, 20-min sessions of in-phase theta-tACS for 5 consecutive weekdays (n = 18) or a sham stimulation (n = 18). Resting-state electroencephalography data were collected at baseline, end of stimulation, and at one-week follow-up. Exact low resolution electromagnetic tomography (eLORETA) was used to compute intra-cortical activity. Lagged phase synchronization (LPS) was used to measure whole-brain source-based functional connectivity across 84 cortical regions at theta frequency (5-7 Hz). EEG data from 35 patients were analyzed. We found that in-phase theta-tACS significantly reduced the LPS between the posterior cingulate (PC) and the parahippocampal gyrus (PHG) in the right hemisphere only at the end of stimulation relative to sham (p = 0.0009, corrected). The reduction in right hemispheric PC-PHG LPS was significantly correlated with negative symptom improvement at the end of the stimulation (r = 0.503, p = 0.039). Our findings suggest that in-phase theta-tACS can modulate theta-band large-scale functional connectivity pertaining to negative symptoms. Considering the failure of right hemispheric PC-PHG functional connectivity to predict improvement in negative symptoms at one-week follow-up, future studies should investigate whether it can serve as a surrogate of treatment response to theta-tACS.

Longitudinal alterations of modular functional-metabolic coupling in first-episode schizophrenia

Altered network organization and aberrant neurometabolic levels have been associated with schizophrenia. However, modular alterations of functional-neurometabolic coupling in various stages of schizophrenia remain unclear. This longitudinal study enrolled 34 drug-naïve first-episode schizophrenia (FES) patients and 30 healthy controls (HC). The FES patients underwent resting-state functional magnetic resonance imaging (rs-fMRI) and proton magnetic resonance spectroscopy (¹H-MRS) at baseline, 2 months, and 6 months of treatment. For ¹H-MRS, the concentrations of γ-aminobutyric acid (GABA), N-acetylaspartate (NAA) and glutamate + glutamine in the ventromedial prefrontal cortex region were measured. A graph theoretical approach was applied for functional connectivity-based modular parcellation. We found that intra-default mode network (DMN) connectivity, inter-modular connectivity between the DMN and the hippocampus, and inter-modular connectivity between the DMN and the frontoparietal module were significantly different across 6-month treatment in the FES patients. The inter-module connectivity of the DMN and hippocampus correlated positively with NAA concentration in the HC group, while this correlation was absent in FES patients. This exploratory study suggests an altered modular connectivity in association with neurometabolite concentrations in FES patients and provides insights into multimodal neuroimaging biomarkers in schizophrenia. Future studies with larger sample sizes are needed to consolidate our findings.

Altered functional connectivity in psychotic disorder not otherwise specified

BACKGROUND

Few studies have investigated functional connectivity (FC) in patients with psychotic disorder not otherwise specified (PNOS). We sought to identify distinct FC differentiating PNOS from schizophrenia (SZ).

METHODS

In total, 49 patients with PNOS, 42 with SZ, and 55 healthy controls (HC) matched for age, sex, and education underwent functional magnetic resonance imaging (fMRI) brain scans and clinical evaluation. Using six functional networks consisting of 40 regions of interest (ROIs), we conducted ROI to ROI and intra- and inter-network FC analyses using resting-state fMRI (rs-fMRI) data. Correlations of altered FC with symptomatology were explored.

RESULTS

We found common brain connectomics in PNOS and SZ including thalamo-cortical (especially superior temporal gyrus) hyperconnectivity, thalamo-cerebellar hypoconnectivity, and reduced within-thalamic connectivity compared to HC. Additionally, features differentiating the two patient groups included hyperconnectivity between the thalamic subregion and anterior cingulate cortex in PNOS compared to SZ and hyperconnectivity of the thalamic subregions with the posterior cingulate cortex and precentral gyrus in SZ compared to PNOS.

CONCLUSIONS

These findings suggest that PNOS and SZ exhibit both common and differentiating changes in neuronal connectivity. Furthermore, they may support the hypothesis that PNOS should be treated as a separate clinical syndrome with distinct neural connectomics.

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.

Functional disconnection between subsystems of the default mode network in schizophrenia

BACKGROUND

A dysfunctional default mode network (DMN) has been reported in patients with schizophrenia. However, the stability of the deficits has not been determined across different stages of the disorder.

METHODS

We examined the functional connectivity of the DMN subsystems of 125 patients with first-episode schizophrenia (FES) or recurrent schizophrenia (RES), compared to that of 82 healthy controls. We tested the robustness of the findings in an independent cohort of 158 patients and 39 healthy controls. We performed resting-state functional connectivity analysis, and examined the strength of the connections within and between the three subsystems of the DMN (core, dorsal medial prefrontal cortex [dMPFC], and medial temporal lobe [MTL]). We also analyzed the connectivity correlations to symptoms and illness duration.

RESULTS

We found reduced connectivity strength between the core and MTL subsystems in schizophrenia patients compared to controls, with no differences between the FES and RES patient groups; these findings were validated in the second sample. Schizophrenia patients also showed a significant reduction in connectivity within the MTL and between the dMPFC-MTL subsystems, similarly between FES and RES groups. The connectivity strength within the core subsystem was negatively correlated with clinical symptoms in schizophrenia. There was no significant correlation between the DMN subsystem connectivity and illness duration.

CONCLUSIONS

DMN subsystem connectivity deficits are present in schizophrenia, and the homochronicity of their appearance indicates the trait-like nature of these alterations. The DMN deficit may be useful for early diagnosis, and MTL dysfunction may be a crucial mechanism underlying schizophrenia.

Treatment Effect of Long-Term Antipsychotics on Default-Mode Network Dysfunction in Drug-Naïve Patients With First-Episode Schizophrenia: A Longitudinal Study

Background: The maintenance of antipsychotic treatment is an efficient way to prevent the relapse of schizophrenia (SCZ). Previous studies have identified beneficial effects of antipsychotics on brain structural and functional abnormalities during mostly the acute phase in SCZ, but seldom is known about the effects of long-term antipsychotics on the brain. The present study focused on the long-term antipsychotic effect on the default mode network (DMN) dysfunction in SCZ.

Methods: A longitudinal study of the functional connectivity (FC) of 11 DMN subdivisions was conducted in 86 drug-naive first-episode patients with SCZ at the baseline and after a long-term atypical antipsychotic treatment (more than 6 months) based on the resting-state functional magnetic resonance image. In total, 52 patients completed the follow-up of clinical and neuroimaging investigations.

Results: At the baseline, relative to healthy controls, altered connectivities within the DMN and between the DMN and the external attention system (EAS) were observed in patients. After treatment, along with significant relief of symptoms, most FC alterations between the DMN and the EAS at the baseline were improved after treatment, although the rehabilitation of FC within the DMN was only observed at the link between the posterior cingulate cortex and precuneus. Greater reductions in negative and positive symptoms were both related to the changes of DMN-EAS FC in patients.

Conclusion: Our findings provide evidence that maintenance antipsychotics on SCZ is beneficial for the improvement of DMN-EAS competitive imbalance, which may partly contribute to the efficient relapse prevention of this severe mental disorder.

Neurometabolic and functional changes of default-mode network relate to clinical recovery in first-episode psychosis patients: A longitudinal 1H-MRS and fMRI study

BACKGROUND

Antipsychotic treatment has improved the disrupted functional connectivity (FC) and neurometabolites levels of the default mode network (DMN) in schizophrenia patients, but a direct relationship between FC change, neurometabolic level alteration, and symptom improvement has not been built. This study examined the association between the alterations in DMN FC, the changes of neurometabolites levels in the medial prefrontal cortex (MPFC), and the improvementsinpsychopathology in a longitudinal study of drug-naïve first-episode psychosis (FEP) patients.

METHODS

Thirty-two drug-naïve FEP patients and 30 matched healthy controls underwent repeated assessments with the Positive and Negative Syndrome Scale (PANSS) and 3T proton magnetic resonance spectroscopy as well as resting-state functional magnetic resonance imaging. The levels of γ-aminobutyric acid, glutamate, N-acetyl-aspartate in MPFC, and the FC of DMN were measured. After 8-week antipsychotic treatment, 24 patients were re-examined.

RESULTS

After treatment, the changes in γ-aminobutyric acid were correlated with the alterations of FC between the MPFC and DMN, while the changes in N-acetyl-aspartate were associated with the alterations of FC between the posterior cingulate cortex/precuneus and DMN. The FC changes of both regions were correlated with patients PANSS positive score reductions. The structural equation modeling analyses revealed that the changes of DMN FC mediated the relationship between the changes of neurometabolites and the symptom improvements of the patients.

CONCLUSIONS

The derived neurometabolic-functional changes underlying the clinical recovery provide insights into the prognosis of FEP patients. It is noteworthy that this is an exploratory study, and future work with larger sample size is needed to validate our findings.

Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage

Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early‐onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early‐onset (EOS) and adult‐onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co‐varying patterns by jointly analyzing three MRI features: fractional amplitude of low‐frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug‐naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub‐cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug‐naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug‐naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.

Role of Brain Imaging in Drug Development for Psychiatry

Background:

Over the last decades, many brain imaging studies have contributed to new insights in the pathogenesis of psychiatric disease. However, in spite of these developments, progress in the development of novel therapeutic drugs for prevalent psychiatric health conditions has been limited.

Objective:

In this review, we discuss translational, diagnostic and methodological issues that have hampered drug development in CNS disorders with a particular focus on psychiatry. The role of preclinical models is critically reviewed and opportunities for brain imaging in early stages of drug development using PET and fMRI are discussed. The role of PET and fMRI in drug development is reviewed emphasizing the need to engage in collaborations between industry, academia and phase I units.

Conclusion:

Brain imaging technology has revolutionized the study of psychiatric illnesses, and during the last decade, neuroimaging has provided valuable insights at different levels of analysis and brain organization, such as effective connectivity (anatomical), functional connectivity patterns and neurochemical information that may support both preclinical and clinical drug development. Since there is no unifying pathophysiological theory of individual psychiatric syndromes and since many symptoms cut across diagnostic boundaries, a new theoretical framework has been proposed that may help in defining new targets for treatment and thus enhance drug development in CNS diseases. In addition, it is argued that new proposals for data-mining and mathematical modelling as well as freely available databanks for neural network and neurochemical models of rodents combined with revised psychiatric classification will lead to new validated targets for drug development.

Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score

Background: Prior studies have separately demonstrated that magnetic resonance imaging (MRI) and schizophrenia polygenic risk score (PRS) are predictive of antipsychotic medication treatment outcomes in schizophrenia. However, it remains unclear whether MRI combined with PRS can provide superior prognostic performance. Besides, the relative importance of these measures in predictions is not investigated.

Methods: We collected 57 patients with schizophrenia, all of which had baseline MRI and genotype data. All these patients received approximately 6 weeks of antipsychotic medication treatment. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up. We divided these patients into responders (N = 20) or non-responders (N = 37) based on whether their percentages of PANSS total reduction were above or below 50%. Nine categories of MRI measures and PRSs with 145 different p-value thresholding ranges were calculated. We trained machine learning classifiers with these baseline predictors to identify whether a patient was a responder or non-responder.

Results: The extreme gradient boosting (XGBoost) technique was applied to build binary classifiers. Using a leave-one-out cross-validation scheme, we achieved an accuracy of 86% with all MRI and PRS features. Other metrics were also estimated, including sensitivity (85%), specificity (86%), F1-score (81%), and area under the receiver operating characteristic curve (0.86). We found excluding a single feature category of gray matter volume (GMV), amplitude of low-frequency fluctuation (ALFF), and surface curvature could lead to a maximum accuracy drop of 10.5%. These three categories contributed more than half of the top 10 important features. Besides, removing PRS features caused a modest accuracy drop (8.8%), which was not the least decrease (1.8%) among all feature categories.

Conclusions: Our classifier using both MRI and PRS features was stable and not biased to predicting either responder or non-responder. Combining with MRI measures, PRS could provide certain extra predictive power of antipsychotic medication treatment outcomes in schizophrenia. PRS exhibited medium importance in predictions, lower than GMV, ALFF, and surface curvature, but higher than measures of cortical thickness, cortical volume, and surface sulcal depth. Our findings inform the contributions of PRS in predictions of treatment outcomes in schizophrenia.

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

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

Aberrant default mode connectivity in adolescents with early-onset psychosis: A resting state fMRI study

Abnormal default mode network (DMN) connectivity has been found in schizophrenia and other psychotic disorders. However, there are limited studies on early onset psychosis (EOP), and their results show lack of agreement. Here, we investigated within-network DMN connectivity in EOP compared to healthy controls (HC), and its relationship to clinical characteristics. A sample of 68 adolescent patients with EOP (mean age 16.53 ± 1.12 [SD] years, females 66%) and 95 HC (mean age 16.24 ± 1.50 [SD], females 60%) from two Scandinavian cohorts underwent resting state functional magnetic resonance imaging (rsfMRI). A group independent component analysis (ICA) was performed to identify the DMN across all participants. Dual regression was used to estimate spatial maps reflecting each participant's DMN network, which were compared between EOP and HC using voxel-wise general linear models and permutation-based analyses. Subgroup analyses were performed within the patient group, to explore associations between diagnostic subcategories and current use of psychotropic medication in relation to connectivity strength. The analysis revealed significantly reduced DMN connectivity in EOP compared to HC in the posterior cingulate cortex, precuneus, fusiform cortex, putamen, pallidum, amygdala, and insula. The subgroup analysis in the EOP group showed strongest deviations for affective psychosis, followed by other psychotic disorders and schizophrenia. There was no association between DMN connectivity strength and the current use of psychotropic medication. In conclusion, the findings demonstrate weaker DMN connectivity in adolescent patients with EOP compared to healthy peers, and differential effects across diagnostic subcategories, which may inform our understanding of underlying disease mechanisms in EOP.

How recent learning shapes the brain: Memory-dependent functional reconfiguration of brain circuits

The process of storing recently encoded episodic mnestic traces so that they are available for subsequent retrieval is accompanied by specific brain functional connectivity (FC) changes. In this fMRI study, we examined the early processing of memories in twenty-eight healthy participants performing an episodic memory task interposed between two resting state sessions. Memory performance was assessed through a forced-choice recognition test after the scanning sessions. We investigated resting state system configuration changes via Independent Component Analysis by cross-modeling baseline resting state spatial maps onto the post-encoding resting state, and post-encoding resting state spatial maps onto baseline. We identified both persistent and plastic components of the overall brain functional configuration between baseline and post-encoding. While FC patterns within executive, default mode, and cerebellar circuits persisted from baseline to post-encoding, FC within the visual circuit changed. A significant session × performance interaction characterized medial temporal lobe and prefrontal cortex FC with the visual circuit, as well as thalamic FC within the executive control system. Findings reveal early-stage FC changes at the system-level subsequent to a learning experience and associated with inter-individual variation in memory performance.

Increased Homotopic Connectivity in the Prefrontal Cortex Modulated by Olanzapine Predicts Therapeutic Efficacy in Patients with Schizophrenia

Background

Previous studies have revealed the abnormalities in homotopic connectivity in schizophrenia. However, the relationship of these deficits to antipsychotic treatment in schizophrenia remains unclear. This study explored the effects of antipsychotic therapy on brain homotopic connectivity and whether the homotopic connectivity of these regions might predict individual treatment response in schizophrenic patients.

Methods

A total of 21 schizophrenic patients and 20 healthy controls were scanned by the resting-state functional magnetic resonance imaging. The patients received olanzapine treatment and were scanned at two time points. Voxel-mirrored homotopic connectivity (VMHC) and pattern classification techniques were applied to analyze the imaging data.

Results

Schizophrenic patients presented significantly decreased VMHC in the temporal and inferior frontal gyri, medial prefrontal cortex (MPFC), and motor and low-level sensory processing regions (including the fusiform gyrus and cerebellum lobule VI) relative to healthy controls. The VMHC in the superior/middle MPFC was significantly increased in the patients after eight weeks of treatment. Support vector regression (SVR) analyses revealed that VMHC in the superior/middle MPFC at baseline can predict the symptomatic improvement of the positive and negative syndrome scale after eight weeks of treatment.

Conclusions

This study demonstrated that olanzapine treatment may normalize decreased homotopic connectivity in the superior/middle MPFC in schizophrenic patients. The VMHC in the superior/middle MPFC may predict individual response for antipsychotic therapy. The findings of this study conduce to the comprehension of the therapy effects of antipsychotic medications on homotopic connectivity in schizophrenia.

External speech processing and auditory verbal hallucinations: A systematic review of functional neuroimaging studies

It has been documented that individuals who hear auditory verbal hallucinations (AVH) exhibit diminished capabilities in processing external speech. While functional neuroimaging studies have attempted to characterise the cortical regions and networks facilitating these deficits in a bid to understand AVH, considerable methodological heterogeneity has prevented a consensus being reached. The current systematic review investigated the neurobiological underpinnings of external speech processing deficits in voice-hearers in 38 studies published between January 1990 to June 2020. AVH-specific deviations in the activity and lateralisation of the temporal auditory regions were apparent when processing speech sounds, words and sentences. During active or affective listening tasks, functional connectivity changes arose within the language, limbic and default mode networks. However, poor study quality and lack of replicable results plague the field. A detailed list of recommendations has been provided to improve the quality of future research on this topic.

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.

Tracking positive and negative symptom improvement in first-episode schizophrenia treated with risperidone using individual-level functional connectivity

BACKGROUND

To improve the treatment outcomes of patients with schizophrenia, research efforts have focused on identifying brain-based markers of treatment response. Personal characteristics regarding disease-related behaviors likely stem from inter-individual variability in the organization of brain functional systems. This study aimed to track dimension-specific changes in psychotic symptoms following risperidone treatment using individual-level functional connectivity (FC).

METHODS

A reliable cortical parcellation approach that accounts for individual heterogeneity in cortical functional anatomy was used to localize functional regions in a longitudinal cohort, consisting of 42 drug-naive first-episodes schizophrenia (FES) patients at baseline and after 8 weeks of risperidone treatment. FC was calculated in individually specified brain regions and used to predict the baseline severity and improvement of positive and negative symptoms in FES.

RESULTS

Distinct sets of individual-specific FC were separately associated with the positive and negative symptom burden at baseline, which could be used to track the corresponding symptom resolution in FES patients following risperidone treatment. Between-network connections of the fronto-parietal network (FPN) contributed the most to predicting the positive symptom domain. A combination of between-network connections of the default mode network, FPN, and within-network connections of the FPN contributed markedly to the prediction model of negative symptom.

CONCLUSION

This novel study, which accounts for individual brain variation, take a step toward establishing individual-specific theranostic biomarkers in schizophrenia.

A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space

We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. The discriminative component of our model uses logistic regression on the projection vectors for disease diagnosis. This prediction task implicitly guides our framework to find interpretable biomarkers that are substantially different between a healthy and disease population. We exploit the interconnectedness of different brain regions by incorporating a graph regularization penalty into the joint objective function. We also use a group sparsity penalty to find a representative set of genetic basis vectors that span a low dimensional space where subjects are easily separable between patients and controls. We have evaluated our model on a population study of schizophrenia that includes two task fMRI paradigms and single nucleotide polymorphism (SNP) data. Using ten-fold cross validation, we compare our generative-discriminative framework with canonical correlation analysis (CCA) of imaging and genetics data, parallel independent component analysis (pICA) of imaging and genetics data, random forest (RF) classification, and a linear support vector machine (SVM). We also quantify the reproducibility of the imaging and genetics biomarkers via subsampling. Our framework achieves higher class prediction accuracy and identifies robust biomarkers. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.

Dynamic functional connectivity and its anatomical substrate reveal treatment outcome in first-episode drug-naïve schizophrenia

Convergent evidence has suggested a significant effect of antipsychotic exposure on brain structure and function in patients with schizophrenia, yet the characteristics of favorable treatment outcome remains largely unknown. In this work, we aimed to examine how large-scale brain networks are modulated by antipsychotic treatment, and whether the longitudinal changes could track the improvements of psychopathologic scores. Thirty-four patients with first-episode drug-naïve schizophrenia and 28 matched healthy controls were recruited at baseline from Shanghai Mental Health Center. After 8 weeks of antipsychotic treatment, 24 patients were re-scanned. Through a systematical dynamic functional connectivity (dFC) analysis, we investigated the schizophrenia-related intrinsic alterations of dFC at baseline, followed by a longitudinal study to examine the influence of antipsychotic treatment on these abnormalities by comparing patients at baseline and follow-up. A structural connectivity (SC) association analysis was further carried out to investigate longitudinal anatomical changes that underpin the alterations of dFC. We found a significant symptomatic improvement-related increase in the occurrence of a dFC state characterized by stronger inter-network integration. Furthermore, symptom reduction was correlated with increased FC variability in a unique connectomic signature, particularly in the connections within the default mode network and between the auditory, cognitive control, and cerebellar network to other networks. Additionally, we observed that the SC between the superior frontal gyrus and medial prefrontal cortex was decreased after treatment, suggesting a relaxation of normal constraints on dFC. Taken together, these findings provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network. Moreover, our identified neuroimaging markers tied to the neurobiology of schizophrenia could be used as potential indicators in predicting the treatment outcome of antipsychotics.

Altered dynamics of the prefrontal networks are associated with the risk for postpartum psychosis: a functional magnetic resonance imaging study

Postpartum psychosis (PP) is a severe mental disorder that affects women in the first few weeks after delivery. To date there are no biomarkers that distinguish which women at risk (AR) develop a significant psychiatric relapse postpartum. While altered brain connectivity may contribute to the risk for psychoses unrelated to the puerperium, this remains unexplored in PP. We followed up 32 AR and 27 healthy (HC) women from pregnancy to 8-week postpartum. At this point, we classified women as AR-unwell (n = 15) if they had developed a psychiatric relapse meeting DSM-IV diagnostic criteria, or impacting on daily functioning and requiring treatment, or AR-well (n = 17) if they remained asymptomatic. Women also underwent an fMRI scan at rest and during an emotional-processing task, to study within- and between-networks functional connectivity. Women AR, and specifically those in the AR-well group, showed increased resting connectivity within an executive network compared to HC. During the execution of the emotional task, women AR also showed decreased connectivity in the executive network, and altered emotional load-dependent connectivity between executive, salience, and default-mode networks. AR-unwell women particularly showed increased salience network-dependent modulation of the default-mode and executive network relative to AR-well, who showed greater executive network-dependent modulation of the salience network. Our finding that the executive network and its interplay with other brain networks implicated in goal-directed behavior are intrinsically altered suggest that they could be considered neural phenotypes for postpartum psychosis and help advance our understanding of the pathophysiology of this disorder.

Functional Decoupling of Language and Self-Reference Networks in Patients with Persistent Auditory Verbal Hallucinations

BACKGROUND

Accumulating neuroimaging evidence suggests that abnormal intrinsic neural activity could underlie auditory verbal hallucinations (AVH) in patients with schizophrenia. However, little is known about the functional interplay between distinct intrinsic neural networks and their association with AVH.

METHODS

We investigated functional network connectivity (FNC) of distinct resting-state networks as well as the relationship between FNC strength and AVH symptom severity. Resting-state functional MRI data at 3 T were obtained for 14 healthy controls and 10 patients with schizophrenia presenting with persistent AVH. The data were analyzed using a spatial group independent component analysis, followed by constrained maximal lag correlations to determine FNC within and between groups.

RESULTS

Four components of interest, comprising language, attention, executive control networks, as well as the default-mode network (DMN), were selected for subsequent FNC analyses. Patients with persistent AVH showed lower FNC between the language network and the DMN (p < 0.05, corrected for false discovery rate). FNC strength, however, was not significantly related to symptom severity, as measured by the Psychotic Symptom Rating Scale.

CONCLUSION

These findings suggest that disrupted FNC between a speech-related system and a network subserving self-referential processing is associated with AVH. The data are consistent with a model of disrupted self-attribution of speech generation and perception.

White Matter Microstructural Differences between Hallucinating and Non-Hallucinating Schizophrenia Spectrum Patients

The relation between auditory verbal hallucinations (AVH) and white matter has been studied, but results are still inconsistent. This inconsistency may be related to having only a single time-point of AVH assessment in many studies, not capturing that AVH severity fluctuates over time. In the current study, AVH fluctuations were captured by utilizing a longitudinal design and using repeated (Positive and Negative Symptoms Scale) PANSS questionnaire interviews over a 12 month period. We used a Magnetic Resonance Diffusion Tensor Imaging (MR DTI) sequence and tract-based spatial statistics (TBSS) to explore white matter differences between two subtypes of schizophrenia patients; 44 hallucinating (AVH+) and 13 non-hallucinating (AVH-), compared to 13 AVH- matched controls and 44 AVH+ matched controls. Additionally, we tested for hemispheric fractional anisotropy (FA) asymmetry between the groups. Significant widespread FA-value reduction was found in the AVH+ group in comparison to the AVH- group. Although not significant, the extracted FA-values for the control group were in between the two patient groups, for all clusters. We also found a significant difference in FA-asymmetry between the AVH+ and AVH- groups in two clusters, with significantly higher leftward asymmetry in the AVH- group. The current findings suggest a possible qualitative difference in white matter integrity between AVH+ and AVH- patients. Strengths and limitations of the study are discussed.

Keeping weight off: Mindfulness-Based Stress Reduction alters amygdala functional connectivity during weight loss maintenance in a randomized control trial

Obesity is associated with significant comorbidities and financial costs. While behavioral interventions produce clinically meaningful weight loss, weight loss maintenance is challenging. The objective was to improve understanding of the neural and psychological mechanisms modified by mindfulness that may predict clinical outcomes. Individuals who intentionally recently lost weight were randomized to Mindfulness-Based Stress Reduction (MBSR) or a control healthy living course. Anthropometric and psychological factors were measured at baseline, 8 weeks and 6 months. Functional connectivity (FC) analysis was performed at baseline and 8 weeks to examine FC changes between regions of interest selected a priori, and independent components identified by independent component analysis. The association of pre-post FC changes with 6-month weight and psychometric outcomes was then analyzed. Significant group x time interaction was found for FC between the amygdala and ventromedial prefrontal cortex, such that FC increased in the MBSR group and decreased in controls. Non-significant changes in weight were observed at 6 months, where the mindfulness group maintained their weight while the controls showed a weight increase of 3.4% in BMI. Change in FC at 8-weeks between ventromedial prefrontal cortex and several ROIs was associated with change in depression symptoms but not weight at 6 months. This pilot study provides preliminary evidence of neural mechanisms that may be involved in MBSR’s impact on weight loss maintenance that may be useful for designing future clinical trials and mechanistic studies.

Neural Signatures of Memory Encoding in Schizophrenia Are Modulated by Antipsychotic Treatment

There is no pharmacological treatment to remediate cognitive impairment in schizophrenia (SZ). It is imperative to characterize underlying pathologies of memory processing in order to effectively develop new treatments. In this longitudinal study, we combined functional magnetic resonance imaging during a memory encoding task with proton MR spectroscopy to measure hippocampal glutamate + glutamine (Glx). Seventeen SZ were scanned while unmedicated and after 6 weeks of treatment with risperidone and compared to a group of matched healthy controls (HC) scanned 6 weeks apart. Unmedicated patients showed reduced blood oxygen level dependent (BOLD) response in several regions, including the hippocampus, and greater BOLD response in regions of the default mode network (DMN) during correct memory encoding. Post hoc contrasts from significant group by time interactions indicated reduced hippocampal BOLD response at baseline with subsequent increase following treatment. Hippocampal Glx was not different between groups at baseline, but at week 6, hippocampal Glx was significantly lower in SZ compared to HC. Finally, in unmedicated SZ, higher hippocampal Glx predicted less deactivation of the BOLD response in regions of the DMN. Using 2 brain imaging modalities allowed us to concurrently investigate different mechanisms involved in memory encoding dysfunction in SZ. Hippocampal pathology during memory encoding stems from decreased hippocampal recruitment and faulty deactivation of the DMN, and hippocampal recruitment during encoding can be modulated by antipsychotic treatment. High Glx in unmedicated patients predicted less deactivation of the DMN; these results suggest a mechanism by which faulty DMN deactivation, a hallmark of pathological findings in SZ, is achieved.

Effects of Chronic Pharmacological Treatment on Functional Brain Network Connectivity in Patients with Schizophrenia

Schizophrenia is characterized by the dysfunction of various brain networks. Previous studies suggested that pharmacological treatments for schizophrenia induce functional changes in localized brain regions. However, the effects of antipsychotic treatments on brain networks associated with symptom improvement are still elusive. The elucidation of antipsychotic-induced functional brain changes is essential for the development of biologically informed treatment strategies. Forty-five healthy controls and 44 patients with schizophrenia underwent resting-state fMRI scans at baseline. The patients underwent a second scan after 6 weeks of antipsychotic treatment. At baseline, patients exhibited a significant decrease in functional connectivity of the cingulate gyrus in the default mode network compared to healthy controls, and this decrease was negatively correlated with symptom severity. Clinical improvements were observed after 6 weeks treatment, accompanied by an increase in functional connectivity of the cingulate gyrus in the default mode network and the inferior parietal lobule in the executive control network. The changes in functional connectivity of the inferior parietal lobule were significantly correlated with symptom improvement. These longitudinal neuroimaging findings suggest that schizophrenia might be an outcome of the disruption of the optimal balance of brain networks, and reestablishing this balance through antipsychotic treatment may result in clinical symptom improvement.

Psychotic disorders, dopaminergic agents and EEG/MEG resting-state functional connectivity: A systematic review

Both dysconnectivity and dopamine hypotheses are two well researched pathophysiological models of psychosis. However, little is known about the association of dopamine dysregulation with brain functional connectivity in psychotic disorders, specifically through the administration of antipsychotic medication. In this systematic review, we summarize the existing evidence on the association of dopaminergic effects with electro- and magnetoencephalographic (EEG/MEG) resting-state brain functional connectivity assessed by sensor- as well as source-level measures. A wide heterogeneity of results was found amongst the 20 included studies with increased and decreased functional connectivity in medicated psychosis patients vs. healthy controls in widespread brain areas across all frequency bands. No systematic difference in results was seen between studies with medicated and those with unmedicated psychosis patients and very few studies directly investigated the effect of dopamine agents with a pre-post design. The reported evidence clearly calls for longitudinal EEG and MEG studies with large participant samples to directly explore the association of antipsychotic medication effects with neural network changes over time during illness progression and to ultimately support the development of new treatment strategies.

Resting-state functional MRI of the default mode network in epilepsy

The default mode network (DMN) is a major neuronal network that deactivates during goal-directed tasks. Recent advances in neuroimaging have shed light on its structure and function. Alterations in the DMN are increasingly recognized in a range of neurological and psychiatric conditions including epilepsy. This review first describes the current understanding of the DMN in health, normal aging, and disease as it is acquired via resting-state functional magnetic resonance imaging (MRI), before focusing on how it is affected in various types of focal and generalized epilepsy. These findings support the potential use of DMN parameters as future biomarkers in epilepsy research, diagnosis, and management.

Resting-state hyperconnectivity within the default mode network impedes the ability to initiate cognitive performance in first-episode schizophrenia patients

Among multiple cognitive impairments present in schizophrenia, a decline in fast information processing is one of the most severe neuropsychological deficit. Reduced ability to efficiently launch a coherent cognitive activity might be a significant factor contributing to poor results in time-limited tasks obtained by schizophrenia patients. The aim of this study was to identify neurophysiological predictors of expected cognitive initiation failures in a group of first-episode schizophrenia individuals (SZ). To evaluate the effectiveness of initiation, a dynamic analysis of design fluency test was applied, assessing to what extent the productivity was focused within the first interval of the performance, what is a typical way healthy subjects execute this task. Resting-state EEG recordings were obtained from SZ patients (n = 34) and controls (n = 30) to examine functional connectivity between 84 intra-cortical current sources determined by eLORETA (exact low-resolution brain electromagnetic tomography) for six conventionally analyzed frequencies. The nonparametric randomization approach was used to identify hypo- and hyper-connections, i.e. synchronizations significantly differentiating the studied samples in terms of connectivity strength. Generally, SZ patients obtained poor outcomes in fluency test and dynamic analysis of performance confirmed the presence of initiation deficit in clinical sample, which was a single factor explaining the intergroup difference regarding the entire task. In the majority of frequencies, the arrangement of synchronizations in SZ group was dominated by hypo-connections, except for the theta band, in which the strength of synchronizations between posterior cingulate cortex, cuneus and precuneus was significantly higher for SZ group. These theta-band hyper-connections turned out to be significant predictors of cognitive initiation failure in the clinical sample. Additionally, theta hyper-connections correlated negatively with the total number of unique designs generated by patients, however, the strength of this correlation was weaker than regarding initiation index. The results of this study suggest that baseline hyperconnectivity within the posterior hub of the Default Mode Network, containing posterior cingulate gyrus and precuneus, might disturb effective cognitive outcome, not only by interfering with task-positive functional networks but also by delaying the starting phase of performance, which might be specifically deleterious for the execution of time-limited tests.

Resting-state functional connectivity of the striatum predicts improvement in negative symptoms and general functioning in patients with first-episode psychosis: A 1-year naturalistic follow-up study

OBJECTIVE

The persistent disease burden of psychotic disorders often comes from negative symptoms; however, prognostic biomarkers for negative symptoms have not been fully understood. This study investigated whether the altered functional connectivity of the striatum predicts improvement in negative symptoms and functioning after 1 year of usual treatment in patients with first-episode psychosis.

METHODS

Resting-state functional magnetic imaging was obtained from 40 first-episode psychosis patients and 40 age- and sex-matched healthy control subjects. Whole-brain functional connectivity maps were generated with subdivisions of the striatum as seed regions and compared between first-episode psychosis patients and healthy controls. In 22 patients with first-episode psychosis, follow-up assessments of negative symptom severity and general functional status were conducted after 1 year of usual treatment. Multiple regression analyses were performed to examine factors predictive of symptomatic or functional improvements over the 1-year period.

RESULTS

First-episode psychosis patients showed greater functional connectivity between the left dorsal caudate and left primary motor cortex, as well as between the left ventral rostral putamen and right temporal occipital fusiform cortex, than healthy controls. Lower functional connectivity between the right dorsal rostral putamen and anterior cingulate cortex was observed in the first-episode psychosis patients than in healthy controls. In multiple regression analyses, lower functional connectivity of the left dorsal caudate-left primary motor cortex/right dorsal rostral putamen-anterior cingulate cortex predicted improvement in negative symptoms. In addition, lower right dorsal rostral putamen-anterior cingulate cortex functional connectivity predicted improvement in general functioning.

CONCLUSION

These results suggest that altered striatal functional connectivity can be a potent neurobiological marker in the prognosis prediction of first-episode psychosis. Furthermore, altered striatal functional connectivity may provide a potential target in developing treatments for negative symptoms.

A temporal chronnectomic framework: Cigarette smoking preserved the prefrontal dysfunction in schizophrenia

The widespread cigarette smoking behavior in schizophrenia is generally attributed to its alleviation of patients' symptomatology by the self-medication hypothesis. The prefrontal cortex (PFC), which predominantly supports orchestrating thoughts and actions, might underlie the biological underpinnings of smoking behavior in schizophrenia. However, few studies have focused on the impact of smoking on the prefrontal function in schizophrenia. This study assumed that smoking-related alterations on the prefrontal dynamics of information integration (chronnectome) were different between healthy control (HC) and schizophrenia patient (SP). We recruited SP smokers (N = 22)/nonsmokers (N = 27) and HC smokers (N = 22)/nonsmokers (N = 21) who underwent resting-state functional magnetic resonance imaging (rsfMRI) with a total of 240 volumes (lasting for 480 s). We employed a chronnectomic density analysis on the rsfMRI signal by using a sliding-window method. We examined the interaction effect between smoking status and diagnosis utilizing two-way analysis of covariance under permutation test. Whereas disease-related reduced effects were found on the bilateral dorsolateral PFC chronnectomic density, no smoking effect was observed. As regards interaction effect, a smoking-related reduced effect was found on the right dorsolateral PFC chronnectomic density in HC, while a smoking-related increased effect was observed in SP. Nevertheless, post-hoc analysis revealed significant group difference between SP smokers and HC nonsmokers. Therefore, these results indicated a smoking-related preservation effect on disrupted prefrontal dynamics in schizophrenia that cannot restore it to normal levels. The novel findings yield a prefrontal-based chronnectome framework to elaborate upon the self-medication hypothesis in schizophrenia.

Effects of risperidone monotherapy on the default-mode network in antipsychotic-naïve first-episode schizophrenia: Posteromedial cortex heterogeneity and relationship with the symptom improvements

The default mode network (DMN) has been consistently detected abnormally in schizophrenia. However, the effects of antipsychotics on this network are still under debate, and inconsistent findings may be due to the functional heterogeneity within the DMN, especially in the component regions of the posteromedial cortex (PMC). Here, we conducted a longitudinal research on the resting-state functional connectivity of the PMC subdivisions on 33 treatment-naive first-episode patients with schizophrenia at baseline and after 8 weeks of risperidone treatment through resting-state functional magnetic resonance imaging. At baseline, the patients demonstrated decreased connectivity of the three PMC seeds with several brain regions (target regions) compared with healthy controls. We then tested the effect of antipsychotic treatment on the functional connectivity between the three seeds and the target regions. We found that, one of the three seeds encompassed in PMC, namely, posterior cingulate cortex (PCC), was observed to have increased functional connectivity with the bilateral thalamus and the left lingual gyrus (LG). On the contrary, the functional connectivity between the target regions and the two remaining seeds, namely, the retrosplenial cortex and precuneus, was unaffected by risperidone treatment. Correlation analysis revealed a positive correlation between longitudinal change of PCC-LG connectivity and symptom improvement. These findings indicated the heterogeneity of the PMC in response to antipsychotic treatment and suggested the role of PCC as a treatment biomarker for schizophrenia.

Triple Network Model Dynamically Revisited: Lower Salience Network State Switching in Pre-psychosis

Emerging evidence has attributed altered network coordination between the default mode, central executive, and salience networks (DMN/CEN/SAL) to disturbances seen in schizophrenia, but little is known for at-risk psychosis stages. Moreover, pinpointing impairments in specific network-to-network interactions, although essential to resolve possibly distinct harbingers of conversion to clinically diagnosed schizophrenia, remains particularly challenging. We addressed this by a dynamic approach to functional connectivity, where right anterior insula brain interactions were examined through co-activation pattern (CAP) analysis. We utilized resting-state fMRI in 19 subjects suffering from subthreshold delusions and hallucinations (UHR), 28 at-risk for psychosis with basic symptoms describing only self-experienced subclinical disturbances (BS), and 29 healthy controls (CTR) matched for age, gender, handedness, and intelligence. We extracted the most recurring CAPs, compared their relative occurrence and average dwell time to probe their temporal expression, and quantified occurrence balance to assess the putative loss of competing relationships. Our findings substantiate the pivotal role of the right anterior insula in governing CEN-to-DMN transitions, which appear dysfunctional prior to the onset of psychosis, especially when first attenuated psychotic symptoms occur. In UHR subjects, it is longer active in concert with the DMN and there is a loss of competition between a SAL/DMN state, and a state with insula/CEN activation paralleled by DMN deactivation. These features suggest that abnormal network switching disrupts one's capacity to distinguish between the internal world and external environment, which is accompanied by inflexibility and an excessive awareness to internal processes reflected by prolonged expression of the right anterior insula-default mode co-activation pattern.

A Review of Default Mode Network Connectivity and Its Association With Social Cognition in Adolescents With Autism Spectrum Disorder and Early-Onset Psychosis

Recent studies have demonstrated substantial phenotypic overlap, notably social impairment, between autism spectrum disorder (ASD) and schizophrenia. However, the neural mechanisms underlying the pathogenesis of social impairments across these distinct neuropsychiatric disorders has not yet been fully examined. Most neuroimaging studies to date have focused on adults with these disorders, with little known about the neural underpinnings of social impairments in younger populations. Here, we present a narrative review of the literature available through April 2020 on imaging studies of adolescents with either ASD or early-onset psychosis (EOP), to better understand the shared and unique neural mechanisms of social difficulties across diagnosis from a developmental framework. We specifically focus on functional connectivity studies of the default mode network (DMN), as the most extensively studied brain network relevant to social cognition across both groups. Our review included 29 studies of DMN connectivity in adolescents with ASD (Mean age range = 11.2-21.6 years), and 14 studies in adolescents with EOP (Mean age range = 14.2-24.3 years). Of these, 15 of 29 studies in ASD adolescents found predominant underconnectivity when examining DMN connectivity. In contrast, findings were mixed in adolescents with EOP, with five of 14 studies reporting DMN underconnectivity, and an additional six of 14 studies reporting both under- and over-connectivity of the DMN. Specifically, intra-DMN networks were more frequently underconnected in ASD, but overconnected in EOP. On the other hand, inter-DMN connectivity patterns were mixed (both under- and over-connected) for each group, especially DMN connectivity with frontal, sensorimotor, and temporoparietal regions in ASD, and with frontal, temporal, subcortical, and cerebellar regions in EOP. Finally, disrupted DMN connectivity appeared to be associated with social impairments in both groups, less so with other features distinct to each condition, such as repetitive behaviors/restricted interests in ASD and hallucinations/delusions in EOP. Further studies on demographically well-matched groups of adolescents with each of these conditions are needed to systematically explore additional contributing factors in DMN connectivity patterns such as clinical heterogeneity, pubertal development, and medication effects that would better inform treatment targets and facilitate prediction of outcomes in the context of these developmental neuropsychiatric conditions.

EEG Microstate Differences in Medicated vs. Medication-Naïve First-Episode Psychosis Patients

There has been considerable interest in the role of synchronous brain activity abnormalities in the pathophysiology of psychotic disorders and their relevance for treatment; one index of such activity are EEG resting-state microstates. These reflect electric field configurations of the brain that persist over 60-120 ms time periods. A set of quasi-stable microstates classes A, B, C, and D have been repeatedly identified across healthy participants. Changes in microstate parameters coverage, duration and occurrence have been found in medication-naïve as well as medicated patients with psychotic disorders compared to healthy controls. However, to date, only two studies have directly compared antipsychotic medication effects on EEG microstates either pre- vs. post-treatment or between medicated and unmedicated chronic schizophrenia patients. The aim of this study was therefore to directly compare EEG resting-state microstates between medicated and medication-naïve (untreated) first-episode (FEP) psychosis patients (mFEP vs. uFEP). We used 19-channel clinical EEG recordings to compare temporal parameters of four prototypical microstate classes (A-D) within an overall sample of 47 patients (mFEP n = 17; uFEP n = 30). The results demonstrated significant decreases of microstate class A and significant increases of microstate class B in mFEP compared to uFEP. No significant differences between groups were found for microstate classes C and D. Further studies are needed to replicate these results in longitudinal designs that assess antipsychotic medication effects on neural networks at the onset of the disorder and over time during illness progression. As treatment response and compliance in FEP patients are relatively low, such studies could contribute to better understand treatment outcomes and ultimately improve treatment strategies.

Metacognitive Training Modulates Default-Mode Network Homogeneity During 8-Week Olanzapine Treatment in Patients With Schizophrenia

BACKGROUND

Previous studies have revealed the efficacy of metacognitive training for schizophrenia. However, the underlying mechanisms of metacognitive training on brain function alterations, including the default-mode network (DMN), remain unknown. The present study explored treatment effects of metacognitive training on functional connectivity of the brain regions in the DMN.

METHODS

Forty-one patients with schizophrenia and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. Patients were randomly assigned to drug plus psychotherapy (DPP) and drug therapy (DT) groups. The DPP group received olanzapine and metacognitive training, and the DT group received only olanzapine for 8 weeks. Network homogeneity (NH) was applied to analyze the imaging data, and pattern classification techniques were applied to test whether abnormal NH deficits at baseline might be used to discriminate patients from healthy controls. Abnormal NH in predicting treatment response was also examined in each patient group.

RESULTS

Compared with healthy controls, patients at baseline showed decreased NH in the bilateral ventral medial prefrontal cortex (MPFC), right posterior cingulate cortex (PCC)/precuneus, and bilateral precuneus and increased NH in the right cerebellum Crus II and bilateral superior MPFC. NH values in the right PCC/precuneus increased in the DPP group after 8 weeks of treatment, whereas no substantial difference in NH value was observed in the DT group. Support vector machine analyses showed that the accuracy, sensitivity, and specificity for distinguishing patients from healthy controls were more than 0.7 in the NH values of the right PCC/precuneus, bilateral ventral MPFC, bilateral superior MPFC, and bilateral precuneus regions. Support vector regression analyses showed that high NH levels at baseline in the bilateral superior MPFC could predict symptomatic improvement of positive and negative syndrome scale (PANSS) after 8 weeks of DPP treatment. No correlations were found between alterations in the NH values and changes in the PANSS scores/cognition parameters in the patients.

CONCLUSION

This study provides evidence that metacognitive training is related to the modulation of DMN homogeneity in schizophrenia.

Frontostriatal functional connectivity and striatal dopamine synthesis capacity in schizophrenia in terms of antipsychotic responsiveness: an [18F]DOPA PET and fMRI study

BACKGROUND

Given that only a subgroup of patients with schizophrenia responds to first-line antipsychotic drugs, a key clinical question is what underlies treatment response. Observations that prefrontal activity correlates with striatal dopaminergic function, have led to the hypothesis that disrupted frontostriatal functional connectivity (FC) could be associated with altered dopaminergic function. Thus, the aim of this study was to investigate the relationship between frontostriatal FC and striatal dopamine synthesis capacity in patients with schizophrenia who had responded to first-line antipsychotic drug compared with those who had failed but responded to clozapine.

METHODS

Twenty-four symptomatically stable patients with schizophrenia were recruited from Seoul National University Hospital, 12 of which responded to first-line antipsychotic drugs (first-line AP group) and 12 under clozapine (clozapine group), along with 12 matched healthy controls. All participants underwent resting-state functional magnetic resonance imaging and [18F]DOPA PET scans.

RESULTS

No significant difference was found in the total PANSS score between the patient groups. Voxel-based analysis showed a significant correlation between frontal FC to the associative striatum and the influx rate constant of [18F]DOPA in the corresponding region in the first-line AP group. Region-of-interest analysis confirmed the result (control group: R2 = 0.019, p = 0.665; first-line AP group: R2 = 0.675, p < 0.001; clozapine group: R2 = 0.324, p = 0.054) and the correlation coefficients were significantly different between the groups.

CONCLUSIONS

The relationship between striatal dopamine synthesis capacity and frontostriatal FC is different between responders to first-line treatment and clozapine treatment in schizophrenia, indicating that a different pathophysiology could underlie schizophrenia in patients who respond to first-line treatments relative to those who do not.

Preservation Effect: Cigarette Smoking Acts on the Dynamic of Influences Among Unifying Neuropsychiatric Triple Networks in Schizophrenia

OBJECTIVE

The high prevalence of cigarette smoking in schizophrenia (SZ) is generally explained by the self-medication theory. However, its neurobiological mechanism remains unclear. The impaired dynamic of influences among unifying neuropsychiatric triple networks in SZ, including the central executive network (CEN), the default mode network (DMN), and the salience network (SN), might explain the nature of their syndromes, whereas smoking could regulate the dynamics within networks. Therefore, this study examined whether cigarette smoking could elicit a distinct improvement in the dynamics of triple networks in SZ and associated with the alleviation of symptoms.

METHODS

Four groups were recruited, namely, SZ smoking (n = 22)/nonsmoking (n = 25), and healthy controls smoking (n = 22)/nonsmoking (n = 21). All participants underwent a resting-state functional magnetic resonance imaging (fMRI). The dynamics among unifying neuropsychiatric triple networks were measured using Granger causality analysis on the resting-sate fMRI signal. Interaction effects between SZ and smoking on dynamics were detected using 2-way analysis of covariance, correcting for sex, age, and education level.

RESULTS

Whereas smoking reduced SN→DMN dynamic in healthy controls, it preserved the dynamic in SZ, thus suggesting a preservation effect. Moreover, smoking additionally increased DMN→CEN dynamic in SZ.

CONCLUSIONS

This finding from neural pathways shed new insights into the prevailing self-medication hypothesis in SZ. More broadly, this study elaborates on the neurobiological dynamics that may assist in the treatment of the symptomatology of SZ.

A Dissociation in Effects of Risperidone Monotherapy on Functional and Anatomical Connectivity Within the Default Mode Network

Respective changes in functional and anatomical connectivities of default mode network (DMN) after antipsychotic treatment have been reported. However, alterations in structure-function coupling after treatment remain unknown. We performed diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging in 42 drug-naive first-episode schizophrenia patients (FESP) both at baseline and after 8-weeks risperidone monotherapy, and in 38 healthy volunteers. Independent component analysis was used to assess voxel-wise DMN synchrony. A 3-step procedure was used to trace fiber paths between DMN components. Structure-function couplings were assessed by Pearson's correlations between mean fractional anisotropy and temporal correlation coefficients in major tracts of DMN. Pretreatment, FESP showed impaired functional connectivity in posterior cingulate cortex/precuneus (PCC/PCUN) and medial prefrontal cortex (mPFC), but no abnormalities in fibers connecting DMN components. After treatment, there were significant increases in functional connectivities of PCC/PCUN. Increases in functional connectivity between PCC/PCUN and mPFC correlated with improvement in positive symptoms. The structure-function coupling in tracts connecting PCC/PCUN and bilateral medial temporal lobes decreased after treatment. No alterations in DMN fiber integrity were detected. This combination of functional and anatomical findings in FESP contributes novel evidence related to neurobehavioral treatment effects. Increased functional connectivities between PCC/PCUN and mPFC may be treatment response biomarkers for positive symptoms. Increases in functional connectivities, no alterations in fiber integrity, combined with decreases in structural-functional coupling, suggest that DMN connectivities may be dissociated by modality after 8-week treatment. Major limitations of this study, however, include lack of repeat scans in healthy volunteers and control group of patients taking placebo or comparator antipsychotics.

Two patterns of anterior insular cortex functional connectivity in bipolar disorder and schizophrenia

OBJECTIVES

Bipolar disorder (BD) and schizophrenia (SZ) share structural abnormalities in the anterior insula cortex (AIC). The AIC appears to have a crucial role in emotional processing and regulation and cognitive control in BD and SZ.

METHODS

Forty-six participants with BD, 68 with SZ and 66 healthy controls (HC) underwent functional magnetic resonance imaging scanning. Resting-state functional connectivity (rsFC) from AIC subregions (ventral and dorsal) was compared among the three groups.

RESULTS

Compared to HC group, both BD and SZ groups exhibited increased rsFC from the ventral AIC (vAIC) and dorsal AIC (dAIC) to bilateral frontal pole and thalamus, the left middle frontal gyrus and the hippocampus. Meanwhile, the BD group demonstrated increased rsFC from the vAIC to the perigenual anterior cingulate cortex, the SZ group presented increased rsFC from the vAIC and dAIC to the right caudate. Compared with the BD group, the SZ group showed significantly increased rsFC from the vAIC and dAIC to the left middle frontal gyrus.

CONCLUSIONS

The shared AIC rsFC abnormalities in both BD and SZ support the importance of the AIC in the common pathophysiology of BD and SZ. There were also disorder-specific features of AIC rsFC, which might implicate potential avenues for differentiating during the early stages.

Treatment effects of olanzapine on homotopic connectivity in drug-free schizophrenia at rest

OBJECTIVES

Deficits in homotopic connectivity have been implicated in schizophrenia. However, alterations in homotopic connectivity associated with antipsychotic treatments in schizophrenia remain unclear due to lack of longitudinal studies.

METHODS

Seventeen drug-free patients with recurrent schizophrenia and 24 healthy controls underwent resting-state functional magnetic resonance imaging scans. The patients were scanned at three time points (baseline, at 6 weeks of treatment, and at 6 months of treatment). Voxel-mirrored homotopic connectivity (VMHC) was applied to analyse the imaging data to examine alterations in VMHC associated with antipsychotic treatment.

RESULTS

The results showed that patients with schizophrenia exhibited decreased VMHC in the default-mode network (such as the precuneus and inferior parietal lobule) and the motor and sensory processing regions (such as the lingual gyrus, fusiform gyrus and cerebellum lobule VI), which could be normalised or denormalised by olanzapine treatment. In addition, negative correlations were found between decreased VMHC and symptom severity in the patients at baseline.

CONCLUSIONS

The present study shows that olanzapine treatment can normalise or denormalise decreased homotopic connectivity in schizophrenia. The findings also provide a new perspective to understand treatment effects of antipsychotic drugs on homotopic connectivity in schizophrenia that contribute to the disconnection hypothesis of this disease.