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

Association of core brain networks with antipsychotic therapeutic effects in first-episode schizophrenia

Elucidating neurobiological mechanisms underlying the heterogeneity of antipsychotic treatment will be of great value for precision medicine in schizophrenia, yet there has been limited progress. We combined static and dynamic functional connectivity (FC) analysis to examine the abnormal communications among core brain networks [default-mode network (DMN), central executive network (CEN), salience network (SN), primary network (PN), and subcortical network (SCN) in clinical subtypes of schizophrenia (responders and nonresponders to antipsychotic monotherapy). Resting-state functional magnetic resonance imaging data were collected from 79 first-episode schizophrenia and 90 healthy controls. All patients received antipsychotic monotherapy for up to 12 weeks and underwent a second scan. We found that significantly reduced static FC in CEN-DMN/SN and SN-SCN were observed in nonresponders after treatment, whereas almost no difference was observed in responders. The nonresponders showed significantly higher dynamic FC in PN-DMN/SN than responders at baseline. Further, the baseline FC in core brain networks were treated as moderators involved in symptom relief and distinguished response subtypes with high classification accuracy. Collectively, the current work highlights the potential of communications among five core brain networks in searching biomarkers of antipsychotic monotherapy response and neuroanatomical subtypes, advancing the understanding of antipsychotic treatment mechanisms in schizophrenia.

Altered patterns of dynamic functional connectivity of brain networks in deficit and non-deficit schizophrenia

This study aims to investigate the altered patterns of dynamic functional network connectivity (dFNC) between deficit schizophrenia (DS) and non-deficit schizophrenia (NDS), and further explore the associations with cognitive impairments. 70 DS, 91 NDS, and 120 matched healthy controls (HCs) were enrolled. The independent component analysis was used to segment the whole brain. The fMRI brain atlas was used to identify functional networks, and the dynamic functional connectivity (FC) of each network was detected. Correlation analysis was used to explore the associations between altered dFNC and cognitive functions. Four dynamic states were identified. Compared to NDS, DS showed increased FC between sensorimotor network and default mode network in state 1 and decreased FC within auditory network in state 4. Additionally, DS had a longer mean dwell time of state 2 and a shorter one in state 3 compared to NDS. Correlation analysis showed that fraction time and mean dwell time of states were correlated with cognitive impairments in DS. This study demonstrates the distinctive altered patterns of dFNC between DS and NDS patients. The associations with impaired cognition provide specific neuroimaging evidence for the pathogenesis of DS.

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade

BACKGROUND AND HYPOTHESIS

Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years.

STUDY DESIGN

The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade.

STUDY RESULTS

Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders.

CONCLUSIONS

We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet

Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) methods have displayed outstanding performance over traditional approaches. However, challenges such as imbalanced data, noise, poor generalization, high cost, and time-consuming processes hinder progress in this field. To overcome the above challenges, we propose a DL-based model termed DrugSchizoNet for drug interaction (DI) prediction of Schizophrenia. Our model leverages drug-related data from the DrugBank and repoDB databases, employing three key preprocessing techniques. First, data cleaning eliminates duplicate or incomplete entries to ensure data integrity. Next, normalization is performed to enhance security and reduce costs associated with data acquisition. Finally, feature extraction is applied to improve the quality of input data. The three layers of the DrugSchizoNet model are the input, hidden and output layers. In the hidden layer, we employ dropout regularization to mitigate overfitting and improve generalization. The fully connected (FC) layer extracts relevant features, while the LSTM layer captures the sequential nature of DIs. In the output layer, our model provides confidence scores for potential DIs. To optimize the prediction accuracy, we utilize hyperparameter tuning through OB-MOA optimization. Experimental results demonstrate that DrugSchizoNet achieves a superior accuracy of 98.70%. The existing models, including CNN-RNN, DANN, CKA-MKL, DGAN, and CNN, across various evaluation metrics such as accuracy, recall, specificity, precision, F1 score, AUPR, and AUROC are compared with the proposed model. By effectively addressing the challenges of imbalanced data, noise, poor generalization, high cost and time-consuming processes, DrugSchizoNet offers a promising approach for accurate DTI prediction in Schizophrenia. Its superior performance demonstrates the potential of DL in advancing drug discovery and development processes.

Levodopa therapy affects brain functional network dynamics in Parkinson's disease

Levodopa (L-dopa) therapy is the most effective pharmacological treatment for motor symptoms of Parkinson's disease (PD). However, its effect on brain functional network dynamics is still unclear. Here, we recruited 26 PD patients and 24 healthy controls, and acquired their resting-state functional MRI data before (PD-OFF) and after (PD-ON) receiving 400 mg L-dopa. Using the independent component analysis and the sliding-window approach, we estimated the dynamic functional connectivity (dFC) and examined the effect of L-dopa on the temporal properties of dFC states, the variability of dFC and functional network topological organization. We found that PD-ON showed decreased mean dwell time in sparsely connected State 2 than PD-OFF, the transformation of the dFC state is more frequent and the variability of dFC was decreased within the auditory network and sensorimotor network in PD-ON. Our findings provide new insights to understand the dynamic neural activity induced by L-dopa therapy in PD patients.

Effects on Multimodal Connectivity Patterns in Female Schizophrenia During 8 Weeks of Antipsychotic Treatment

BACKGROUND AND HYPOTHESIS

Respective abnormal structural connectivity (SC) and functional connectivity (FC) have been reported in individuals with schizophrenia. However, transmodal associations between SC and FC following antipsychotic treatment, especially in female schizophrenia, remain unclear. We hypothesized that increased SC-FC coupling may be found in female schizophrenia, and could be normalized after antipsychotic treatment.

STUDY DESIGN

Sixty-four female drug-naïve patients with first-diagnosed schizophrenia treated with antipsychotic drugs for 8 weeks, and 55 female healthy controls (HCs) were enrolled. Magnetic resonance imaging (MRI) data were collected from HCs at baseline and from patients at baseline and after treatment. SC and FC were analyzed by network-based statistics, calculating nonzero SC-FC coupling of the whole brain and altered connectivity following treatment. Finally, an Elastic-net logistic regression analysis was employed to establish a predictive model for evaluating the clinical efficacy treatment.

STUDY RESULTS

At baseline, female schizophrenia patients exhibited abnormal SC in cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, and limbic-cerebellar connectivity compared to HCs, while FC showed no abnormalities. Following treatment, cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar connectivity were altered in both SC and FC. Additionally, SC-FC coupling of altered connectivity was higher in patients at baseline than in HC, trending toward normalization after treatment. Furthermore, identified FC or/and SC predicted changes in psychopathological symptoms and cognitive impairment among female schizophrenia following treatment.

CONCLUSIONS

SC-FC coupling may be a potential predictive biomarker of treatment response. Cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar could represent major targets for antipsychotic drugs in female schizophrenia.

Progressive alterations of resting-state hypothalamic dysconnectivity in schizophrenia

BACKGROUND

The hypothalamus may be involved in the pathogenesis of schizophrenia. Investigating hypothalamus dysfunction in schizophrenia and probing how it is related to symptoms and responds to antipsychotic medication is crucial for understanding the potential mechanism of hypothalamus dysfunction under the long-term illness.

METHODS

We recruited 216 patients with schizophrenia, including 140 antipsychotic-naïve first-episode patients (FES, including 44 patients with 1-year follow-up data), 76 chronically treated schizophrenia (CTS), and 210 healthy controls (HC). Hypothalamic seed-based functional connectivity (FC) was calculated and compared among the FES, CTS, and HC groups using analysis of covariance. Exploratory analysis was conducted between the FES patients at baseline and after 1-year follow-up. Significantly altered hypothalamic FCs were then related to clinical symptomology, while age- and illness-related regression analyses were also conducted and compared between diagnostic groups.

RESULTS

The FES patients showed decreased hypothalamic FCs with the midbrain and right thalamus, whereas the CTS patients showed more severe decreased hypothalamic FCs with the midbrain, right thalamus, left putamen, right caudate, and bilateral anterior cingulate cortex compared to HCs. These abnormalities were not correlated to the symptomology or illness duration, or not reversed by the antipsychotic treatment. Age-related hypothalamic FC decrease was also identified in the abovementioned regions, and a faster age-related decline of the hypothalamic FC was observed with the left putamen and bilateral anterior cingulate cortex.

CONCLUSION

Age-related hypothalamic FC decrease extends the functional alterations that characterize the neurodegenerative nature of schizophrenia. Future studies are required to further probe the hormonal or endocrinal underpinnings of such alterations and trace the precise progressive trajectories.

Static and Dynamic Dysconnectivity in Early Psychosis: Relationship With Symptom Dimensions

BACKGROUND AND HYPOTHESIS

Altered functional connectivity (FC) has been frequently reported in psychosis. Studying FC and its time-varying patterns in early-stage psychosis allows the investigation of the neural mechanisms of this disorder without the confounding effects of drug treatment or illness-related factors.

STUDY DESIGN

We employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore FC in individuals with early psychosis (EP), who also underwent clinical and neuropsychological assessments. 96 EP and 56 demographically matched healthy controls (HC) from the Human Connectome Project for Early Psychosis database were included. Multivariate analyses using spatial group independent component analysis were used to compute static FC and dynamic functional network connectivity (dFNC). Partial correlations between FC measures and clinical and cognitive variables were performed to test brain-behavior associations.

STUDY RESULTS

Compared to HC, EP showed higher static FC in the striatum and temporal, frontal, and parietal cortex, as well as lower FC in the frontal, parietal, and occipital gyrus. We found a negative correlation in EP between cognitive function and FC in the right striatum FC (pFWE = 0.009). All dFNC parameters, including dynamism and fluidity measures, were altered in EP, and positive symptoms were negatively correlated with the meta-state changes and the total distance (pFWE = 0.040 and pFWE = 0.049).

CONCLUSIONS

Our findings support the view that psychosis is characterized from the early stages by complex alterations in intrinsic static and dynamic FC, that may ultimately result in positive symptoms and cognitive deficits.

Disruption of network hierarchy pattern in bulimia nervosa reveals brain information integration disorder

The human brain works as a hierarchical organization that is a continuous axis spanning sensorimotor cortex to transmodal cortex (referring to cortex that integrates multimodal sensory information and participates in complex cognitive functions). Previous studies have demonstrated abnormalities in several specific networks that may account for their multiple behavioral deficits in patients with bulimia nervosa (BN), but whether and how the network hierarchical organization changes in BN remain unknown. This study aimed to investigate alterations of the hierarchy organization in BN network and their clinical relevance. Connectome gradient analyses were applied to depict the network hierarchy patterns of fifty-nine patients with BN and thirty-nine healthy controls (HCs). Then, we evaluated the network- and voxel-level gradient alterations of BN by comparing gradient values in each network and each voxel between patients with BN and HCs. Finally, the association between altered gradient values and clinical variables was explored. In the principal gradient, patients with BN exhibited reduced gradient values in dorsal attention network and increased gradient values in subcortical regions compared to HCs. In the secondary gradient, patients with BN showed decreased gradient values in ventral attention network and increased gradient values in limbic network. Regionally, the areas with altered principal or secondary gradient values in BN group were mainly located in transmodal networks, i.e., the default-mode and frontoparietal network. In BN group, the principal gradient values of right inferior frontal gyrus were negatively associated with external eating behavior. This study revealed the disordered network hierarchy patterns in patients with BN, which suggested a disturbance of brain information integration from attention network and subcortical regions to transmodal networks in these patients. These findings may provide insight into the neurobiological underpinnings of BN.

Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia

Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.

Aberrant brain functional connectivity mediates the effects of negative symptoms on cognitive function in schizophrenia: A structural equation model

BACKGROUND

Schizophrenia is a severe psychiatric disorder, characterized by positive symptoms, negative symptoms, and cognitive deficits. Elucidating the mechanism of negative symptom and cognitive deficits could contribute to the treatment and prognosis of schizophrenia. We hypothesized that abnormal functional connectivity would be involved in the indirect effects of negative symptoms on cognitive function.

METHODS

A total of 150 schizophrenia male patients and 108 healthy controls matched for age, education and gender were enrolled in the study. The scores of Brief Negative Symptom Scale were divided into two factors: motivation and pleasure deficits (MAP) and diminished expression (EXP). Subsequently, a series of classic neurocognitive tests were used to evaluate cognitive functions. Resting-state fMRI data was collected from all participants. The Anatomical Automatic Labeling template was employed to establish regions of interest, thereby constructing the functional connectivity network across the entire brain. Eventually, scores of patients' negative symptoms scale, cognitive function, and strengths of abnormal functional connectivity were incorporated into a structural equation model to explore the interactions among variables.

RESULTS

MAP exhibited a distinctly and significantly negative impact on cognitive function. The functional connectivity between the left insula and left precuneus, along with that between the left precuneus and right angular gyrus, collectively served as intermediaries, contributing to the indirect effects of MAP and EXP on cognitive function.

CONCLUSIONS

Our findings demonstrated the moderating role of aberrant brain functional connectivity between negative symptoms and cognitive function, providing clues about the neural correlates of negative symptoms and cognitive deficits in schizophrenia.

Alterations of Functional Connectivity Dynamics in Affective and Psychotic Disorders

BACKGROUND

Patients with psychosis and patients with depression exhibit widespread neurobiological abnormalities. The analysis of dynamic functional connectivity (dFC) allows for the detection of changes in complex brain activity patterns, providing insights into common and unique processes underlying these disorders.

METHODS

We report the analysis of dFC in a large sample including 127 patients at clinical high risk for psychosis, 142 patients with recent-onset psychosis, 134 patients with recent-onset depression, and 256 healthy control participants. A sliding window-based technique was used to calculate the time-dependent FC in resting-state magnetic resonance imaging data, followed by clustering to reveal recurrent FC states in each diagnostic group.

RESULTS

We identified 5 unique FC states, which could be identified in all groups with high consistency (mean r = 0.889 [SD = 0.116]). Analysis of dynamic parameters of these states showed a characteristic increase in the lifetime and frequency of a weakly connected FC state in patients with recent-onset depression (p < .0005) compared with the other groups and a common increase in the lifetime of an FC state characterized by high sensorimotor and cingulo-opercular connectivities in all patient groups compared with the healthy control group (p < .0002). Canonical correlation analysis revealed a mode that exhibited significant correlations between dFC parameters and clinical variables (r = 0.617, p < .0029), which was associated with positive psychosis symptom severity and several dFC parameters.

CONCLUSIONS

Our findings indicate diagnosis-specific alterations of dFC and underline the potential of dynamic analysis to characterize disorders such as depression and psychosis and clinical risk states.

Dynamic structure-function coupling across three major psychiatric disorders

BACKGROUND

Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure-function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ).

METHODS

We quantified the dynamic structure-function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure-function coupling with the topological features of functional networks to examine how the structure-function relationship facilitates brain information communication over time.

RESULTS

The dynamic structure-function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure-function coupling and the topological features of functional networks are altered in a manner indicative of state specificity.

CONCLUSIONS

These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure-function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.

Right superior frontal gyrus: A potential neuroimaging biomarker for predicting short-term efficacy in schizophrenia

Antipsychotic drug treatment for schizophrenia (SZ) can alter brain structure and function, but it is unclear if specific regional changes are associated with treatment outcome. Therefore, we examined the effects of antipsychotic drug treatment on regional grey matter (GM) density, white matter (WM) density, and functional connectivity (FC) as well as associations between regional changes and treatment efficacy. SZ patients (n = 163) and health controls (HCs) (n = 131) were examined by structural magnetic resonance imaging (sMRI) at baseline, and a subset of SZ patients (n = 77) were re-examined after 8 weeks of second-generation antipsychotic treatment to assess changes in regional GM and WM density. In addition, 88 SZ patients and 81 HCs were examined by resting-state functional MRI (rs-fMRI) at baseline and the patients were re-examined post-treatment to examine FC changes. The Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery (MCCB) were applied to measure psychiatric symptoms and cognitive impairments in SZ. SZ patients were then stratified into response and non-response groups according to PANSS score change (≥50 % decrease or <50 % decrease, respectively). The GM density of the right cingulate gyrus, WM density of the right superior frontal gyrus (SFG) plus 5 other WM tracts were reduced in the response group compared to the non-response group. The FC values between the right anterior cingulate and paracingulate gyrus and left thalamus were reduced in the entire SZ group (n = 88) after treatment, while FC between the right inferior temporal gyrus (ITG) and right medial superior frontal gyrus (SFGmed) was increased in the response group. There were no significant changes in regional FC among the non-response group after treatment and no correlations with symptom or cognition test scores. These findings suggest that the right SFG is a critical target of antipsychotic drugs and that WM density and FC alterations within this region could be used as potential indicators in predicting the treatment outcome of antipsychotics of SZ.

Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia

Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.

Brain mechanisms underlying catatonia: A systematic review

BACKGROUND

Catatonia is a complex psychomotor disorder characterized by motor, affective, and behavioral symptoms. Despite being known for almost 150 years, its pathomechanisms are still largely unknown.

METHODS

A systematic research on PubMed, Web of Science, and Scopus was conducted to identify neuroimaging studies conducted on group or single individuals with catatonia. Overall, 33 studies employing structural magnetic resonance imaging (sMRI, n = 11), functional magnetic resonance imaging (fMRI, n = 10), sMRI and fMRI (n = 2), functional near-infrared spectroscopy (fNIRS, n = 1), single positron emission computer tomography (SPECT, n = 4), positron emission tomography (PET, n = 4), and magnetic resonance spectroscopy (MRS, n = 1), and 171 case reports were retrieved.

RESULTS

Observational sMRI studies showed numerous brain changes in catatonia, including diffuse atrophy and signal hyperintensities, while case-control studies reported alterations in fronto-parietal and limbic regions, the thalamus, and the striatum. Task-based and resting-state fMRI studies found abnormalities located primarily in the orbitofrontal, medial prefrontal, motor cortices, cerebellum, and brainstem. Lastly, metabolic and perfusion changes were observed in the basal ganglia, prefrontal, and motor areas. Most of the case-report studies described widespread white matter lesions and frontal, temporal, or basal ganglia hypoperfusion.

CONCLUSIONS

Catatonia is characterized by structural, functional, perfusion, and metabolic cortico-subcortical abnormalities. However, the majority of studies and case reports included in this systematic review are affected by considerable heterogeneity, both in terms of populations and neuroimaging techniques, which calls for a cautious interpretation. Further elucidation, through future neuroimaging research, could have great potential to improve the description of the neural motor and psychomotor mechanisms underlying catatonia.

Dynamic functional connectivity in schizophrenia and bipolar disorder: A review of the evidence and associations with psychopathological features

Alterations of functional network connectivity have been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). Recent studies also suggest that the temporal dynamics of functional connectivity (dFC) can be altered in these disorders. Here, we summarized the existing literature on dFC in SCZ and BD, and their association with psychopathological and cognitive features. We systematically searched PubMed, Web of Science, and Scopus for studies investigating dFC in SCZ and BD and identified 77 studies. Our findings support a general model of dysconnectivity of dFC in SCZ, whereas a heterogeneous picture arose in BD. Although dFC alterations are more severe and widespread in SCZ compared to BD, dysfunctions of a triple network system underlying goal-directed behavior and sensory-motor networks were present in both disorders. Furthermore, in SCZ, positive and negative symptoms were associated with abnormal dFC. Implications for understanding the pathophysiology of disorders, the role of neurotransmitters, and treatments on dFC are discussed. The lack of standards for dFC metrics, replication studies, and the use of small samples represent major limitations for the field.

Association of aberrant brain network dynamics with gut microbial composition uncovers disrupted brain-gut-microbiome interactions in irritable bowel syndrome: Preliminary findings

BACKGROUND AND PURPOSE

Growing evidence suggests that abnormalities in brain-gut-microbiome (BGM) interactions are involved in the pathogenesis of irritable bowel syndrome (IBS). Our study aimed to explore alterations in dynamic functional connectivity (DFC), the gut microbiome and the bidirectional interaction in the BGM.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI), fecal samples and clinical chacteristics were collected from 33 IBS patients and 32 healthy controls. We performed a systematic DFC analysis on rs-fMRI. The gut microbiome was analyzed by 16S rRNA gene sequencing. Associations between DFC characteristics and microbial alterations were explored.

RESULTS

In the DFC analysis, four dynamic functional states were identified. IBS patients exhibited increased mean dwell and fraction time in State 4, and reduced transitions from State 3 to State 1. Aberrant temporal properties in State 4 were only evident when choosing a short window (36 s or 44 s). Decreased functional connectivity (FC) variability was found in State 1 and State 3 in IBS patients, two of which (independent component [IC]51-IC91, IC46-IC11) showed significant correlations with clinical characteristics. Additionally, we identified nine significantly differential abundances in microbial composition. We also found that IBS-related microbiota were associated with aberrant FC variability, although these exploratory results were obtained at an uncorrected threshold of significance.

CONCLUSIONS

Although future studies are needed to confirm our results, the findings not only provide a new insight into the dysconnectivity hypothesis in IBS from a dynamic perspective, but also establish a possible link between DFC and the gut microbiome, which lays the foundation for future research on disrupted BGM interactions.

fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review

BACKGROUND

Antipsychotic drugs are the first-choice therapy for psychotic episodes, but antipsychotic treatment response (AP-R) is unpredictable and only becomes clear after weeks of therapy. A biomarker for AP-R is currently unavailable. We reviewed the evidence for the hypothesis that functional magnetic resonance imaging functional connectivity (fMRI-FC) is a predictor of AP-R or could serve as a biomarker for AP-R in psychosis.

METHOD

A systematic review of longitudinal fMRI studies examining the predictive performance and relationship between FC and AP-R was performed following PRISMA guidelines. Technical and clinical aspects were critically assessed for the retrieved studies. We addressed three questions: Q1) is baseline fMRI-FC related to subsequent AP-R; Q2) is AP-R related to a change in fMRI-FC; and Q3) can baseline fMRI-FC predict subsequent AP-R?

RESULTS

In total, 28 articles were included. Most studies were of good quality. fMRI-FC analysis pipelines included seed-based-, independent component- / canonical correlation analysis, network-based statistics, and graph-theoretical approaches. We found high heterogeneity in methodological approaches and results. For Q1 (N = 17) and Q2 (N = 18), the most consistent evidence was found for FC between the striatum and ventral attention network as a potential biomarker of AP-R. For Q3 (N = 9) accuracy's varied form 50 till 93%, and prediction models were based on FC between various brain regions.

CONCLUSION

The current fMRI-FC literature on AP-R is hampered by heterogeneity of methodological approaches. Methodological uniformity and further improvement of the reliability and validity of fMRI connectivity analysis is needed before fMRI-FC analysis can have a place in clinical applications of antipsychotic treatment.

Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia

BACKGROUND

Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis.

METHODS

Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes.

RESULTS

Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = -04,y = +30,z = +26;k = 12;cluster-p_FWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively.

CONCLUSION

ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.

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

BACKGROUND AND PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations

Auditory verbal hallucinations (AVH) are a key symptom of schizophrenia. Low-frequency repetitive transcranial magnetic stimulation (rTMS) has shown potential in the treatment of AVH. However, the underlying neural mechanismof rTMS in the treatment of AVH remains largely unknown. In this study, we used a static and dynamic functional network connectivity approach to investigate the connectivity changes among the brain functional networks in schizophrenia patients with AVH receiving 1 Hz rTMS treatment. The static functional network connectivity (sFNC) analysis revealed that patients at baseline had significantly decreased connectivity between the default mode network (DMN) and language network (LAN), and within the executive control network (ECN) as well as within the auditory network (AUD) compared to controls. However, the abnormal network connectivity patterns were normalized or restored after rTMS treatment in patients, instead of increased connectivity between the ECN and LAN, as well as within the AUD. Moreover, the dynamic functional network connectivity (dFNC) analysis showed that the patients at baseline spent more time in this state that was characterized by strongly negative connectivity between the ENC and AUD, as well as within the AUD relative to controls. While after rTMS treatment, the patients showed a higher occurrence rate in this state that was characterized by strongly positive connectivity among the LAN, DMN, and ENC, as well as within the ECN. In addition, the altered static and dynamic connectivity properties were associated with reduced severity of clinical symptoms. Both sFNC and dFNC analyses provided complementary information and suggested that low-frequency rTMS treatment could induce intrinsic functional network alternations and contribute to improvements in clinical symptoms in patients with AVH.

Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis

Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.

Analysis of color vision and cognitive function in first-episode schizophrenia before and after antipsychotic treatment

BACKGROUND

A large body of recent research has demonstrated that patients with schizophrenia exhibit significant changes in visual function and ocular tissue structure in the early stages of onset. It is therefore possible to explore a novel scientific breakthrough in the etiology of schizophrenia by transforming the traditional study of brain structure and function with a view to examining the potential field of eye tissue and function. However, few studies have investigated the correlation between iris characteristics and schizophrenia, and evidence is lacking in this regard. Thus, further exploration is needed.

PURPOSE

This study was designed to analyze the characteristics of iris structure, color vision function and cognitive function, as well as the changes therein in patients with the first-episode drug-free schizophrenia before and after antipsychotic treatment. It aimed to preliminarily identify easily-measurable biomarkers for early clinical screening and diagnosis of schizophrenia.

METHODS

This study recruited 61 patients (22 males) with first-episode schizophrenia. Prior to the commencement of treatment with antipsychotic drugs, the Montreal Cognitive Assessment (MoCA) and Farnsworth-Munsell Dichotomous (D-15 Hue Test) were used as assessment tools to evaluate cognitive function and color vision function, respectively. Over a 6-week period, patients received a second-generation antipsychotic treatment (all converted to olanzapine equivalent dose) as prescribed by the doctor, and the Positive and Negative Syndrome Scale (PANSS) was applied to evaluate the clinical treatment effects before treatment (baseline), as well as at the 2nd, 4th, and 6th weeks after drug treatment. On the basis of iris characteristics, the patients were divided into groups. The observed differences in drug treatment effects between the groups were then compared and analyzed to further clarify the relationship between treatment efficacy and iris characteristics. Finally, changes in the cognitive function and color vision function of patients at baseline and at the 6th week after drug treatment were compared, and the effects of antipsychotic drug treatment on the above-mentioned functions were analyzed.

RESULTS

On the basis of structural iris characteristics, 61 patients were classified as follows: 28 patients without iris crypts and 33 with iris crypts; 35 without iris pigment dots and 26 with iris pigment dots; 42 without iris wrinkles and 19 with iris wrinkles. No significant difference was observed in the PANSS scores of all of the patients at baseline; however, significant differences were found in patients with iris crypts and iris pigment dots at each follow-up timepoint (i.e., at the 2nd, 4th, and 6th week). Moreover, it is noteworthy that, compared with other patients, the PANSS scores of patients without specific iris structure characteristics (iris crypts and pigment dots) decreased significantly (P<0.05), which indicated that the drug therapy was highly effective. Excluding the interference of drug factors, a significant correlation was found between the results of the D-15 (color vision function) and MoCA (cognitive function) in first-episode untreated patients (r = -0.401, P < 0.05). In addition, the MoCA scores (mean difference = 2.36, t = 10.05, P ˂ 0.01) were significantly higher after 6 weeks of antipsychotic drug treatment compared to conditions at baseline.

CONCLUSIONS

The findings of this study demonstrated that color vision function of patients with schizophrenia improved with the improvement of cognitive function. The structural characteristics of the iris with crypts and pigment dots could have a significant impact on the drug treatment effect of schizophrenia and could be considered as a potential biomarker for detecting and recognizing schizophrenia.

Microstructural profiles of thalamus and thalamocortical connectivity in patients with disorder of consciousness

Thalamus and thalamocortical connectivity are crucial for consciousness; however, their microstructural changes in patients with a disorder of consciousness (DOC) have not yet been thoroughly characterized. In the present study, we applied the novel fixel-based analysis to comprehensively investigate the thalamus-related microstructural abnormalities in 10 patients with DOC using 7-T diffusion-weighted imaging data. We found that compared to healthy controls, patients with DOC showed reduced fiber density (FD) and fiber density and cross-section (FDC) in the mediodorsal, anterior, and ventral anterior thalamic nuclei, while fiber-bundle cross-section (FC) was not significantly altered in the thalamus. Impaired thalamocortical connectivity in the DOC cohort was mainly connected to the middle frontal gyrus, anterior cingulate gyrus, fusiform gyrus, and sensorimotor cortices, including the precentral gyrus and postcentral gyrus, with predominant microstructural abnormalities in FD and FDC. Correlation analysis showed that FC of the right mediodorsal thalamus was negatively correlated with the level of consciousness. Our results suggest that microstructural abnormalities of thalamus and thalamocortical connectivity in DOC were mainly attributed to axonal injury. In particular, the microstructural integrity of the thalamus is a vital factor in consciousness generation.