An investigation into the abnormal dynamic connection mechanism of generalized anxiety disorders based on non-homogeneous Markov models.

BACKGROUND: The dynamic and hierarchical nature of the functional brain network. The neural dynamical systems tend to converge to multiple attractors (stable fixed points or dynamical states) in long run. Little is known about how the changes in this brain dynamic "long-term" behavior of the connectivity flow of brain network in generalized anxiety disorder (GAD). METHODS: This study recruited 92 patients with GAD and 77 healthy controls (HC). We applied a reachable probability approach combining a Non-homogeneous Markov model with transition probability to quantify all possible connectivity flows and the hierarchical structure of brain functional systems at the dynamic level and the stationary probability vector (10-step transition probabilities) to describe the steady state of the system in the long run. A random forest algorithm was conducted to predict the severity of anxiety. RESULTS: The dynamic functional patterns in distributed brain networks had larger possibility to converge in bilateral thalamus, posterior cingulate cortex (PCC), right superior occipital gyrus (SOG) and smaller possibility to converge in bilateral superior temporal gyrus (STG) and right parahippocampal gyrus (PHG) in patients with GAD compared to HC. The abnormal transition probability pattern could predict anxiety severity in patients with GAD. LIMITATIONS: Small samples and subjects taking medications may have influenced our results. Future studies are expected to rule out the potential confounding effects. CONCLUSION: Our results have revealed abnormal dynamic neural communication and integration in emotion regulation in patients with GAD, which give new insights to understand the dynamics of brain function of patients with GAD.

Cortical thickness reductions associate with brain network architecture in major depressive disorder.

BACKGROUND: Cortical thickness reductions in major depressive disorder are distributed across multiple regions. Research has indicated that cortical atrophy is influenced by connectome architecture on a range of neurological and psychiatric diseases. However, whether connectome architecture contributes to changes in cortical thickness in the same manner as it does in depression is unclear. This study aims to explain the distribution of cortical thickness reductions across the cortex in depression by brain connectome architecture. METHODS: Here, we calculated a differential map of cortical thickness between 110 depression patients and 88 age-, gender-, and education level-matched healthy controls by using T1-weighted images and a structural network reconstructed through the diffusion tensor imaging of control group. We then used a neighborhood deformation model to explore how cortical thickness change in an area is influenced by areas structurally connected to it. RESULTS: We found that cortical thickness in the frontoparietal and default networks decreased in depression, regional cortical thickness changes were related to reductions in their neighbors and were mainly limited by the frontoparietal and default networks, and the epicenter was in the prefrontal lobe. CONCLUSION: Current findings suggest that connectome architecture contributes to the irregular topographic distribution of cortical thickness reductions in depression and cortical atrophy is restricted by and dependent on structural foundation.

Neural encoding with unsupervised spiking convolutional neural network.

Accurately predicting the brain responses to various stimuli poses a significant challenge in neuroscience. Despite recent breakthroughs in neural encoding using convolutional neural networks (CNNs) in fMRI studies, there remain critical gaps between the computational rules of traditional artificial neurons and real biological neurons. To address this issue, a spiking CNN (SCNN)-based framework is presented in this study to achieve neural encoding in a more biologically plausible manner. The framework utilizes unsupervised SCNN to extract visual features of image stimuli and employs a receptive field-based regression algorithm to predict fMRI responses from the SCNN features. Experimental results on handwritten characters, handwritten digits and natural images demonstrate that the proposed approach can achieve remarkably good encoding performance and can be utilized for "brain reading" tasks such as image reconstruction and identification. This work suggests that SNN can serve as a promising tool for neural encoding.

Macroscale Thalamic Functional Organization Disturbances and Underlying Core Cytoarchitecture in Early-Onset Schizophrenia.

BACKGROUND AND HYPOTHESIS: Schizophrenia is a polygenetic mental disorder with heterogeneous positive and negative symptom constellations, and is associated with abnormal cortical connectivity. The thalamus has a coordinative role in cortical function and is key to the development of the cerebral cortex. Conversely, altered functional organization of the thalamus might relate to overarching cortical disruptions in schizophrenia, anchored in development. STUDY DESIGN: Here, we contrasted resting-state fMRI in 86 antipsychotic-naive first-episode early-onset schizophrenia (EOS) patients and 91 typically developing controls to study whether macroscale thalamic organization is altered in EOS. Employing dimensional reduction techniques on thalamocortical functional connectome (FC), we derived lateral-medial and anterior-posterior thalamic functional axes. STUDY RESULTS: We observed increased segregation of macroscale thalamic functional organization in EOS patients, which was related to altered thalamocortical interactions both in unimodal and transmodal networks. Using an ex vivo approximation of core-matrix cell distribution, we found that core cells particularly underlie the macroscale abnormalities in EOS patients. Moreover, the disruptions were associated with schizophrenia-related gene expression maps. Behavioral and disorder decoding analyses indicated that the macroscale hierarchy disturbances might perturb both perceptual and abstract cognitive functions and contribute to negative syndromes in patients. CONCLUSIONS: These findings provide mechanistic evidence for disrupted thalamocortical system in schizophrenia, suggesting a unitary pathophysiological framework.

Systematically mapping gray matter abnormal patterns in drug-naïve first-episode schizophrenia from childhood to adolescence.

BACKGROUND: Schizophrenia originates early in neurodevelopment, underscoring the need to elaborate on anomalies in the still maturing brain of early-onset schizophrenia (EOS). METHODS: Gray matter (GM) volumes were evaluated in 94 antipsychotic-naïve first-episode EOS patients and 100 typically developing (TD) controls. The anatomical profiles of changing GM deficits in EOS were detected using 2-way analyses of variance with diagnosis and age as factors, and its timing was further charted using stage-specific group comparisons. Interregional relationships of GM alterations were established using structural covariance network analyses. RESULTS: Antagonistic interaction results suggested dynamic GM abnormalities of the left fusiform gyrus, inferior occipital gyrus, and lingual gyrus in EOS. These regions comprise a dominating part of the ventral stream, a ventral occipitotemporal (vOT) network engaged in early social information processing. GM abnormalities were mainly located in the vOT regions in childhood-onset patients, whereas in the rostral prefrontal cortex (rPFC) in adolescent-onset patients. Moreover, compared with TD controls, patients' GM synchronization with the ventral stream was disrupted in widespread high-order social perception regions including the rPFC and salience network. CONCLUSIONS: The current findings reveal age-related anatomical abnormalities of the social perception system in pediatric patients with schizophrenia.

Disassociated and concurrent structural and functional abnormalities in the drug-naïve first-episode early onset schizophrenia.

Schizophrenia which is an abnormally developmental disease has been widely reported to show abnormal brain structure and function. Enhanced functional integration is a predominant neural marker for brain mature. Abnormal development of structure and functional integration may be a biomarker for early diagnosis of schizophrenia. Fifty-five patients with early onset schizophrenia (EOS) and 79 healthy controls were enrolled in this study. Voxel-based morphometry (VBM) and functional connectivity density (FCD) were performed to explore gray matter volume (GMV) lesion, abnormal functional integration, and concurrent structural and functional abnormalities in the brain. Furthermore, the relationships between abnormalities structural and function and clinical characteristics were evaluated in EOS. Compared with healthy controls, EOS showed significantly decreased GMV in the bilateral OFC, frontal, temporal, occipital, parietal and limbic system. EOS also showed decreased FCD in precuneus and increased FCD in cerebellum. Moreover, we found concurrent changes of structure and function in left lateral orbitofrontal cortex (lOFC). Finally, correlation analyses did not find significant correlation between abnormal neural measurements and clinical characteristic in EOS. The results reveal disassociated and bound structural and functional abnormalities patterns in EOS suggesting structural and functional measurements play different roles in delineating the abnormal patterns of EOS. The concurrent structural and functional changes in lOFC may be a biomarker for early diagnosis of schizophrenia. Our findings will deepen our understanding of the pathophysiological mechanisms in EOS.

Reconstructing rapid natural vision with fMRI-conditional video generative adversarial network.

Recent functional magnetic resonance imaging (fMRI) studies have made significant progress in reconstructing perceived visual content, which advanced our understanding of the visual mechanism. However, reconstructing dynamic natural vision remains a challenge because of the limitation of the temporal resolution of fMRI. Here, we developed a novel fMRI-conditional video generative adversarial network (f-CVGAN) to reconstruct rapid video stimuli from evoked fMRI responses. In this model, we employed a generator to produce spatiotemporal reconstructions and employed two separate discriminators (spatial and temporal discriminators) for the assessment. We trained and tested the f-CVGAN on two publicly available video-fMRI datasets, and the model produced pixel-level reconstructions of 8 perceived video frames from each fMRI volume. Experimental results showed that the reconstructed videos were fMRI-related and captured important spatial and temporal information of the original stimuli. Moreover, we visualized the cortical importance map and found that the visual cortex is extensively involved in the reconstruction, whereas the low-level visual areas (V1/V2/V3/V4) showed the largest contribution. Our work suggests that slow blood oxygen level-dependent signals describe neural representations of the fast perceptual process that can be decoded in practice.

Tracking Positive and Negative Symptom Improvement in First-Episode Schizophrenia Treated with Risperidone Using Individual-Level Functional Connectivity.

Background: To improve 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 interindividual 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-episode 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 symptoms. Conclusion: This novel study, which accounts for individual brain variation, takes a step toward establishing individual-specific theranostic biomarkers in schizophrenia. Impact statement This study revealed a theranostic marker for personalized medicine in schizophrenia and may aid in circuit-specific therapies for this disorder.

Cortical patterning of morphometric similarity gradient reveals diverged hierarchical organization in sensory-motor cortices.

The topological organization of the cerebral cortex provides hierarchical axes, namely gradients, which reveal systematic variations of brain structure and function. However, the hierarchical organization of macroscopic brain morphology and how it constrains cortical function along the organizing axes remains unclear. We map the gradient of cortical morphometric similarity (MS) connectome, combining multiple features conceptualized as a "fingerprint" of an individual's brain. The principal MS gradient is anchored by motor and sensory cortices at two extreme ends, which are reliable and reproducible. Notably, divergences between motor and sensory hierarchies are consistent with the laminar histological thickness gradient but contrary to the canonical functional connectivity (FC) gradient. Moreover, the MS dissociates with FC gradients in the higher-order association cortices. The MS gradient recapitulates fundamental properties of cortical organization, from gene expression and cyto- and myeloarchitecture to evolutionary expansion. Collectively, our findings provide a heuristic hierarchical organization of cortical morphological neuromarkers.

Temporal variability profiling of the default mode across epilepsy subtypes.

OBJECTIVE: Epilepsies are a group of neurological disorders sharing certain core features, but also demonstrate remarkable pathogenic and symptomatic heterogeneities. Various subtypes of epilepsy have been identified with abnormal shift in the brain default mode network (DMN). This study aims to evaluate the fine details of shared and distinct alterations in the DMN among epileptic subtypes. METHODS: We collected resting-state functional magnetic resonance imaging (MRI) data from a large epilepsy sample (n = 371) at a single center, including temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), and genetic generalized epilepsy with generalized tonic-clonic seizures (GGE-GTCS), as well as healthy controls (HC, n = 150). We analyzed temporal dynamics profiling of the DMN, including edge-wise and node-wise temporal variabilities, and recurrent dynamic states of functional connectivity, to identify abnormalities common to epilepsies as well as those specific to each subtype. RESULTS: The analyses revealed that hypervariable edges within the specific DMN subsystem were shared by all subtypes (all PNBS  < .005), and deficits in node-wise temporal variability were prominent in TLE (all t(243) ≤ 2.51, PFDR  < .05) and FLE (all t(302) ≤ -2.65, PFDR  < .05) but relatively weak in GGE-GTCS. Moreover, dynamic states were generally less stable in patients than controls (all P's < .001). SIGNIFICANCE: Collectively, these findings demonstrated general DMN abnormalities common to different epilepsies as well as distinct dysfunctions to subtypes, and provided insights into understanding the relationship of pathophysiological mechanisms and brain connectivity.

Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain.

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.

Individual-specific functional connectome biomarkers predict schizophrenia positive symptoms during adolescent brain maturation.

Even with an overarching functional dysconnectivity model of adolescent-onset schizophrenia (AOS), there have been no functional connectome (FC) biomarkers identified for predicting patients' specific symptom domains. Adolescence is a period of dramatic brain maturation, with substantial interindividual variability in brain anatomy. However, existing group-level hypotheses of AOS lack precision in terms of neuroanatomical boundaries. This study aimed to identify individual-specific FC biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation. We used a reliable individual-level cortical parcellation approach to map functional brain regions in each subject, that were then used to identify FC biomarkers for predicting dimension-specific psychotic symptoms in 30 antipsychotic-naïve first-episode AOS patients (recruited sample of 39). Age-related changes in biomarker expression were compared between these patients and 31 healthy controls. Moreover, 29 antipsychotic-naïve first-episode AOS patients (analyzed sample of 25) were recruited from another center to test the generalizability of the prediction model. Individual-specific FC biomarkers could significantly and better predict AOS positive-dimension symptoms with a relatively stronger generalizability than at the group level. Specifically, positive symptom domains were estimated based on connections between the frontoparietal control network (FPN) and salience network and within FPN. Consistent with the neurodevelopmental hypothesis of schizophrenia, the FPN-SN connection exhibited aberrant age-associated alteration in AOS. The individual-level findings reveal reproducible FPN-based FC biomarkers associated with AOS positive symptom domains, and highlight the importance of accounting for individual variation in the study of adolescent-onset disorders.

Progressive brain structural alterations assessed via causal analysis in patients with generalized anxiety disorder.

Accumulating neuroimaging studies implicate widespread brain structural alterations in patients with generalized anxiety disorder (GAD), but little is known regarding the temporal information of these changes and their causal relationships. In this study, a morphometric analysis was performed on T1-weighted structural images, and the progressive changes in the gray matter volume (GMV) in GAD were simulated by dividing the patients into different groups from low illness duration to high illness duration. The duration was defined as the interval between the onset of GAD and the time for magnetic resonance imaging collection. Then, a causal structural covariance network analysis was conducted to describe the causal relationships of the brain structural alterations in GAD. With increased illness duration, the GMV reduction in GAD originated from the subgenual anterior cingulate cortex (sgACC) and propagated to the bilateral ventromedial prefrontal cortex, right dorsomedial prefrontal cortex, left inferior temporal gyrus, and right insula. Intriguingly, the sgACC and the right insula had positive causal effects on each other. Moreover, both sgACC and right insula exhibited positive causal effects on the parietal cortex and negative effects on the posterior cingulate cortex, dorsolateral prefrontal cortex, visual cortex, and temporal lobe. The opposite causal effects were noted on the somatosensory and the ventrolateral prefrontal cortices. In conclusion, patients with GAD show gradual GMV reduction with increasing ilness duration. Furthermore, the causal effects of the sgACC and the right insula GMV reduction with shifts of duration may provide an important new avenue for understanding the pathological anomalies in GAD.

Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder.

The clinical misdiagnosis ratio of bipolar disorder (BD) patients to major depressive disorder (MDD) patients is high. Recent findings hypothesize that the ability to flexibly recruit functional neural networks is differently altered in BD and MDD patients. This study aimed to explore distinct aberrance of network flexibility during dynamic networks configuration in BD and MDD patients. Resting state functional magnetic resonance imaging of 40 BD patients, 61 MDD patients, and 61 matched healthy controls were recruited. Dynamic functional connectivity matrices for each subject were constructed with a sliding window method. Then, network switching rate of each node was calculated and compared among the three groups. BD and MDD patients shared decreased network switching rate of regions including left precuneus, bilateral parahippocampal gyrus, and bilateral dorsal medial prefrontal cortex. Apart from these regions, MDD patients presented specially decreased network switching rate in the bilateral anterior insula, left amygdala, and left striatum. Taken together, BD and MDD patients shared decreased network switching rate of key hubs in default mode network and MDD patients presented specially decreased switching rate in salience network and striatum. We found shared and distinct aberrance of network flexibility which revealed altered adaptive functions during dynamic networks configuration of BD and MDD.

The thalamic functional gradient and its relationship to structural basis and cognitive relevance.

The human thalamus is an integrative hub richly connected with cortical networks, involving diverse cognitive functions. Emerging evidence suggests that multiscale structural and functional gradients integrate various information across modalities into an abstract representation. However, the presence of functional gradients in the thalamus and its relationship to structural properties and cognitive functions remain unknown. We estimated the functional gradients of the thalamus in two independent normal cohorts using a novel diffusion embedding analysis. We identified two main axes of the functional connectivity patterns, and examined associations with thalamic anatomy, morphology, intrinsic geometry, and specific behavioral relevance. We found that the dominant gradient indicated a lateral/medial axis across the thalamus and captured associations with anatomical nuclei and gray matter volume. The second gradient was an anterior/posterior axis and provided a behavioral characterization from lower level perception to higher level cognition. Furthermore, these two gradients strongly correlated with spatial distance, indicating the prominence of intrinsic geometry in functional hierarchies. These findings were replicated in an independent dataset. Overall, our findings suggested that macroscale gradients showed a coordination of structural and functional interactions, with hierarchical organization contributing to behavior characterization.

The anhedonia is differently modulated by structural covariance network of NAc in bipolar disorder and major depressive disorder.

During depressive episode, bipolar disorder (BD) patients share indistinguishable depression symptoms with major depressive disorder (MDD).However, whether neural correlates underlying the anhedonia, a core feature of depression, is different between BD and MDD remains unknown. To explore neural correlates underlying the anhedonia in BD and MDD, structural T1-weighted images from 36 depressed BD patients, 40 depressed MDD patients matched for depression severity and 34 health controls (HCs) were scanned. Considering the vital role of nucleus accumbens (NAc) in the anhedonia, we constructed the structural covariance network of NAc for each subject. Then, we explored altered structural covariance network of NAc and its interaction with the anhedonia severity in BD and MDD patients. As a result, BD and MDD patients shared decreased structural covariance of NAc connected to prefrontal gyrus, bilateral striatum extending to bilateral anterior insula. Apart from these regions, BD patients presented specifically increased structural covariance of NAc connected to left hippocampus extending to thalamus. The interaction between structural covariance network of NAc and the anhedonia severity in MDD was mainly associated anterior insula (AIC), amygdala, anterior cingulate cortex (ACC)and caudate while that in BD was mainly located in striatum and prefrontal cortex. Our results found that BD and MDD patients presented commonly and distinctly altered structural covariance network of NAc. What is more, the neural correlates underlying the anhedonia in BD and MDD might be different.

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.

Impaired interactions among white-matter functional networks in antipsychotic-naive first-episode schizophrenia.

Schizophrenia has been conceptualized as a disorder arising from structurally pathological alterations to white-matter fibers in the brain. However, few studies have focused on white-matter functional changes in schizophrenia. Considering that converging evidence suggests that white-matter resting state functional MRI (rsfMRI) signals can effectively depict neuronal activity and psychopathological status, this study examined white-matter network-level interactions in antipsychotic-naive first-episode schizophrenia (FES) to facilitate the interpretation of the psychiatric pathological mechanisms in schizophrenia. We recruited 42 FES patients (FESs) and 38 healthy controls (HCs), all of whom underwent rsfMRI. We identified 11 white-matter functional networks, which could be further classified into deep, middle, and superficial layers of networks. We then examined network-level interactions among these 11 white-matter functional networks using coefficient Granger causality analysis. We employed group comparisons on the influences among 11 networks using network-based statistic. Excitatory influences from the middle superior corona radiate network to the superficial orbitofrontal and deep networks were disrupted in FESs compared with HCs. Additionally, an extra failure of suppression within superficial networks (including the frontoparietal network, temporofrontal network, and the orbitofrontal network) was observed in FESs. We additionally recruited an independent cohort (13 FESs and 13 HCs) from another center to examine the replicability of our findings across centers. Similar replication results further verified the white-matter functional network interaction model of schizophrenia. The novel findings of impaired interactions among white-matter functional networks in schizophrenia indicate that the pathophysiology of schizophrenia may also lie in white-matter functional abnormalities.

Disconnectivity between the raphe nucleus and subcortical dopamine-related regions contributes altered salience network in schizophrenia.

Numerous studies strongly have suggested the significant role of serotonin in the pathomechanism of schizophrenia. However, few studies have directly explored the altered serotonin function in schizophrenia. In the current study, we explored the altered serotonin function in first-episode treatment-naive patients with schizophrenia with resting-state functional magnetic resonance imaging. A total 42 first-episode treatment-naive patients with schizophrenia and carefully matched healthy controls are included in the study. Considering that the raphe nucleus providing a substantial proportion of the serotonin innervation to the forebrain, the raphe nucleus was chosen as the seed to construct voxel-based functional connectivity (FC) maps. In the results, subcortical dopamine-related regions presented decreased FC with the raphe nucleus, such as the bilateral striatum, pallidum, and thalamus, in patients with schizophrenia. Decreased FC in these regions was significantly correlated with the total negative scores in PANSS. Furthermore, these regions presented with decreased FC connection to salience network. Our results presented that the raphe nucleus played an important role in the dysfunction of subcortical DA-related regions, and contributed to the altered salience network in schizophrenia. Our study emphasized the importance of the raphe nucleus in the pathophysiology of schizophrenia.