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Summary: Background: Disturbances in emotion regulation are the hallmarks of major depressive disorder (MDD). The incapacity to control negative emotion in patients has been associated with abnormal hyperactivation of the limbic system and hypoactivation of the frontal cortex. The amygdala and orbital frontal cortex (OFC) are two critical regions of the emotion regulation neural systems. Methods: This study investigated the anatomical basis of abnormal emotion regulation by tracking the fiber tracts connecting the amygdala and OFC. In addition, using dynamic casual modeling on resting‐state fMRI data of 20 MDD patients and equivalent controls, we investigated the exact neural mechanism through which abnormal communications between these two nodes were mediated in MDD. Key Results: The results revealed disrupted white matter integrity of fiber tracts in MDD, suggesting that functional abnormalities were accompanied by underlying anatomical basis. We also detected a failure of inhibition of the OFC on the activity of the amygdala in MDD, suggesting dysconnectivity was mediated through "top‐down" influences from the frontal cortex to the amygdala. Following 8 weeks of antidepressant treatment, the patients showed significant clinical improvement and normalization of the abnormal OFC‐amygdala structural and effective connectivity in the left hemisphere. Conclusions & Inferences: Our findings suggest that pathways connecting these two nodes may be core targets of the antidepressant treatment. In particular, it raised the intriguing question: Does the reversal of structural markers of connectivity reflect a response to antidepressant medication or activity‐dependent myelination following a therapeutic restoration of effective connectivity? [ABSTRACT FROM AUTHOR]

Objective: The symptom-related neurobiology characteristic of schizophrenia in the brain from a network perspective is still poorly understood, leading to a lack of potential biologically-based markers and difficulty identifying therapeutic targets. We aim to test the dysregulated cross-network interactions among the Salience Network (SN), Central Executive Network (CEN) and Default Mode Network (DMN) and how they contributed to different symptoms in schizophrenia patients.Methods: We examined network interactions among the SN, CEN and DMN in 76 patients with schizophrenia vs. 80 well-matched controls using dynamic causal modeling (DCM). We further analyzed the relation between network dynamics and Positive and Negative Syndrome Scale (PANSS).Results: We observed that the DMN, CEN and SN across healthy controls and schizophrenia patients showed several similarities within or between-network pattern in the resting state. Comparing schizophrenia to controls, SN-centered cross-network interactions were most significantly reduced. Crucially, the strength of connections from CEN subnetwork 1 to DMN subnetwork 1 was positively correlated with the Positive Score of PANSS. The connection from the DMN subnetwork 2 to CEN subnetwork 2 was negatively correlated with the Negative Score of PANSS.Conclusions: Our study provides strong evidence for the dysregulation among SN, CEN and DMN in a triple-network perspective in schizophrenia. The connection between DMN and CEN could be clinically-relevant neurobiological signature of schizophrenia symptoms. Our study indicated that the description of brain triple network hypothesis could be a novel and possible bio-marker for schizophrenia. [ABSTRACT FROM AUTHOR]

Multimodal neuroimaging features provide opportunities for accurate classification and personalized treatment options in the psychiatric domain. This study aimed to investigate whether brain features predict responses to the overall treatment of schizophrenia at the end of the first or a single hospitalization. Structural and functional magnetic resonance imaging (MRI) data from two independent samples (N = 85 and 63, separately) of schizophrenia patients at baseline were included. After treatment, patients were classified as responders and non‐responders. Radiomics features of gray matter morphology and functional connectivity were extracted using Least Absolute Shrinkage and Selection Operator. Support vector machine was used to explore the predictive performance. Prediction models were based on structural features (cortical thickness, surface area, gray matter regional volume, mean curvature, metric distortion, and sulcal depth), functional features (functional connectivity), and combined features. There were 12 features after dimensionality reduction. The structural features involved the right precuneus, cuneus, and inferior parietal lobule. The functional features predominately included inter‐hemispheric connectivity. We observed a prediction accuracy of 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the one using structural features. Our model combining both structural and functional features achieved a higher accuracy of 85.03%, with 92.04% responder and 80.23% non‐responders to the overall treatment to be correctly predicted. These results highlight the power of structural and functional MRI‐derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies. [ABSTRACT FROM AUTHOR]

Objective: We aimed to evaluate the functional network properties in first-episode schizophrenia (SZ) patients at baseline and after 4-months treatment with second-generation antipsychotic drugs.Methods: Resting-state functional magnetic resonance imaging and graph theory approaches were utilized to evaluate the functional integration and segregation of brain networks in 36 first-episode patients (20 male/16 female) with SZ and 36 age and sex matched healthy controls (20 male/16 female).Results: Compared with healthy controls, SZ at baseline showed lower clustering coefficient (Cp) and local network efficiency (Eloc), and this abnormal pattern was modulated with treatment of antipsychotic drugs at follow-up. Longitudinally, the increase of Cp was associated with the improvement of negative symptom. We found that the strength of functional connectivity between brain regions were significantly increased in three connections after treatment, mainly involving the frontal, parietal and occipital lobes.Conclusion: The current study suggested that antipsychotic drugs could modulate the faulty local clustering of the functional connectome in SZ. Furthermore, Cp, the parameter that reflects local clustering of topological organization, demonstrated the potential to be a connectome-based biomarker of treatment response to second-generation antipsychotics in patients with SZ. [ABSTRACT FROM AUTHOR]

Understanding the neural substrates of depression is crucial for diagnosis and treatment. Here, we review recent studies of functional and effective connectivity in depression, in terms of functional integration in the brain. Findings from these studies, including our own, point to the involvement of at least four networks in patients with depression. Elevated connectivity of a ventral limbic affective network appears to be associated with excessive negative mood (dysphoria) in the patients; decreased connectivity of a frontal‐striatal reward network has been suggested to account for loss of interest, motivation, and pleasure (anhedonia); enhanced default mode network connectivity seems to be associated with depressive rumination; and diminished connectivity of a dorsal cognitive control network is thought to underlie cognitive deficits especially ineffective top‐down control of negative thoughts and emotions in depressed patients. Moreover, the restoration of connectivity of these networks—and corresponding symptom improvement—following antidepressant treatment (including medication, psychotherapy, and brain stimulation techniques) serves as evidence for the crucial role of these networks in the pathophysiology of depression. [ABSTRACT FROM AUTHOR]

Objective: Auditory verbal hallucinations (AVHs) are one of the cardinal symptoms of schizophrenia (SZ). Cerebral dysfunction may represent pathophysiological underpinnings behind AVHs in SZ. However, regional and network functional deficits for AVHs in SZ remain to be identified. Methods: Seventeen medication-naïve first-episode SZ patients with AVHs, 15 without AVHs, and 19 healthy controls (HCs) were studied using resting-state functional magnetic resonance imaging. We compared the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) among these subjects. Areas with both ALFF and ReHo alterations were used as seeds in functional connectivity (FC) analysis. Then we performed correlation analysis between image measures and symptoms and receiver operating characteristic analysis. Results: One-way analysis of variance showed significant differences of ALFF and ReHo in the bilateral putamen, thereby being used as seeds. SZ patients with AVHs showed decreased ALFF in the left putamen, increased ReHo in the right dorsolateral prefrontal cortex (DLPFC), and increased right putamen-seeded FC with the left DLPFC and Broca's area relative to those without AVHs. Furthermore, the increased strength of the connectivity between the right putamen and left Broca's area correlated with the severity of SZ symptoms. Both patient groups demonstrated hypoconnectivity within frontal/parietal/temporal cortico-striatal-cerebellar networks compared with HCs. Conclusion: AVHs in SZ may be caused by abnormal regional function in the putamen and prefrontal cortex, as well as hyperconnectivity between them. The putamen-related regional and network functional deficits may reflect imbalance in neuromodulation of AVHs in SZ. Furthermore, dysconnectivity within cortico-striatal-cerebellar networks might subserve the pathogenesis of SZ. [ABSTRACT FROM AUTHOR]

Introduction: Treatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response. Objects: We aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia. Methods: Amplitude of low‐frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFFratio, was evaluated based on diagnostic tests and clinical correlates. Results: In the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non‐responders. ALFFratio of responders before treatment was significantly higher than that of non‐responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFFratio to distinguish responders from non‐responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset. Conclusions: Baseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho‐neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia. The amplitude of low‐frequency fluctuation (ALFF) was measured at the beginning of hospitalization. Baseline regional activity of the brain seems to be able to predict early response to treatment for schizophrenia. [ABSTRACT FROM AUTHOR]