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Background and Hypothesis Disrupted control of brain state transitions may contribute to the diverse dysfunctions of cognition, emotion, and behavior that are fundamental to schizophrenia. Control theory provides the rationale for evaluating brain state transitions from a controllability perspective, which may help reveal the brain mechanism for clinical features such as cognitive control deficits associated with schizophrenia. We hypothesized that brain controllability would be altered in patients with schizophrenia, and that controllability of brain networks would be related to clinical symptomatology. Study Design Controllability measurements of functional brain networks, including average controllability and modal controllability, were calculated and compared between 125 first-episode never-treated patients with schizophrenia and 133 healthy controls (HCs). Associations between controllability metrics and clinical symptoms were evaluated using sparse canonical correlation analysis. Study Results Compared to HCs, patients showed significantly increased average controllability (P FDR =.023) and decreased modal controllability (P FDR =.023) in dorsal anterior cingulate cortex (dACC). General psychopathology symptoms and positive symptoms were positively correlated with average controllability in regions of default mode network and negatively associated with average controllability in regions of sensorimotor, dorsal attention, and frontoparietal networks. Conclusions Our findings suggest that altered controllability of functional activity in dACC may play a critical role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations. The demonstration of associations of functional controllability with psychosis symptoms suggests that the identified alterations in average controllability of brain function may contribute to the severity of acute psychotic illness in schizophrenia. [ABSTRACT FROM AUTHOR]

Background: The intrinsic brain functional connectivity of suicide attempts in major depressive disorder (MDD) remains incompletely understood. Purpose: To investigate graph‐theoretical based functional connectivity strength (FCS) alterations in MDD patients with suicidal behavior. Study Type: Prospective. Subjects: Fifty medication‐free MDD patients, with (suicide attempters, SA, N = 15) and without (non‐attempters, nSA, N = 35) a history of a suicide attempt, and 37 healthy controls (HC). Field Strength/Sequence: Resting‐state functional magnetic resonance imaging (fMRI) using a gradient‐echo imaging sequence was acquired at 3.0 T. Assessment: For each individual, voxel‐wise whole‐brain functional network was constructed and graph‐theoretical based FCS map was calculated. For each individual in two patient groups, the seed‐based functional connectivity map was constructed. Statistical Tests: Non‐parameter permutation tests, analysis of covariance, two‐sample t‐test, Chi‐square test, and Pearson correlation analysis. A P value <0.05 was considered statistically significant. Results: Relative to the HC group, two MDD patient groups showed significantly lower FCS in the left hippocampus, while nSA patients showed additionally lower FCS in more widespread regions (P < 0.05). Importantly, comparing to nSA patients, SA patients had significantly higher FCS in the right orbitofrontal cortex (OFC) and bilateral dorsomedial prefrontal cortex (dmPFC) (P < 0.05). Further seed‐based functional connectivity analysis revealed that the right OFC exhibited significantly higher connectivity to right middle frontal gyrus and lower connectivity to the left anterior cingulate cortex and left calcarine sulcus, and the bilateral dmPFC had significantly higher connectivity to the left middle frontal gyrus and right inferior temporal gyrus in the SA patients than in the nSA patients (P < 0.05). Data Conclusion: This study identified disconnections of the OFC and dmPFC which were putatively related to a higher risk of suicidal behavior in MDD patients, thus extended the understanding of suicidal behavior at a brain circuit level. Level of Evidence: 3 Technical Efficacy Stage: 3 [ABSTRACT FROM AUTHOR]

Background: Affective and anxiety disorders including major depression disorder (MDD), post-traumatic stress disorder (PTSD), and social anxiety disorder (SAD) are characterized by network dysconnectivity. Network controllability quantifies the capability of specific brain regions to impact functional dynamics based on the underlying structural connectome. This study aimed to investigate transdiagnostic and illness-specific network controllability alterations across these three disorders., Materials and Methods: The study enrolled 233 currently untreated and non-comorbid subjects, including 68 MDD patients, 51 PTSD patients, 46 SAD patients, and 68 healthy controls (HCs). White matter network controllability was compared among the four groups, and its associations with symptom severity and duration of untreated illness were evaluated., Results: Compared with HCs, patients with PTSD, MDD and SAD exhibited reduced average controllability in the somatomotor, subcortical, and default mode network, notably in brain regions such as the superior frontal gyrus, postcentral gyrus, paracentral gyrus, pallidum, posterior cingulate, and putamen. MDD and SAD patients exhibited reduced average controllability in the left lateral occipital gyrus and bilateral accumbens. SAD patients showed reduced average controllability in the dorsal attention network. These controllability changes did not correlate with illness duration or symptom severity., Limitations: The cross-sectional design limits causal inference, and adjusting for age and sex differences may not completely eliminate their influence on the results., Conclusion: The present study revealed shared and specific alterations of network controllability in MDD, PTSD, and SAD, suggesting reduced ability of specific brain regions/networks in driving the brain system into different functional states across these disorders., Competing Interests: Declaration of competing interest Dr. Wenjing Zhang consult to VeraSci. The remaining authors have no competing interests or financial support to disclose., (Copyright © 2024. Published by Elsevier B.V.)

Background: Antipsychotic medications offer limited long-term benefit to about 30% of patients with schizophrenia. We aimed to explore the individual-specific imaging markers to predict 1-year treatment response of schizophrenia., Methods: Structural morphology and functional topological features related to treatment response were identified using an individualized parcellation analysis in conjunction with machine learning (ML). We performed dimensionality reductions using the Pearson correlation coefficient and three feature selection analyses and classifications using 10 ML classifiers. The results were assessed through a 5-fold cross-validation (training and validation cohorts, n = 51) and validated using the external test cohort (n = 17)., Results: ML algorithms based on individual-specific brain network proved more effective than those based on group-level brain network in predicting outcomes. The most predictive features based on individual-specific parcellation involved the GMV of the default network and the degree of the control, limbic, and default networks. The AUCs for the training, validation, and test cohorts were 0.947, 0.939, and 0.883, respectively. Additionally, the prediction performance of the models constructed by the different feature selection methods and classifiers showed no significant differences., Conclusion: Our study highlighted the potential of individual-specific network parcellation in treatment resistant schizophrenia prediction and underscored the crucial role of feature attributes in predictive model accuracy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Yao, Zhang, Zhao, Qu, Lui, Zhao and Gong.)