About this Research Topic
Psychotic symptoms are a cross-sectional dimension affecting multiple diagnostic categories. About a decade ago, the National Institute of Mental Health (NIMH) proposed an innovative framework, the Research Domain Criteria (RDoC), to classify psychiatric disorders. This complementary approach has been used with existing diagnostic systems to identify transdiagnostic factors that inform early detection of mental health disturbances. The development of functional MRI analytical techniques helps to constitute the concept of psychosis as a disorder marked by a deficit of functional integration or segregation, contributing to the formulation of the dysconnection Hypothesis in 1995. Imbalance of the brain segregation and integration of incoming neural stimuli would lead to inflexible cognition and psychotic symptoms. Nowadays the brain functional network reconfiguration persists in the conceptualization of psychosis enriched by neuroimaging findings which corroborate the hypothesis. Specifically, many topological measures from graph theory applied on neuroimaging studies of connectomics were proposed to evaluate how brain networks reorganize due to psychopathological damage. However, how brain-network topology can shape neural responses to psychiatric damage is still needed to better understand the cross-disorder landscape.
In recent years, hypothesis-free connectome-wide association studies (CWAS), allows for pinpointing local functional disconnectivity in many psychiatric disorders at the whole-brain scale with high-throughput capacity. Moreover, brain dynamics received a lot of attention by the scientific community and astonishing findings emerged about the rearrangement of brain dynamic states under the effect of psychosis. Recent whole-brain computational modelling approaches have enabled us to start assessing the effect of input perturbations on brain dynamics, helping examine imbalance of brain segregation and integration in silico for mental patients. We believe development of these novel methods would allow big success on clarifying principles that underlie abnormal neural information transmission in psychosis.
The aim of this Research Topic is to explore novel brain network analytical methods to comprehensively evaluate the deficit of functional integration or segregation along the psychiatric disorder trajectory, or alterations after drug or neuromodulation intervention. Using functional MRI techniques, we focus on pinpointing and quantifying the brain disconnection signature at different levels and its relationship with mental disability. Interdisciplinary research and cross-disorder designs are highly encouraged.
We welcome research articles and review articles on the following topics among others:
• CWAS studies to clarify and validate brain network reconfiguration associated with psychopathology in mental patients, including schizophrenia, ADHD, autism, bipolar affective disorders, major depression, drug addiction, etc.
• Advanced brain dynamic fMRI approaches to quantify transition signatures of brain states in psychosis.
• Novel frameworks and guidelines to establish end-to-end AI model for psychosis diagnosis or prognosis, including graph embedding and graph neural network (GNN) algorithm research.
• Possible connectome-behavior biomarkers or brain network mediators of mental treatment response.
• Discovery of biotypes of psychiatric disorder based on connectome-behavior associations.
In recent years, hypothesis-free connectome-wide association studies (CWAS), allows for pinpointing local functional disconnectivity in many psychiatric disorders at the whole-brain scale with high-throughput capacity. Moreover, brain dynamics received a lot of attention by the scientific community and astonishing findings emerged about the rearrangement of brain dynamic states under the effect of psychosis. Recent whole-brain computational modelling approaches have enabled us to start assessing the effect of input perturbations on brain dynamics, helping examine imbalance of brain segregation and integration in silico for mental patients. We believe development of these novel methods would allow big success on clarifying principles that underlie abnormal neural information transmission in psychosis.
The aim of this Research Topic is to explore novel brain network analytical methods to comprehensively evaluate the deficit of functional integration or segregation along the psychiatric disorder trajectory, or alterations after drug or neuromodulation intervention. Using functional MRI techniques, we focus on pinpointing and quantifying the brain disconnection signature at different levels and its relationship with mental disability. Interdisciplinary research and cross-disorder designs are highly encouraged.
We welcome research articles and review articles on the following topics among others:
• CWAS studies to clarify and validate brain network reconfiguration associated with psychopathology in mental patients, including schizophrenia, ADHD, autism, bipolar affective disorders, major depression, drug addiction, etc.
• Advanced brain dynamic fMRI approaches to quantify transition signatures of brain states in psychosis.
• Novel frameworks and guidelines to establish end-to-end AI model for psychosis diagnosis or prognosis, including graph embedding and graph neural network (GNN) algorithm research.
• Possible connectome-behavior biomarkers or brain network mediators of mental treatment response.
• Discovery of biotypes of psychiatric disorder based on connectome-behavior associations.
Keywords: brain segregation and integration, psychosis, CWAS, dysconnection, connectome-behavior association
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