Patients with neuropsychiatric disorders often suffer from functional impairments in cognition, emotion and social behaviors, which seriously affect the quality of life of patients and bring a great burden to family and society. However, the neuropathology underlying dysfunctions in neuropsychiatric disorders remains unclear.
Functional connectivity (FC) has been demonstrated to be an effective and powerful way to study the neuropathology of neuropsychiatric disorders by examining the alterations in the neural circuitry functions. Conventional FC measures the correlation of signals, and reflects the interaction and communication between regions. However, this method considers that the role of two regions is equal in information transformation between them and does not estimate the direction of FC. In recent years, studies have proposed several advanced models, such as Granger Causality Model, Dynamic Causal Model, and Structural Equation Model, to calculate the effective connectivity (EC), which can estimate the direction and strength of connections between the brain regions, and provide richer information than traditional FC in revealing the neuropathology underlying the clinical disorders. Thus, this Research Topic focuses on the recent developments in methods and models based on EC in fMRI, EEG and MEG, and their applications in neuropsychiatric disorders. We solicit both reviews and original research articles on the use of advanced functional connectivity analysis.
This Research Topic will explore neuropathological mechanisms underlying functional deficits in neuropsychiatric disorders by using effectivity connectivity in fMRI, EEG and MEG. This topic will also address important conceptual and methodological questions in understanding how effectivity connectivity characterizes the information flow of brain regions. We expect that this topic will be beneficial for clinicians to understand the nature, origins and neuropathological mechanisms of clinical symptoms in neuropsychiatric disorders.
Research areas covered by this Research Topic include, but are not limited to:
• Studies using effectivity connectivity in neuropsychiatric disorders, such as schizophrenia, depressive disorder, bipolar disorder, somatization disorder, anxiety disorder, panic disorder, obsessive compulsive disorder, epilepsy, disorders of consciousness, eating disorder, cervical dystonia, and hypochondriasis
• Diagnosis, prognosis, and assessment of therapy using effectivity connectivity measures
• Novel algorithms, model and analytics frameworks for effectivity connectivity
• Relationship between effectivity connectivity changes and behavioral changes
• Review articles on the developments of advanced effectivity connectivity
• Application of effectivity connectivity in synchronous fMRI-EEG study
Patients with neuropsychiatric disorders often suffer from functional impairments in cognition, emotion and social behaviors, which seriously affect the quality of life of patients and bring a great burden to family and society. However, the neuropathology underlying dysfunctions in neuropsychiatric disorders remains unclear.
Functional connectivity (FC) has been demonstrated to be an effective and powerful way to study the neuropathology of neuropsychiatric disorders by examining the alterations in the neural circuitry functions. Conventional FC measures the correlation of signals, and reflects the interaction and communication between regions. However, this method considers that the role of two regions is equal in information transformation between them and does not estimate the direction of FC. In recent years, studies have proposed several advanced models, such as Granger Causality Model, Dynamic Causal Model, and Structural Equation Model, to calculate the effective connectivity (EC), which can estimate the direction and strength of connections between the brain regions, and provide richer information than traditional FC in revealing the neuropathology underlying the clinical disorders. Thus, this Research Topic focuses on the recent developments in methods and models based on EC in fMRI, EEG and MEG, and their applications in neuropsychiatric disorders. We solicit both reviews and original research articles on the use of advanced functional connectivity analysis.
This Research Topic will explore neuropathological mechanisms underlying functional deficits in neuropsychiatric disorders by using effectivity connectivity in fMRI, EEG and MEG. This topic will also address important conceptual and methodological questions in understanding how effectivity connectivity characterizes the information flow of brain regions. We expect that this topic will be beneficial for clinicians to understand the nature, origins and neuropathological mechanisms of clinical symptoms in neuropsychiatric disorders.
Research areas covered by this Research Topic include, but are not limited to:
• Studies using effectivity connectivity in neuropsychiatric disorders, such as schizophrenia, depressive disorder, bipolar disorder, somatization disorder, anxiety disorder, panic disorder, obsessive compulsive disorder, epilepsy, disorders of consciousness, eating disorder, cervical dystonia, and hypochondriasis
• Diagnosis, prognosis, and assessment of therapy using effectivity connectivity measures
• Novel algorithms, model and analytics frameworks for effectivity connectivity
• Relationship between effectivity connectivity changes and behavioral changes
• Review articles on the developments of advanced effectivity connectivity
• Application of effectivity connectivity in synchronous fMRI-EEG study
