There is a growing appreciation that many psychiatric (and neurological) conditions can be understood as functional disconnection syndromes – as reflected in aberrant functional integration and synaptic connectivity. This Research Topic considers recent advances in understanding psychopathology in terms of aberrant effective connectivity – as measured noninvasively using functional magnetic resonance imaging (fMRI). For example, major depressive disorder (MDD) is a common psychiatric disorder that is characterized by persistent feelings of negative moods, low self-esteem and a loss of interest or pleasure in formerly pleasurable activities. Convergent findings from neuroimaging studies suggest that MDD is associated with dysregulation of specific brain networks, rather than dysfunction of single brain regions. Functional connectivity – defined as statistical dependency among regional time series – has been widely used to identify candidate networks implicated in MDD. These networks include the default mode network, the cognitive control network, the affective network etc. However, because functional connectivity does not support inferences about directed (causal) brain connections, the changes in information flow in these distributed systems remain largely unknown.
Recently, there has been increasing interest in inferring directed connectivity (effective connectivity) from fMRI data. Effective connectivity refers to the influence that one neural system exerts over another and quantifies the directed coupling among brain regions – and how they change with pathophysiology. Compared to functional connectivity, effective connectivity allows one to understand how brain regions interact with each other in terms of context sensitive changes and directed coupling – and therefore may provide mechanistic insights into the neural basis of psychopathology.
Established models of effective connectivity include psychophysiological interaction (PPI), structural equation modeling (SEM) and dynamic causal modelling (DCM). DCM is unique because it explicitly models the interaction among brain regions in terms of latent neuronal activity. Moreover, recent advances in DCM such as stochastic and spectral DCM, make it possible to characterize the interaction between different brain regions both at rest and during a cognitive task.
This Research Topic aims to investigate the neural circuitry of major psychiatric disorders (e.g., MDD, schizophrenia, Huntington's and Parkinson’s disease, etc.) using fMRI. In addition, this research topic focuses on the effects of treatment on directed coupling among brain regions – and whether alterations in effective connectivity persist in remitted subjects. Furthermore, we hope to address the relationship between persistent changes in effective connectivity and relapse.
There is a growing appreciation that many psychiatric (and neurological) conditions can be understood as functional disconnection syndromes – as reflected in aberrant functional integration and synaptic connectivity. This Research Topic considers recent advances in understanding psychopathology in terms of aberrant effective connectivity – as measured noninvasively using functional magnetic resonance imaging (fMRI). For example, major depressive disorder (MDD) is a common psychiatric disorder that is characterized by persistent feelings of negative moods, low self-esteem and a loss of interest or pleasure in formerly pleasurable activities. Convergent findings from neuroimaging studies suggest that MDD is associated with dysregulation of specific brain networks, rather than dysfunction of single brain regions. Functional connectivity – defined as statistical dependency among regional time series – has been widely used to identify candidate networks implicated in MDD. These networks include the default mode network, the cognitive control network, the affective network etc. However, because functional connectivity does not support inferences about directed (causal) brain connections, the changes in information flow in these distributed systems remain largely unknown.
Recently, there has been increasing interest in inferring directed connectivity (effective connectivity) from fMRI data. Effective connectivity refers to the influence that one neural system exerts over another and quantifies the directed coupling among brain regions – and how they change with pathophysiology. Compared to functional connectivity, effective connectivity allows one to understand how brain regions interact with each other in terms of context sensitive changes and directed coupling – and therefore may provide mechanistic insights into the neural basis of psychopathology.
Established models of effective connectivity include psychophysiological interaction (PPI), structural equation modeling (SEM) and dynamic causal modelling (DCM). DCM is unique because it explicitly models the interaction among brain regions in terms of latent neuronal activity. Moreover, recent advances in DCM such as stochastic and spectral DCM, make it possible to characterize the interaction between different brain regions both at rest and during a cognitive task.
This Research Topic aims to investigate the neural circuitry of major psychiatric disorders (e.g., MDD, schizophrenia, Huntington's and Parkinson’s disease, etc.) using fMRI. In addition, this research topic focuses on the effects of treatment on directed coupling among brain regions – and whether alterations in effective connectivity persist in remitted subjects. Furthermore, we hope to address the relationship between persistent changes in effective connectivity and relapse.
