Throughout this decade, the proceedings of studies regarding brain network have revealed the dynamical behaviors of brain networks including multi-temporal scale dynamics, from ultra-slow to moment-to-moment behaviors. The network dynamics is captured by the degree of synchronization and information flow between pair-wise brain regional neural activities, which is called dynamical functional connectivity. Moreover, besides the pair-wise neural interaction, the temporal itinerancy of global topology of the whole brain functional network is present. Under pathological conditions, the multi-temporal-scale characteristics of such network dynamics exhibit disease-specific alternations. Therefore, these characteristics pose the possibility for biomarker identification of psychiatric disorders. For its achievement, two major approaches exist.
The first one is a physiological-data-driven approach with neuroimaging of electroencephalography, magnetoencephalography, and functional magnetic resonance imaging. For this approach, the method for utilizing the temporal variation of functional connectivity within a short-time window has been developed along with the subsequent technique of using the instantaneous temporal patterns produced by neural interaction, required for the achievement of high temporal resolution to capture the characteristics of moment-to-moment dynamical functional connectivity.
The second one involves a simulation-based approach using mathematical models with high pathological validity typified as spiking neural networks. Recent mathematical modeling for brain networks focuses on large hieratical neural characteristics from the molecular/cellular level and the local neural circuit level to global whole brain levels. Therefore, embedding the disease-specific impairments into the modeled-brain network studies would aid in revealing the mechanisms by which these individual impairments affect the alternations of brain network dynamics. This research topic aims to inspire further research to focus on both approaches and facilitate the mutual use of findings of network dynamics and individual approaches.
We welcome Original Research, Brief Research Reports, Reviews and Mini-reviews addressing, but not limited to, the following issues:
1. Alternations of dynamical functional connectivity under the pathological conditions of psychiatric disorders.
2. Mathematical modeling for describing disease-specific characteristics of network dynamics for psychiatric disorders.
Throughout this decade, the proceedings of studies regarding brain network have revealed the dynamical behaviors of brain networks including multi-temporal scale dynamics, from ultra-slow to moment-to-moment behaviors. The network dynamics is captured by the degree of synchronization and information flow between pair-wise brain regional neural activities, which is called dynamical functional connectivity. Moreover, besides the pair-wise neural interaction, the temporal itinerancy of global topology of the whole brain functional network is present. Under pathological conditions, the multi-temporal-scale characteristics of such network dynamics exhibit disease-specific alternations. Therefore, these characteristics pose the possibility for biomarker identification of psychiatric disorders. For its achievement, two major approaches exist.
The first one is a physiological-data-driven approach with neuroimaging of electroencephalography, magnetoencephalography, and functional magnetic resonance imaging. For this approach, the method for utilizing the temporal variation of functional connectivity within a short-time window has been developed along with the subsequent technique of using the instantaneous temporal patterns produced by neural interaction, required for the achievement of high temporal resolution to capture the characteristics of moment-to-moment dynamical functional connectivity.
The second one involves a simulation-based approach using mathematical models with high pathological validity typified as spiking neural networks. Recent mathematical modeling for brain networks focuses on large hieratical neural characteristics from the molecular/cellular level and the local neural circuit level to global whole brain levels. Therefore, embedding the disease-specific impairments into the modeled-brain network studies would aid in revealing the mechanisms by which these individual impairments affect the alternations of brain network dynamics. This research topic aims to inspire further research to focus on both approaches and facilitate the mutual use of findings of network dynamics and individual approaches.
We welcome Original Research, Brief Research Reports, Reviews and Mini-reviews addressing, but not limited to, the following issues:
1. Alternations of dynamical functional connectivity under the pathological conditions of psychiatric disorders.
2. Mathematical modeling for describing disease-specific characteristics of network dynamics for psychiatric disorders.
