Functional connectivity (FC) is one of the widely used approaches that enables us to explore the intrinsic network organization of the human brain in typical or atypical situations. FC analysis is typically calculated as the static correlation between two region-specific temporal series, with the assumption that intrinsic FC of the brain remains constant during the entire brain scan. While this assumption keeps FC analysis from becoming vastly more complex, it also, unfortunately, represents a gross oversimplification by ignoring the highly dynamic nature of brain activity. In recent years, we have witnessed a rapid growth of interest in moving traditional FC analysis beyond simple whole-length averages and into dynamic FC analysis approaches that allow a detailed mapping of time-varying FC in the human brain.
While a number of studies have demonstrated the feasibility of using this relatively new dynamic FC analysis approach to characterize the intrinsic brain network during different states with more fine-grained temporal resolution, there remain various questions to be answered in terms of the test-retest reliability, usable modalities and applicable practice of the dynamic FC analysis approach. Specifically, we are interested in evaluating the reliability of dynamic FC analysis, such as optimal parameters selection and generalized abilities across multi-run tests. In addition, the majority of existing dynamic FC-based studies have been focused on functional magnetic resonance imaging (fMRI), however, whether this approach can be extended to other popular recording techniques, such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), electrocorticography (ECoG), local field potential (LFP), calcium imaging, and spike trains from either brain wide or local circuits needs to be explored further. For example, FC can be defined by oscillatory synchrony between various brain regions (EEG, LFP), as well as synchronized firing of neuron pairs within a brain region or brain circuit (calcium imaging, single units), using signal processing and machine learning techniques. Finally, there is also a critical need to evaluate the applicable practice of using dynamic FC analysis to investigate the intrinsic brain network in certain research/clinical scenarios, such as social-emotional processing and cognitive impairment. In sum, the goal of this Research Topic is to provide a relatively comprehensive guideline for using dynamic FC analysis to study the typical and atypical patterns of brain dynamics in healthy patients, as well as those with different brain disorders.
The scope of this Topic focuses on developments and findings that provide further insight into the fundamental methodology of dynamic FC analysis and novel investigations on the brain’s dynamic nature in both normal and abnormal populations, using different brain imaging techniques. We welcome contributions that address, but are not limited to, the following themes:
- Novel analytical methods for optimal parameter selection in dynamic FC analysis, such as task duration, length of time window, algorithms for brain states determination
- Reliability evaluation across multiple or longitudinal runs/tests
- Dynamic FC analysis using different modalities and multi-modality, including, but not limited to, fMRI, EEG, fNIRS, ECoG, LFP, Calcium Imaging, Spike trains.
- Novel tools for analysis and visualization of dynamic FC
- Characterization of dynamic FC under specific states, such as social interaction, emotion processing, spatial navigation, memory and sleep disorder, linking statistical significance to biological significance in previous unachievable time scales.
- Review of recent developments neuronal dynamics tracing methods, not limited to dynamic FC analysis
Functional connectivity (FC) is one of the widely used approaches that enables us to explore the intrinsic network organization of the human brain in typical or atypical situations. FC analysis is typically calculated as the static correlation between two region-specific temporal series, with the assumption that intrinsic FC of the brain remains constant during the entire brain scan. While this assumption keeps FC analysis from becoming vastly more complex, it also, unfortunately, represents a gross oversimplification by ignoring the highly dynamic nature of brain activity. In recent years, we have witnessed a rapid growth of interest in moving traditional FC analysis beyond simple whole-length averages and into dynamic FC analysis approaches that allow a detailed mapping of time-varying FC in the human brain.
While a number of studies have demonstrated the feasibility of using this relatively new dynamic FC analysis approach to characterize the intrinsic brain network during different states with more fine-grained temporal resolution, there remain various questions to be answered in terms of the test-retest reliability, usable modalities and applicable practice of the dynamic FC analysis approach. Specifically, we are interested in evaluating the reliability of dynamic FC analysis, such as optimal parameters selection and generalized abilities across multi-run tests. In addition, the majority of existing dynamic FC-based studies have been focused on functional magnetic resonance imaging (fMRI), however, whether this approach can be extended to other popular recording techniques, such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), electrocorticography (ECoG), local field potential (LFP), calcium imaging, and spike trains from either brain wide or local circuits needs to be explored further. For example, FC can be defined by oscillatory synchrony between various brain regions (EEG, LFP), as well as synchronized firing of neuron pairs within a brain region or brain circuit (calcium imaging, single units), using signal processing and machine learning techniques. Finally, there is also a critical need to evaluate the applicable practice of using dynamic FC analysis to investigate the intrinsic brain network in certain research/clinical scenarios, such as social-emotional processing and cognitive impairment. In sum, the goal of this Research Topic is to provide a relatively comprehensive guideline for using dynamic FC analysis to study the typical and atypical patterns of brain dynamics in healthy patients, as well as those with different brain disorders.
The scope of this Topic focuses on developments and findings that provide further insight into the fundamental methodology of dynamic FC analysis and novel investigations on the brain’s dynamic nature in both normal and abnormal populations, using different brain imaging techniques. We welcome contributions that address, but are not limited to, the following themes:
- Novel analytical methods for optimal parameter selection in dynamic FC analysis, such as task duration, length of time window, algorithms for brain states determination
- Reliability evaluation across multiple or longitudinal runs/tests
- Dynamic FC analysis using different modalities and multi-modality, including, but not limited to, fMRI, EEG, fNIRS, ECoG, LFP, Calcium Imaging, Spike trains.
- Novel tools for analysis and visualization of dynamic FC
- Characterization of dynamic FC under specific states, such as social interaction, emotion processing, spatial navigation, memory and sleep disorder, linking statistical significance to biological significance in previous unachievable time scales.
- Review of recent developments neuronal dynamics tracing methods, not limited to dynamic FC analysis
