Time-Varying Neural Connectivity: New Concepts, Methods and Their Clinical Applications in Brain Disorders

From the microscopic to the macroscopic scales, brain functioning is grounded on dynamical phenomena which allow highly efficient information processing and are invariably altered in the presence of many neurological and neuropsychiatric conditions. Methods to determine and analyze the structural and functional networks which underlie such phenomena, therefore, play a crucial role in both research and clinical settings. Until recently, endeavors to study brain connectivity almost invariably assumed that the links are static. This represents an oversimplification because, on par with other complex dynamical systems, brain networks show temporal changes not only in the nodal activity but also in the functional and structural link strengths. Moreover, technological advancements, as well as the growing availability of multimodal datasets, have made it easier to query the principles of brain organization simultaneously at the high spatial and high temporal resolution, thus prompting the reconciliation and combination of efforts focused on mapping topographical and dynamical aspects.

The purpose of this Research Topic is to coalesce conceptual, technical, and clinical developments regarding mapping time-varying brain connectivity. At the conceptual level, the field of dynamic graphs is developing floridly and offers theoretical tools that go beyond extensions of the established measures of static network topology, and which are only beginning to be translated into indices applied to neuroscientific settings. Similarly, the field of nonlinear dynamics offers countless examples of the intricate relationship between structural couplings and the emergence of synchronization patterns, which have an obvious but as-yet under-explored relevance to interpreting brain data. At the technical level, advancements in diverse data acquisition technologies such as fast functional neuroimaging approaches, optical imaging, and high-density electrical recordings, converge towards finally bridging the gap between the studies focusing on mapping the topography of brain functions and connectivity, and those targeting dynamical phenomena through detailed signal analysis. At the clinical level, the potential impact of a more principled modeling of network dynamics on differential diagnosis, staging, and follow-up remains almost entirely to be addressed. A deeper, more complete, and consistent understanding of network dynamics made possible by advanced datasets could be highly instrumental in illuminating the way toward successful applications based on parsimonious data acquisition requirements.

This Research Topic will exclusively accept manuscripts offering new and relevant neuroscientific or physical concepts, experimental results based on emerging data acquisition techniques or data analysis methods, insights into the relevance of modeling time-varying brain connectivity to the clinical workup of neurological and neuropsychiatric conditions, as well as reviews and informative opinions around these topics. Manuscripts trivially presenting applications of well-established methods or based on datasets having an insufficient temporal resolution will be rejected outright.

This Research Topic will welcome, in particular, contributions around the following areas:

- New concepts, theoretical tools and estimators relevant to the analysis of time-varying neural connectivity;

- Technical advancements in the acquisition, processing, and analysis of high-temporal resolution neuroscientific data applied to the study of time-varying connectivity;

- Experimental investigations of time-varying neural connectivity grounded on new concepts, analyses, or data acquisition settings (human and animal in vivo and in vitro recordings are equally welcome);

- Studies of time-varying neural connectivity across multiple temporal and spatial scales;

- Multidisciplinary studies of time-varying neural connectivity juxtaposing it to data from other systems in physics, biology, and engineering;