Network representation of multicellular activity in pancreatic islets: Technical considerations for functional connectivity analysis.

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

The brain as a complex network: assessment of EEG-based functional connectivity patterns in patients with childhood absence epilepsy.

The human brain is increasingly seen as a dynamic neural system, the function of which relies on a diverse set of connections between brain regions. To assess these complex dynamical interactions, formalism of complex networks was suggested as one of the most promising tools to offer new insight into the brain's structural and functional organization, with a potential also for clinical implications. Irrespective of the brain mapping technique, modern network approaches have revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, and the presence of hubs. Moreover, the utility of these approaches, to gain a better understanding of neurological diseases, is of great interest. In the present contribution, we first describe the basic network measures and how the brain networks are constructed on the basis of brain activity data in order to introduce clinical neurologists to this new theoretical paradigm. We then demonstrate how network formalism can be used to detect changes in EEG-based functional connectivity patterns in six paediatric patients with childhood absence epilepsy. Notably, our results do not only indicate enhanced synchronicity during epileptic episodes but also reveal specific spatial changes in the electrical activity of the brain. We argue that the network-based evaluation of functional brain networks can provide clinicians with more detailed insight into the activity of a pathological brain and can also be regarded as a support for objective diagnosis and treatment for various neurological diseases.