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

Abstract

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.

NMDA receptor inhibition increases, synchronizes, and stabilizes the collective pancreatic beta cell activity: Insights through multilayer network analysis

“…To this end, we combined advanced high-frequency functional multicellular imaging using a novel stable and bright low-affinity Ca 2+ probe with classical physiological analyses, as well as functional and multilayer network tools [13,25,[52][53][54][55][56][57]. The latter have proven to be a much richer computational framework than classical network analyses and particularly suitable for the description of time-varying networks and for exploring the temporal robustness of functional connectivity patterns [25,[58][59][60][61][62][63]. Representing the spreading Ca 2+ waves in islets of Langerhans as network layers, to the best of our knowledge for the first time, enabled us to interpret the effects of NMDAR inhibition on the multicellular level of beta cell activity, far beyond classical physiological and network methods for cellular signaling analysis.…”

Frequency-Dependent Dynamics of Functional Connectivity Networks During Seizure Termination in Childhood Absence Epilepsy: A Magnetoencephalography Study

“…Epileptic seizures were considered the result of hypersynchronous and abnormal discharges among neurons. It was found that the synchronicity of neural activity was enhanced during ictal episodes (6). Some scholars have reported that hypersynchronized neuronal activity increases the chance of epileptic discharges and eventually leads to epileptic seizures (7)(8)(9).…”

“…The brain can be seen as a complex network in which nodes of a network represent brain areas, and edges reflect either structural or functional connections between different nodes (6). An increasing amount of evidence has indicated that epilepsy is a network disease and that epileptic discharges spread to the whole brain through the network (6,(13)(14)(15). Previous studies have also revealed altered functional and structural networks in patients with epilepsy (13,(16)(17)(18).…”

“…The clustering coefficient and shortest path length are the representative parameters in GT. Also, brain networks could be classified into three types, including small-world networks, random networks, and regular networks, according to these parameters ( 6 , 19 ). Specific altered topological characteristics were observed in patients with epilepsy reported by several studies ( 21 – 23 ).…”

Information Optimized Multilayer Network Representation of High Density Electroencephalogram Recordings

“…In the context of EEG we can assign distinct layers to different time windows and/or different frequency bands and assign each electrode to a node in each single-layer network. For example, a time-based multilayer complex network analysis was perfomed on EEG recordings in patients with epilepsy (Leitgeb et al, 2020). The central issues in multilayer network based methods for EEG signal is to find a representation that minimizes information loss and introduce suitable statistical tools to extract readable information from the networks.…”