Brain network dynamics codify heterogeneity in seizure propagation

Abstract

Dynamic functional brain connectivity facilitates adaptive cognition and behavior. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures. A central but unresolved question concerns the mechanisms by which extraordinarily diverse dynamics of seizures emerge. Here, we apply a graph-theoretical approach to assess dynamic reconfigurations in the functional brain connectivity before, during, and after seizures that display heterogeneous propagation patterns despite sharing similar origins. We demonstrate unique reconfigurations in globally-defined network properties preceding seizure onset that predict propagation patterns of impending seizures, and in locally-defined network properties that differentiate post-onset dynamics. These results characterize quantitative network features underlying the heterogeneity of seizure dynamics and the accompanying clinical manifestations. Decoding these network properties could improve personalized preventative treatment strategies for epilepsy as well as other neurological disorders.

Intermittent brain network reconfigurations and the resistance to social media influence

“…A critical feature of our findings is the fact that the temporal profiles of the estimated community structure is more diagnostic (e.g., intermittence ) than simply the fact that dynamic network reconfigurations occur ( Figure 3 ). The temporal profile of interactions has a fundamental importance on a wide range of phenomena such as the dynamics of neuron populations that lead to seizures ( Jirsa et al, 2014 ; Rungratsameetaweemana et al, 2021 ), weather models and turbulent systems such as the Lorenz attractor ( Ruelle, 1976 ) and the many synchronization phenomena in which many units share the same temporal profile ( Pikovsky et al, 2001 ; Strogatz, 2000 ). From the point of view of dynamical systems, processes of opinion spreading have been extensively studied using models such as the Voter ( Holley & Liggett, 1975 ) and majority rule models ( Krapivsky & Redner, 2003 ), suggesting a complex interactive scheme that gives rise to opinion formation and change.…”

Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

“…Then, a filter based on Fourier transform was applied to divide the EEG into five bands. These five bands-gamma (30-60 Hz), beta (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), alpha (8-15 Hz), theta (4-8 Hz) and delta (0.5-4 Hz)-and unfiltered raw EEG were used for subsequent calculations.…”

Intermittent brain network reconfigurations and the resistance to social media influence

“…al. 2014, Rungratsameetaweemana et al, 2021, weather models and turbulent systems such as the Lorenz attractor (Ruelle 1976) and the many synchronization phenomena in which many units share the same temporal profile (Pikovsky et. al.…”