2026-10-09T22:16:53Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33958212 2026-09-21T17:52:22Z category_1 category_146 category_15 category_16 category_272 category_734 category_61 category_106 portal_5 item_type_3 month_year_09_2026

<p>Onset bifurcation characteristics.</p> Diana M. Karosas (25084908) Marisa Saggio (25084911) William C. Stacey (25084914) Biophysics Space Science Neuroscience Physiology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Developmental Biology Science Policy used dynamical modeling patients &# 8217 consider two nodes exhibit baseline shifts display baseline shifts novel coupling method dependent upon dynamotype recently proposed model investigate seizure propagation unusual recruitment behaviors seizure recruitment properties onset behaviors observed study seizure propagation potential propagation zone node 2 ), node 1 ), seizure onset zone seizure propagation baseline shift node 2 unusual combinations study provide single dynamotype seizure networks coupling factors node 1 model predicted theoretical framework studies focused specific whole seizure dynamotypes previous studies poorly understood particular attention multiclass epileptor less likely independently epileptogenic diverse array create patient bursts autonomously brain models amplitude scaling also measure <div><p>Seizure propagation – how epileptogenic brain tissue recruits less excitable tissue – is poorly understood. Previous studies have used dynamical modeling to study seizure propagation and to create patient-specific whole-brain models of seizure spread. However, these studies focused on seizures of a single dynamotype (onset and offset bifurcation pair). Here, we implement a novel coupling method to investigate seizure propagation in a diverse array of dynamotypes. We utilize the Multiclass Epileptor, a recently proposed model that captures a wide range of seizure dynamotypes in a cortical mass (“node”). We consider two nodes: the seizure onset zone (node 1), which bursts autonomously, and the potential propagation zone (node 2), which is not independently epileptogenic but can be recruited by node 1. We examine the impact of intrinsic and coupling factors on the likelihood and speed of recruitment, with particular attention to the onset bifurcation of node 1. We also measure the range of onset behaviors observed in node 2 with respect to the onset behavior of node 1. The model predicted that seizures that display baseline shifts at onset are less likely to spread, and spread more slowly, compared to seizures that do not exhibit baseline shifts at onset. Seizures that present with amplitude scaling at onset were unlikely to propagate. Further, the model predicted the potential for unusual combinations of onset dynamics, such as a baseline shift in node 2 but not node 1. We confirmed the possibility for several of these unusual recruitment behaviors in humans using intracranial electroencephalography data. The results of the study provide a theoretical framework for seizure propagation, establishing a basis for innovations in characterization of patients’ seizure networks and identification of the seizure onset zone.</p></div> 2026-09-21T17:55:22Z Dataset Dataset 10.1371/journal.pcbi.1013975.t001 https://figshare.com/articles/dataset/_p_Onset_bifurcation_characteristics_p_/33958212 CC BY 4.0