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Seizure pathways: A model-based investigation

Karoly, Philippa J ; Kuhlmann, Levin ; Soudry, Daniel ; Grayden, David B ; Cook, Mark J ; Freestone, Dean R Marinazzo, Daniele

PLoS computational biology, 2018-10, Vol.14 (10), p.e1006403-e1006403 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Seizure pathways: A model-based investigation
Autor: Karoly, Philippa J ; Kuhlmann, Levin ; Soudry, Daniel ; Grayden, David B ; Cook, Mark J ; Freestone, Dean R
Marinazzo, Daniele
Assuntos: Algorithms ; Biology and Life Sciences ; Biomedical engineering ; Brain ; Computation ; Computational Biology ; Computational neuroscience ; Convulsions & seizures ; Databases, Factual ; Dynamics ; Electrocorticography - methods ; Engineering ; Epilepsy ; Estimates ; Evolution ; Experimental research ; Funding ; Hospitals ; Humans ; Mathematical models ; Medicine and Health Sciences ; Models, Neurological ; Neural networks ; Neurosciences ; Parameters ; Patients ; Physical Sciences ; Research and Analysis Methods ; Seizures ; Seizures - diagnosis ; Seizures - physiopathology ; Signal Processing, Computer-Assisted ; Software
É parte de: PLoS computational biology, 2018-10, Vol.14 (10), p.e1006403-e1006403
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I have read the journal’s policy and the authors of this manuscript have the following competing interests: PJK, DRF and MJC are shareholders/employees of Seer Medical Pty Ltd. Data for this study was obtained from NeuroVista Pty Ltd.
Current address: Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Melbourne, Australia
Descrição: We present the results of a model inversion algorithm for electrocorticography (ECoG) data recorded during epileptic seizures. The states and parameters of neural mass models were tracked during a total of over 3000 seizures from twelve patients with focal epilepsy. These models provide an estimate of the effective connectivity within intracortical circuits over the time course of seizures. Observing the dynamics of effective connectivity provides insight into mechanisms of seizures. Estimation of patients seizure dynamics revealed: 1) a highly stereotyped pattern of evolution for each patient, 2) distinct sub-groups of onset mechanisms amongst patients, and 3) different offset mechanisms for long and short seizures. Stereotypical dynamics suggest that, once initiated, seizures follow a deterministic path through the parameter space of a neural model. Furthermore, distinct sub-populations of patients were identified based on characteristic motifs in the dynamics at seizure onset. There were also distinct patterns between long and short duration seizures that were related to seizure offset. Understanding how these different patterns of seizure evolution arise may provide new insights into brain function and guide treatment for epilepsy, since specific therapies may have preferential effects on the various parameters that could potentially be individualized. Methods that unite computational models with data provide a powerful means to generate testable hypotheses for further experimental research. This work provides a demonstration that the hidden connectivity parameters of a neural mass model can be dynamically inferred from data. Our results underscore the power of theoretical models to inform epilepsy management. It is our hope that this work guides further efforts to apply computational models to clinical data.
Editor: United States: Public Library of Science
Idioma: Inglês

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Seizure pathways: A model-based investigation

Karoly, Philippa J ; Kuhlmann, Levin ; Soudry, Daniel ; Grayden, David B ; Cook, Mark J ; Freestone, Dean R Marinazzo, Daniele

United States: Public Library of Science

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