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eNeuro. 2017 Mar 8;4(1). pii: ENEURO.0091-16.2017. doi: 10.1523/ENEURO.0091-16.2017. eCollection 2017 Jan-Feb.

Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy.

Author information

1
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104; Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104.
2
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104; Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104.
3
Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104.
4
Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104; Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104.
5
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104; Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104.

Abstract

Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network.

KEYWORDS:

dynamic network neuroscience; epileptic network; functional subgraphs; interictal; non-negative matrix factorization; prediction

PMID:
28303256
PMCID:
PMC5343278
DOI:
10.1523/ENEURO.0091-16.2017
[Indexed for MEDLINE]
Free PMC Article

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