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      Millimeter-scale epileptiform spike patterns and their relationship to seizures.

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          Abstract

          Advances in neural electrode technology are enabling brain recordings with increasingly fine spatial and temporal resolution. We explore spatio-temporal (ST) patterns of local field potential spikes using a new high-density active electrode array with 500 μm resolution. We record subdural micro-electrocorticographic (μECoG) signals in vivo from a feline model of acute neocortical epileptiform spikes and seizures induced with local administration of the GABA antagonist, picrotoxin. We employ a clustering algorithm to separate 2-dimensional (2-D) spike patterns to isolate distinct classes of spikes unique to the interictal and ictal states. Our findings indicate that the 2-D patterns can be used to distinguish seizures from non-seizure state. We find two statistically significant ST patterns that uniquely characterize ictal epochs. We conclude that millimeter-scale ST spike dynamics contain useful information about ictal state. This finding may be important to understanding mechanisms underlying local circuit activity during seizure generation. Further work will investigate whether patterns we identify can increase our understanding of seizure dynamics and their underlying mechanisms and inform new electrical stimulation protocols for seizure termination.

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          Author and article information

          Journal
          Conf Proc IEEE Eng Med Biol Soc
          Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
          Institute of Electrical and Electronics Engineers (IEEE)
          1557-170X
          1557-170X
          2011
          : 2011
          Affiliations
          [1 ] Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA. achamb@seas.upenn.edu
          Article
          NIHMS762475
          10.1109/IEMBS.2011.6090174
          4807854
          22254422
          196c3b84-1227-467a-959a-8e3d292cff8f
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