Multi-Unit Activity in the Human Cortex as a Predictor of Seizure Onset
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CitationRozman, Peter Andrew. 2015. Multi-Unit Activity in the Human Cortex as a Predictor of Seizure Onset. Doctoral dissertation, Harvard Medical School.
AbstractEpilepsy is a neurological disorder affecting 50 million people worldwide. It consists of a large number of syndromes, all of which are characterized by a predisposition to recurrent, unprovoked seizures, while differing by degree of focality, clinical manifestation and many other factors. Despite the prevalence of this disorder, relatively little is known about the basic physiological mechanisms that underlie the seizures themselves. Additionally, roughly 25% of patients are refractory to existing therapies. The need for more highly targeted therapies for focal epilepsies has driven decades of research on seizure prediction. While most of these studies have relied on scalp or intracranial EEG, more recent studies have taken advantage of electrodes that capture single- or multi-unit activity. We utilized a linear microelectrode array to capture multi-unit activity in humans with refractory epilepsy with the expectation that such microscale activity may provide a signal in advance of changes on electroencephalography.
Twelve patients underwent long-term monitoring with both clinical electrocorticography (ECoG) and the laminar microelectrode array, which consists of linearly arranged contacts that sample all layers of the human cortex. Multi-unit (300-5000 Hz) power was compared between thirty-minute preictal and interictal time windows.
Several parameters characterizing the multi-unit power were compared between preictal and interictal time windows. Parameters included proximity to seizure focus, depth of recording, and directionality of changes in multi-unit power. Optimization of these parameters resulted in a best-performing classifier with sensitivity and specificity of 0.70 and 0.80, respectively.
These results demonstrate reproducible increases and decreases in multi-unit activity prior to seizure onset and suggest that multi-unit information may be useful in the development of future seizure prediction systems.
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