The epilepsies are a family of disorders of brain dynamics. Epilepsy patients are susceptible to seizures (changes in sensation, awareness, or behavior caused by brief electrical disturbances in the brain). Often, the electrical disturbances originate from a """"""""focus"""""""" or """"""""epileptogenic zone"""""""" in part of the brain, from which the disturbances are propagated to other parts of the brain. Epilepsy affects approximately 2.5 million persons in the United States, and over 50 million persons worldwide, and 150,000 to 200,000 new cases occur annually in the U.S. Although most epilepsy patients can control their seizures with the use of antiepileptic drugs, between 20% to 25% of patients cannot bring their seizures under control using drug therapy. Many patients with pharmacologically intractable seizures can eliminate their disability largely or completely by neurosurgical intervention, which typically involves resection of tissue in the epileptogenic zone to prevent the spread of electrical disturbances. Candidates for epilepsy surgery are generally evaluated first with scalp EEG. In approximately 20% of cases, however, intracranial telemetry (iEEG) with brain surface (ElectroCorticoGraphy, or ECoG) and/or depth electrodes is required to determine the site(s) of seizure onset, but determination of the seizure onset zone is often ambiguous. We propose to develop software tools that will aid the clinician in the identification of the epileptogenic zone from iEEG data, through the multimodal integration of iEEG data with structural and functional imaging data, visualization of iEEG data, both in space and time, and analysis of iEEG data. During Phase I, we designed, implemented, and validated software to integrate the visualization of ECoG electrode locations on sMRI data, on a subject-specific basis, and visualize ECoG activity patterns in both space and time. During Phase II, we will extend these results to include depth electrode data. We will also develop, implement and verify statistical signal processing tools to aid in the identification and localization of the seizure onset zone. These methods will be validated by comparison with clinical findings. A principal design objective is the creation of software that can be used by a trained technician to produce a reliable visualization with less than 1 hour of preparation time.
The work described in this proposal addresses the need to develop improved software tools for the visualization and analysis of intracranial electroencephalographic (iEEG) measurements obtained from the surface (electrocorticography, or ECoG) or from the depths of the human brain. If successful, these tools will advance the clinical practice of surgical management for epilepsy, as well as providing an enabling technology for basic and clinical human brain research.
| Ossadtchi, A; Greenblatt, R E; Towle, V L et al. (2010) Inferring spatiotemporal network patterns from intracranial EEG data. Clin Neurophysiol 121:823-35 |
