Emerging roles of network analysis for epilepsy
| Autor: | Richard J. Staba, Kareem A. Zaghloul, Mark A. Kramer, Kristin M. Gunnarsdottir, Jennifer Stiso, Virginia B. Liu, William C. Stacey, Rachel J. Smith, Ankit N. Khambhati, Danielle S. Bassett, Sridevi V. Sarma, Beth A. Lopour, Sara K. Inati, Jorge Gonzalez-Martinez |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Computer science Clinical Sciences Network science Neurodegenerative Electroencephalography Article Session (web analytics) Functional connectivity 03 medical and health sciences Epilepsy 0302 clinical medicine Control theory medicine Humans EEG Brain Mapping Neurology & Neurosurgery Infantile spasms medicine.diagnostic_test Neurosciences Brain Graph theory Special Interest Group medicine.disease Data science Brain Disorders ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Neurology Neurological Network analysis Neurology (clinical) Nerve Net 030217 neurology & neurosurgery |
| Zdroj: | Epilepsy Res |
| ISSN: | 0920-1211 |
| Popis: | In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals. |
| Databáze: | OpenAIRE |
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