Unsupervised Spike Sorting of extracellular electrophysiological recording in subthalamic nucleus of Parkinsonian patients
| Autor: | Tetyana I. Aksenova, Olga K. Chibirova, Jean Rouat, Alessandro E. P. Villa, Steeve Larouche, Stephan Chabardes, Alim-Louis Benabid |
|---|---|
| Přispěvatelé: | Neurosciences précliniques, Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institute of Applied System Analysis, Ukrainian Academy of Sciences, Laboratoire de Neurobiophysique, Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenble, Département de génie électronique et de génie informatique (IMSI), Univercité de Sherbrooke 2, Issartel, Jean-Paul |
| Rok vydání: | 2005 |
| Předmět: |
Statistics and Probability
Deep brain stimulation Computer science medicine.medical_treatment Action Potentials General Biochemistry Genetics and Molecular Biology MESH: Software 03 medical and health sciences Bursting 0302 clinical medicine Subthalamic Nucleus Basal ganglia medicine Humans Premovement neuronal activity MESH: Action Potentials MESH: Subthalamic Nucleus 030304 developmental biology 0303 health sciences MESH: Humans Applied Mathematics Sorting Parkinson Disease General Medicine Electrophysiology Subthalamic nucleus Spike sorting Modeling and Simulation Neuroscience Software MESH: Parkinson Disease 030217 neurology & neurosurgery |
| Zdroj: | BioSystems BioSystems, Elsevier, 2005, 79 (1-3), pp.159-71. ⟨10.1016/j.biosystems.2004.09.028⟩ |
| ISSN: | 0303-2647 |
| DOI: | 10.1016/j.biosystems.2004.09.028 |
| Popis: | International audience; The present study demonstrates the application of the Unsupervised Spike Sorting algorithm (USS) to separation of multi-unit recordings and investigation of neuronal activity patterns in the subthalamic nucleus (STN). This nucleus is the main target for deep brain stimulation (DBS) in Parkinsonian patients. The USS comprises a fast unsupervised learning procedure and allows sorting of multiple single units, if any, out of a bioelectric signal. The algorithm was tested on a simulated signal with different levels of noise and with application of Time and Spatial Adaptation (TSA) algorithm for denoising. The results of the test showed a good quality of spike separation and allow its application to investigation of neuronal activity patterns in a medical application. One hundred twenty-four single channel multi-unit records from STN of 6 Parkinsonian patients were separated with USS into 492 single unit trains. Auto- and crosscorrellograms for each unit were analyzed in order to reveal oscillatory, bursting and synchronized activity patterns. We analyzed separately two brain hemispheres. For each hemisphere the percentage of units of each activity pattern were calculated. The results were compared for the first and the second operated hemispheres of each patient and in total. |
| Databáze: | OpenAIRE |
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