Unsupervised Spike Sorting of extracellular electrophysiological recording in subthalamic nucleus of Parkinsonian patients.
Identifieur interne : 001800 ( PubMed/Checkpoint ); précédent : 001799; suivant : 001801Unsupervised Spike Sorting of extracellular electrophysiological recording in subthalamic nucleus of Parkinsonian patients.
Auteurs : Olga K. Chibirova [France] ; Tetyana I. Aksenova ; Alim-Louis Benabid ; Stephan Chabardes ; Steeve Larouche ; Jean Rouat ; Alessandro E P. VillaSource :
- Bio Systems [ 0303-2647 ]
English descriptors
- KwdEn :
- MESH :
- physiopathology : Parkinson Disease, Subthalamic Nucleus.
- Action Potentials, Humans, Software.
Abstract
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.
DOI: 10.1016/j.biosystems.2004.09.028
PubMed: 15649601
Affiliations:
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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. 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Aksenova</name><name sortKey="Benabid, Alim Louis" sort="Benabid, Alim Louis" uniqKey="Benabid A" first="Alim-Louis" last="Benabid">Alim-Louis Benabid</name><name sortKey="Chabardes, Stephan" sort="Chabardes, Stephan" uniqKey="Chabardes S" first="Stephan" last="Chabardes">Stephan Chabardes</name><name sortKey="Larouche, Steeve" sort="Larouche, Steeve" uniqKey="Larouche S" first="Steeve" last="Larouche">Steeve Larouche</name><name sortKey="Rouat, Jean" sort="Rouat, Jean" uniqKey="Rouat J" first="Jean" last="Rouat">Jean Rouat</name><name sortKey="Villa, Alessandro E P" sort="Villa, Alessandro E P" uniqKey="Villa A" first="Alessandro E P" last="Villa">Alessandro E P. Villa</name></noCountry><country name="France"><noRegion><name sortKey="Chibirova, Olga K" sort="Chibirova, Olga K" uniqKey="Chibirova O" first="Olga K" last="Chibirova">Olga K. Chibirova</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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