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| 001 | 863851 | ||
| 005 | 20240313103130.0 | ||
| 024 | 7 | _ | |2 doi |a 10.1016/j.neuroscience.2019.07.005 |
| 024 | 7 | _ | |2 ISSN |a 0306-4522 |
| 024 | 7 | _ | |2 ISSN |a 1873-7544 |
| 024 | 7 | _ | |2 Handle |a 2128/23051 |
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| 100 | 1 | _ | |0 P:(DE-Juel1)171989 |a Sukiban, Jeyathevy |b 0 |
| 245 | _ | _ | |a Evaluation of Spike Sorting Algorithms: Application to Human Subthalamic Nucleus Recordings and Simulations |
| 260 | _ | _ | |a Amsterdam [u.a.] |b Elsevier Science |c 2019 |
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| 520 | _ | _ | |a An important prerequisite for the analysis of spike synchrony in extracellular recordings is the extraction of single-unit activity from the multi-unit signal. To identify single units, potential spikes are separated with respect to their potential neuronal origins (‘spike sorting’). However, different sorting algorithms yield inconsistent unit assignments, which seriously influences subsequent spike train analyses. We aim to identify the best sorting algorithm for subthalamic nucleus recordings of patients with Parkinson's disease (experimental data ED). Therefore, we apply various prevalent algorithms offered by the ‘Plexon Offline Sorter’ and evaluate the sorting results. Since this evaluation leaves us unsure about the best algorithm, we apply all methods again to artificial data (AD) with known ground truth. AD consists of pairs of single units with different shape similarity embedded in the background noise of the ED. The sorting evaluation depicts a significant influence of the respective methods on the single unit assignments. We find a high variability in the sortings obtained by different algorithms that increases with single units shape similarity. We also find significant differences in the resulting firing characteristics. We conclude that Valley-Seeking algorithms produce the most accurate result if the exclusion of artifacts as unsorted events is important. If the latter is less important (‘clean’ data) the K-Means algorithm is a better option. Our results strongly argue for the need of standardized validation procedures based on ground truth data. The recipe suggested here is simple enough to become a standard procedure. |
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| 700 | 1 | _ | |0 P:(DE-Juel1)168479 |a Voges, Nicole |b 1 |e Corresponding author |
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| 773 | _ | _ | |0 PERI:(DE-600)1498423-4 |a 10.1016/j.neuroscience.2019.07.005 |g p. S0306452219304750 |p 168-185 |t Neuroscience |v 414 |x 0306-4522 |y 2019 |
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