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| Home > Publications database > Evaluation of spike sorting results obtained from neuronal activity in the Subthalamic Nucleus of Parkinson patients |
| Conference Presentation (Other) | FZJ-2016-05177 |
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2016
Abstract: In Parkinson’s disease (PD) the STN plays an important role in the formation of pathological oscillatoryactivity within the basal ganglia-cortex loop. The primary measure to reveal such oscillations is the localfield potential (LFP). While it is assumed that the LFP reflects synaptic input to groups of neurons, therelationship between this population signal and the single neuron activity is still a matter of debate [1, 2].Our long-term goals are to investigate the spike-LFP relationship in STN recordings obtained during deepbrain stimulation surgery, as well as to assess the amount of synchrony between individual neurons in orderto elucidate how oscillations on the population level translate to neuronal synchrony.A critical step to achieve this goal is to correctly isolate the spiking activity of single units in extracellularSTN recordings from Parkinson patients measured with a Ben Gun five channel micro-marcro-electrodeholder. We employed a number of spike sorting algorithms [e.g., 3] and found that different spike sortingmethods yield inconsistent results. We quantify these differences by the number of detected single unitsand the individual assignment of spikes to the detected units. Our long-term goal critically depends onthe spike sorting quality [4], as, e.g., spike synchrony evaluation depends on the percentage of correctlyidentified spikes [5]. Hence, we introduced two additional approaches. Firstly, we developed a set of toolsthat estimates the isolation quality of single units [6]. These tools calculate the similarity of the spike shapeswithin one unit compared to other units. Secondly, we generated synthetic ground truth spike data of mixedunits with the statistical features of the STN recordings: We selected the two most different spike shapeswhich we combined linearly to obtain pairs of spikes with a controlled distinctness. Assuming Poisson spikerates we generated spike trains by inserting such spike pairs into a noisy background obtained by phaseshifting the original noise. These data enable us to calibrate and verify our spike sorting results, i.e., tocheck if the number of extracted units and the spike-to-unit assignment is correct. By use of these twoapproaches, we compare and evaluate various spike sorting methods to finally select and apply the mostappropriate one for the analysis of our STN recordings.
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