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| Poster (Other) | FZJ-2017-00619 |
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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 inorder to elucidate how oscillations on the population level translate to neuronal synchrony. A critical step toachieve this goal is to correctly isolate the spiking activity of single units in extracellular STN recordings fromParkinson patients measured with a Ben Gun five channel micro-marcro-electrode holder. We employed anumber of spike sorting algorithms [e.g., 3] and found that different spike sorting methods yield inconsistentresults. We quantify these differences by the number of detected single units and the individual assignmentof spikes to the detected units. Our long-term goal critically depends on the spike sorting quality [4], as,e.g., spike synchrony evaluation depends on the percentage of correctly identified spikes [5]. Hence, weintroduced two additional approaches. Firstly, we developed a set of tools that estimates the isolationquality of single units [6]. These tools calculate the similarity of the spike shapes within one unit comparedto other units. Secondly, we generated synthetic ground truth spike data of mixed units with the statisticalfeatures of the STN recordings: We selected the two most different spike shapes which we combinedlinearly to obtain pairs of spikes with a controlled dissimilarity. Assuming Poisson spike rates we generatedspike trains by inserting such spike pairs into a noisy background obtained by phase shifting the originalnoise. These data enable us to calibrate and verify our spike sorting results, i.e., to check if the number ofextracted units and the spike-to-unit assignment is correct. By use of these two approaches, we compareand evaluate various spike sorting methods to finally select and apply the most appropriate one for theanalysis of our STN recordings.
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