Spike Sorting of Microelectrode Single-channel Recordings
Type of document
disertační práceAuthor
Wild, Jiří
Supervisor
Novák, Daniel
Štěpánková, Olga
Field of study
Umělá inteligence a biokybernetikaStudy program
Elektrotechnika a informatikaInstitutions assigning rank
České vysoké učení technické v Praze. Fakulta elektrotechnická. Katedra kybernetikyMetadata
Show full item recordAbstract
Until now the experimental research in medical neuroscience has been limited to analyzing
summary activity of large neuron populations. However, thanks to recent e orts in using
neuroinformatics, arti cial intelligence and machine learning methods in neuroscience
as well as medical and technological advances, new opportunities to record activity of
individual neurons arises. These opportunities allow us to better understand the neural
mechanism of complex behavior, as well as identify parts of the brain responsible for
speci c tasks.
In this thesis, we were focusing on applying such methods to data recorded from patients
with Parkinson's disease that were treated with deep brain stimulation, to improve
our understanding of the human brain and the mechanism of the deep brain stimulation
in particular.
This thesis concentrated mainly on two problems in this eld. First, the evaluation
of the state-of-the-art methods used to identify and classify neuronal action potentials
(i.e spike sorting methods) in microelectrode recordings, which required devising and
implementation of signal generator that produced arti cial signals with similar properties
as the signals recorded from basal ganglia.
Second, to use these methods to discriminate individual neurons from a microelectrode
recording and to use this knowledge to identify neurons with speci c functions in
basal ganglia and better understanding of the human brain in general. Spike sorting
methods allowed us to nd approx. 20% of basal ganglia neurons with activity related to
control of eye movements and 17% of basal ganglia neurons with activity related to processing
emotional stimuli or responding to di erent types of emotional stimuli. We were
also able to nd several statistically signi cant relations between severity of Parkinson's
disease symptoms (described using Uni ed Parkinson's Disease Rating Scale subscores)
and statistical characteristics of both microelectrode records and by individual neuron
ring patterns using linear mixed-e ects models.
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