The paper analyses signals that have been measured by brain probes during surgery. First background noise is removed from the signals. The remaining signals are a superposition of spike trains which are subsequently assigned to different families. For this two techniques are used: classic PCA and code vectors. Both techniques confirm that amplitude is the distinguishing feature of spikes. Finally the presence of various types of periodicity in spike trains are examined using correlation and the interval shift histogram. The results allow the development of a visual aid for surgeons

Ali Salah, Albert

Archer, Claude

Hochstenbach, Michiel

Hoede, Kees

Meijer, Hil

Meinsma, Gjerrit

Stolk, Chris

Swist, Tomasz

Zyprych, Joanna

English

University of Twente Research Information

Neural spike sorting with spatio-temporal features

Mathematics in Industry Information Service Eprints Archive

Characterising and Analysing
Intracerebral Electrical Activity
http://www.math.utwente.nl/swi2008
During stereotactic functional neurosurgery, intracerebral electrical activity is typically recorded
to monitor and refine the target position. Methods are needed to analyse the recorded data. In
particular the effects of different types of neurons on the data need to be characterized so as to
localise the appropriate neurons.
Transfer of information in the human brain is largely determined by electrical activity of neurons.
Individual neurons have characteristic electrical properties that are determined by the electrodynamical
behaviour of the cell membrane. The amount of electrical discharge of a neuron depends on several
factors including the type of channels present in the cell membrane, the sensitivity of the neurons for
specific neurotransmitters and the changes in the extracellular and intracellular electrical potential.
During stereotactic functional neurosurgery, basically two types of signals can be obtained. First, the
electrical discharges of individual neurons can be recorded. With a specially designed micro recording
needle single/multi unit activity (MER) within an area with a diameter of 100 − 200 micrometer can be
picked up. Secondly, with a more course electrode, local field potentials (LFP) can be measured across
a larger area with a 1 − 2 mm diameter, that reflect potential variations caused by a larger population of
neurons (50− 500).
With respect to microelectrode recordings, the signals measured consist of a mixture of measurements
noise, superimposed neuronal activity from large numbers of neurons in the general vicinity of the elec-
trode, larger action potentials from individual neurons very close to the electrode, and various artefacts,
including 50 Hz power-line contamination and vibrations of the stereotactic frame. To evaluate the activity
of individual neurons after artefact correction, it is necessary to not only separate out the action potentials
of nearby neurons from the background activity, but also to then separate those superimposed multi-unit
action potentials into clusters, such that each cluster represents the activity of a single neuron. Several cri-
teria can be used including the amplitude and profile of an individual spike. In addition, the characteristics
of the spike train assigned to a single neuron may be used to classify a cluster, since each type of neuron
has a specific firing pattern, such as burst, pause or tonic firing. This process is commonly referred to as
spike-sorting, i.e., to separate multi-unit activity into activity of different single units.
Considerable investigation of spike-sorting methods has been made in the field of cortical microelec-
trode recordings, but the application of spike-sorting to intra-operative electrophysiology brings with it
several specific complications. First, the data tends to be highly non-stationary, which not only confounds
most standard statistical approaches, but also means that units may be present only during part of the
recording. Second, the analysis must be done on-line. Most methods are quite computationally intensive,
and often require a substantial learning period, which is not feasible intra-operatively. Finally, the method
must be automatic. Most methods available are, at best, semi-automatic, requiring the user to set certain
data-specific parameters that are based on a subjective interpretation of the data.
Mathematical assistance would be valuable on the following specific points:
1. Automatic assessment of periods of approximate stationarity. Such an assessment must be reliably
applicable to relatively short data sets.
2. Automatic sorting of events into different units (where each unit represents the activity of a single
neuron. An event is defined to be a short time interval of data, with a high likelihood of being an
action potential, and is selected according to some set of criteria.
Adjacent Structure
Target Deep
Presumed Target
Target Superficial
Figure 1: Single/multi unit activity of the presumed target structure for various depths (-2 to
+5 mm). The superficial part of the target (-1 to 0 mm) is clearly recognized by a
sudden increase in discharge rate characterized by rhythmic bursts of activity of 15
to 25 Hz. Deeper layers of the target region (+1.5 tot 3.5 mm) show an irregular
high frequency discharge pattern. Activity of adjacent structures (+5 mm) is clearly
distinguishable and consists of a low frequency tonic discharges.


Archer, C

Hochstenbach, ME Michiel

Hoede, C

Meinsma, G

Meijer, H

Salah, AA

Stolk, C

Swist, T

Zyprych, J

Repository TU/e

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https://ris.utwente.nl/ws/files/5393943/swibook08_neural.pdf

Neural spike sorting with spatio-temporal features

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