Fig.1 Method of amplitude discrimination. The separation of five neuronal groups from the recording of multi-unit activity is shown. The abscissa axis - time.
The method of implanted electrodes makes it possible to investigate directly the
electrical activity of neuronal populations of deep brain structures and to detail
their role in the realization of mental activity (Bechtereva, 1971). The development
of precise methods of study of the neurodynamics of these populations in
connection with the type of activity being realized has thus become of great
importance. One of the methods of such study is the investigation of the dynamics
of multi-unit activity of neuronal populations (Bechtereva et al., 1971; Bundzen et
al., 1971). The complicated noise-like form of the signal under consideration and
its wide frequency spectrum (from 500 Hz to 3000 Hz) make any manual
processing of the data practically impossible and the use of digital and analog
computers for that purpose is desirable.
In this paper a complex method of analysis of multi-unit activity is described,
which can be used for the investigation of background activity, functional
reorganization of activity under different conditions, and the search of patterns formed
by different stimuli. This method was used in the investigation by Bechtereva and
Bundzen (see the results published in this book).
The process consists of two stages: first the discrimination analysis stage, and
second the digital computer stage, several programs being used during the latter
stage.
Discrimination analysis is carried out with the help of "MH-14" analog
computer and consists of separating the recorded signal into several amplitude
levels. The signal at the output of each level appears only in case the amplitude of
spike is limited by the upper and lower thresholds of the level (Fig. 1). In such
cases the level separates from the whole recording the signals of the comparatively
small group of neurons, the size of which is determined by the distance between
the thresholds. The number of groups separated is equal to the number of levels
and can be changed.
Fig.1 Method of amplitude discrimination. The separation of five neuronal groups from the recording of multi-unit activity is shown. The abscissa axis - time.
The measurement of current frequency of each level is carried out simultaneously
with the discrimination analysis, i.e., the number of impulses per fixed
sequential time intervals is counted, time intervals may be chosen within the
interval of 20-60 msec.
The second stage of processing consists of factor analysis, dynamic selective
correlation analysis, and classification analysis. For that purpose a digital computer
"Minsk-32" is being used.
From the large number of factor analysis methods available principal component
analysis was chosen (Lawley and Maxwell, 1963), and was used to investigate
links and relationships existing between different discrimination levels. In
other words, it makes it possible to investigate the interactions of chosen neuronal
groups. During the factor analysis procedure not only the correlation coefficients
between the discrimination levels are calculated, but also groups of factors,
defining the interconnections of different levels.
For example, factor analysis was applied for investigation of relations between
neuronal groups when they formed a specific trace (Fig. 2). In such cases the
current frequency was analyzed for a period of 500-800 msec, sampling time
intervals being 20 msec.
Fig.2 Results of factor analysis. The abscissa axis - the numbers of discriminant levels; the ordinate axis - the coefficient of interconnection. Curves show the main and second-order interconnections between different levels.
Principal component analysis could be applied not only to the correlation
matrix, but also to the information matrix calculated after Shannon theory. It
should be noted that this form of analysis deals only with the informational
properties of interaction between neuronal groups, but not with the way of coding
and processing of external signals by these groups. The described methods of
mathematical analysis also permit the reception of detailed characteristics of the
functional structure of neuronal populations under the action of different kinds of
stimuli, and permit a statistical description of the trace to be given.
Investigation of the dynamics of traces is carried out with the help of the
dynamic selective correlation method, the aim of which is to calculate the correla-
tion coefficients between the specially chosen fragment of activity, usually recorded
at the moment of a stimulus presentation, and the fragments of the same
length taken from the whole recording of the process under consideration, step by
step with a comparatively small delay (Fig. 3). Instead of the special fragment of
activity, it is also possible to use some other fragments, such as the time dependence
of different parameters of stimulus. These parameters could be, for example,
the current frequency or some other characteristics of verbal stimulus. This
method proved the existence of a high degree of correlation between the frequency
of multi-unit activity at the moment of presentation of the verbal signal to a patient
and spectral characteristics of this signal (Bechtereva et al., 1971).
Fig.3 The Method of dynamic selective correlation. (A) Fragment of activity under consideration, (B) a shorter fragment, which was registered during the presentation of the signal, (C) sequence of correlation coefficients. The abscissa axis - time. The clearly seen maxima on C prove the existence of the high degree of correlation at several moments.
The methods of classification analysis determine the existence of classes having
a high degree of resemblance of dynamics of frequency within a large number of
fragments of multi-unit activity recordings. It helps to find periodical changes of
current frequency and to investigate the reproducibility and specificity of patterns
of multi-unit activity during the presentation of different signals.
The method of automatic classification of multiparametric experimental data
(Klimenko et al., 1972) does not require any a priori information about the
structure and number of classes due to the fact that all these parameters are
completely defined by the data when this method is used. Central points of classes
which are considered to be characteristic representatives of the structures found are
calculated automatically and described with the aid of multi-dimensional vectors.
It is also possible to use the principal components method for the classification,
and in that case one can separate groups using several methods. Each one
clarifying the resemblance of the data from a different point of view.
The third classification method, which may also be used during the second stage
of data processing, is the potential function classification. It is based on the
investigation of the resemblance of classified objects, with the aid of their geomet-
ric description. For example, as was used for the investigation of multi-unit
activity during the presentation of short-term memory tests, unknown English
words were presented to a patient before and after he had learned the meaning of
them. and the fragments of multi-unit activity during presentation, retention, and
verbal response stages were chosen for analysis (Gogolitsin, 1972). When the
meaning of a word was unknown to the patient, the frequency patterns appeared to
he quite stable during all three stages of the test. After learning, when an engram
was formed in the long-term memory, the same pattern was found to reproduce
during the presentation stage only. hut during the retention and verbal response it
appeared to he quite different (Fig. 4).
Fig.4 The stability of patterns of multi-unit activity in the case of presentation of unknown English words, which were learned afterwards: (I) before learning, (II) and (III) alter learning. Curve III shows the stability of changed pattern. The abscissa axis displays the stages of test: (A) presentation of word, (B) retention, (C) verbal response. The ordinate axis displays the degree ol pattern stability.
1. Bechtereva, N.P. Neurophysiological aspects of mental activity of man. Meditsina, 1971.
2. Bechtereva, N.P., Bundzen, P.V., Matveev, Yu. K., and Kaplunovsky, A.S. "Functional
organization of activity of cerebral neuronal assemblies in man during short-term memory."
Sechenov Physiol. Journ. USSR 57, (12), 1971.
3. Bundzen, P.V., Vassilevsky, N.N., Kaplunovsky, A.S., and Shabaev, V.V. "Factor analysis in
studies of functional organization of the brain electrical activity." Sechenov Physiol. Journ. USSR
57, (7), 1971.
4. Gogolitsin, Yu. L. Report at the symposium "Neurophysiological Mechanisms of Mental Activity"
(Leningrad. July 2-5. 1972, to be published).
5. Klimenko, V.M., Kaplunovsky, A.S., and Neroslavsky, N.A. "Automatic Classification of
Multiparametric Experimental Data." Sechenov Physiol. Journ. USSR 58, (4), 1972.
6. Lawley, D.N., and Maxwell, A.E. Factor Analysis as a Statistical Method. Butterworths,
London, 1963.
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