CN109589113B - Time-space network construction method of multi-electrode array neuron discharge sequence - Google Patents

Time-space network construction method of multi-electrode array neuron discharge sequence Download PDF

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CN109589113B
CN109589113B CN201811257058.9A CN201811257058A CN109589113B CN 109589113 B CN109589113 B CN 109589113B CN 201811257058 A CN201811257058 A CN 201811257058A CN 109589113 B CN109589113 B CN 109589113B
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neuron
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discharge
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于海涛
雷新宇
孟紫寒
王江
邓斌
魏熙乐
刘晨
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Tianjin University
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Abstract

The invention relates to a method for constructing a space-time network of a multi-electrode array neuron discharge sequence, which comprises the following steps: firstly, dividing a time window for each neuron discharge time sequence; secondly, constructing a space-time network; thirdly, analyzing network characteristics: multi-neuron spatiotemporal network
Figure DDA0001842971710000011
The structural parameters of (1) include the number D of connected neurons in each time window, the number M of mutually connected neuron clusters, the cluster connection existing time length T and the cluster connection existing width W.

Description

Time-space network construction method of multi-electrode array neuron discharge sequence
Technical Field
The invention relates to the field of neuron complex networks, in particular to a space-time network construction method of a multi-electrode array neuron discharge sequence.
Background
The brain contains a large number of neurons, which are interconnected to form an extremely complex network of neurons, and which conduct and process neural impulse signals rapidly. Each process of the response of the brain to external events and information processing needs to involve the combined action of a plurality of neurons, and the complex mechanism of the brain for completing the information processing needs to be revealed, so that the sufficient number of electrical activity information of the neurons can be obtained. In recent years, the emergence and development of multi-electrode array technology provide very convenient conditions for carrying out neuron discharge related research. The multi-electrode array recording system arranges electrodes on the micro-area glass surface lattice shape with the diameter of about 5mm, the diameter and the spacing of the electrodes are micron-sized, the change condition of the neurons can be observed at any time, and the discharge information of a large number of neurons can be collected and recorded at the same time, so that the multi-electrode array recording system is suitable for researching the electrophysiological characteristics of nerve cells, retina cells and myocardial cells and the biological characteristics of ion channels. In addition, by developing brain network research on neuron clusters measured by a multi-electrode array, a network structure is analyzed by a complex network method, and the brain network research becomes a hot spot of current neuroelectrophysiology research.
With the introduction of the concept of network science in the field of neuroscience, more and more neuroscience researches the electroencephalogram characteristics by using a network method. Currently, the research on brain networks is mainly focused on the macroscopic scale based on EEG, MEG and fMRI technologies, and the related research is performed on the tissue patterns of brain networks among neural populations, nuclei or brain regions. These macroscopic studies measure the overall response of the neuron population directly or indirectly, lack direct measurement of functional connectivity between neurons, and have the problem that temporal resolution and spatial resolution cannot be compatible. The neuron discharge sequence measured by the multi-electrode array can be used for extracting and constructing a neuron-level brain network from a multi-channel electrical activity record, so that a local tissue mode in brain activity can be more finely disclosed. Although the method for constructing the static network by taking the multi-electrode array as the sensor for the nerve cell electric signals is mature, the nerve cell network has complex transient characteristics due to the rapid change characteristics of the nerve cells, and the static space network is difficult to express the characteristics of a time dimension and does not consider the information transmission characteristics of the nerve cell clusters in continuous time. Therefore, the research on time and space dimensions of a network formed by a plurality of neurons can reflect the evolution process of the transient structure of the neuron network along with time, and the method has important significance for understanding the organization principle, the formation mechanism, the regular evolution and other aspects of brain activities.
Disclosure of Invention
The invention aims to provide a method for constructing a space-time network of a multi-electrode array neuron discharge sequence, which establishes a neuron space-time network through the division of a plurality of neuron discharge sequences and the analysis of correlation among neurons, and is used for researching the evolution process of the instantaneous structure of the neuron network along with time. The technical scheme of the invention is as follows.
A method for constructing a space-time network of a multi-electrode array neuron discharge sequence comprises the following steps:
firstly, dividing a time window for each neuron discharge time sequence:
the discharge time sequence of each neuron is obtained by processing the data of the multi-electrode array, the width of a time window is set to be t, the discharge number n of a single neuron in each time window is guaranteed to be less than or equal to 1, the discharge sequence of the neuron recorded by the multi-electrode array is uniformly divided into K sections, and the K instantaneous states of the neuron cluster are obtained in total
Secondly, constructing a space-time network:
testing N neurons (X) in a multiple electrode array1、X2、……、XN) As a network node, analyzing the discharge correlation of internal neurons in the same time window K, K is more than or equal to 1 and less than or equal to K, and if the neurons are not less than
Figure BDA0001842971690000021
And neurons
Figure BDA0001842971690000022
All have electric discharges, i.e.
Figure BDA0001842971690000023
Then consider the neuron
Figure BDA0001842971690000024
And
Figure BDA0001842971690000025
correlation exists between corresponding nodes, otherwise, no connection exists, and according to the rule, a spatial network G corresponding to each time window is finally obtained; then, the correlation of the neuron discharge in the adjacent time windows k and k +1 is analyzed, if the neuron is
Figure BDA0001842971690000026
And neurons
Figure BDA0001842971690000027
All have electric discharges, i.e.
Figure BDA0001842971690000028
Then
Figure BDA0001842971690000029
And
Figure BDA00018429716900000210
correlation exists between corresponding nodes, otherwise, no connection exists, and further, the space-time network of multiple neurons is obtained on the basis of the obtained space network
Figure BDA00018429716900000211
Thirdly, analyzing network characteristics:
multi-neuron spatiotemporal network
Figure BDA00018429716900000212
The structural parameters of (1) include the number D of connected neurons in each time window, the number M of interconnected neuron clusters, the length T of cluster connection existing time, and the width W of cluster connection existing
Wherein the number of connected neurons in the time window k
Figure BDA00018429716900000213
The interconnected neuron clusters C refer to neuron groups which are interconnected in a space-time network, connection only exists in the interior of the clusters, no connection exists between neurons in different clusters, and the number M of the interconnected neuron clusters is obtained by counting the space-time network
Connection existence time length T of mth clusterm：
Figure BDA00018429716900000214
Wherein the content of the first and second substances,
Figure BDA00018429716900000215
is represented in a time window k0Neurons with connections to time window k' and all belonging to cluster Cm
The width of cluster connection is the width W of the single window in the cluster containing the most neurons, and the m-th cluster connectionm＝max{Dk|Xk∈Cm}。
Compared with the prior art, the invention has the beneficial effects that:
(1) the discharge activity of a large number of neurons can be collected and recorded simultaneously by adopting a multi-electrode array; (2) according to the second step in the method, a neuron network with two dimensions of space and time can be constructed according to the discharge correlation of neurons in the same time window and the discharge correlation of neurons in adjacent time windows; (3) the multi-neuron space-time network constructed by the method can embody the information transfer characteristic of a neuron cluster in continuous time on one hand and can embody the evolution process of the transient structure of the neuron network along with time on the other hand.
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FIG. 1 is a schematic diagram of the principle of the method for constructing the neuron spatiotemporal network of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described and illustrated below with reference to the embodiments and the accompanying drawings, but the present invention is not limited thereto.
The invention discloses a method for constructing a space-time network of a multi-electrode array neuron discharge sequence, which comprises the following steps (as shown in figure 1):
1) dividing a time window: setting the width t of a time window, and continuously dividing a multi-neuron discharge time sequence recorded by a multi-electrode array;
2) constructing a space-time network: regarding each neuron X as a network node, and constructing a space-time network of all neurons according to the discharge correlation of the neurons in the same time window and the discharge correlation of the neurons in adjacent time windows;
3) network characteristic analysis: and extracting structural parameters of the neuron space-time network, wherein the structural parameters comprise the number D of connected neurons in each time window, the number M of mutually connected neuron clusters, the cluster connection existence time length T and the cluster connection existence width W.
In the first step of this example, a 16-channel multi-electrode array system MEA-2100 (including an amplifier and a digitizer) with a diameter of 5mm was used to perform in vitro studies on mouse brain cortex tissue, wherein the diameter of the electrode was 10um, the electrode spacing was 30um, and the sampling frequency was 50 kHz. In this example, the experimental data of the multi-electrode array is first processed, at least 1 neuron can be detected in each channel, the discharge signal with the frequency of 80-2000Hz is retained by a three-order Butterworth band-pass filter, and the discharge time sequence of 15 neurons in the cerebral cortex can be separated by wavelet-based sorting algorithm, as shown in FIG. 1A. Furthermore, the width of the time window is set to be 0.01s, and the neuron discharge sequence recorded by the multi-electrode array is uniformly divided into 13 segments by adopting the continuous time window, namely, 13 transient states of the neuron cluster are obtained in total.
In step two of this example, 15 neurons (X) measured by the multi-electrode array are used1、X2、……、X15) As network nodes, analyzing the discharge correlation of the neurons in the same time window k (k is more than or equal to 1 and less than or equal to 13) if the neurons
Figure BDA0001842971690000031
And neurons
Figure BDA0001842971690000032
All have electric discharges, i.e.
Figure BDA0001842971690000033
Then consider the neuron
Figure BDA0001842971690000034
And
Figure BDA0001842971690000035
in correlation, there is a connection between the corresponding nodes, otherwise there is no connection, and a spatial network G is obtained, as shown in fig. 1 b.a. In time window 1, neurons
Figure BDA0001842971690000041
And neurons
Figure BDA0001842971690000042
If discharge occurs, the two are considered to be connected,
Figure BDA0001842971690000043
if no discharge occurs, it is determined that
Figure BDA0001842971690000044
No connection with other neurons in the same window; then, the correlation of the neuron firing in the adjacent time windows (k and k +1) is analyzed if the neuron is firing
Figure BDA0001842971690000045
And neurons
Figure BDA0001842971690000046
All have electric discharge generated
Figure BDA0001842971690000047
Then
Figure BDA0001842971690000048
And
Figure BDA0001842971690000049
correlation exists between corresponding nodes, otherwise, no connection exists, and further, the space-time network of multiple neurons is obtained on the basis of the space network
Figure BDA00018429716900000410
Spatio-temporal network as shown in FIG. 1B.b
Figure BDA00018429716900000411
Only nodes where connections exist are shown. In time windows 1 and 2, neurons
Figure BDA00018429716900000412
And neurons
Figure BDA00018429716900000413
When both discharges occur, the two are considered to be connected, and
Figure BDA00018429716900000414
without discharging, then
Figure BDA00018429716900000415
There is no connection to neurons in time window 1.
In the third step of this embodiment, the structural parameters of the neuron space-time network are extracted, including the number D of connected neurons in each time window, the number M of interconnected neuron clusters, the length T of cluster connection existence time, and the width W of cluster connection existence. According to the formula
Figure BDA00018429716900000416
Calculating the number D of connected neurons in the time window k1＝5，D2＝3，D3＝7……
The interconnected neuron cluster C refers to a neuron group which is interconnected in a space-time network, and the connection only exists in the interior of the cluster, and no connection exists between neurons in different clusters. The number M of the interconnected neuron clusters is 3 by counting the time-space network.
Connection existence time length T of mth clusterm：
Figure BDA00018429716900000417
In this example, cluster C1In a time window k0There is a persistent link between 1 and k' 5, so the cluster link available from the above formula exists for a time period T 15. All in oneTo cluster C2And C3The length of cluster connection existing time is T2＝3，T3＝3。
The width of cluster connection is the maximum number of neurons contained in a single window in the cluster, and the width of the mth cluster connection is Wm＝max{Dk|Xk∈CmW is obtained by calculation1＝7，W2＝4，W3＝4。
In summary, the present embodiment is a method for constructing a spatiotemporal network of a multi-electrode array neuron discharge sequence, the method includes the steps of: firstly, setting the width of a time window, and uniformly dividing a multi-neuron discharge time sequence recorded by a multi-electrode array; secondly, regarding each neuron as a network node, and constructing a space-time network of all neurons according to the discharge correlation of the neurons in the same time window and the discharge correlation of the neurons in adjacent time windows; and finally, extracting structural parameters of the neuron space-time network, wherein the structural parameters comprise the number of connected neurons in each time window, the number of mutually connected neuron clusters and the cluster connection existence time length. The invention provides a method for constructing a neuron space-time network, which can be used for researching the evolution process of the transient structure of the neuron network along with time.

Claims (1)

1. A method for constructing a space-time network of a multi-electrode array neuron discharge sequence comprises the following steps:
firstly, dividing a time window for each neuron discharge time sequence:
processing data of the multi-electrode array to obtain discharge time sequences of each neuron, further setting the width of a time window to be t, ensuring that the discharge number n of a single neuron in each time window is less than or equal to 1, and uniformly dividing the discharge sequences of the neurons recorded by the multi-electrode array into K sections, namely obtaining K instantaneous states of a neuron cluster in total;
secondly, constructing a space-time network:
testing N neurons (X) in a multiple electrode array1、X2、……、XN) As network nodes, for the same time window k,1K is more than or equal to K, and the correlation of the discharge of the internal neurons is analyzed, if the neurons are not less than K
Figure FDA0001842971680000011
And neurons
Figure FDA0001842971680000012
All have electric discharges, i.e.
Figure FDA0001842971680000013
Then consider the neuron
Figure FDA0001842971680000014
And
Figure FDA0001842971680000015
correlation exists between corresponding nodes, otherwise, no connection exists, and according to the rule, a spatial network G corresponding to each time window is finally obtained; then, the correlation of the neuron discharge in the adjacent time windows k and k +1 is analyzed, if the neuron is
Figure FDA0001842971680000016
And neurons
Figure FDA0001842971680000017
All have electric discharges, i.e.
Figure FDA0001842971680000018
Then
Figure FDA0001842971680000019
And Xj k+1Correlation exists between corresponding nodes, otherwise, no connection exists, and further, the space-time network of multiple neurons is obtained on the basis of the obtained space network
Figure FDA00018429716800000110
；
Thirdly, analyzing network characteristics:
multi-neuron spatiotemporal network
Figure FDA00018429716800000111
The structural parameters of (1) comprise the number D of connected neurons in each time window, the number M of mutually connected neuron clusters, the cluster connection existing time length T and the cluster connection existing width W;
wherein the number of connected neurons in the time window k
Figure FDA00018429716800000112
；
The interconnected neuron clusters C are neuron groups which are interconnected in a space-time network, connection only exists in the clusters, neurons in different clusters are not connected, and the number M of the interconnected neuron clusters is obtained by counting the space-time network;
connection existence time length T of mth clusterm：
Figure FDA00018429716800000113
；
Wherein the content of the first and second substances,
Figure FDA00018429716800000114
is represented in a time window k0Neurons with connections to time window k' and all belonging to cluster Cm；
The width of cluster connection is the width W of the single window in the cluster containing the most neurons, and the m-th cluster connectionm＝max{Dk|Xk∈Cm}。
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