EVALUATION METHOD, EVALUATION DEVICE, AND COMPUTER PROGRAM FOR EVALUATING NEUROTRANSMITTER SIGNALS CONDUCTED THROUGH AXON BUNDLES

An evaluation method for evaluating a neurotransmission signal that is conducted through an axon bundle includes: performing a leveling that estimates a baseline of a signal waveform and subtracts an estimated baseline from the signal waveform; grouping a plurality of peaks that is contained in the signal waveform after the leveling into a plurality of groups each corresponding to one of the axons included in the axon bundle; calculating a similarity between a first signal waveform acquired by a first measurement electrode among a plurality of measurement electrodes and a second signal waveform acquired by a second measurement electrode, which is distinct from the first measurement electrode, after the grouping; and calculating a signal conduction time between the first measurement electrode and the second measurement electrode, based on a calculated similarity.

TECHNICAL FIELD

The present disclosure relates to an evaluation method, an evaluation apparatus, and a computer program for evaluating a neurotransmission signal that is conducted through an axon bundle.

BACKGROUND ART

Neurodegenerative diseases are known as incurable diseases of the modern era. Neurodegenerative diseases are diseases in which neurodegeneration occurs for unknown causes. An example of a neurodegenerative disease is amyotrophic lateral sclerosis (ALS). In ALS, neurodegeneration causes muscular atrophy, whereby the body gradually becomes immobile. Neurodegeneration means that degenerative changes occur in nerve cells. A nerve cell is composed of a cell body, an axon, and dendrites.

One reason for the current lack of sufficient progress in identifying the causes of neurodegenerative diseases and in drug discovery research may be the fact that nerve cells that fully reproduce the patient's pathology are not available in large quantities, because it is impossible to collect large quantities of nerve cells from an actual patient.

Therefore, organoids (artificial nerves) that have differentiated from iPS cells (induced Pluripotent Stem cells), which can reproduce patients' pathology, are drawing attention for their availability in research. One known organoid is a 2D organoid, which is obtained by seeding cell bodies in an MEA (Micro Electrode Array) having an array of measurement electrodes, and culturing them to allow axons to randomly extend from the cell bodies. Nerve development and functionality as well as pathological progress (e.g., axonal degeneration (axonal regression)) and degree, which are important factors in promoting 2D organoid research, are recognized through neurotransmission signal measurement. “Neurotransmission signal measurement” is, regarding two specific points, a measurement of similarity between a signal that is acquired at one point and a signal that is acquired at the other point. In a known neurotransmission signal measurement, by taking advantage of the tendency that axons randomly extend from a cell body that is seeded on an MEA and connect to other cell bodies, means are taken to prevent axons from overlapping and tangling, so that a single axon can be subjected to measurement.

On the other hand, in recent years, culture and growth of 3D organoids utilizing a microfluidic device (disclosed in e.g. Patent Document 1) have begun to attract attention. A 3D organoid is more similar in structure to a nerve cell within the human body than a 2D organoid. In a nerve cell within the human body, axons extending form a mass of cell bodies belonging to multiple nerve cells spontaneously become entangled to form a bundle (called an “axon bundle”). Currently, research is actively under way for drug discovery screening and etiological studies utilizing 3D organoids.

CITATION LIST

Patent Literature

SUMMARY

Technical Problem

Known methods of measuring neurotransmission signals cannot be applied to a structure that has an axon bundle, such as a 3D organoid. In other words, no method for evaluating a neurotransmission signal that is conducted through an axon bundle has been established.

Embodiments of the present disclosure have been made in view of the above problems, and an objective thereof is to provide an evaluation method and an evaluation apparatus which can evaluate a neurotransmission signal that is conducted through an axon bundle.

Solution to Problem

According to embodiments of the present disclosure, solutions as recited in the following Items are provided.

An evaluation method for evaluating a neurotransmission signal that is conducted through an axon bundle which is a bundle of axons of a plurality of nerve cells, the evaluation being based on a signal waveform that is acquired by using each of a plurality of measurement electrodes disposed at respectively different positions relative to the axon bundle, the evaluation method comprising:

a leveling step of performing a leveling that estimates a baseline of the signal waveform and subtracts the estimated baseline from the signal waveform;

a grouping step of grouping a plurality of peaks that are contained in the signal waveform after having undergone the leveling into a plurality of groups each corresponding to one of the axons included in the axon bundle;

a similarity calculation step of calculating a similarity between a first signal waveform acquired by a first measurement electrode among the plurality of measurement electrodes and a second signal waveform acquired by a second measurement electrode which is distinct from the first measurement electrode after the grouping step; and

a conduction time calculation step of calculating a signal conduction time between the first measurement electrode and the second measurement electrode based on the calculated similarity.

The evaluation method of Item 1, wherein, in the leveling step, the baseline is estimated by using an asymmetric least squares method.

The evaluation method of Item 1 or 2, wherein, in the grouping step, with respect to a given pair of peaks among the plurality of peaks, a time difference between the peaks is calculated, and grouping is performed based on the calculated time difference and peak heights of the pair of peaks.

The evaluation method of any of Items 1 to 3, wherein, in the similarity calculation step, for vicinities of all peaks in the first signal waveform that belong to a certain group, inner product values with a second signal waveform obtained by being shifted by a predetermined shift amount in a time axis direction are calculated and added up, a resultant value thereof being determined as the similarity.

The evaluation method of Item 4, wherein,

the similarity calculation step is performed a plurality of times while varying the shift amount; and,

in the conduction time calculation step, a shift amount that maximizes the similarity is determined as the signal conduction time.

An evaluation apparatus for evaluating a neurotransmission signal that is conducted through an axon bundle which is a bundle of axons of a plurality of nerve cells, the evaluation being based on a signal waveform that is acquired by using each of a plurality of measurement electrodes disposed at respectively different positions relative to the axon bundle, the evaluation apparatus comprising:

a leveling section to perform a leveling that estimates a baseline of the signal waveform and subtracts the estimated baseline from the signal waveform;

a grouping section to group a plurality of peaks that are contained in the signal waveform after having undergone the leveling into a plurality of groups each corresponding to one of the axons included in the axon bundle;

a similarity calculation section to calculate a similarity between a first signal waveform acquired by a first measurement electrode among the plurality of measurement electrodes and a second signal waveform acquired by a second measurement electrode which is distinct from the first measurement electrode after the grouping by the grouping section; and

a conduction time calculation section to calculate a signal conduction time between the first measurement electrode and the second measurement electrode based on the calculated similarity.

The evaluation apparatus of Item 6, wherein the leveling section estimates the baseline by using an asymmetric least squares method.

The evaluation apparatus of Item 6 or 7, wherein, with respect to a given pair of peaks among the plurality of peaks, the grouping section calculates a time difference between the peaks, and performs grouping based on the calculated time difference and peak heights of the pair of peaks.

The evaluation apparatus of any of Items 6 to 8, wherein, for vicinities of all peaks in the first signal waveform that belong to a certain group, the similarity calculation section calculates and adds up inner product values with a second signal waveform obtained by being shifted by a predetermined shift amount in a time axis direction, a resultant value thereof being determined as the similarity.

The evaluation apparatus of Item 9, wherein,

the similarity calculation section calculates the similarity a plurality of times while varying the shift amount; and

the conduction time calculation section determines a shift amount that maximizes the similarity as the signal conduction time.

A computer program for evaluating a neurotransmission signal that is conducted through an axon bundle which is a bundle of axons of a plurality of nerve cells, the evaluation being based on a signal waveform that is acquired by using each of a plurality of measurement electrodes disposed at respectively different positions relative to the axon bundle, the computer program causing a computer to execute:

a leveling step of performing a leveling that estimates a baseline of the signal waveform and subtracts the estimated baseline from the signal waveform;

a grouping step of grouping a plurality of peaks that are contained in the signal waveform after having undergone the leveling into a plurality of groups each corresponding to one of the axons included in the axon bundle;

a similarity calculation step of calculating a similarity between a first signal waveform acquired by a first measurement electrode among the plurality of measurement electrodes and a second signal waveform acquired by a second measurement electrode which is distinct from the first measurement electrode after the grouping step; and

a conduction time calculation step of calculating a signal conduction time between the first measurement electrode and the second measurement electrode based on the calculated similarity.

Advantageous Effects of Invention

According to embodiments of the present disclosure, an evaluation method and an evaluation apparatus which can evaluate a neurotransmission signal that is conducted through an axon bundle are provided.

DESCRIPTION OF EMBODIMENTS

Hereinafter, with reference to the drawings, embodiments of the present disclosure will be described. Note that embodiments of the present disclosure are not limited to the configuration exemplified below.

As will be described below, an evaluation method and an evaluation apparatus according to embodiments of the present disclosure are able to suitably evaluate a neurotransmission signal that is conducted through an axon bundle, which is a bundle of axons of a multitude of nerve cells. Before describing the evaluation method and the evaluation apparatus, the structure of a nerve cell, the structure of a 3D organoid including multiple nerve cells, means for culturing a 3D organoid, and the like will be described.

FIG.1is a diagram schematically showing a nerve cell1. The nerve cell1has a function of, when a stimulus is input to the nerve cell1, generating an action potential for propagating information to other cells. As shown inFIG.1, the nerve cell1includes a cell body2, an axon3, and dendrites (not shown here). The axon3and the dendrites may collectively be referred to as neurites.

The cell body2is a place in the nerve cell1where organelles such as a nucleus2aare concentrated. The cell body2has a diameter on the order of several μm to ten and several μm.

The axon3is a part in the nerve cell1that is responsible for outputting signals to other cells. The axon3is structured as a projection that extends from the cell body2. Basically only one axon3extends from a single cell body1, but it may branch out. The end of the axon3opposite to the cell body2(called an “axon terminal”)3ais connected to other cells.

A dendrite receives signals from other cells (i.e., they are responsible for inputs). A dendrite is structured so as to spread while ramifying as if branches of a tree from the cell body2. More than one dendrite may exist in a single nerve cell1.

FIG.2is a diagram schematically showing a 3D organoid5. As shown inFIG.2, the 3D organoid5includes multiple nerve cells1. The number of nerve cells1included in the 3D organoid5may be e.g. several thousand to ten and several thousand.

In the 3D organoid5, the cell bodies2of multiple nerve cells1make a mass CL. Moreover, the axons3of the multiple nerve cells1make a bundle. In other words, the 3D organoid5is created (cultured) so that the axon bundle BU, being a bundle of axons3, extends from the mass CL of cell bodies2.

With reference toFIG.3AandFIG.3B, an incubator (microfluidic device)10as an example means with which the 3D organoid5can be cultured will be described.FIG.3Aand FIG.3B are a plan view and a cross-sectional view schematically showing the incubator10.

As shown inFIG.3AandFIG.3B, the incubator10includes an incubation space11. The incubation space11is a space for culturing the 3D organoid5.

In the illustrated example, the incubator10includes a substrate12, and a plurality of measurement electrodes13and an upper plate14that are provided upon the substrate12. The aforementioned incubation space11is created in the upper plate14.

The substrate12is a plate member having a substantially rectangular shape in a plan view. The substrate12is a glass substrate, for example.

As will be described later, the plurality of measurement electrodes13are used for the measurement of neurotransmission signals. The measurement electrodes13are made of an electrically conductive material such as silver, copper, or aluminum.

The incubation space11includes a first chamber11aand a second chamber11band a channel11c. The channel11cinterconnects lower portions of the first chamber11aand the second chamber11b.

The plurality of measurement electrodes13are disposed so as to overlap the channel11cin a plan view. The number of measurement electrodes13is not limited to what is illustrated inFIG.3AandFIG.3B.

In the incubation space11(i.e., in the first chamber11a, the second chamber11b, and the channel11c), a culture fluid15is placed.

When the 3D organoid5is cultured in the incubator10, first, as shown inFIG.3C, multiple cell bodies2are seeded in the first chamber11ain a closely huddled state. At this point, the axon3has not grown out of each cell body2yet. As the culture progresses, as shown inFIG.3D, the axon3extends from each cell body2, such that a number of axons3make a bundle within the channel11c. In this manner, a 3D organoid5is created which is structured so that the axon bundle BU extends from the mass CL of cell bodies2. The mass CL of cell bodies2is located in the first chamber11a. When viewed from the normal direction of the substrate12(i.e., in a plan view), the axon bundle BU overlaps the plurality of measurement electrodes13within the channel11c.

FIG.4Ashows another example means for culturing the 3D organoid5. In the example shown inFIG.4A, a cell-adhesive coating layer17is formed in a predetermined pattern on the cultivation substrate16. The cultivation substrate16may be what is used for culturing 2D organoids. The coating layer17includes a first region17aof a substantially circular shape and a second region17bextending in a linear shape from the first region17a.

When multiple cell bodies2are seeded on the first region17aof the coating layer17in a closely huddled state, as shown inFIG.4B, an axon3extends from each cell body2along the second region17bof the coating layer17, a number of axons3make a bundle within the second region17b. In this manner, a 3D organoid5which is structured so that the axon bundle BU extends from the mass CL of cell bodies2can be created.

Alternatively, it is also possible to create a 3D organoid5in a manner shown inFIG.5A,FIG.5BandFIG.5C. First, as shown inFIG.5A, a cultivation substrate16having a cell-adhesive coating layer17formed on substantially the entire surface thereof is provided, and multiple cell bodies2are seeded on the coating layer17. Then, the axons3which have grown from the cell bodies2are gathered by using tweezers7or the like to allow the axons3to turn into a bundle, as shown inFIG.5B. In this manner, as shown inFIG.5C, a 3D organoid5which is structured so that the axon bundle BU extends from the mass CL of cell bodies2can be created.

Problems in Evaluation of a Neurotransmission Signal that is Conducted Through an Axon Bundle

Problems associated with evaluating a neurotransmission signal that is conducted through the axon bundle5will be described.(1) As has already been described, the measurement electrodes13are provided on the microfluidic device (incubator)10with predetermined interspaces. However, each axon3in the axon bundle BU of the 3D organoid5may not necessarily be in direct contact with the measurement electrodes13; moreover, the axon bundle BU has a structure in which axons3in projection shape have spontaneously become entangled to form a bundle. Therefore, the distance between a measurement electrode13and each axon3in the axon bundle BU cannot be controlled.

Moreover, the cell bodies2of the nerve cells1each emit a continuous subtle signal in a disparate manner. Furthermore, the speed with which a neurotransmission signal conducts (nerve conduction velocity) differs from axon3to axon3due to the degree of development (thickness and length) of the axon3, and even the same axon3may have a different nerve conduction velocity from day to day. Therefore, a signal that is measured by each measurement electrode13is a composite signal that is generated as a result of the disparately-emitted continuous signals from the multiple cell bodies2having been conducted at a different nerve conduction velocity for each axon3. For example, when signals as shown inFIGS.6(b), (c) and (d)are disparately emitted from three cell bodies2A,2B and2C as shown inFIG.6(a), a composite signal as shown inFIG.6(e)is emitted.(2) In order to capture subtle signals, the microfluidic device10measures a potential within a cell as a capacitance, where the cell membrane of any axon3above a measurement electrode13is regarded as a dielectric of a capacitor, thereby obtaining a measurement signal (signal waveform).

On the other hand, the magnitude of the potential of a measurement signal that is obtained from each axon3in the axon bundle BU that is located above a measurement electrode13depends on the distance between the measurement electrode13and the axon3. As shown inFIG.7(a), the axons3composing an axon bundle BU include axons3A that are relatively close to the measurement electrodes13and axons3B that are relatively distant from the measurement electrodes13. Therefore, as shown inFIG.7(b), a measurement signal corresponding to any axon3A that is close to a measurement electrode13has a relatively large potential; on the other hand, as shown inFIG.7(c), a measurement signal corresponding to any axon3B that is distant from a measurement electrode13has a relatively small potential. Thus, a measurement signal that is obtained through capacitance measurement is considerably affected by changes in potential that are commensurate with the distance between a measurement electrode13and an axon3.(3) As shown inFIG.8, a measurement signal is susceptible to a phenomenon where its baseline wobbles up and down (called a “baseline drift”). This baseline drift is another factor that may hinder evaluation of a neurotransmission signal.

[Evaluation Apparatus and Evaluation Method]

With reference toFIG.9, an evaluation apparatus100according to an embodiment of the present disclosure will be described.FIG.9is a diagram showing the overall configuration of a neurotransmission signal measurement system that includes the evaluation apparatus100(which hereinafter will be simply referred to as the “measurement system”)200.

As shown inFIG.9, the measurement system200includes the incubator10, a data acquisition apparatus20, and the evaluation apparatus100.

As has already been described, the incubator10includes the incubation space11, the measurement electrodes13, and the like.

The data acquisition apparatus20is connected to the incubator10, and acquires measurement data containing signal waveforms that are measured by the measurement electrodes13of the incubator10. Although not shown herein, the data acquisition apparatus20includes a transmission section to transmit the measurement data to the evaluation apparatus100and the like, for example.

The evaluation apparatus100may be a calculation apparatus that receives the measurement data from the data acquisition apparatus20, and performs various calculations. The evaluation apparatus100may be a personal computer, for example. Alternatively, the evaluation apparatus100may be a dedicated apparatus that functions as an assistance tool to assist in the evaluation of a neurotransmission signal.

FIG.10is a block diagram showing an example hardware configuration for the evaluation apparatus100. The evaluation apparatus100includes an input device31, a display device32, a communications I/F33, a storage device34, a processor35, a ROM (Read Only Memory)36, and a RAM (Random Access Memory)37. These component elements are connected so as to be capable of communication with one another via a bus38.

The input device31is a device for converting instructions from a user into data for input to a computer. The input device31may be a keyboard, a mouse, a touchscreen panel, or a microphone, for example.

The communications I/F33is an interface for performing data communication between the evaluation apparatus100and the outside, and its form and protocol are not limited. For example, the communications I/F33is able to perform wired communication based on USB, IEEE1394 (registered trademark), or Ethernet (registered trademark), etc. The communications I/F33may be able to perform wireless communication under the Bluetooth (registered trademark) standards and/or Wi-Fi (registered trademark) standards. These standards all include wireless communication standards utilizing frequencies in the 2.4 GHz band.

The storage device34is a magnetic storage device, an optical storage device, or a combination thereof, for example. Examples of optical storage devices include optical disc drives, magneto-optical disc (MD) drives, and the like. Examples of magnetic storage devices include hard disk drives (HDD), floppy disk (FD) drives, and magnetic tape recorders.

The processor35is a semiconductor integrated circuit, also referred to as a central processing unit (CPU) or a microprocessor. The processor35consecutively executes a computer program that is stored in a ROM160to achieve desired processes. The processor35is to be broadly interpreted as a term that encompasses an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit) or an ASSP (Application Specific Standard Product) on which a CPU is mounted.

The ROM36may be a writable memory (e.g., a PROM), a rewritable memory (e.g., a flash memory), or a read-only memory, for example. The ROM36stores a program for controlling the operation of the processor. The ROM36does not need to be a single storage medium, but may be an aggregation of multiple storage media. A part of such an aggregate storage may be a removable memory.

The RAM37provides a work area for a control program stored in the ROM36to be laid out once at boot time. The RAM37does not need to be a single storage medium, but may be an aggregation of multiple storage media.

FIG.11is a functional block diagram showing the evaluation apparatus100in functional blocks. As shown inFIG.11, the evaluation apparatus100includes a leveling section110, a grouping section120, a similarity calculation section130, and a conduction time calculation section140.

The evaluation apparatus100evaluates a neurotransmission signal that is conducted through the axon bundle BU from a signal waveform that is acquired by using each of the plurality of measurement electrodes13. As will be seen from what has been described, the plurality of measurement electrodes13are disposed at respectively different positions relative to the axon bundle BU.

The leveling section110performs leveling for the acquired signal waveform. “Leveling” is a process of estimating a baseline of a signal waveform and subtracting the estimated baseline from the signal waveform.

The grouping section120groups a plurality of peaks that are contained in the signal waveform after having undergone the leveling into a plurality of groups. As used herein, the “plurality of groups” respectively correspond to one of the axons3included in the axon bundle BU. In other words, grouping means identifying two or more peaks that pertain to the same axon3.

After the grouping by the grouping section120, the similarity calculation section130calculates a similarity between: a signal waveform (hereinafter referred to as the “first signal waveform”) acquired by a certain measurement electrode13(hereinafter denoted as the “first measurement electrode13A”) among the plurality of measurement electrodes13; and a signal waveform (hereinafter referred to as the “second signal waveform”) acquired by another measurement electrode13(hereinafter referred to as the “second measurement electrode13B”).

Based on the calculated similarity, the conduction time calculation section140calculates a signal conduction time between the first measurement electrode13A and the second measurement electrode13B.

With the aforementioned configuration, the evaluation apparatus100according to an embodiment of the present disclosure is able to suitably evaluate a neurotransmission signal that is conducted through the axon bundle BU of the 3D organoid5. Hereinafter, with reference toFIG.12, a specific example of a processing procedure (algorithm) by the evaluation apparatus100will be described. A computer program including instructions that describe the algorithm may be distributed via the Internet, or marketed as packaged software, for example.FIG.12is a flowchart showing an example processing procedure.

First, the leveling section110performs leveling for the acquired signal waveform (step S1). For example, baseline estimation can be suitably performed by using an asymmetric least squares method.

An asymmetric least squares method is a technique that is used in spectroscopic studies or the like, as disclosed in Non-Patent Document 1, for example. The entire enclosure of Non-Patent Document 1 is incorporated herein by reference. In an asymmetric least squares method, an evaluation function is expressed by formula (1) below.

In formula (1), yiis a measured value, and ziis an estimated value. The first term in this evaluation function is a term that indicates a degree of fit of the estimated value, whereas the second term is a penalty term to adjust smoothness (degree of extension). wiis a weight, such that: the weight wiis p (where p is a parameter) when the difference between the measured value yiand the estimated value ziis positive (i.e., when yi−zi>0); and the weight wiis 1-p when the difference between the measured value yiand the estimated value ziis negative (i.e., when yi-zi<0). λ is a parameter for adjusting the balance between these two terms.

As has already been described, a measurement signal is susceptible to a phenomenon where its baseline wobbles up and down (baseline drift), and this baseline drift is a factor that hinders measurement of a neurotransmission signal. Merely applying a filter to such a measurement signal, or deriving a moving average, will distort the characteristics of the signal. On the other hand, by using an asymmetric least squares method to estimate a baseline and subtracting it from the original signal waveform, for example, it is possible to suitably perform waveform extraction without much distorting the characteristics of the original signal waveform.

An example of subtracting an estimated baseline from the actual signal waveform is illustrated inFIG.13.FIG.13shows the original signal waveform and the estimated baseline, as well as a signal waveform after leveling. It can be seen fromFIG.13that the influences of the baseline drift are eliminated.

Then, from the signal waveform after leveling, a desired portion (a portion to be subjected to the grouping described below) is extracted (step S2).

Next, the grouping section120groups a plurality of peaks that are contained in the signal waveform after having undergone the leveling into a plurality of groups (step S3). The grouping may involve, with respect to a given pair of peaks among the plurality of peaks, calculating a time difference between peaks, and be performed based on the calculated time difference and the peak heights, for example. Since the axon3transmits information based on the transmission interval of signals and the intensity thereof, it is possible to perform grouping (i.e., identifying two or more peaks that pertain to the same axon3) based on the time difference between peaks and the peak heights.

For example, consider a case where eight peaks A to H as shown inFIG.14Aare grouped. With respect to a given pair of peaks (hereinafter simply referred to as a “pair”) among these eight peaks, a time difference between the peaks is calculated.FIG.14Bis a graph where each pair is denoted in the order of the earlier peak and the later peak (for example, a pair of peak A and peak B is denoted as “A-B”), and each pair is plotted with the time difference between the peaks reading on the horizontal axis and the peak height of the later peak reading on the vertical axis.

As shown inFIG.14B, peaks A, C, E and H, which are contained in mutually proximate (i.e., essentially at the same position) pairs “A-C”, “C-E” and “E-H”, may be determined as corresponding to the same axon. Peaks B, D, F and G, which are contained in mutually proximate (i.e., essentially at the same position) pairs “B-D”, “D-F” and “F-G”, may be determined as corresponding to the same axon.

The grouping by the grouping section120may be semi-automated (or fully-automated) by using machine learning. As a technique of machine learning, clustering can be used, for example. An example of clustering may be the k-means method.

Then, the similarity calculation section130calculate a similarity between: a signal waveform (first signal waveform) acquired by a certain measurement electrode13(first measurement electrode13A); and a signal waveform (second signal waveform) acquired by another measurement electrode13(second measurement electrode13B) (step S4).

Thereafter, based on the calculated similarity, the conduction time calculation section140calculates a signal conduction time between the first measurement electrode13A and the second measurement electrode13B (step S5).

The similarity calculation step S4and the conduction time calculation step S5can be suitably performed as follows, for example. In the similarity calculation step S4, for vicinities of all peaks in a first signal waveform that belong to a certain group (i.e., pertaining to the same axon3), inner product values with a second signal waveform obtained by being shifted by a predetermined shift amount in a time axis direction are calculated and added up, the resultant value thereof being determined as the similarity. Then, the similarity calculation step S4is performed a plurality of times while varying the shift amount; and, at the conduction time calculation step S5, a shift amount that maximizes the similarity is determined as a signal conduction time. As a result, similar aspects can be emphasized while suppressing noise, whereby similarity calculation and conduction time calculation can be suitably performed. Hereinafter, with reference toFIG.15toFIG.20, this technique will be described more specifically.FIG.15is a flowchart of a specific technique of similarity calculation and conduction time calculation.

First, from a first signal waveform, vicinities of all peaks (hereinafter referred to as “peak vicinities”) that are determined as belonging to the same group by grouping are extracted. A “peak vicinity” means a predetermined interval that includes a peak (e.g., substantially centered around the peak). Although the length of the interval is not particularly limited, if one observation step of signal potential takes 0.05 ms, it may be 300 steps, for example.FIG.16shows an example of peak vicinities that may be extracted from a first signal waveform. In the example shown inFIG.16, from a first signal waveform SW1, peak vicinities R1, R2and R3are extracted with respect to peaks P1, P2and P3belonging to a certain group. At this time, as shown inFIG.17, the same intervals (intervals corresponding to the peak vicinities R1, R2and R3) of a second signal waveform SW2are also extracted. Also, at this time, a pair consisting of each of the peak vicinities R1, R2and R3of the first signal waveform SW1and a corresponding extraction interval in the second signal waveform SW2is subjected to normalization for a maximum value of 1 (normalized around zero while maintaining large-small relationships) (step S41inFIG.15).

Next, inner product values of the respective pairs are calculated and added up, and the resultant value is determined as a similarity (step S42). Then, as shown inFIG.18, a process of shifting the second signal waveform SW2in a time axis direction (e.g., in a negative direction as in here) by a predetermined shift amount Δt is performed (step S43). Thereafter, normalization, similarity calculation, and a process of shifting the second signal waveform SW2are repeated a predetermined number of times.

Thereafter, a shift amount Δti that maximizes the similarity is calculated as a signal conduction time (step S5).FIG.19is a graph to visualize a relationship between the shift amount Δt and the similarity where the shift amount Δt reads on the horizontal axis and the similarity reads on the vertical axis; andFIG.20shows a state where the second signal waveform SW2has been shifted by the shift amount Δti that maximizes the similarity. It can be seen fromFIG.19that the similarity becomes maximum at a certain shift amount Δti. The shift amount Δti that maximizes the similarity can be regarded as a delay time in the waveform between the first measurement electrode13A and the second measurement electrode13B, i.e., signal conduction time.

Note that, as shown inFIG.21, due to noise components residing on the entire measurement system, the calculated similarity may take a greater value in the neighborhood of shift amount Δt=0 than what is really the maximum value. For this reason, it is preferable to eliminate the neighborhood of Δt=0 from the evaluation, and the shift amount Δt is to be set to an appropriate value that is greater than 0. The shift amount Δt may be set in a range of s/vmax to s/min, which is calculated backwards from a range of vmin to vmax of signal speed that is expectable to the skilled artisan and the distance s between electrodes.

For ease of understanding, the flowchart shown inFIG.15illustrates an example where the similarity calculation is first performed in a state of shift amount Δt=0. However, an initially-calculated similarity (i.e., the similarity in the case of Δt=0) may be eliminated at the time of conduction time calculation; or, as shown inFIG.22, the second signal waveform SW2may already be shifted in the beginning. In the example shown inFIG.22, a process of shifting the second signal waveform SW2in a time axis direction (e.g., in a negative direction as in here) by a predetermined shift amount Δt is first performed (step S43), and then data normalization (step S41) and similarity calculation (step S42) are performed in order.

Note that the aforementioned technique may be regarded as an application of Code Division Multiple Access (CDMA). By applying CDMA, similar aspects can be emphasized while suppressing noise, whereby similarity calculation and conduction time calculation between waveforms can be suitably performed.

Thus, the evaluation apparatus100according to an embodiment of the present disclosure includes the leveling section110, the grouping section120, the similarity calculation section130, and the conduction time calculation section140, thereby being able to suitably evaluate a neurotransmission signal that is conducted through the axon bundle BU.

As has been illustrated, by using an asymmetric least squares method to perform a baseline estimation at the time of leveling, waveform extraction can be suitably performed without much distorting the characteristics of the original signal waveform.

With respect to a given pair of peaks among a plurality of peaks, a time difference between peaks is calculated, and the calculated time difference and peak heights are utilized to suitably perform grouping (identifying two or more peaks that pertain to the same axon3).

Furthermore, by using the aforementioned technique which is an application of CDMA, similar aspects can be emphasized while suppressing noise, whereby similarity calculation and conduction time calculation can be suitably performed.

INDUSTRIAL APPLICABILITY

According to an embodiment of the present disclosure, an evaluation method and an evaluation apparatus which can evaluate a neurotransmission signal that is conducted through an axon bundle can be provided.

REFERENCE SIGNS LIST