JP5641355B2 - Biological light measurement device, program, and biological light measurement method - Google Patents

Description

  The present invention relates to a biological light measurement device, a program, and a biological light measurement method for measuring a signal derived from biological activity using near infrared light.

  As a biological light measurement method for measuring a signal derived from biological activity using near-infrared light, a fNIRS measurement method (hereinafter referred to as a single distance method) by a single distance probe arrangement is known. In this method, as shown in FIG. 1, near-infrared light irradiated from the light irradiation unit is guided to the head 10 via the irradiation probe 1, and the guided light is arranged close to the head 10. The light detection unit 2 detects the light through the light receiving probe 2. In the figures, 3 is the scalp, 4 is the skull, 5 is the cerebrospinal fluid, 6 is the cerebral gray matter, and 7 is the cerebral white matter layer.

  The conventional fNIRS measurement data based on the single distance method includes components such as 1) brain function signals, 2) physiological signals other than brain function signals, and 3) signal fluctuations derived from probe contact defects. (Refer nonpatent literature 1). In many cases, signal fluctuations resulting from probe contact failures are relatively easy to identify because they exhibit temporal behaviors different from other components, such as instantaneous baseline shifts and extreme noise. In contrast, certain portions of signals derived from physiological activities other than brain function signals exhibit temporal behavior very similar to brain function signals. Specifically, these signal components are derived from blood flow fluctuations in the surface tissue, etc., but for example, when performing repetitive opposition (tapping) of the fingertips or cognitive tasks, brain activity in the corresponding functional areas It is known that systemic blood flow changes occur at the same time, and signal changes caused by this change are observed in a wide area of the head (see Non-Patent Documents 2 and 3). In such a case, unless we have a priori knowledge about the correlation between the channel position and the position of the brain function, we cannot determine whether the observed signal change is derived from the brain function. There is. Therefore, it is necessary to separate the change in the light absorption coefficient associated with brain activity and the others by some method.

Until now, several methods for extracting brain function signals have been attempted, but they have some problems for the following reasons.
First, a technique such as adaptive filtering (see Non-Patent Document 4) that separates signals using characteristics of temporal changes is based on the premise that brain function signals have low correlation with other components. Since a component (artifact) other than the brain function signal generated in synchronization with task execution exists in a part of the sexual blood flow signal, this method cannot sufficiently separate the brain function signal from the rest.
In addition, since ICA (Independent Component Analysis; Independent Component Analysis; see Non-Patent Document 5) is also a method of performing component separation based on independence between multi-component signals included in multi-channel measurement data, it is also a brain function signal. It is not suitable for the separation of these artifacts that have high simultaneity and waveform similarity.
Similarly, a method of separating signal components and local components generated globally by applying PCA (Principal Component Analysis) to multichannel measurement data has been proposed (see Non-Patent Document 2). Is limited to the case where brain activity occurs only in a single area, and even in that case, it is necessary to install probes in a wide area other than the area, If there is a defect in the probe contact, there is a problem that the analysis accuracy is greatly affected.

On the other hand, attempts have been made to separate artifacts and brain function signals induced by task performance by devising the task content and arrangement of the experiment.
For example, in the above tapping example, when a task is inserted into a resting section such as “resting-tapping-resting” in the task sequence, the influence of systemic blood flow changes that occur simultaneously with brain activity is observed in the tasking section. It is impossible to exclude the possibility of being lost. On the other hand, in the method using a problem sequence such as “right hand tapping—left hand tapping—right hand tapping—left hand tapping” (see Non-Patent Document 6), the influence of systemic blood flow is superimposed throughout the entire section. In the measurement of primary motor areas, there is a high probability that only signal changes due to brain functional activity are detected.
However, there are several problems with this approach. That is, the brain function signal extraction through such an experimental design generally requires a high degree of experimental design skill for the measurer. In addition, this type of experimental paradigm is based on the assumption that all physiological activities other than the relevant brain function activity occur in the same way as the target task in the prepared control task. Most often difficult.

In order to overcome these difficulties, we have proposed a multi-distance probe arrangement method (hereinafter referred to as multi-distance method; see Patent Document 1 and Non-Patent Document 3).
This proposed method is a technique of arranging the reference probe 8 at a position closer to the irradiation probe 1 than the light receiving probe 2 as shown in FIG. 2 in addition to the probe arrangement of the single distance method shown in FIG. The distance between the irradiation probe 1 and the reference probe 8 is set to 20 mm with respect to the distance 30 mm between the irradiation probe 1 and the light receiving probe 2), and the distance between the light source and the detector is short, and a relatively shallow position passes from the scalp 3. The detected light is detected from the reference probe 8, and the detection result is detected from the light detection probe 2 as a signal that includes signals other than brain function signals such as intrinsic fluctuations associated with heartbeat, respiration, and autonomic nerve activity. It is assumed that only a signal derived from the brain function is detected by taking a difference from a light signal having a long distance between the detectors and a relatively deep position from the scalp 3.

According to this proposed method, brain function signal extraction can be realized by overcoming most of the above problems.
However, it requires special probe placement and creates two problems when trying to perform this approach with commercially available equipment.
One is that the above-described probe arrangement cannot be easily realized by general users due to the size of the probe fixture of a commercially available device. The other is that the measured light quantity of the two detection probes differs by about 10 times based on the difference in the distance between the light source and the detector, so that the mechanism for adjusting the dynamic range of the detector is not sufficiently provided. Measurement is difficult with a commercially available device.
Therefore, no proposal has been made to solve these problems and extract only the signals derived from brain activity from only the data that can be obtained by general users without modifying the device in most of the commercially available devices. Is the current situation.

  An object of the present invention is to solve the above-described problems and achieve the following objects. That is, the present invention provides a biological light measurement device, a program, and a biological light measurement method for extracting only a specific signal in a biological activity using data that can be acquired by a general user without modifying a commercially available fNIRS device. For the purpose.

In order to solve the above-mentioned problem, based on various empirical findings, we refer to near-infrared light signal changes caused by blood flow fluctuations in living tissues as volumetric fluctuation components and flow velocity fluctuation components. We propose to categorize and view in two ways. And the usefulness of this proposal was verified as follows.
First, the fluctuation components according to the two modes are characterized as components in which a positive / negative proportional relationship is established between oxy and deoxy changes in the bloodstream. Next, the observation signal of the fNIRS apparatus is described as a linear sum of these fluctuation components. And the coefficient of the proportional relationship of these fluctuation components is determined. Further, the proposed method and the multi distance method (refer to Patent Document 1 and Non-Patent Document 3) capable of measuring hemoglobin change associated with brain function activity during an artifact task and a brain function task accompanied by the artifact can be measured. To calculate the degree of coincidence between the two. If the degree of coincidence is high, the proposed method can be considered useful.
As a result, by classifying into two modes, volumetric fluctuation component and flow velocity fluctuation component, the signal activity of the near-infrared light caused by blood flow fluctuations in the living tissue can be influenced by the biological activity of the subject. A biological light measurement device and program that can extract only a specific signal and extract only a specific signal in life activity from only data that can be acquired by a general user by applying arithmetic processing related to the extraction method to a commercially available device And the knowledge that the biological light measuring method can be provided was acquired.

The present invention is based on the above knowledge, and means for solving the above problems are as follows. That is,
<1> A light irradiation unit that irradiates a subject with near-infrared light, a light detection unit that detects light that is irradiated on the subject and passes through the inside of the subject, and the light that is detected by the light detection unit An arithmetic processing unit that arithmetically processes and outputs the information acquired from the light, and the arithmetic processing unit includes an oxyhemoglobin change amount O and a deoxyhemoglobin change amount D in the blood of the subject acquired from the light. From the above information, the blood flow change component OF of the flow velocity variation in the oxyhemoglobin change amount O _F and the blood flow of flow velocity variation in the deoxyhemoglobin change amount D expressed by the following formulas (6) and (7). change component D _F, and / or the oxyhemoglobin variation blood change component of O volume of variations in O _V and blood flow change component D _V volume of variation in the deoxy-hemoglobin variation D Living body light measuring device, characterized in that the out output.
However, in the formula (6) and (7), _{k F} and _{k V} are numerical der Rutotomoni satisfying _{k F} ≠ _{k V,} a negative value of _{k F} is -1 ≦ _k F <0, k _V is Ru positive numbers der of 0 _{<k V.}
<2> _{k F} is -0.85 meet _<k F _<-0.35, k _V, the biological light measuring apparatus according to a numerical value satisfying _{0 <k V ≦ 1 <1} >.
< 3 > The biological light measurement device according to any one of <1> to < 2 >, wherein the head of the subject is irradiated with near infrared rays.
< 4 > From <1> to < 3 >, including an irradiation probe that guides near-infrared light irradiated from the light irradiation unit to the subject, and a light-receiving probe that guides light passing through the subject to the light detection unit. The biological light measurement device according to any one of the above.
<5> together with the light detection unit detects each light detected from a plurality of light receiving probe, the arithmetic processing unit, and outputs the respective calculation processing said detected information obtained from the optical to <4> The biological light measuring device described.
< 6 > The living body light measurement apparatus according to any one of < 4 > to < 5 >, further including a probe holder that can be attached along the surface of the subject, wherein the irradiation probe and the light receiving probe are held by the probe holder. .
< 7 > A light irradiation unit that irradiates a subject with near-infrared light, a light detection unit that detects light that has been irradiated on the subject and passed through the subject, and the light that is detected by the light detection unit Change in oxyhemoglobin in blood in the subject obtained from the light with respect to the arithmetic processing unit. from each information amount O and deoxyhemoglobin variation D, the following formula (6) and (7) represented by the blood flow change component O _F and the deoxy-hemoglobin variation of the flow velocity of variation in the oxyhemoglobin variation O In blood flow change component D _F of flow velocity fluctuation in D and / or blood change component O _V of volumetric fluctuation in oxyhemoglobin change amount O and deoxyhemoglobin change amount D Program for causing output and calculates blood flow change component D _V of that volume resistance variation.
However, in the formula (6) and (7), _{k F} and _{k V} are numerical der Rutotomoni satisfying _{k F} ≠ _{k V,} a negative value of _{k F} is -1 ≦ _k F <0, k _V is Ru positive numbers der of 0 _{<k V.}
< 8 > A light irradiation step of irradiating the subject with near-infrared light, a light detection step of irradiating the subject and detecting light passing through the subject, and the light detected in the light detection step An arithmetic processing step for arithmetically processing and outputting information acquired from the light, and the arithmetic processing step includes an oxyhemoglobin change amount O and a deoxyhemoglobin change amount D in the blood in the subject acquired from the light. From the above information, the blood flow change component OF of the flow velocity variation in the oxyhemoglobin change amount O _F and the blood flow of flow velocity variation in the deoxyhemoglobin change amount D expressed by the following formulas (6) and (7). Change component D _F and / or blood change component O _V of volumetric variation in oxyhemoglobin change amount O and deoxyhemoglobin change amount D Biological optical measurement method characterized by calculating and outputting a blood flow change component D _V volume of variation.
However, in the formula (6) and (7), _{k F} and _{k V} are numerical der Rutotomoni satisfying _{k F} ≠ _{k V,} a negative value of _{k F} is -1 ≦ _k F <0, k _V is Ru positive numbers der of 0 _{<k V.}
< 9 > The biological light measurement according to < 8 >, wherein the blood flow fluctuation in the cerebral gray matter layer of the subject associated with the neural activity is measured based on each information of the blood flow change components O _F and D _F of the flow velocity fluctuation. Method.
<10> based on the information of the blood change component O _V, D _V volume of variation, the biological optical measurement according to any one of <9> from the <8> measuring the blood flow change in scalp layer of the subject Method.

  According to the present invention, the above-mentioned problems in the prior art can be solved, and a living body that extracts only a specific signal in a biological activity using data that can be acquired by a general user without modifying a commercially available fNIRS device. A light measurement device, a program, and a biological light measurement method can be provided.

It is explanatory drawing which shows the outline | summary of the single distance method. It is explanatory drawing which shows the outline | summary of the multi distance method. It is explanatory drawing which shows the various aspects of the blood-flow change which arises based on various physiological attributes. It is explanatory drawing which shows an example of probe arrangement | positioning. It is explanatory drawing which shows an example of a probe holder. It is a disassembled perspective view which shows an example of a probe holder. Amount mutual information calculated from the data in each experiment object of the subject ^{_{1 I (k F *, k}} V) is a graph showing a k _V dependency. All subjects are graphs showing the k _V ^* histogram of obtained from the data of the problem conditions. It is a graph which shows the result comparison of the conventional method regarding the hemoglobin change accompanying the subject's 1 body inclination task performance, and the proposal method which concerns on the Example of this invention. Each notation in the graph is as follows. Conventional O & D: conventional methods, Proposed O _F & _{D F:} flow rate of fluctuation component, Proposed O _v & _{D v:} volume resistance variation component. Gray line: oxyhemoglobin, black line: deoxyhemoglobin. Black thick line: Task execution time. The unit of the vertical axis in each graph is [mM × cm]. It is a graph which shows the result comparison of the conventional method regarding the hemoglobin change accompanying the subject's 1 respiratory stop task performance, and the proposal method which concerns on the Example of this invention. Each notation in the graph is as follows. Conventional O & D: conventional methods, Proposed O _F & _{D F:} flow rate of fluctuation component, Proposed O _v & _{D v:} volume resistance variation component. Gray line: oxyhemoglobin, black line: deoxyhemoglobin. Black thick line: Task execution time. The unit of the vertical axis in each graph is [mM × cm]. It is a graph which shows the result comparison of the conventional method regarding the hemoglobin change accompanying the subject's 1 fingertip movement (tapping) task execution, and the proposed method based on the Example of this invention. Each notation in the graph is as follows. Conventional O & D: conventional methods, Proposed O _F & _{D F:} flow rate of fluctuation component, Proposed O _v & _{D v:} volume resistance variation component. Gray line: oxyhemoglobin, black line: deoxyhemoglobin. Black line: Task execution time (0-20 seconds: left hand, 40-60 seconds: right hand). The unit of the vertical axis in each graph is [mM × cm]. It is a graph which shows the result comparison with the proposed method and the multi distance method which concern on the Example of this invention regarding the hemoglobin change accompanying each subject's subject 1 performance. Each notation in the graph is as follows. Body tilting: upper body tilt, Breath holding: breathing stop, Finger tapping: fingertip movement. Black line: Flow velocity fluctuation component obtained by 30 mm arrangement of proposed method, Gray line: Change in cerebral gray matter layer obtained by multidistance method. Black line: Task execution time (0-20 seconds: left hand, 40-60 seconds: right hand). The unit of the vertical axis in each graph is [mM × cm]. It is a graph which shows the dependence with respect to the light source-detector distance of the contribution to the optical attenuation of each fluctuation component isolate | separated by the proposed method based on the Example of this invention. In the figure, the left part shows the volumetric fluctuation component, and the right part shows the flow velocity fluctuation component. Each notation in the graph is as follows. Gray line: Dependence curve obtained for each subject, black line: Dependence curve of partial optical path length of scalp layer (left) and cerebral gray matter layer (right) obtained by Monte Carlo simulation.

(Biological light measurement device and program)
The biological light measurement device of the present invention includes at least a light irradiation means, a light detection unit, and an arithmetic processing unit.

<Light irradiation means>
The light irradiation means is means for irradiating the subject with near infrared light.
The specific means is not particularly limited and may be appropriately selected according to the purpose. Examples thereof include all known light irradiation means such as near infrared light sources used in commercially available fNIRS apparatuses. Can do.
In the present specification, the fNIRS (Functional Near-Infrared Spectroscopy) device refers to a device that measures a biological signal obtained by irradiating a subject with near-infrared light, a functional near-infrared light device, It refers to all devices called near-infrared light imaging devices.

<Light detector>
The light detection unit detects light passing through the subject.
There is no restriction | limiting in particular as a concrete thing of the said light detection part, According to the objective, it can select suitably, All the examples of well-known apparatuses, such as what is used with the commercially available fNIRS apparatus, can be mentioned. .

<Operation processing unit>
The arithmetic processing unit is expressed by the following formulas (6) and (7) from each information of oxyhemoglobin change amount O and deoxyhemoglobin change amount D in blood in the subject acquired from the light, Blood flow change component O _F of flow velocity variation in oxyhemoglobin variation O and blood flow variation component D _F of flow velocity variation in deoxyhemoglobin variation D and / or blood of volumetric variation in oxyhemoglobin variation O change component O _V and calculates the blood flow change component D _V volume of variation in the deoxy-hemoglobin variation D outputs.
However, in the formula (6) and (7), _{k F} and _{k V} is a numerical value satisfying _{k F} ≠ _{k V.}

  An example of the arithmetic processing unit is not particularly limited and may be appropriately selected according to the purpose. An electronic computer disposed in a commercially available fNIRS device, an electronic computer that can be externally connected to the fNIRS device, and the like are listed. It is done.

-Program-
The program of the present invention is a program for executing the arithmetic processing in cooperation with the biological light measurement device of the present invention. Specifically, the light is transmitted to the arithmetic processing unit of the biological light measurement device. From the respective information on the oxyhemoglobin change amount O and deoxyhemoglobin change amount D in the blood obtained from the subject, the flow rate characteristic in the oxyhemoglobin change amount O represented by the formulas (6) and (7) blood flow change component of the variation O _F and the blood flow change component of the flow velocity of variation in deoxyhemoglobin variation D D _F, and / or the blood change component O _V and the deoxyhemoglobin volume of variations in oxyhemoglobin variation O and calculates the blood flow change component D _V volume of variation in the amount of change D, characterized in that to the output.

  The specific significance of the above-described arithmetic processing will be described below while clarifying the aspect of blood flow fluctuation of the subject.

[Two modes of blood flow fluctuation (modality)]
-Concept-
In the measurement by the fNIRS apparatus, changes in the oxyhemoglobin amount and deoxyhemoglobin amount (O, D) associated with the activity of the body tissue of the subject can be estimated as independent bivariates. Many reports have been made so far regarding what O and D temporal changes are typical of hemodynamics associated with brain activity.
Many early studies have reported an outstanding increase in oxy (see Non-Patent Documents 7 and 8), which considers the possibility of blood flow changes in living tissues other than the brain in the optical path. Not.
On the other hand, relatively recent studies paying sufficient attention to measurement errors, body movements, effects of systemic physiological activities, etc. have reported that deoxy decreases simultaneously with increased oxy in synchronization with tasks. (See Non-Patent Documents 3 and 9). Similar reciprocal changes of oxy and deoxy, that is, negative correlations are observed in the brain function signal measurement results under craniotomy using rats (see Non-Patent Documents 10 and 11).

From studies such as PET and MRI using contrast media, rCBF increases by several tens of percent in local blood flow fluctuations associated with brain activity, while rCBV is less than 10% at most. It has been known for a long time that it does not increase (see Non-Patent Documents 12, 13, and 14).
Although it has been confirmed under a microscope that the arteriole of a rat causes vasodilation with neural activity as an entity of CBV increase (see Non-Patent Document 15), Compared with blood vessels, it is negligibly small (see Non-Patent Document 16).
On the other hand, the capillaries that serve as oxygen supply sites have tube diameters that are smaller than the radius of red blood cells in still blood, and the red blood cells are deformed by the shear stress generated by the flow velocity gradient of the plasma flow, thereby causing these capillaries. It is known to pass through (see Non-Patent Document 17). Due to this mechanism, it is considered that the capillary diameter hardly fluctuates due to increase or decrease in blood flow volume in capillaries.
These items suggest that the increase in blood volume is never large in hemodynamics related to oxygen exchange accompanying brain activity.

Assuming that the blood vessel diameter is almost constant in capillaries, if the inflow of arterial blood with high oxygen saturation from the upstream side of the blood vessel increases accompanying cranial nerve activity, it balances with local oxygen consumption. As deoxy-hemoglobin, which has achieved low oxygen saturation, is swept away and replaced by oxy-hemoglobin, oxy-increase and deoxy-decrease should occur complementarily in the blood vessel.
That is, the relationship D ₁ = k ₁ O ₁ is established there, and k ₁ can be described as a constant coefficient of − sign. The coefficient k ₁ will vary depending on the change in the hematocrit value depending on the flow rate and the presence or absence of slight passive vasodilation, but is expected to take a negative value between -1 and at most 0.
In the present specification, this mode of change in blood flow (modality) is referred to as flow velocity blood flow-sensitive component (flow velocity-sensitive component).

On the other hand, some researchers know empirically that blood flow fluctuations derived from body movements and systemic physiological activities increase or decrease oxy and deoxy in the same sign direction. In Patent Document 18), bowing, and breath holding (see Non-Patent Document 3), it is reported that oxy and deoxy change in phase. Such systemic blood flow changes are considered to be accompanied by arteriovenous passive volume fluctuations such as arterio-arteriolar vasoconstriction / dilation via the autonomic nerve and blood pressure changes / posture changes. Along with such a change in blood vessel volume, the amount of oxy and deoxy changes in phase with each other according to the respective abundance ratios.
That is, the relationship D ₂ = k ₂ O ₂ still holds there, but k ₂ is a constant coefficient of + sign. The coefficient k ₂ is determined by the oxygen saturation of the blood in the blood vessel, and therefore takes 0 when the oxygen saturation is 100% and + ∞ when the oxygen saturation is 0%. Expected to take.
In these thick blood vessel regions, almost no oxygen is supplied to the tissue, so the oxygen saturation of blood may be considered to be substantially constant. Therefore, even if the blood flow velocity changes in these blood vessels, the flow velocity blood flow change described above does not occur.
Therefore, in this specification, the mode (modality) of the blood flow change is considered as a component independent of the flow velocity blood flow change, and is referred to as volume-sensitive blood flow change (volume-sensitive component).

The above blood flow modalities and physiological attributes are shown in FIG. The values of the proportional coefficients k ₁ and k ₂ that characterize each modality are determined by the hematocrit value, the blood oxygen saturation, the elasticity of the blood vessel wall, and the like. Since these are parameters accompanied by bioconstancy, k ₁ and k ₂ can be regarded as sufficiently constant within the measurement time.
Therefore, the fNIRS measurement signal according to the conventional single distance method can be considered as a mixture of two blood flow modalities with different generation mechanisms.
Based on this interpretation, a method for separating the temporal changes of the respective modality components from the fNIRS measurement signal by the single distance method will be described in detail below.

-Modeling-
Information measured by fNIRS by the single distance method is a bivariate of oxy-hemoglobin change amount and deoxy-hemoglobin change amount. Hereinafter, this is referred to as O and D. These are considered blood flow change component O _F flow rate of _variation, D _F and blood flow change component O _V volume of _variation, and is constituted by a D _V. That is,

Here, _{k F} is negative constant of _{-1 ≦ k F <0, k} V is a positive constant of 0 _{<k V.} At this time, the formula (1) can be transformed into the following form.

Since k _F ≠ k _V , the matrix on the right side of the equation (3) has an inverse matrix. Therefore, the following two equations are obtained.

The formulas for each blood flow change component are summarized as follows.

  As described above, it is included in the bivariate O and D obtained by the fNIRS measurement of the single distance method based on the two models of blood flow change modalities described by the equations (1) and (2). Volumetric blood flow change and flow velocity blood flow change can be separated as the above formulas (6) and (7).

- determination of each modality coefficient _k V, _{k F} -
In order to effectively execute the arithmetic processing based on the above component separation, specific numerical values of k _V and k _F must be determined.
There are no particular restrictions on the method used to determine this value, and it can be selected appropriately according to the purpose.Here, there are two approaches to determine a more appropriate value: an arithmetic approach and an empirical approach. The method will be explained.

-Arithmetic approach-
The arithmetic approach is a technique for determining a numerical value using an arithmetic technique.
The arithmetic technique is not particularly limited as long as it is a technique capable of determining specific numerical values of k _V and k _F , and can be appropriately selected according to the purpose. Arithmetic methods such as a method using a function for discriminating the independence of flow velocity blood flow changes can be mentioned.

  There is no restriction | limiting in particular as a function which discriminate | determines the independence of the above-mentioned volumetric blood flow change and flow velocity blood flow change, According to the objective, it can select suitably, For example, (1) Mutual information (Mutual information) ) Or a function that defines an amount called “Transformation Information”, and (2) a function that defines an amount called “correlation coefficient” or “Pearson product-moment correlation coefficient”. Can be mentioned.

(1) A function that defines an amount called mutual information amount or transmitted information amount. This amount is given as follows when two variables O _F (k _F ) and O _V (k _V ) take discrete values. .
However, in the above formula (8), p (O _F , O _V ) is a simultaneous distribution function of O _F , O _V , and p (O _F ) and p (O _V ) are O _F and O _V , respectively. Is a marginal probability distribution function.

Further, the amount _is, O _F (k _F), if the O _{V (k} V) takes continuous values, given as follows.
However, in the formula _{(9), p (O F} , O V) _is O F, a joint distribution density function of _{O V,} p _{(O F)} and p _{(O V),} respectively _{O F} and O _{It is} a marginal probability density function of _V.

In both cases of the above formulas (8) and (9), p (O _F , O _V ), p (O _F ), and p (O _V ) are O _F (k _F ) and O _V (k _V ). Can be obtained based on the frequency distribution. I (k _F , k _V ) always takes a positive value, and takes a smaller value as the independence of the values taken by the two variables O _F (k _F ) and O _V (k _V ) is higher. Using this property, a combination of (k _F , k _V ) that minimizes I (k _F , k _V ) can be obtained.

(2) A function that defines a quantity called correlation coefficient or Pearson's product moment correlation coefficient In the proposed method using the equations (6) and (7), the variable O _{F, i} (k _F ) for each time i. And n sets of O _{V, i} (k _V ). In this case, this amount is given as:

In the equation (10), ρ (k _F , k _V ) takes a value from −1 to 1, and becomes zero as the correlation between the two variables O _F (k _F ) and O _V (k _V ) is lower. Get closer. Using this property, a combination of (k _F , k _V ) that minimizes the absolute value of ρ (k _F , k _V ) can be obtained.

-Empirical approach-
The empirical approach is a method for determining an appropriate numerical value from consideration of biological mechanisms and past research results. Details will be described below.

First, consider the numerical value of _{k F.}
It can be assumed that the diameter of the capillary blood vessel in which this blood flow component can occur does not change with time. When the inflow of arterial blood with high oxygen saturation from the upstream side of the blood vessel increases accompanying cranial nerve activity, deoxy-hemoglobin is swept away and replaced with oxy-hemoglobin. When only this effect is considered, k _F = −1.
However, in actuality, the hematocrit value (ratio of the volume of blood cells in the blood) may increase as the blood flow velocity increases, or passive vasodilation may occur.
However, since they are responsive to the increase in blood flow velocity, the effects of only the increase in blood flow velocity cannot be completely countered. That is, k _F <0 is guaranteed.
From the above, −1 ≦ k _F <0 can be considered logically valid.

Of the inner and outer performed in the last 10 years studies, conditions that may guess the correct value of k _F (i.e., visual stimulation experiments, tactile stimulation experiments, with appropriate control experiments motor task experiment, blood of cerebral gray matter any experiments with assays for extracting flow changes only was calculated a value of k _F based on the data obtained in any of the experiments performed in the skull removed state), the value in table 1 As shown, it was in the range of −0.85 <k _F <−0.35.
Therefore, −0.85 <k _F <−0.35 is found from past research examples.

Considering that the animal has acquired an important organ called the brain and the supply mechanism of oxygen and nutrients to it, it has a common mechanism, that is, a common k _F across different species, regardless of the brain area. We can expect to find a wide range of values. As far as the above Table 1, the case showing the value of k _F within a range of ± 10% of -0.6 is over half of the total.
Accordingly, as the value of the common _{k F,} it may be preferable to employ about -0.6 (about -0.6 ± 10%).

Next, consider the _{k V.}
Since oxygen is not directly distributed to tissues from arteries (other than capillaries), arterioles, venules, and veins where this blood flow component can occur, the proportion of blood oxy- and deoxy-hemoglobin in these blood vessels is respectively It can be assumed that a constant value of If the blood volume increases under these conditions, oxy- and deoxy-hemoglobin increases according to the proportion of each. Assuming that the ratio of oxyhemoglobin to the total hemoglobin in blood is S _x O ₂ , the relationship can be expressed as follows.
In the above formula (11), the maximum value that can be logically considered as S _x O ₂ is 100% when all the blood vessels are filled with oxyhemoglobin, and the minimum value is all filled with deoxyhemoglobin. 0% of the case. The k _V corresponding to these are 0 and + ∞ in each case from the equation (11).
Therefore, logically, a range of 0 <k _V <+ ∞ can be assumed.

In fact, it is known that physiologically S _x O ₂ in arterial blood is very close to 100% and not as easy as 95% or less. In addition, it is known that S _x O ₂ of venous blood shows about 70% at rest (see Non-Patent Document 30), and does not decrease to about 50% or less even when intense exercise continues.
If this blood flow component occurs only in arterial blood and its S _x O ₂ is 100%, k _V takes the lowest value of 0. On the other hand, if the blood flow component is generated only by venous blood and its S _x O ₂ is about 50%, which is the lowest that can be considered physiologically, k _V takes the highest value of 1.
From the above consideration, it is considered physiologically appropriate to show a range of 0 <k _V ≦ 1.

In practice, both arterial and venous blood contribute to this blood flow component. Under normal measurement conditions in which no intense exercise load is applied, S _x O _{2 of} arterial blood is about 100% and S _x O _{2 of} venous blood is about 70%. From the equation (11), the _{k V} value corresponding to each is 0 and 0.43.
Therefore, it is considered that the measurement performed under the quasi-static condition takes a value in the range of about 0 <k _V <0.5.

In the determination of k _V and k _F , the arithmetic approach and the empirical approach can be considered independently. However, the k _V and k _F may be determined using these in combination.
For example, in the arithmetic approach, as a result of performing the arithmetic processing of the formulas (8) to (10), the minimum point of I (k _F , k _V ), ρ (k _F , k _V ), that is, the measurement waveform In the case where the local minimum point is not surely confirmed, either k _V or k _F may be determined by an empirical approach and processed so that the local minimum point is confirmed.

<Example of device>
A device example of the biological light measurement device will be described.
As described above, the biological light measurement device includes the light irradiation unit and the light detection unit. These are optically connected through the subject, but normally, an irradiation probe that guides the light emitted from the light irradiating unit to the subject, and light that has passed through the subject to the light detecting unit. And a light receiving probe for guiding (see FIG. 1). Although the present invention does not exclude the configuration having the reference probe in the multi distance method (see FIG. 2), the arithmetic processing unit performs arithmetic processing for analyzing two modes (modalities) of blood flow fluctuations. By doing so, even without such a reference probe, it is possible to extract and measure only the signal derived from the brain function.

Regarding the configuration having the irradiation probe and the light receiving probe, a plurality of these may be configured in the biological light measurement device.
In this case, the light detection unit detects the light guided from the plurality of light receiving probes, respectively, and the calculation processing unit performs calculation processing and outputs information acquired from each of the detected lights. Composed.

The biological light measurement device is suitable for measuring a biological signal in the head of a subject. For example, the irradiation probe may be arranged in a right brain portion and a left brain portion of the head. A plurality of the light receiving probes may be arranged around the irradiation probe at a constant distance. This is shown in FIG. In the drawing, reference numeral 11 denotes an irradiation probe, and reference numeral 12 denotes a light receiving probe as an arrangement location of each probe.
With such a probe arrangement, various nerve activities in each functional area of the brain can be effectively measured.

Further, the biological light measurement device may have a probe holder that can be attached along the surface of the subject, and the irradiation probe and the light receiving probe may be held by the probe holder. .
By having such a probe holder, each probe can be arranged in a stable state with respect to the subject.

  The specific configuration of the probe holder is not particularly limited and can be appropriately selected according to the purpose. For example, the one used in a commercially available fNIRS device can be applied, but depending on individual differences. For example, a probe holder 20 shown in FIGS. 5 and 6 is used from the viewpoint of keeping the probes in close contact with each other on the head of the subject having various curved surfaces and keeping the arrangement of the probes constant. You may comprise.

  As shown in FIG. 5, the probe holder 20 includes at least two probe sockets 13 for holding the irradiation probe 11 and the light receiving probe 12, and an upper connector 14 and a lower connector 15 on a long plate for connecting these sockets. And a cloth-like member 19 that is fixed on the lower connector 15 so as to fit the surface of the subject and blocks external light.

As shown in FIG. 6, the probe socket 13 is screwed into the socket body 16 with the screw portions of the lower mounting member 17 and the upper mounting member 18 while being fitted into the holes of the upper connector 14 and the lower connector 15. Arranged.
Here, unlike the round hole arranged in the lower connector 15, the hole arranged in the upper connector 14 is formed as a long hole expanded in the length direction of the upper connector, and the probe socket 13. The mounting position of the upper mounting member 18 can be arbitrarily adjusted in the length direction of the upper connector 14 while fixing the mounting position of the lower mounting member 17 in the lower connector 15.
Therefore, when the probe holder 20 shown in FIG. 5 is curved and attached along the surface shape of the subject head, the upper attachment member 18 is longer than the position corresponding to the hole of the lower connector 15. The probes 11 and 12 can be arranged so as to be held in close contact with the subject's head vertically by adjusting and attaching to the position outside the direction.

(Biometric light measurement method)
The biological light measurement method of the present invention includes at least a light irradiation step, a light detection step, and an arithmetic processing step.

<Light irradiation step>
In the light irradiation step, the subject is irradiated with near infrared light.
There is no restriction | limiting in particular as a specific means to perform the said light irradiation step, According to the objective, it can select suitably, For example, the light irradiation means in the said biological light measuring device of this invention is mentioned.

<Light detection step>
In the light detection step, light that is irradiated to the subject and passes through the subject is detected.
There is no restriction | limiting in particular as a specific means to perform the said light detection step, According to the objective, it can select suitably, For example, the light detection part in the said biological light measuring device of this invention is mentioned.

<Calculation processing steps>
In the calculation processing step, the information acquired from the light detected in the light detection step is calculated and output.
Here, the calculation processing step is expressed by the following formulas (6) and (7) from each information of oxyhemoglobin change amount O and deoxyhemoglobin change amount D in blood in the subject acquired from the light. that the oxy blood flow change component of hemoglobin change quantity O velocity of variations in O _F and the blood flow change component of the flow velocity of variation in deoxyhemoglobin variation D D _F, and / or volume of in the oxyhemoglobin variation O wherein the calculating and outputting a blood flow change component D _V volume of variation in the blood change component O _V and the deoxy-hemoglobin variation D of the variation.
However, in the formula (6) and (7), _{k F} and _{k V} is a numerical value satisfying _{k F} ≠ _{k V.}
There is no restriction | limiting in particular as a concrete means to perform the said arithmetic processing step, According to the objective, it can select suitably, For example, the arithmetic processing part in the said biological light measuring device of this invention is mentioned.

<Experiment details>
Connect the probe and holder system developed uniquely (refer to Patent Document 2 and Non-Patent Document 31) characterized by the configuration shown in FIGS. 5 and 6 to a near-infrared imaging device (Shimadzu Corporation, OMM-3000). And the arithmetic processing based on said Formula (6), (7) was performed with respect to the detected optical signal. The wavelengths used were 776 nm, 808 nm, and 828 nm. The absorbance data for each wavelength obtained at a sampling rate of 40 Hz was down-sampled to 10 Hz, and then passed through a 1 Hz low-pass filter.
The placement of the probe on the head of the subject is based on the placement method described in Non-Patent Document 3, and the left and right motor area activities are performed on the subject whose primary motor area is specified by MRI measurement using the same tapping task. One light source and four detection probes were installed immediately above the site. The light source-detector was placed on a straight line, and the distances from the light sources of the four detectors were 10 mm, 20 mm, 30 mm, and 40 mm, respectively.

  Subjects were selected as follows. In other words, based on the experimental design approved by the AIST ergonomics experiment committee, each person in charge of the experiment received an oral and written explanation of the experiment individually and obtained cooperation from multiple healthy adult subjects who understood it.

Based on the above experimental configuration, first, the subject was asked to perform a non-functional task.
The subject was seated on a chair, and after 20 seconds of initial rest, the task 20 seconds and rest 20 seconds were repeated five times. The total time is 220 seconds. The contents of the task were upper body inclination and breath holding.
In the task of tilting the upper body, only the upper body was tilted forward with a visual and auditory instruction as a cue, and the nose was brought into contact with a bar placed at a tilt angle of 30 ° to maintain the posture. After 20 seconds, the next visual and auditory instruction was given as a cue to return to the upper body upright state at the initial rest and hold the posture.
In the breath holding task, breathing was stopped with the same instruction as a cue and held for 20 seconds. Breathing was resumed with an instruction after 20 seconds.

Next, the subject was asked to perform the task of functional task.
The subject was seated on the chair in the same manner as in the non-functional task, and the body was kept in an upright posture. In this state, following the initial rest 20 seconds, the left hand tapping operation 20 seconds, the rest 20 seconds, the right hand tapping operation 20 seconds, and the rest 20 seconds were repeated five times. The total time is 420 seconds. The tapping operation was performed under the condition that the opposing operation of the thumb and forefinger of each hand was performed at 4 Hz.

-K _F determination of -
Based on the empirical approach described above, here, and the _k ^F * = 0.6 as the value of _{k F.}

-K _V of convergence -
Also relates k _V, based on a function that defines the mutual information of the type in arithmetic approaches described above (8), the information independence I from assumptions (k F _*, ^k _V) such that the minimum defining the k _V ^*. As a result, (O _F , D _F ) and (O _V , D _V ) were uniquely separated for each measurement data.
FIG. 7 shows how the _kV dependence of the mutual information I (k _F ^* , k _V ) between the two blood flow modalities differs depending on the conditions of different light source-detector distances and task types. The data of the subject 1 are shown. Under almost all conditions, I (k _F ^* , k _V ) has a minimum point, and the value k _V ^* of k _V at that time is a range that is reasonable for the proposed model of the present invention (0 <k _V It can be seen that it is in <1).

Next, FIG. 8 shows a histogram of k _V ^{* when} the distance between the light source and the detector is 30 mm obtained using data of different subjects. According to the _histogram, most _k ^{V _*,} is distributed in the range of ^{0 <k} V * ^<0.4. This is because the currently performed task is not accompanied by significant exercise, oxygen saturation of the venous blood is a value about 70% of the resting state, and considering, most k _V ^* value 0.4 I can understand that the results are below.
In addition, the fact that there are more that have a value close to zero suggests that the ratio of the volume fluctuation in the arterial blood vessel contributes relatively large in the whole volumetric blood flow fluctuation.
From the above results, it is considered that the model assuming two blood flow modalities is generally appropriate as a blood flow change model under these problems and measurement conditions, and k _F ^* and k _V ^* are estimated appropriately. It is done.

-Time change during task execution-
FIG. 9 shows hemoglobin changes O and D in the conventional method (a technique in which hemoglobin changes O and D are not separated under the conditions of the above-described experiment) accompanying the execution of the task of tilting the upper body, and a proposal according to an embodiment of the present invention. volume resistance variation component _O V technique, _{D V} and a flow rate of fluctuation component _O F, a result of the subject 1 in the _{D F} shown. As we have already reported (see Non-Patent Document 3), O and D of the conventional method change greatly by tilting the upper body.
It can be seen that this change shows a similar shape even under various conditions with different light source-detector distances.
Further, the amplitude becomes larger as the distance between the light source and the detector is longer. This is considered to be because correction by the optical path length is not performed in the calculation of O and D. It can be seen that the change in the volumetric fluctuation component of the proposed method behaves very similar to the time change of the conventional method in any channel.
On the other hand, it can be seen that the velocity variation component has a very small amplitude of change compared to the other two, and is hardly affected by the body inclination.

Next, FIG. 10 shows the result of the subject 1 on each hemoglobin change during the performance of the respiratory arrest task. Again, the artifacts associated with respiratory arrest that we reported previously (see Non-Patent Document 3) were confirmed in the conventional hemoglobin change.
The behavior is very similar even under different light source-detector distance conditions.
The volumetric variation component of the proposed method again showed very similar behavior to the hemoglobin change under each condition of the conventional method.
On the other hand, it can be seen that the flow velocity fluctuation component has a smaller change amplitude than the other two, and is hardly affected by respiratory arrest.

And in FIG. 11, the result in the test subject 1 of each hemoglobin change accompanying a finger tapping task performance is shown. In the hemoglobin change obtained by the conventional method, a clear unilaterality with respect to the difference between the left and right of the tapped finger is not recognized.
At the same time, in the comparison between conditions with different distances between the light source and the detector, there is no similarity in the shape of time change as in the case of body tilt or breathing stop.
The volumetric fluctuation component of the proposed method shows a gradual baseline fluctuation that occurs in a time span of several tens of seconds and an increase that synchronizes with the performance of tasks that do not depend on the left and right. This shape is very similar between conditions with different light source-detector distances.
Meanwhile, the flow rate of fluctuation component, the light source - detector distance 30 mm, under the condition of 40 mm, at the right side with respect to the left tapping, rise clear O _F on the left side for the right tapping (reduction of D _F) is observed It is done. This change is hardly recognized under the condition that the distance between the light source and the detector is 20 mm and 10 mm.

-Comparison of flow rate fluctuation component and brain function signal by multi distance method-
By using the measurement data measured under the conditions where the distance between the light source and the detector is 20 mm and 30 mm, it is possible to calculate the hemoglobin change in the cerebral gray matter using the multi distance method proposed by us. it can.
Figure 12, the hemoglobin change and the light source in the cerebral gray matter, obtained by the method - shows a comparison of the resulting flow rate of fluctuation component at a distance 30mm between the detectors O _F, D _F.
The hemoglobin changes obtained by the two methods of the multi distance method and the proposed method according to the embodiment of the present invention have different amplitude scaling. For this reason, in FIG. 12, the scaling of the hemoglobin change obtained by the multi distance method is adjusted and displayed so that the temporal variations of the two under the conditions listed here most closely match as a whole.
It can be seen that the hemoglobin changes obtained by the respective methods are in good agreement with each other in the problems of body tilt and tapping.
On the other hand, the time changes in performing the breathing stop task do not match very well, and the amplitude of the flow velocity fluctuation component in the proposed method is larger than that in the multi distance method. This meaning will be described later.
As described above, it is possible to separate the two components of the volumetric variation and the flow velocity variation from the fNIRS measurement data obtained by the conventional probe arrangement using the proposed method, and the flow velocity variation component of the components is the multi distance method. It was clarified that the change of blood flow of cerebral gray matter obtained in the experiment was very similar.

<Discussion>
The two blood flow modalities are only defined as differences in the correlation between oxy and deoxy, at least in the model. We can now verify the correspondence between them and brain function signals or other signals.
In FIGS. 9 and 10, it was clearly confirmed that the amplitude of the volumetric variation of the same shape increases as the distance between the light source and the detector increases.
Also, in the tapping experiment of FIG. 11, relatively similar behavior is recognized between channels having different distances between the light source and the detector with respect to the volumetric variation. On the other hand, in the case of the flow rate fluctuation, different from this, it is recognized only when the distance between the light source and the detector exceeds 30 mm.

As described above, the amplitude of each blood flow modality component has different light source-detector distance dependency. In order to discuss this point in more detail, we quantified the difference in the ratio of the time-varying amplitude of the volumetric component and the velocity component obtained in the tapping experiment for each subject. did. The results are shown in FIG. The amplitude of the volume component and the flow velocity component is determined by the product of the hemoglobin concentration change and the partial optical path length.
Propagation of near-infrared light in human head tissue has also been studied in simulations, and the partial optical path length in each tissue layer also varies depending on the distance between the light source and the detector, and its dependence varies from tissue layer to tissue layer. Is known (see Non-Patent Document 32).
If, as we have assumed, the physiological origins of the velocity component and the volume component are different, the velocity component actually occurs only in the cerebral gray matter, and the majority of the volume component is in the scalp layer. It is thought to occur.
In that case, the dependency of the amplitude of each component shown in FIG. 13 on the light source-detector distance is expected to be similar to the dependency on the light source-detector distance of the partial optical path length of the tissue layer where the component occurs. .

Therefore, we simulated the dependence of the partial optical path length on the scalp layer and the cerebral gray matter layer at 776 nm, which is one of the observation wavelengths, using the stack model. The result is shown in FIG. In this simulation, a calculation code published by Massachusetts General Hospital was used (http://www.nmr.mgh.harvard.edu/PMI/index.html), and the optical constants of living tissues were the literature values ( Non-Patent Document 33) was used.
As a result, the light source-detector distance dependence of the partial optical path length of the scalp layer and gray matter layer is clearly different, the former being very similar to the volumetric fluctuation component and the latter being very similar to the flow velocity fluctuation component. I understand.
Furthermore, considering that the hemoglobin change of the gray matter layer and the flow velocity fluctuation component of the gray matter layer shown in FIG. 7 are very similar, the volumetric fluctuation component is the scalp layer, and the flow velocity fluctuation component is The above can be understood consistently by considering that it occurs mainly in the gray matter layer.
Based on the above considerations, it is considered that the flow velocity fluctuation component separated by the proposed method reflects the blood flow fluctuation accompanying the nerve activity in the gray matter layer to a considerable extent.

In this experiment, the k _F ^* characterizing the flow velocity variation was determined to be 0.6 from the literature value, but differences between species and between regions were not taken into account. This is because the mechanism that causes this blood flow fluctuation in brain tissue is considered to be relatively well preserved in mammals.
However, in some pathological cases such as cerebral ischemia, the value of k _F ^* may be different. When measuring such a pathology Example This technique is believed to Issaku also be used to modify the algorithm to determine the converged solution both be k _F and k _V at the expense of convergence as a free variable .

When observing the flow rate fluctuation component at the time of performing the breathing stop task (see FIG. 10), many subjects showed a slight tendency for oxy to gradually decrease and deoxy to increase.
This is thought to reflect a slight decrease in blood oxygen saturation due to the cessation of oxygen supply, but this fluctuation in flow velocity is generally small, so it was not serious for such low-load tasks. However, large in the measurement of a challenge under the exercise is accompanied, this in mind also, it is believed that Issaku to determine the k _F and k _V.

On the assumption that the inverse correlation between oxy and deoxy in the fNIRS measurement signal is reduced by the inclusion of artifacts, there is a report that proposes removal of artifacts mainly due to probe miscontact (see Non-Patent Document 34).
The apparent hemoglobin change due to probe miscontact is caused by fluctuations in incidence and light reception efficiency, and thus shows a characteristic characteristic of the apparatus according to the wavelength used.
The fluctuation has a positive correlation between oxy and deoxy like the volumetric fluctuation described above, and the proportionality coefficient corresponding to k _V ^* differs depending on the apparatus, but takes a value of approximately 0.5 or more.
The experiments we have performed use our own probe holder system, so it is thought that the problem of probe miscontact hardly occurs. This is also indicated by the fact that the frequency at which k _V ^* takes a value of 0.5 or more is extremely small in the histogram of k _V ^* (see FIG. 8). However, it is also possible to separately consider the convergence of _kV ^* and the performance of the brain function signal extraction as a result when the sufficient experimental consideration is not taken.

  As described above, in the measurement under the standard fNIRS measurement condition, the flow velocity fluctuation component extracted by the proposed method is considered to reflect the blood flow change accompanying the nerve activity in the cerebral gray matter layer. By using this method, a user who already has a conventional fNIRS device can perform more highly reliable fNIRS measurement without having to update the hardware.

DESCRIPTION OF SYMBOLS 1,11 Irradiation probe 2,12 Light reception probe 3 Scalp 4 Skull 5 Cerebrospinal fluid 6 Cerebral gray matter layer 7 Cerebral white matter layer 8 Reference probe 10 Head 13 Probe socket 14 Upper connector 15 Lower connector 16 Socket body 17 Lower Mounting member 18 Upper mounting member 19 Cloth-like member 20 Probe holder

JP 2009-136434 A JP 2009-178192 A

Huppert, T .; J. et al. , Diamond, S .; G. , Franceschini, M .; A. Boas, D .; A. , 2009. Homer: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl. Opt. 48, D280-D298. M.M. A. Franceschini, D.C. K. Joseph, T.M. J. et al. Huppert, S.M. G. Diamond, and D.D. A. Boas, "Diffuse optical imaging of the whole head," J. Biomed. Opt. 11,054007,2006. Yamada T, Umeyama S, Matsuda K. et al. Multidistance probe array to elimi- nate artifacts in functional near-infrared spectroscopy. J Biomed Opt. 2009 Nov-Dec; 14 (6): 064034. Zhang Q, Brown EN, Strangman GE. Adaptive filtering for global interference cancellation and real-time recovery of evolved brain activity: a Monte Carlo simulation study. J Biomed Opt. 2007 Jul-Aug; 12 (4): 044014. S. Kohno, I .; Miyai, A .; Seiyama, I .; Oda, A .; Ishikawa, S .; Tsuneishi, T .; Amita, and K.K. Shimizu, “Re-moval of the blood flow artifact in functional near-infrared spectroscopic imaging data intensive component.” Biomed. Opt. 12,062111 2007. S. Boden, H.M. Obrig, C.I. Kohncke ("o" with umlaut), H.C. Benav, S.M. P. Koch, and J.M. Steinblink, The oxygenation response to functional simulation: Is the aphysical measuring to the lag beetween parameters? NeuroImage 36 (2007) 100-107 Yamamoto, T .; , Kato, T .; , 2002. Paradoxical correlation between signal in functional magnetic resonance imaging and deoxygenated haemoglobine content in capillaries. Phys. Med. Biol. 47, 1121-1114. Strangman, G.M. , Culver, J. et al. P. Thompson, J .; H. Boas, D .; A. , 2002. A quality comparison of simulated BOLD fMRI and NIRS recordings functioning functional brain activity. NeuroImage 17 (2), 719-731. Huppert TJ, Hoge RD, Diamond SG, Franceschini MA, Boas DA. A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. Neuroimage. 2006 Jan15; 29 (2): 368-82. Epub 2005 Nov 21. Shet SA, Nemoto M, Guiou M, Walker M, Polishan N, Toga AW. Linear and Nonlinear Relations between Neuronal Activity, Oxygen Metabolism, and Hemodynamic Responses Neuron. 2004 Apr 22; 42 (2): 347-55. Huppert TJ, Allen MS, Benav H, Jones PB, Boas DA. A multicompartment basal model for inferring baseline and functional changes in cerebral oxygen metabolism and articulation. J Cereb Blood Flow Metab. 2007 Jun; 27 (6): 1262-79. Epub 2007 Jan 3. Mandeville JB, Marota JA, Ayata C, Zaharchuk G, Moskowitz MA, Rosen BR, Weisskoff RM Evidence of a cerebral biospowders in windshield. Journal of Cerebral Blood Flow & Metabolism (1999) 19, 679-689 Ostergaard L, Smith DF, Vestergaard-Poulsen P, Hansen SB, Gee AD, Gjedde A, Gyldensted C, Absolute cerebral blood flow and blood volume measured by magnetic resonance imaging bolus tracking: Comparison with positron emission tomography values. Journal of Cerebral Blood Flow & Metabolism (1998) 18, 425-432 Schumann P, Touzani O, Young AR, Baron JC, Morello R, MacKenzie ET Evaluation of the ratio of cerebral blood flow to cerebral blood volume. Brain (1998), 121, 1369-1379. Zonta M, Angulo MC, Gobo S, Rosengarten B, Hossmann KA, Pozzan T, Carignoto G. Neuron-to-astrocyte signaling is central to the dynamic control of brain microcirculation. Nat Neurosci. 2003 Jan; 6 (1): 43-50. Integritata M, Johnson PC, Winslow RM Microvascular and tissue oxygen distribution. Cardiovascular Research 32 (1996) 632-643 Popel AS, Johnson PC Microcirculation and hemology. Annu. Rev. Fluid Mech. 2005.37: 43-69 Obrig H, Villringer A .; Beyond the visible imaging the human brain with light. J Cereb Blood Flow Metab. 2003 Jan; 23 (1): 1-18. Jasdzewski G, Strangman G, Wagner J, Kwon KK, Poldrac RA, Boas DA. Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy. Neuroimage. 2003 Sep; 20 (1): 479-88. Tang L, Avison MJ, Gore JC. Nonlinear flood oxygen level-dependent responses for transient activations and deactivations in V1-insights into the hemodynamic response. Magnetic Resonance Imaging 27 (2009) 449-459 McIntosh MA, Shahani U, Boughton RG, McCulloch DL. Absolute quantification of oxygenated within the visual cortex with functional near infrared spectroscopy (fNIRS). Invest Ophthalmol Vis Sci. 2010 Sep; 51 (9): 4856-60. Epub 2010 Mar 31. Siegel AM, Culver JP, Mandeville JB, Boas DA. Temporal comparison of functional brain imaging with diffuse optical tomography and fMRI durating rat forepaw simulation. Phys Med Biol. 2003 May 21; 48 (10): 1391-403. Villringer A, Planck J, Hock C, Schleinkofer L, Dirnagl U .; Near Infrared Spectroscopy (NIRS): a new tool to study hemodynamics dur- ing activation of brain functions in human adults. Neuroscience Letters, 154 (1993) 101-104. Obrig H, Wenzel R, Kohl M, Horst S, Worst P, Steinbrink J, Thomas F, Villringer A. et al. Near-infrared spectroscopy: does it function in function activation studies of the adult brain? Int J Psychophysiol. 2000 Mar; 35 (2-3): 125-42. Zeff BW, White BR, Deghani H, Schlaggar BL, Culver JP. Retinotopic mapping of adult human cortex with high-density diffusive optical tomography. Proc Natl Acad Sci USA. 2007 Jul 17; 104 (29): 12169-74. Epub 2007 Jul 6. Dunn AK, Devor A, Dale AM, Boas DA. Spatial extent of oxygen metabolism and hemodynamic changes during the activation of the somatosensory cortex. Neuroimage. 2005 Aug 15; 27 (2): 279-90. Collier WN, Quaresima V, Wenzel R, van der Sluijs MC, Oeseburg B, Ferrari M, Villringer A. et al. Simulaneous near-infrared spectroscopic monitoring of left and right octopital areal revitalizations chem. Vision Res. 2001 Jan; 41 (1): 97-102. Berwick J, Martin C, Martindale J, Jones M, Johnston D, Zheng Y, Redgrave P, Mayhew J. et al. Hemodynamic response in the unanesthetized rat: intrinsic optical imaging and the spectroscopy of the barrel cortex. J Cereb Blood Flow Metab. 2002 Jun; 22 (6): 670-9. Francescinini MA, Fantini S, Thompson JH, Culver JP, Boas DA. Hemodynamic evoked response of the sensory cortex, measured non-innovative with near-infrared optical imaging. Psychophysology. 2003 Jul; 40 (4): 548-60. Madsen PL, Secher NH. Near-infrared oximetry of the brain. Prog Neurobiol. 1999 Aug; 58 (6): 541-60. Yamada T, Umeyama S, Matsuda K. et al. A multi-probe probe arrangement NIRS for detecting absorption changes in cerebral gray matter layer. Proc. of SPIE Vol. 7557 75570X Okada E, Firebank M, Schweiger M, Arrow SR, Copy M, Delpy DT. Theoretical and experimental investing of near-infrared light propagating in a model of the adult head. Appl Opt. 1997 Jan 1; 36 (1): 21-31. Okui N, Okada E .; Wavelength dependency of crosstalk in dual-wavelength measurement of oxy- and deoxy-hemoglobin. J Biomed Opt. 2005 Jan-Feb; 10 (1): 11015. Cui X, Bray S, Reiss AL. Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobulin. NeuroImage 49 (2010) 3039-3046

Claims (10)

A light irradiation unit that irradiates the subject with near infrared light; and
A light detection unit that detects light that is irradiated to the subject and passes through the subject;
An arithmetic processing unit that calculates and outputs information acquired from the light detected by the light detection unit, and
The arithmetic processing unit is expressed by the following formulas (6) and (7) from each information of oxyhemoglobin change amount O and deoxyhemoglobin change amount D in blood in the subject acquired from the light, Blood flow change component O _F of flow velocity variation in oxyhemoglobin variation O and blood flow variation component D _F of flow velocity variation in deoxyhemoglobin variation D and / or blood of volumetric variation in oxyhemoglobin variation O living body light measuring device, characterized in that calculates and outputs the blood flow change component D _V volume of variation in change component O _V and the deoxy-hemoglobin variation D.
However, in the formula (6) and (7), _{k F} and _{k V} are numerical der Rutotomoni satisfying _{k F} ≠ _{k V,} a negative value of _{k F} is -1 ≦ _k F <0, k _V is Ru positive numbers der of 0 _{<k V.} k _F Is -0.85 <k _F <-0.35 is satisfied, k _V Is 0 <k _V The biological light measurement device according to claim 1, wherein the biological light measurement device has a numerical value satisfying ≦ 1. The living body light measurement apparatus according to claim 1, wherein near-infrared rays are irradiated to the head of the subject. 4. The apparatus according to claim 1, further comprising: an irradiation probe that guides near-infrared light emitted from the light irradiation unit to the subject; and a light-receiving probe that guides light passing through the subject to the light detection unit. Biological light measurement device. While the light detection unit detects light detected from the plurality of light receiving probes,
The biological light measurement device according to claim 4, wherein information obtained from each light detected by the arithmetic processing unit is arithmetically processed and output. The living body light measurement apparatus according to claim 4, further comprising a probe holder that can be attached along a surface of the subject, wherein the irradiation probe and the light receiving probe are held by the probe holder. It is obtained from a light irradiation unit that irradiates a subject with near-infrared light, a light detection unit that detects light that irradiates the subject and passes through the subject, and the light detected by the light detection unit. Used in a biological light measurement apparatus having an arithmetic processing unit that performs arithmetic processing and outputs information,
From the respective information of the oxyhemoglobin change amount O and deoxyhemoglobin change amount D in the blood of the subject acquired from the light, the calculation processing unit is expressed by the following formulas (6) and (7). the oxy blood flow change component of hemoglobin change quantity O velocity of variations in O _F and the blood flow change component of the flow velocity of variation in deoxyhemoglobin variation D D _F, and / or volume of variations in the oxyhemoglobin variation O a program characterized by blood change component O _V and calculates the blood flow change component D _V volume of variation in the deoxy-hemoglobin change amount D is output.
However, in the formula (6) and (7), _{k F} and _{k V,} along with a numerical value satisfying _{k F} ≠ _{k V, k F} is a negative value of -1 ≦ _k F <0, _{k V} is a positive numerical value of 0 _{<k V.} A light irradiation step for irradiating the subject with near infrared light; and
A light detection step of detecting light irradiated on the subject and passing through the subject;
A calculation processing step for calculating and outputting information acquired from the light detected in the light detection step,
The calculation processing step is expressed by the following formulas (6) and (7) from each information of oxyhemoglobin change amount O and deoxyhemoglobin change amount D in blood in the subject obtained from the light, Blood flow change component O _F of flow velocity variation in oxyhemoglobin variation O and blood flow variation component D _F of flow velocity variation in deoxyhemoglobin variation D and / or blood of volumetric variation in oxyhemoglobin variation O change component O _V and biological optical measurement method characterized by calculating and outputting a blood flow change component D _V volume of variation in the deoxy-hemoglobin variation D.
However, in the formula (6) and (7), _{k F} and _{k V,} along with a numerical value satisfying _{k F} ≠ _{k V, k F} is a negative value of -1 ≦ _k F <0, _{k V} is a positive numerical value of 0 _{<k V.} Blood flow change component O of flow velocity fluctuation _F , D _F The biological light measurement method according to claim 8, wherein blood flow fluctuations in the cerebral gray matter layer of the subject associated with neural activity are measured based on each of the information. Blood change component O of volumetric variation _V , D _V The biological light measurement method according to any one of claims 8 to 9, wherein blood flow fluctuations in the scalp layer of the subject are measured based on each information.