Determination method, information processing device, computer program and system

Abstract

This method allows for early and highly accurate assessment of the likelihood of developing central nervous system diseases (especially Parkinson's disease) in the future or currently being affected. [Solution] Based on the activity level of the pontine nuclei in the brain of a living organism, disease probability information is obtained, which indicates the likelihood of developing a central nervous system disease in the future.

Description

Technical Field

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[0001] The present invention relates to a determination method, an information processing apparatus, a computer program, and a system.

Background Art

[0002] A method for evaluating the risk of developing Parkinson's disease by measuring the concentration of isatin in a blood sample taken from a subject not suffering from Parkinson's disease is known (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the conventional technology, the evaluation period and accuracy were not sufficient. Further, such a problem was not limited to Parkinson's disease, but was a problem common to Parkinson's syndrome including Parkinson's disease and central nervous system diseases in general.

[0005] In view of the above circumstances, an object of the present invention is to provide a technique for evaluating the possibility of developing a central nervous system disease (particularly Parkinson's disease) in the future or the possibility of currently suffering from it with higher accuracy at an early stage.

Means for Solving the Problems

[0006] [1] In order to solve the above problems, a determination method according to an aspect of the present invention includes an acquisition step of acquiring onset possibility information, which is information indicating the possibility of developing a future central nervous system disease, based on the activity amount of the pontine nucleus of the brain of a living body.

[0007] [2] In order to solve the above problems, an information processing device according to one aspect of the present invention includes a control unit that acquires disease possibility information, which is information indicating the possibility of developing a central nervous system disease in the future, based on the activity level of the pontine nuclei of the brain of a living organism.

[0008] [3] In order to solve the above problems, a computer program according to one aspect of the present invention causes a computer to perform an acquisition step of acquiring disease possibility information, which is information indicating the likelihood of developing a central nervous system disease in the future, based on the activity level of the pontine nuclei of the brain of a living organism.

[0009] [4] In order to solve the above problems, a system according to one aspect of the present invention comprises a functional MRI for measuring the brain of a living organism, and an information processing device that acquires disease possibility information, which is information indicating the possibility of developing a central nervous system disease in the future, based on the activity level of the pontine nuclei of the brain obtained by the measurement of the brain by the functional MRI.

[0010] [5] In order to solve the above problems, one aspect of the present invention is a determination method which includes an acquisition step of acquiring disease possibility information, which is information indicating the current possibility of suffering from a central nervous system disease, based on the activity level of the pontine nuclei of the brain of a living organism. [Effects of the Invention]

[0011] According to the above aspects of the present invention, it is possible to evaluate the likelihood of developing a central nervous system disease (particularly Parkinson's disease) in the future or the likelihood of currently suffering from it at an early stage with high accuracy. [Brief explanation of the drawing]

[0012] [Figure 1] This is an example of a graph showing the activity levels of four brain regions, obtained from fMRI brain scans of multiple samples (all of the same age) of wild-type and genetically modified marmosets. [Figure 2]This is an example of a graph showing the activity levels of four brain regions by age, obtained from fMRI brain scans of multiple samples of wild-type and genetically modified marmosets. [Figure 3] This is an example of a graph showing the relationship between pontine nucleus activity in the brain and the amount of motor activity in the samples of genetically modified marmosets, obtained from fMRI brain scans of multiple samples, before and after the onset of Parkinson's disease. [Figure 4] This is an example of a graph showing the activity levels of four brain regions, obtained from fMRI brain scans of multiple samples of healthy individuals and individuals belonging to families with familial Parkinson's disease (non-symptomatic individuals). [Figure 5] This is a schematic diagram of an analysis system to which a data analysis device according to the first embodiment of the present invention is applied. [Figure 6] This is an example of how the output device displays information regarding the likelihood of developing an illness. [Figure 7] This flowchart shows an example of the general processing procedure of the analysis system. [Figure 8] This figure shows a schematic example of the hardware configuration of an information processing device. [Figure 9] This is a schematic diagram of an analysis system to which a data analysis device is applied according to a second embodiment of the present invention. [Modes for carrying out the invention]

[0013] [Basic Experiments] In their research on central nervous system diseases, the inventors created several genetically modified marmosets (Tg marmosets) that developed Parkinson's disease and observed their condition from birth until the onset of motor symptoms of Parkinson's disease. As part of this observation, they conducted fMRI brain scans and discovered that the Tg marmosets showed increased activity in the pontine nucleus of the brain even before the onset of motor symptoms of Parkinson's disease.

[0014] Figure 1 shows an example of a graph representing the activity states of four brain regions obtained from fMRI experiments on the brains of multiple samples (all of the same age) of wild-type marmosets (WT marmosets) and Tg marmosets. The four graphs in Figure 1 are graphs representing the activity states of the pontine nucleus, caudate nucleus, thalamus, and external globus pallidus (GPe) in order from the left end. In each graph, WT on the horizontal axis represents WT marmosets without genetic modification (number of samples: 18), and Tg represents Tg marmosets (number of samples: 12). The vertical axis represents the voxel intensity obtained from a plurality of two-dimensional slice images that are fMRI images of the brain.

[0015] In Figure 1, it can be seen that in the caudate nucleus, thalamus, and external globus pallidus, the voxel intensity of Tg is smaller than that of WT, while in the pontine nucleus, the voxel intensity of Tg is larger than that of WT. That is, from the graphs in Figure 1, it can be seen that the Tg samples have a higher activity level in the pontine nucleus than the WT samples. Although the experimental results of the caudate nucleus, thalamus, and external globus pallidus as brain regions other than the pontine nucleus are shown, considering the similar experimental results for other brain regions, it was found that the activity state of the pontine nucleus of Tg compared to WT is clearly significantly different compared to the activity states of other brain regions.

[0016] Figure 2 shows an example of a graph representing the activity states of four brain regions obtained from fMRI experiments on the brains of multiple samples of WT marmosets and Tg marmosets, presented by age. The four graphs in Figure 2 are graphs representing the activity states of the pontine nucleus, caudate nucleus, thalamus, and external globus pallidus in order from the left end. In each graph, 2y on the horizontal axis indicates 2 years old, 3y indicates 3 years old, and 4y~ indicates 4 years old or older. For each age, the left graph represents WT marmosets and the right graph represents Tg marmosets. The vertical axis represents the voxel intensity obtained from a plurality of two-dimensional slice images that are fMRI images of the brain.

[0017] The graph of the Tg marmoset shown in Figure 2 is an example of a case where Parkinson's disease developed at the age of 3 years. The onset here refers to the appearance of the movement symptoms specific to Parkinson's disease (such as tremors, muscle rigidity, bradykinesia, akinesia, etc.). According to the graph at the left end of Figure 2, it can be seen that in the Tg marmoset, at the age of 2 years before the onset of Parkinson's disease, the voxel intensity of the pontine nucleus is greater than that of the WT, and the activity of the pontine nucleus is higher than that of the WT. Also, the Tg marmoset developed Parkinson's disease at the age of 3 years, and it was found that even after the age of 4 years when the movement symptoms appeared, the voxel intensity of the pontine nucleus remained greater than that of the WT, and the activity of the pontine nucleus remained higher than that of the WT.

[0018] Figure 3 shows an example of a graph representing the relationship between the activity of the pontine nucleus in the brain and the amount of movement of the movement symptoms of the sample before and after the onset of Parkinson's disease obtained from the fMRI examination experiment of the brain for multiple samples of Tg marmosets. In Figure 3, the horizontal axis represents the data (Normalized voxel intensity) obtained by normalizing the voxel intensity obtained from a plurality of two-dimensional slice images, which are fMRI images of the pontine nucleus of the Tg marmoset before the onset of Parkinson's disease (at the age of 2 years). The vertical axis represents the data (Activity score) quantifying the motor function of the Tg marmoset after the onset of Parkinson's disease (after the age of 3 years). As shown in Figure 3, for the Tg marmosets (number of samples: 5, all developed at the age of 3 years), it was found that there is a strong correlation between the activity level of the pontine nucleus in the brain before the onset of Parkinson's disease and the motor function after the onset. For example, it was found that the relationship between the activity (x) of the pontine nucleus in the brain before and after the onset of Parkinson's disease shown in Figure 3 and the amount of movement (y) of the movement symptoms of the sample is represented by the regression linear equation: y = -122.7 * x + 391.7.

[0019] Based on the experimental results described above, the inventors hypothesized that increased activity in the pontine nuclei of the brain in living organisms could serve as an early biomarker indicating the likelihood of developing Parkinson's disease in the future, and that it could also be a predictive indicator of the likelihood of currently having Parkinson's disease and the severity of symptoms. To investigate this, they performed fMRI scans of the brains of individuals in families with familial Parkinson's disease who had not yet developed the disease, and examined their brain activity. As a result of this study, two cases were selected from the participants in whom activity in the pontine nuclei of the brain was increased.

[0020] Figure 4 shows an example of a graph representing the activity status of four brain regions obtained from fMRI brain scans of multiple samples of healthy individuals and individuals belonging to families with familial Parkinson's disease (non-symptomatic individuals). In each graph, the horizontal axis HC represents healthy individuals (sample size: 5), and PARK4 represents non-symptomatic individuals belonging to families with familial Parkinson's disease (sample size: 2). The vertical axis shows normalized voxel intensity data obtained from multiple two-dimensional slice images of the brain using fMRI.

[0021] In Figure 4, there is almost no difference between the HC data and the PARK4 data in the caudate nucleus, thalamus, and external segment of the globus pallidus, and no significant difference can be found in either case. However, in the pontine nuclei, the PARK4 data is clearly larger than that of HC. In other words, the graph in Figure 4 shows that the PARK4 sample has higher activity in the pontine nuclei than the HC sample. The experimental results for the caudate nucleus, thalamus, and external segment of the globus pallidus are shown as brain regions other than the pontine nuclei. Considering similar experimental results for other brain regions, it is clear that the activity level of the pontine nuclei in PARK4 compared to HC is significantly different from the activity level of other brain regions. In addition, although not shown in the diagram, we observed cases where the activity of the pontine nuclei in the brains of patients who had recently developed Parkinson's disease was elevated.

[0022] This invention is based on the results of the above research and experiments. The embodiments of this invention will be described in detail below.

[0023] [First Embodiment] Figure 5 is a schematic diagram of an analysis system to which a data analysis device is applied according to the first embodiment of the present invention. As shown in Figure 5, the analysis system 1 includes a functional MRI (fMRI) 10, a data analysis device 20, an input device 30, and an output device 40.

[0024] Functional MRI10 is a functional magnetic resonance imaging (MRI) device. Functional MRI10 uses magnetic resonance imaging (MRI) to continuously image the brain and spinal cord of a living organism for a set period of time, thereby measuring fluctuations in MRI signals that correlate with brain activity, for example. The fluctuations in MRI signals measured by Functional MRI10 represent fluctuations in cerebral blood flow. Since high blood flow in the brain indicates high activity (vigor), fluctuations in MRI signals can be used to obtain information about changes in brain activity.

[0025] In the first embodiment, the functional MRI 10 measures fluctuations in the MRI signal of a region of the brain of a living human being (also referred to as a subject) that includes at least the pontine nuclei. The pontine nuclei are regions located in the ventral part of the pons in the brain. The measurement data output by the functional MRI 10 when measuring the brain of a living being consists of multiple two-dimensional slice image data of the brain obtained sequentially through imaging, and includes data indicating the hemodynamics of brain blood flow.

[0026] The functional MRI 10 supplies the measurement data obtained to the data analysis device 20. The functional MRI 10 and the data analysis device 20 may be connected via a telecommunications line, or they may be connected directly with a dedicated cable. Alternatively, the functional MRI 10 and the data analysis device 20 may not be connected, and the functional MRI 10 may record the measurement data on a portable recording medium, and the data analysis device 20 may acquire the measurement data via the portable recording medium on which the measurement data is recorded.

[0027] The input device 30 is configured using existing input devices such as a keyboard, pointing device (mouse, tablet, etc.), buttons, or touch panel. The input device 30 is operated by the user when inputting user instructions to the data analysis device 20. The input device 30 may also be configured using a microphone and a speech recognition device. In this case, the input device 30 acquires acoustic signals generated by the user's speech, performs speech recognition on the words spoken by the user, and inputs the recognized string information to the data analysis device 20. The speech recognition process may be performed by the processor of the data analysis device 20. The input device 30 may be configured in any way that allows user instructions to be input to the data analysis device 20.

[0028] The output device 40 outputs information in a format that the user can recognize. The output device 40 may be an image display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display. The output device 40 may also be an audio output device such as a speaker or headphones. The output device 40 may be configured as a touch panel integrated with the input device 30.

[0029] The data analysis device 20 is an example of an information processing device. As shown in Figure 5, the data analysis device 20 has a measurement data acquisition unit 11, a storage unit 12, an analysis unit 13, and an analysis result output unit 14.

[0030] The measurement data acquisition unit 11 takes in the measurement data output from the functional MRI 10 and stores it in the storage unit 12. The measurement data acquisition unit 11 may also acquire measurement data by reading measurement data recorded on a recording medium such as a USB memory from the recording medium. The measurement data acquisition unit 11 may also acquire measurement data by communicating data with the functional MRI 10 via wired or wireless communication. The measurement data acquisition unit 11 may also acquire measurement data by communicating data with another information processing device that stores the measurement data of the functional MRI 10 via wired or wireless communication. The measurement data acquisition unit 11 may acquire measurement data in any other manner.

[0031] The storage unit 12 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The storage unit 12 stores data used by the control unit 15. The storage unit 12 also stores measurement data acquired by the measurement data acquisition unit 11.

[0032] The analysis unit 13 is an example of a control unit in an information processing device. The analysis unit 13 is composed of a processor such as a CPU (Central Processing Unit) and memory (main memory). The analysis unit 13 functions when the processor executes a program. Note that all or part of the functions of the analysis unit 13 may be implemented using hardware such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). The above program may be recorded on a computer-readable recording medium. Computer-readable recording media include, for example, portable media such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, semiconductor memory devices (e.g., SSDs: Solid State Drives), and memory devices such as hard disks and semiconductor memory devices built into computer systems. The above program may be transmitted via a telecommunications line.

[0033] The analysis unit 13 takes in the measurement data stored in the memory unit 12 and generates disease probability information based on the taken measurement data, which is information indicating the likelihood that a subject corresponding to the measurement data will develop a central nervous system disease in the future. The disease probability information is a biomarker or surrogate marker. Central nervous system diseases include Parkinson's disease and Parkinsonian syndromes.

[0034] Specifically, the analysis unit 13 acquires multiple two-dimensional slice image data from the memory unit 12, generates multiple voxel data from the acquired two-dimensional slice image data, and calculates voxel intensity values ​​for the region corresponding to the pontine nuclei of the brain. The average value or median value of the voxel data included in the region corresponding to the pontine nuclei may also be used as the voxel intensity value. This calculated voxel intensity value represents the degree of blood flow in the pontine nuclei of the brain, that is, the degree of activity of the pontine nuclei. A larger voxel intensity value indicates a higher blood flow in the pontine nuclei and a higher (more active, increased) level of activity in the pontine nuclei.

[0035] Furthermore, the analysis unit 13 may calculate statistical values ​​of voxel data included in the entire brain region (for example, mean value, central value, centroid value weighted according to brain region) as the whole brain voxel intensity value, and calculate the ratio of the voxel intensity value corresponding to the pontine nuclei to the whole brain voxel intensity value, and use this ratio as the voxel intensity value indicating the activity level of the pontine nuclei. Since the brain of a living organism is awake (active) and therefore has a generally higher intensity, normalizing the intensity of the part corresponding to the pontine nuclei by dividing it by the statistical value of the whole brain makes it easier to capture the characteristics of the pontine nuclei.

[0036] The analysis unit 13 pre-samples voxel intensity values ​​for the pontine nuclei of multiple healthy individuals and stores them as a reference. The analysis unit 13 generates pair information of the voxel intensity values ​​calculated for the subject and the reference as disease probability information.

[0037] Alternatively, the analysis unit 13 may use information corresponding to the difference or variance of voxel intensity values ​​calculated for the subject relative to the reference as information indicating the likelihood of developing the disease. Information corresponding to the variance is, for example, a value obtained by normalizing the variance value so that the numerical range containing the variance value falls within the range of 0 to 1.

[0038] The analysis unit 13 may store voxel intensity values ​​for the pontine nuclei of multiple healthy individuals as references classified by, for example, age or age group of healthy individuals (e.g., life stage (infancy: 0-4 years, childhood: 5-14 years, adolescence: 15-24 years, adulthood: 25-44 years, middle age: 45-64 years, elderly: 65 years and older) or age group such as 20s, 40s-60s, etc.). In this case, the analysis unit 13 may generate information as disease probability information, which consists of paired voxel intensity values ​​calculated for a subject and references corresponding to the subject's age or age group.

[0039] Furthermore, as shown in Figure 3, based on the strong correlation between the activity level of the pontine nuclei of the brain before the onset of Parkinson's disease or Parkinsonian syndrome and motor function after the onset, the analysis unit 13 may, for example, sample the relationship between the activity of the pontine nuclei of the brain before the onset of Parkinson's disease and the amount of motor activity of the person if the disease develops, according to the age of onset, for individuals belonging to a family with familial Parkinson's disease (non-affected individuals), and calculate a regression line as shown in Figure 3 to use as a reference. For example, by substituting the voxel intensity value calculated for a subject into the regression line equation according to the subject's age, a predicted value for the amount of motor activity of the subject if the disease develops in the future can be obtained. This regression line serves as a predictive index for the future severity of Parkinson's disease symptoms. Therefore, the analysis unit 13 may use the pair information of the voxel intensity value calculated for a subject and the regression line equation (reference) according to the subject's age as information on the likelihood of developing the disease.

[0040] The analysis result output unit 14 outputs the disease probability information generated by the analysis unit 13 to the output device 40. For example, the analysis result output unit 14 outputs the disease probability information generated by the analysis unit 13, which is a pair of voxel intensity values ​​and references, to the output device 40 as graph data that can be visually compared.

[0041] Alternatively, the analysis result output unit 14 outputs information corresponding to the difference or variance of voxel intensity values ​​relative to the reference, which is disease probability information generated by the analysis unit 13, to the output device 40 as an indicator or graphics.

[0042] The output device 40 displays the disease probability information output by the analysis result output unit 14.

[0043] Figure 6 shows an example of how the probability of developing the disease is displayed by the output device 40. Figure 6(1) is an example of the probability of developing the disease being represented as a graph. On the horizontal axis of the graph, HP represents healthy individuals and OP represents subjects (test subjects). The vertical axis represents the voxel intensity values ​​or normalized voxel intensity values ​​calculated by the analysis unit 13. By displaying the data for HP (healthy individuals) and OP (subjects) corresponding to the vertical axis, the probability of developing the disease can be easily grasped.

[0044] Figure 6(2) shows an example of displaying information corresponding to the difference in voxel intensity values ​​relative to the reference, which is information regarding the likelihood of developing the disease, as an indicator.

[0045] Figure 6(3) is an example of a graph showing the predicted amount of motor activity for a subject who develops motor symptoms in the future, assuming they are age n. The horizontal axis of the graph represents the voxel intensity value of the pontine nuclei in the subject's brain at age n, and the vertical axis represents the predicted amount of motor activity for motor symptoms if they develop motor symptoms in the future.

[0046] Next, the operation of the analysis system 1 according to the first embodiment will be described. Figure 7 is a flowchart showing an example of the general processing procedure of analysis system 1. First, let's explain how Functional MRI10 works. Functional MRI10 measures fluctuations in the MRI signal in areas of the subject's brain that include at least the pontine nuclei (step S11).

[0047] Next, the functional MRI 10 supplies the measurement data obtained online or offline to the data analysis device 20 (step S12).

[0048] Next, the operation of the data analysis device 20 will be described. The measurement data acquisition unit 11 takes in the measurement data output from the functional MRI 10 and stores it in the storage unit 12 (step S21).

[0049] Next, the analysis unit 13 takes in the measurement data stored in the storage unit 12 and generates disease probability information, which is information indicating the possibility that the subject corresponding to the measurement data will develop a central nervous system disease in the future, based on the taken measurement data (step S22). Such processing may be performed in response to a processing instruction received from the user via the input device 30.

[0050] Next, the analysis result output unit 14 outputs the disease probability information generated by the analysis unit 13 to the output device 40 for display (step S23).

[0051] Alternatively, the functional MRI 10 may be configured to include the functions of a data analysis device 20.

[0052] Figure 8 is a schematic diagram of an example hardware configuration of an information processing device 90 applied to the first embodiment. The information processing device 90 comprises a processor 91, main memory 92, communication interface 93, auxiliary storage device 94, input / output interface 95, and internal bus 96. The processor 91, main memory 92, communication interface 93, auxiliary storage device 94, and input / output interface 95 are connected to each other via the internal bus 96 so as to be able to communicate with each other. The information processing device 90 may be applied to, for example, a data analysis device 20. In this case, for example, the measurement data acquisition unit 11 may be configured using a communication interface 93 or an input / output interface 95. For example, the storage unit 12 may be configured using an auxiliary storage device 94. The analysis unit 13 may be configured using a processor 91 and a main memory device 92. The analysis result output unit 14 may also be configured using a communication interface 93 or an input / output interface 95.

[0053] [Second Embodiment] Figure 9 is a schematic diagram of an analysis system applying a data analysis device according to a second embodiment of the present invention. As shown in Figure 9, the analysis system 1a includes a functional MRI (fMRI) 10, a data analysis device 20a, an input device 30, and an output device 40. The analysis system 1a according to the second embodiment differs from the analysis system 1 according to the first embodiment in that the data analysis device 20a is different. The data analysis device 20a is an example of an information processing device. As shown in Figure 9, the data analysis device 20a includes a measurement data acquisition unit 11, a storage unit 12, an analysis unit 13a, and an analysis result output unit 14. The data analysis device 20a according to the second embodiment differs from the data analysis device 20 according to the first embodiment in that the analysis unit 13a is different. In the second embodiment, only the configuration and operation that differ from the first embodiment will be described, and the common parts will not be explained.

[0054] The analysis unit 13a, like the analysis unit 13, is an example of a control unit in an information processing device. The analysis unit 13 takes in measurement data stored in the storage unit 12 and generates disease possibility information based on the taken measurement data, which is information indicating the possibility that the subject corresponding to the measurement data is currently suffering from a central nervous system disease. The disease possibility information is a biomarker or surrogate marker. Central nervous system diseases include Parkinson's disease and Parkinsonian syndromes.

[0055] The specific configuration and operation of the analysis unit 13a, which acquires measurement data from the memory unit 12 and calculates voxel intensity values ​​for the region corresponding to the pontine nuclei of the brain from the acquired measurement data, are the same as in the first embodiment, so a detailed explanation is omitted here.

[0056] The analysis unit 13a pre-samples voxel intensity values ​​for the pontine nuclei of multiple healthy individuals and stores them as a reference. The analysis unit 13a generates disease probability information by pairing the voxel intensity values ​​calculated for the subject with the reference.

[0057] Alternatively, the analysis unit 13a may use information corresponding to the difference or variance of voxel intensity values ​​calculated for the subject relative to the reference as disease probability information. Information corresponding to the variance is, for example, a value obtained by normalizing the variance value so that the numerical range containing the variance value falls within the range of 0 to 1.

[0058] Furthermore, the analysis unit 13a may store voxel intensity values ​​for the pontine nuclei of multiple healthy individuals as references classified by, for example, age or age group of healthy individuals (e.g., life stage (infancy: 0-4 years, childhood: 5-14 years, adolescence: 15-24 years, adulthood: 25-44 years, middle age: 45-64 years, elderly: 65 years and older) or age group such as 20s, 40s-60s, etc.). In this case, the analysis unit 13a may generate disease probability information by pairing the voxel intensity values ​​calculated for the subject with the references of the age or age group corresponding to the subject.

[0059] The analysis result output unit 14 outputs the disease probability information generated by the analysis unit 13a to the output device 40. For example, the analysis result output unit 14 outputs the disease probability information generated by the analysis unit 13a, which is a pair of voxel intensity values ​​and references, to the output device 40 as graph data that can be visually compared.

[0060] Alternatively, the analysis result output unit 14 outputs information corresponding to the difference or variance of voxel intensity values ​​relative to the reference, which is disease probability information generated by the analysis unit 13a, to the output device 40 as an indicator or graphics.

[0061] Next, the operation of the analysis system 1a according to the second embodiment will be explained using the flowchart in Figure 7, focusing only on the parts where the processing procedure differs from that of the first embodiment.

[0062] The operation of the data analysis device 20a will now be described. The measurement data acquisition unit 11 takes in the measurement data output from the functional MRI 10 and stores it in the storage unit 12 (step S21).

[0063] Next, the analysis unit 13a takes in the measurement data stored in the storage unit 12 and generates disease possibility information, which is information indicating the possibility that the subject corresponding to the measurement data is currently suffering from a central nervous system disease, based on the taken measurement data (step S22). Such processing may be performed in response to a processing instruction received from the user via the input device 30.

[0064] Next, the analysis result output unit 14 outputs the disease probability information generated by the analysis unit 13a to the output device 40 for display (step S23).

[0065] Alternatively, the functional MRI 10 may be configured to include the functions of the data analysis device 20a.

[0066] A schematic example of the hardware configuration of the information processing device applied to the second embodiment is the same as in Figure 8, so a detailed explanation is omitted here.

[0067] [Modified version of the first embodiment] In the first embodiment described above, some or all of the operations of the data analysis device 20 may be performed by a person. In particular, if all operations are performed by a person rather than the data analysis device 20, the data analysis device 20 is not an essential component in the implementation of the present invention. The data analysis device 20 may be implemented using multiple information processing devices. For example, the data analysis device 20 may be implemented using a cloud or similar device.

[0068] Alternatively, the processing performed by the analysis unit 13 and the analysis result output unit 14 of the data analysis device 20 using the patient measurement data output by the functional MRI 10 may be performed sequentially by an examiner or other human. For example, the examiner may perform the following tasks. (1) The examiner shall prepare in advance sampled data of voxel intensity values ​​for the pontine nuclei of the brains of multiple healthy individuals as a reference. For example, the sampled data of voxel intensity values ​​may be displayed on a computer device or tablet terminal screen, or prepared as printed material as a reference. Alternatively, for individuals belonging to a family with familial Parkinson's disease (non-affected individuals), the relationship between pontine nucleus activity in the brain before the onset of Parkinson's disease and the amount of motor activity in their motor symptoms after the onset is sampled according to the age of onset, and a regression line equation as shown in Figure 3 is calculated and prepared as a reference. (2) The examiner obtains as measurement data the fluctuations in the MRI signal of the subject's brain, including at least the pontine nuclei, as measured by the functional MRI10. (3) The examiner calculates the voxel intensity value using the acquired measurement data and obtains the pair information of the calculated voxel intensity value for the subject and the reference as information on the likelihood of developing the disease. The examiner may also obtain information corresponding to the difference or difference in the voxel intensity value calculated for the subject compared to the reference as information on the likelihood of developing the disease. Alternatively, the examiner may obtain paired information of the voxel intensity value calculated for the subject and the regression line equation (reference) corresponding to the subject's age as information on the likelihood of developing the disease. (4) Based on the acquired information on the likelihood of developing the disease, the examiner will determine whether the subject is likely to develop Parkinson's disease in the future. For example, the examiner may visualize the information on the likelihood of developing the disease by displaying it on a computer device or tablet terminal, or by printing it out, and the examiner will determine the likelihood of developing the disease themselves.

[0069] [Modified version of the second embodiment] In the second embodiment described above, some or all of the operations of the data analysis device 20a may be performed by a person. In particular, if all operations are performed by a person rather than the data analysis device 20a, the data analysis device 20a is not an essential component in the implementation of the present invention. The data analysis device 20a may be implemented using multiple information processing devices. For example, the data analysis device 20a may be implemented using a cloud or other similar device.

[0070] Furthermore, the processing performed by the analysis unit 13a and the analysis result output unit 14 of the data analysis device 20a using the patient measurement data output by the functional MRI 10 may be performed sequentially by an examiner or other human. For example, the examiner may perform the following tasks. (1) The examiner shall prepare in advance sampled data of voxel intensity values ​​for the pontine nuclei of the brains of multiple healthy individuals as a reference. For example, the sampled data of voxel intensity values ​​may be displayed on a computer device or tablet terminal screen, or prepared as printed material as a reference. (2) The examiner obtains as measurement data the fluctuations in the MRI signal of the subject's brain, including at least the pontine nuclei, as measured by the functional MRI10. (3) The examiner calculates voxel intensity values ​​using the acquired measurement data and obtains pair information of the calculated voxel intensity values ​​for the subject and the reference as disease probability information. The examiner may also obtain information corresponding to the difference or difference in voxel intensity values ​​calculated for the subject compared to the reference as disease probability information. (4) Based on the acquired disease possibility information, the examiner will determine whether the subject may currently have Parkinson's disease. For example, the examiner may visualize the disease possibility information by displaying it on a computer device or tablet screen or printing it out, and the examiner will determine the disease possibility themselves.

[0071] As described above, according to the first embodiment of the present invention or a modified version thereof, the possibility of the onset of a central nervous system disease can be detected earlier and with higher accuracy.

[0072] Furthermore, according to the second embodiment of the present invention or a modified version thereof, it is possible to detect the possibility of currently suffering from a central nervous system disease at an earlier stage and with higher accuracy.

[0073] Although embodiments of the present invention have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments and may include designs and the like that do not depart from the spirit of the present invention. [Explanation of Symbols]

[0074] 1, 1a Analysis system, 10 Functional MRI, 20, 20a Data analysis device, 11 Measurement data acquisition unit, 12 Storage unit, 13, 13a Analysis unit, 14 Analysis result output unit, 30 Input device, 40 Output device

Claims

1. Acquisition step: Obtaining disease probability information, which indicates the likelihood of future central nervous system disease onset, based on the activity level of the pontine nuclei in the brain of living organisms. A method for determining the thumbnail.

2. In the acquisition step, the activity level of the pontine nuclei of the brain is obtained as the ratio of the value representing the activity level of the pontine nuclei to the statistical value representing the activity level of the entire brain. The determination method according to claim 1.

3. The aforementioned information regarding the likelihood of developing the disease indicates the possibility of future development of Parkinson's disease or Parkinsonian syndrome. The determination method according to claim 1 or claim 2.

4. The acquisition step involves acquiring the activity level of the pontine nuclei based on the blood flow of the pontine nuclei of the brain obtained by measuring the brain using functional MRI. The determination method according to claim 1 or claim 2.

5. A control unit acquires disease probability information, which indicates the likelihood of future central nervous system disease development, based on the activity level of the pontine nuclei in the brain of living organisms. An information processing device equipped with the following features.

6. On the computer, Acquisition step: Obtaining disease probability information, which indicates the likelihood of future central nervous system disease onset, based on the activity level of the pontine nuclei in the brain of living organisms. A computer program designed to execute something.

7. Functional MRI, which measures the brain of a living organism, An information processing device that acquires disease probability information, which is information indicating the likelihood of future central nervous system disease onset, based on the activity level of the pontine nuclei of the brain obtained by the measurement of the brain by the functional MRI, A system equipped with these features.

8. Acquisition step: Obtaining disease probability information, which indicates the current likelihood of suffering from a central nervous system disease, based on the activity level of the pontine nuclei in the brain of a living organism. A method for determining the thumbnail.

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