CN115836839A - Positioning system of individual nerve regulation target point - Google Patents
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Abstract
The invention provides a positioning system of an individual nerve regulation target spot, which comprises: the data acquisition module is used for acquiring functional magnetic resonance data of a target brain area of a detected person; the target brain area functional network construction module is used for constructing a target brain area functional connection network according to the functional magnetic resonance data; the target brain area dividing module is used for dividing the target brain area into a plurality of sub brain areas based on the target brain area functional connection network; the functional network construction module of the sub-brain area is used for calculating a second functional connection value between each sub-brain area and the rest sub-brain areas and constructing a functional connection network of the sub-brain area relative to the sub-brain area of the whole brain; and the abnormal positioning module is used for calculating the abnormal degree index of each sub-brain region functional connection network of the detected person by adopting an abnormal value detection method, comparing the abnormal degree index of the sub-brain region of each target point brain region and positioning the sub-brain region with the maximum abnormal degree index as the abnormal target point brain region.
Description
Technical Field
The invention mainly relates to the field of medical instruments, in particular to a positioning system of an individual nerve regulation target spot based on functional magnetic resonance data.
Background
With the continuous development of science and technology, modern nerve regulation and control technologies with micro-wound, reversibility and adjustability, such as deep brain stimulation, transcranial magnetic stimulation and the like, are gaining more and more acceptance in the fields of clinical mental diseases, neurological diseases and rehabilitation, and show good prospects. However, current neuromodulation techniques still face significant challenges. On one hand, the position and the stimulation intensity of the nerve regulation target point are mainly subjectively judged by doctors according to clinical experience, even contradictions exist among judgment results of different doctors, and serious consequences are generated if the selection is wrong; on the other hand, target selection, setting of regulatory parameters, etc. in a general treatment regimen may not achieve good therapeutic effects in all individual patients, and may even lead to unpredictable side effects. Therefore, it is desirable to be able to objectively select neuromodulation targets for individual patients, and to set regulatory parameters to achieve optimal intervention results.
Disclosure of Invention
The invention aims to provide a positioning system for positioning a nerve regulation and control target according to individual conditions.
In order to solve the technical problems, the invention provides a positioning system for an individualized nerve regulation target spot, which is characterized by comprising: the data acquisition module is used for acquiring functional magnetic resonance data of a target brain area of a detected person; the target brain area functional network construction module is used for constructing a target brain area functional connection network according to the functional magnetic resonance data, and the target brain area functional connection network comprises first functional connection values among the target brain areas; a target brain region segmentation module for segmenting the target brain region into a plurality of sub-brain regions based on the target brain region functional connection network; the functional network construction module of the sub-brain areas is used for calculating a second functional connection value between each sub-brain area and the rest sub-brain areas and constructing a functional connection network of the sub-brain areas relative to the sub-brain areas of the whole brain; and the abnormal positioning module is used for calculating the abnormal degree index of each sub-brain region functional connection network of the detected person by adopting an abnormal value detection method, comparing the abnormal degree index of the sub-brain region of each target brain region and positioning the sub-brain region with the maximum abnormal degree index as the abnormal target brain region.
In an embodiment of the invention, the target brain region functional network construction module extracts a time series of each voxel in each of the target brain regions, calculates a pearson correlation coefficient between the time series of any voxels in each two of the target brain regions, transforms the pearson correlation coefficient into a Z-value using Fisher-Z transformation, and uses the Z-value as the first functional connection value between the relevant voxels.
In an embodiment of the invention, the target brain region segmentation module segments the target brain region into a plurality of sub-brain regions using an unsupervised machine learning algorithm.
In an embodiment of the invention, the unsupervised machine learning algorithm comprises a k-means clustering method.
In an embodiment of the invention, the sub-brain region functional network construction module is further configured to extract a time series of each voxel in each of the sub-brain regions, calculate a pearson correlation coefficient between the sub-brain region average time series of each sub-brain region in each target brain region and the average time series of the remaining brain regions in the whole brain region, and transform the pearson correlation coefficient into a Z-value using a Fisher-Z transform, and use the Z-value as the second functional connection value between the sub-brain region and the remaining brain regions.
In one embodiment of the present invention, the whole brain region includes 116 brain regions divided according to an AAL template, and the brain regions include the target brain region.
In an embodiment of the present invention, the abnormal value detection method includes a grubbs method in which a functional connection network of a sub-brain region of a normal person is taken as a normal distribution, and the abnormal value detection method calculates an abnormal degree index of the functional connection network of the sub-brain region of the subject with respect to the normal distribution.
In an embodiment of the present invention, the abnormality localization module is further configured to calculate an average value of the abnormality degree indicators of each of the sub-brain regions of the subject with respect to the rest of the whole brain regions, and use the average value as the abnormality degree indicator of the sub-brain region.
In an embodiment of the invention, the abnormality localization module is further configured to divide each target brain region into a left side and a right side, calculate a left-right functional connection value between each voxel on the left side and each voxel on the right side, and compare the left-right functional connection value of the subject with the left-right functional connection value of the normal person.
In an embodiment of the present invention, the abnormality localization module further calculates abnormality degree indexes of the left side and the right side to obtain left and right differences of a target brain region, and compares the left and right differences of the target brain region of the subject with the left and right differences of the target brain region of the normal person.
In an embodiment of the present invention, the target brain areas include a left and right posterior combined brain area and a left and right temporal vertex combined brain area.
In an embodiment of the present invention, the image processing device further includes an output module, configured to register the sub-brain region with the largest abnormality degree index and the voxel with the strongest sub-brain region signal to the brain space of the subject, and display the sub-brain region with the largest abnormality degree index and the voxel with the strongest sub-brain region signal in the image of the brain space.
The positioning system refines the target brain area into sub-brain areas based on the physiological significance of functional connection of the brain areas, can accurately position the nerve regulation target point aiming at an individual, and can assist in optimizing parameters of nerve regulation, thereby obtaining better regulation and control effects.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the principle of the invention. In the drawings:
FIG. 1 is a block diagram of a localization system for an individualized neuromodulation target according to an embodiment of the invention;
FIG. 2 is a schematic diagram of the position of a target brain region of a subject obtained by the positioning system according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a relationship between a Silhouette parameter and the number of clustering centers obtained by a target brain region segmentation module in the positioning system according to an embodiment of the present invention using a k-means clustering method;
FIG. 4 is a diagram illustrating the result of clustering the second brain region into 2 sub-brain regions by the positioning system according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a t-distribution of normalized statistical values of functional connection values of sub-brain regions obtained by an abnormal location module of the location system according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an image output and displayed by an output module of the positioning system according to an embodiment of the invention.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
The relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as exemplary only and not as limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
It should be noted that the terms "first", "second", and the like are used to define the components, and are only used for convenience of distinguishing the corresponding components, and the terms have no special meanings unless otherwise stated, and therefore, the scope of protection of the present application is not to be construed as being limited. Further, although the terms used in the present application are selected from publicly known and used terms, some of the terms mentioned in the specification of the present application may be selected by the applicant at his or her discretion, the detailed meanings of which are described in relevant parts of the description herein. Further, it is required that the present application is understood not only by the actual terms used but also by the meaning of each term lying within.
A state of micro-consciousness is a state of consciousness with little but well-defined behavioral evidence to demonstrate the perception of severe changes in consciousness of self and the environment, a particular state of consciousness between coma and conscious awareness. The micro-conscious state is a necessary stage for all patients with chronic disturbance of consciousness to recover. There is currently a lack of effective treatment for the promotion of wakefulness in patients with micro-conscious states. Neural modulation can be implemented as a wake-promoting means by targeting locations of the relevant brain regions. Research shows that nerve and mental diseases are probably caused by the dysfunction of the connection between brain areas, and the nerve regulation and control means can reverse the abnormal connection of brain networks by adjusting the connection of the brain networks so as to achieve the effect of adjuvant therapy of the diseases.
In this specification, the term "functional magnetic resonance data" refers to image data scanned using a magnetic resonance imaging technique. The term "neuromodulation" is the high-end application of neuro-interventional technology in the neuroscience field, and is a biomedical engineering technology which utilizes an implanted or non-implanted technology and adopts physical means (such as electrical stimulation and magnetic stimulation) or medicinal means (implantation of a micro pump) to change the activity of a central nerve, a peripheral nerve or an autonomic nervous system so as to improve the symptoms of a sick population and improve the life quality. Compared with the traditional brain destruction and resection operation, the emphasis is on regulation, namely the process is reversible, and the treatment parameters can be adjusted in vitro. The term "neuropsychiatric disorders" refers to cognitive, sensory, motor disorders and the like due to abnormalities in the neural circuits, such as autism in children, middle-aged affective disorders (depression, obsessive compulsive disorder, addiction, anorexia), and senile neurodegenerative disorders (parkinson, alzheimer's disease) and the like. The neural circuits of the normal body are an intrinsic balance system (i.e., normal brain network) composed of electrical stimulation and chemical signals, but diseases (including congenital and acquired factors) break this balance, resulting in impaired sensation, movement or cognition (i.e., brain network abnormalities). Although the etiology of these diseases is complex, there is a commonality that is often accompanied by dysfunction of the neural circuits of the brain. The treatment of such diseases requires the overall regulation of the neural circuits to restore the normal functional operation of the brain network, thereby achieving the effect of curing the diseases. Therefore, based on the neural regulation and control means, the effect of assisting in treating diseases can be achieved by adjusting the neural circuits in the brain network, radiating and reversing the network properties of the whole brain network. The scientific hypothesis is combined with a large amount of clinical evidences at home and abroad, namely, the intervention is carried out through physical (electric, magnetic and the like) means on proper targets, so that the brain diseases can be effectively treated.
FIG. 1 is a block diagram of a system for locating a personalized neuromodulation target according to an embodiment of the present invention. Referring to fig. 1, a localization system 100 of this embodiment includes a data acquisition module 110, a target brain region functional network construction module 120, a target brain region segmentation module 130, a sub-brain region functional network construction module 140, and an abnormality localization module 150.
The data acquiring module 110 is configured to acquire functional magnetic resonance data of a target brain region of a subject. The present invention is not particularly limited to the subject, and the subject may include an abnormal subject having abnormal brain function and a normal subject having normal brain function.
In some embodiments, the abnormal is a patient with a neuro-psychiatric disorder.
In some embodiments, the psychiatric neurological condition comprises a micro-conscious state.
In some embodiments, the normal human is a healthy human who is not clinically diagnosed with a neuro-psychiatric disorder.
In some embodiments, the functional magnetic resonance data comprises resting state functional magnetic resonance data.
The invention is not limited as to the manner in which the data acquisition module 110 acquires functional magnetic resonance data. The data acquisition module 110 may be part or all of a magnetic resonance apparatus, and is directly used for measuring functional magnetic resonance data of the subject. The data acquisition module 110 may also be a separate device that acquires functional magnetic resonance data of the subject from the magnetic resonance apparatus. The positioning system 100 may establish a communication connection with a magnetic resonance device in a wired or wireless manner, and the magnetic resonance device may send the acquired functional magnetic resonance data to the data acquisition module 110 in the positioning system 100.
The invention does not limit the format of the functional magnetic resonance data. In some embodiments, the formats include a DICOM format and a NIFTI format.
In some embodiments, the target brain region of the subject comprises any of the brain regions in the AAL template. The human cerebral cortex was divided into 116 brain regions according to AAL (atomic Automatic Labeling) template, as shown in Table 1 below. The invention does not limit the specific position and number of the brain area corresponding to the functional magnetic resonance data to be acquired.
Table 1: standard brain partition information according to AAL template
Fig. 2 is a schematic diagram of the position of the target brain region of the subject acquired by the positioning system according to an embodiment of the present invention. Fig. 2 shows a schematic diagram of a nuclear magnetic resonance image of a subject's brain, in which four brain regions, a first brain region 210, a second brain region 220, a third brain region 230, and a fourth brain region 240, are circled with ellipses. Table 2 lists the names of the brain regions corresponding to the four brain regions and the number of voxels included in the nmr image. In an embodiment of the present invention, the voxel is a minimum unit of a three-dimensional segmentation of a medical image that can be provided by a magnetic resonance scanning apparatus.
Table 2: target brain region information shown in FIG. 2
Referring to fig. 1, the target brain region functional network construction module 120 is configured to construct a target brain region functional connection network according to the functional magnetic resonance data obtained by the data obtaining module 110, where the target brain region functional connection network includes a first functional connection value between target brain regions. It is understood that a target brain region functional connection network is constructed for each subject.
In some embodiments, the target brain region functional network construction module 120 extracts a time series of each voxel in each target brain region, calculates a pearson correlation coefficient between time series of any voxels in every two target brain regions, and transforms the pearson correlation coefficient into a Z-value using a Fisher-Z transform, the Z-value being a first functional connection value between the relevant voxels. In these embodiments, the network of functional connections of the target brain region is constructed by first functional connections between voxels of different target brain regions.
Taking the four brain regions shown in table 2 as an example, the target brain region functional connection network includes a first functional connection value between each voxel in the first brain region 210 and 408 voxels in the remaining 3 brain regions, a first functional connection value between each voxel in the second brain region 220 and 425 voxels in the remaining 3 brain regions, and so on.
According to these embodiments, the target brain region functional network construction module 120 may construct the target brain region functional connection network by the following steps.
Step S11: the functional magnetic resonance data is transformed to a standard space for standard calculations.
Specifically, the standard space in step S11 may be an MNI space. The MNI space is a coordinate system established by the Montreal Neurological Institute from a series of magnetic resonance images of normal human brain. The magnetic resonance images of different examinees are not comparable in original space, and the functional magnetic resonance data of different examinees can be made to be comparable by registering the images of all examinees on a standardized template and unifying parameters such as dimensions, origins and voxels, so that the functional magnetic resonance data of the different examinees can be used for analyzing multiple samples. Using the MNI standard template, functional magnetic resonance data of different subjects can be converted to MNI space for analysis and comparison. Fig. 2 shows a magnetic resonance image of a subject in an MNI space.
Step S12: and extracting the time signal of each voxel in each target brain area and the average time signal of all voxels in each target brain area.
In step S12, taking table 2 as an example, the time signal of each voxel in the 4 target brain regions in table 2 and the average time signal of all voxels in the 4 target brain regions are extracted. Taking the first brain region 210 as an example, in step S12, the time signal of each voxel in the first brain region 210 and the average time signal of all 57 voxels are extracted. In these embodiments, the temporal signal of the voxel is a time series.
Step S13: for each target brain region, calculating a Pearson correlation coefficient r between the time signal of each voxel in the target brain region and the time signals of each voxel in the remaining target brain regions, and transforming the r value into a Z value using Fisher-Z transformation, as shown in the following equation (1):
step S14: and taking the z value as a first functional connection value between related voxels, thereby constructing a functional connection matrix between each target brain region and the voxels of the rest target brain regions. For the four brain regions shown in table 2, the size of the functional connection matrix between the first brain region 210 and the rest of the target brain region voxels is 57 × 408, the size of the functional connection matrix between the second brain region 220 and the rest of the target brain region voxels is 40 × 425, the size of the functional connection matrix between the third brain region 230 and the rest of the target brain region voxels is 131 × 334, and the size of the functional connection matrix between the fourth brain region 240 and the rest of the target brain region voxels is 237 × 228. The size of the functional connection matrix between each brain region and the voxels of the rest target brain regions is mxn, wherein m is equal to the number of the voxels in the target brain region, and n is equal to the total number of the voxels in the rest target brain regions except the target brain region.
It is to be understood that the illustration in table 2 is merely an example. In other embodiments, any other target brain region may be selected.
Referring to fig. 1, the target brain region segmentation module 130 is configured to segment the target brain region into a plurality of sub-brain regions based on the functional connection network of the target brain region.
In some embodiments, the target brain region segmentation module 130 segments the target brain region into a plurality of sub-brain regions using an unsupervised machine learning algorithm.
In some embodiments, the unsupervised machine learning algorithm includes a k-means clustering method. In these embodiments, the following steps are used to perform the segmentation of the target brain region.
Step S21: and clustering the functional connection values in the target brain area functional connection network by using a k-means clustering method.
Continuing with the example shown in table 2, all 57 × 408,40 × 425,131 × 334,237 × 228 first functional connection values of voxels belonging to the four target brain regions are clustered using k-means at step S21, with the initial cluster center number traversing from 2 to 8.
Step S22: traversing the initial cluster center number k from 2 to 8, and calculating a Silhouette parameter according to the following formulas (2), (3):
b(j)＝min{e(j,Cs)}，s＝1,2,…k (3)
wherein Cs (s =1,2, … k) is k clusters obtained after clustering, a (j) represents an average euclidean distance between a sample j in a cluster Cs and other members in the cluster Cs, e (j, cs) represents an average euclidean distance between the sample j in the cluster Cs and members in other clusters, and b (j) represents a minimum value of the distances between the sample j and other cluster members.
The larger the Silhouuette parameter calculated according to the step S22 is, the larger the intra-class compactness and the inter-class separability are, the better the clustering quality is.
Fig. 3 is a graph showing a relationship between the number of clustering centers and the silouette parameter obtained by the target brain region segmentation module in the localization system according to the embodiment of the present invention using the k-means clustering method. Wherein, the horizontal axis is the number of the clustering centers from 2 to 8; the vertical axis is the Silhouette parameter. Referring to fig. 3, when the number of the cluster centers is 2, the corresponding silouette parameter is the largest, which indicates that in this embodiment, the cluster quality is the best when the number of the cluster centers is 2.
Fig. 4 is a diagram illustrating the result of clustering the second brain region into 2 sub-brain regions by the positioning system according to an embodiment of the present invention. Referring to fig. 4, the second brain region 220 shown in fig. 2, i.e., the ppc.c target brain region, is clustered into 2 sub-brain regions 410, 420 using the localization system 100 of the present invention. Fig. 4 is a three-dimensional view of the brain image of the same subject, in which the upper left is coronal, the upper right is sagittal, and the lower side is transverse. In fig. 4, a pattern filled with a blank space indicates a sub-brain region 410, and a pattern filled with oblique lines indicates a sub-brain region 420. The sub-brain regions 410, 420 shown in the different views represent the same sub-brain region 410, 420, respectively, and therefore the same reference numerals are used.
The illustration in fig. 4 is merely an example and is not intended to limit the number of cluster centers in the clustering method of the present invention.
After k-means clustering is performed on the four target brain areas in the example shown in table 2, the first brain area 210 is divided into 2 sub-brain areas, the second brain area 220 is divided into 3 sub-brain areas, the third brain area 230 is divided into 2 sub-brain areas, and the fourth brain area 240 is divided into 2 sub-brain areas.
Referring to fig. 1, the sub-brain region functional network construction module 140 is configured to calculate a second functional connection value between each sub-brain region and the remaining sub-brain regions, and construct a sub-brain region functional connection network of the sub-brain region relative to the whole brain.
In some embodiments, the sub-brain region functional network construction module 140 is further configured to extract a time series of each voxel in each sub-brain region, calculate a pearson correlation coefficient between the sub-brain region average time series of each sub-brain region in each target brain region and the average time series of the remaining brain regions in the whole brain region, and transform the pearson correlation coefficient into a Z-value using a Fisher-Z transform, the Z-value serving as a second functional connection value between the sub-brain region and the remaining brain regions.
In some embodiments, the whole brain region comprises 116 brain regions partitioned according to the AAL template, the 116 brain regions comprising the target brain region. That is, the target brain region described above is one or more of the 116 brain regions.
In this embodiment, assuming that the target brain region is a brain region a of 116 brain regions, which includes sub-brain regions A1 and A2, the sub-brain region functional network construction module 140 is to extract a time series of each voxel in each sub-brain region A1, A2 in the brain region a, calculate a sub-brain region average time series At1, at2 of each sub-brain region A1, A2; extracting the time sequence of each voxel in the rest 115 brain areas in the whole brain area for calculating the average time sequence Ar of the 115 brain areas; calculating a Pearson correlation coefficient between the average time sequence At1 of the sub-brain region and the average time sequence Ar for the sub-brain region A1, and calculating a Pearson correlation coefficient between the average time sequence At2 of the sub-brain region and the average time sequence Ar for the sub-brain region A2; all the Pearson correlation coefficients are transformed into Z values by Fisher-Z transformation.
Continuing with the example shown in table 2, for the four target brain areas, the sub-brain area functional network construction module 140 constructs a sub-brain area functional connection network respectively. According to the number of the sub-brain regions of the four target brain regions, the sizes of the sub-brain region functional connection networks constructed by the sub-brain region functional network construction module 140 are 2 × 115,3 × 115,2 × 115 and 2 × 115, respectively, wherein 115 is the number of the remaining brain regions of 116 brain regions excluding the target brain region corresponding to the sub-brain region. It is understood that the functional connection network of the sub-brain regions also has a matrix form. The size of each sub-brain region functional connection network is the number of z values contained therein.
In some embodiments, the localization system 100 of the present invention also calculates brain features of the sub-brain regions, including low frequency amplitude (Alff), local coherence (Reho), graph theory indices, and the like. The low-frequency amplitude is the amplitude of the low-frequency oscillation signal of the magnetic resonance data, and the condition of local low-frequency amplitude in a resting state can be known through observation of the low-frequency amplitude, so that the change of the spontaneous activity degree of the neuron can be reflected. The local consistency is an index for measuring the function synchronization strength of a local brain area, and the method is based on data driving, and is quantitatively measured by calculating Kendell harmonic coefficients on the assumption that the signal values of adjacent voxels have certain similarity or consistency in a time domain in an active or activated brain area. The graph theory is a theory for researching point sets connected by lines, and the representation of the topological relation of a complex network through a graph theory method is an important means for researching the overall characteristics of different nodes, different connecting edges and networks in the network. The graph theory index is a network characteristic calculated after a research object is modeled according to a graph theory, and includes but is not limited to node degree, node strength, node centrality, characteristic path length and small world attributes.
In these embodiments, the localization system 100 of the present invention can localize the abnormal sub-brain region by comparing the brain features of the sub-brain region of the subject with the brain features of the corresponding sub-brain region of the normal person.
Referring to fig. 1, the abnormal localization module 150 is configured to calculate an abnormal degree index of each sub-brain region functional connection network of the subject by using an abnormal value detection method, compare the abnormal degree indexes of the sub-brain regions of each target brain region, and localize the sub-brain region having the largest abnormal degree index as the abnormal target brain region.
The abnormal value detection method refers to a method for analyzing abnormal data from a large amount of data through a mathematical statistic method according to the characteristics of the data. The abnormal value detection method adopted by the abnormal location module 150 is not limited in the present invention, and any abnormal value detection method in the field can be adopted.
In some embodiments, the abnormal value detection method includes a grubbs method in which the functional connection network of the sub-brain region of the normal person is taken as a normal distribution, and the abnormal value detection method calculates an abnormal degree index of the functional connection network of the sub-brain region of the subject with respect to the normal distribution.
It is to be understood that the positioning system 100 of the present invention has obtained a functional connection network of the sub-brain regions of the normal person in order to locate the abnormal brain region of the abnormal person.
In a related study of the present invention, functional magnetic resonance data of 47 healthy subjects are collected, and the data acquisition module 110, the target brain region functional network construction module 120, the target brain region segmentation module 130, and the sub-brain region functional network construction module 140 in the positioning system 100 of the present invention are used to process these functional magnetic resonance data, so as to obtain sub-brain region functional connection networks of these healthy subjects, which are used as sub-brain region functional connection networks of normal persons. The functional networks of the sub-brain regions of multiple normal persons have a normal distribution.
In these embodiments, the outlier detection method comprises the steps of:
step S41: the mean μ and standard deviation σ of the z-values in all the sub-brain regions were calculated using the following equations (4), (5), respectively:
wherein i is the number of the second function connection values of the sub-brain regions, n is the number of the second function connection values of all the sub-brain regions, and x i Representing the ith second functional connection value.
Step S42: calculating a second functional connection value x of the sub-brain region using the following equation (6) i Normalized statistical value G of i ：
Step S43: the second functional connection value x of the sub-brain region is detected as an abnormal value if G satisfies the following formula (7):
where t represents the critical value of the t-distribution, which is a normal distribution.
Fig. 5 is a schematic diagram illustrating a t distribution of normalized statistical values of functional connection values of sub-brain regions obtained by an abnormal location module of the location system according to an embodiment of the present invention. The horizontal axis represents the value of t, and the vertical axis represents the value of the probability density function. In this embodiment, the normalized statistical value G is calculated i With a hypothetical standard distribution two-tailed t-distribution, the confidence level α is 0.01. Referring to fig. 5, there are shown 3 exemplary confidence intervals 68.3%, 95.5%, 99.7%, corresponding to functional connectivity values of the sub-brain regions at1, 2 and 3 standard deviations from the mean, respectively.
In this embodiment, the t distribution may also give a p-value as a significance degree of the G value, and the p-value is used as a statistical value of the abnormal degree of the sub-brain region connection, and the smaller the p-value, the larger the abnormal degree. Referring to fig. 5, for normal persons, the p-value is in the middle of the t-profile, and for abnormal persons, the p-value is on both sides of the t-profile.
Therefore, the abnormality degree index D of the sub-brain region junction is defined using the following formula (8):
D＝1-p (8)
it is understood that the larger D is, the larger the degree of abnormality indicating the corresponding functional connection value of the sub-brain region is.
Continuing with the example shown in table 2, for each subject, the functional connection networks of the sub-brain regions of the 4 target brain regions can be obtained, where each sub-brain region has an abnormality degree index D relative to the rest brain regions of the whole brain, and the abnormality degree matrices of the functional connection networks of the sub-brain regions of the 4 target brain regions are obtained as follows: 2 × 115,3 × 115,2 × 115,2 × 115.
In some embodiments, the abnormality localization module 150 is further configured to calculate an average value of the abnormality degree indicators of each sub-brain region of the subject with respect to the rest of the whole brain region, and use the average value as the abnormality degree indicator of the sub-brain region. Continuing with the example shown in table 2, for each subject, the average value of the abnormal degree indexes of the sub-brain regions in each target brain region can be calculated according to the abnormal degree matrixes of the 4 target brain regions, and the obtained average value matrixes are respectively: 2 × 1,3 × 1,2 × 1,2 × 1. That is, each of the sub-brain regions has an average value of the abnormality degree indicators, that is, has an abnormality degree indicator.
According to the above embodiment, the abnormality localization module 150 may obtain the sub-brain region with the largest abnormality degree index, and localize the sub-brain region as the new abnormality target brain region. It is understood that the target brain region including the sub-brain region also belongs to the abnormal target brain region. According to the invention, a more detailed result is obtained, and the sub-brain region is used as a new abnormal target brain region, is directly related to the individual condition of a detected person, and can be used as a target to be intervened in subsequent nerve regulation.
According to the positioning system disclosed by the invention, the target brain area is subdivided based on the physiological significance of the functional connection network, a more specific position needing stimulation can be determined, and the accuracy and the effectiveness of neural regulation and control are favorably enhanced.
In some embodiments, the abnormality localization module 150 is further configured to divide each target brain region into a left side and a right side, calculate left and right functional connection values between each voxel on the left side and each voxel on the right side, and compare the left and right functional connection values of the subject with those of a normal person. In these embodiments, the time series of each voxel on the left side and the time series of each voxel on the right side may be extracted separately, the pearson correlation coefficient between the time series of each voxel on the left side and the time series of each voxel on the right side is calculated, and the pearson correlation coefficient is transformed into a Z-value using Fisher-Z transformation, and the Z-value is taken as a left-right functional connection value between the left side and the right side.
And simultaneously obtaining the left and right functional connection values of the normal person, comparing the left and right functional connection values of the detected person with the left and right functional connection values of the normal person, and taking the comparison result as the reference for assisting the neural regulation. For example, the difference between the left and right functional connection values of the subject and the left and right functional connection values of the normal person is calculated, and the magnitude of the stimulation intensity, the length of the stimulation time, and the like are adjusted according to the magnitude of the difference between the left and right functional connection values of the subject and the left and right functional connection values of the normal person.
In some embodiments, the abnormality localization module 150 is further configured to calculate left and right abnormality degree indicators to obtain left and right differences of the target brain region, and compare the left and right differences of the target brain region of the subject with the left and right differences of the target brain region of the normal person. In these embodiments, the difference results obtained from the comparison can be used as a reference for assisting neural regulation. For example, the magnitude of the stimulation intensity, the length of the stimulation time, and the like are controlled according to the magnitude of the difference result.
According to the above-mentioned abnormal location module 150, not only the abnormal brain region to be stimulated can be located individually, but also the neural regulation parameter can be adjusted in an auxiliary manner, so as to optimize the neural regulation parameter and obtain a better regulation effect.
In some embodiments, the target brain regions include a left and right postcerebrum junctional brain region and a left and right temporalis junctional brain region. The left and right hindbrain combined brain region includes the first brain region 210 and the second brain region 220 described above, and the left and right temporal top combined brain region includes the third brain region 230 and the fourth brain region 240 described above.
Referring to fig. 1, in some embodiments, the localization system 100 of the present invention further comprises an output module 160 for registering the sub-brain region with the maximum abnormality degree index and the voxel with the strongest signal of the sub-brain region to the brain space of the subject, and displaying the sub-brain region with the maximum abnormality degree index and the voxel with the strongest signal of the sub-brain region in the image of the brain space.
The present invention is not limited to a specific display manner, and the output module 160 may be various hardware devices having a display function, such as, but not limited to, a display screen.
In some embodiments, the abnormality localization module 150 is further configured to rank the connections between the target brain regions according to the degree of abnormality, and the output module 160 outputs the ranking result. According to the embodiments, the abnormal degree of the connection between the target brain areas can be clear at a glance for doctors, and the personalized nerve regulation and control scheme can be assisted to be established.
In some embodiments, the output module 160 is further configured to output an overall signal average of the target brain region. The overall signal average includes one or more of an overall magnetic resonance signal average, an average of all connected values of the target brain region, an average of an abnormality degree index, and the like.
Fig. 6 is a schematic diagram of an image output and displayed by an output module of the positioning system according to an embodiment of the present invention. Referring to fig. 6, the image is a three-view image with different viewing angles in a standard space, wherein the upper left is a horizontal position, the upper right is a coronal position, and the lower side is a sagittal position. In each view, white color blocks 610 represent the sub-brain regions with the largest abnormality index, and dots 620 represent the voxels with the strongest signal in the sub-brain regions. The magnetic resonance BOLD signal represents the strength of brain activity by measuring blood oxygen level dependency, and the strongest voxel of the sub-brain area signal represents a voxel point with the strongest brain activity in a small area with the largest difference with the normal brain function. The positioning result of the positioning system is directly displayed and marked on the magnetic resonance image according to the mode shown in fig. 6, which is helpful for reducing the error caused by manual marking of doctors. In some embodiments, the intensity of the neuromodulation stimulation, etc. may also be displayed or prompted simultaneously, without the physician carrying redundant data.
The positioning system refines the target brain area into a sub-brain area, and the functional connection modes in the sub-brain area are consistent. In medical theory, it is hypothesized that stimulating this portion of the sub-brain region may result in a similar therapeutic effect. The positioning system can accurately position the nerve regulation target point aiming at an individual based on the physiological significance of functional connection, and can assist in optimizing the parameters of nerve regulation, thereby obtaining better regulation and control effect.
The positioning system of the invention also has the following advantages:
(1) By segmenting the brain area of the nerve regulation target, the method is used for analyzing the difference between the brain functions of individual patients and normal human groups, and deepens the understanding of researchers on the pathological mechanism of neuropsychiatric diseases and the nerve regulation mechanism.
(2) The method realizes the positioning of the individual nerve regulation target spot, and conforms to the trend of individual precise medical treatment compared with the prior group average analysis method, thereby better benefiting the human society.
(3) The data driving algorithm is used for positioning the position with the maximum difference of the normal person of the individual patient in the brain area of the target point and the abnormal degree index of the difference, and the subjectivity of selecting the position of the target point and the stimulation intensity only by the judgment of a doctor in the prior art is avoided.
(4) The positioning system of the invention can analyze all brain areas of the whole brain, can also combine with the brain areas of preselected targets, and has flexibility in scheme.
(5) When the invention positions individual patients, a large amount of more easily obtained normal person data is introduced to guide the model to position the individual patients with complex individual differences, so that the system is more reliable.
(6) The invention not only can provide the neural regulation target location, but also can provide the abnormal degree index for assisting in adjusting the selection of the neural regulation intensity parameter.
(7) According to the invention, the sub-brain regions are segmented based on the functional connection network of the target brain region through a machine learning algorithm, and compared with the traditional method that a doctor roughly selects the brain region based on experience, the method can provide finer and more accurate target positioning.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing disclosure is by way of example only, and is not intended to limit the present application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such alterations, modifications, and improvements are intended to be suggested herein and are intended to be within the spirit and scope of the exemplary embodiments of this application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. The processor may be one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital signal processing devices (DAPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or a combination thereof. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, in one or more computer readable media. For example, computer-readable media can include, but are not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic tape … …), optical disks (e.g., compact Disk (CD), digital Versatile Disk (DVD) … …), smart cards, and flash memory devices (e.g., card, stick, key drive … …).
The computer readable medium may comprise a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. The computer readable medium can be any computer readable medium that can communicate, propagate, or transport the program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, radio frequency signals, or the like, or any combination of the preceding.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single disclosed embodiment.
Where numerals describing the number of components, attributes or the like are used in some embodiments, it is to be understood that such numerals used in the description of the embodiments are modified in some instances by the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
Although the present application has been described with reference to the present specific embodiments, it will be recognized by those skilled in the art that the foregoing embodiments are merely illustrative of the present application and that various changes and substitutions of equivalents may be made without departing from the spirit of the application, and therefore, it is intended that all changes and modifications to the above-described embodiments that come within the spirit of the application fall within the scope of the claims of the application.
Claims (12)
1. A system for locating a personalized neuromodulation target, comprising:
the data acquisition module is used for acquiring functional magnetic resonance data of a target brain area of a detected person;
the target brain area functional network construction module is used for constructing a target brain area functional connection network according to the functional magnetic resonance data, and the target brain area functional connection network comprises first functional connection values among the target brain areas;
a target brain region segmentation module for segmenting the target brain region into a plurality of sub-brain regions based on the target brain region functional connection network;
the function network construction module of the sub-brain area is used for calculating a second function connection value between each sub-brain area and the rest sub-brain areas and constructing a function connection network of the sub-brain area relative to the whole brain; and
and the abnormal positioning module is used for calculating the abnormal degree index of each sub-brain area functional connection network of the detected person by adopting an abnormal value detection method, comparing the abnormal degree index of the sub-brain area of each target point brain area and positioning the sub-brain area with the maximum abnormal degree index as the abnormal target point brain area.
2. The localization system of claim 1, wherein the target brain region functional network construction module extracts a time series of each voxel in each of the target brain regions, calculates a pearson correlation coefficient between time series of any voxels in each two of the target brain regions, and transforms the pearson correlation coefficient into a Z-value using a Fisher-Z transform, the Z-value being the first functional connection value between related voxels.
3. The localization system of claim 1, wherein the target brain region segmentation module segments the target brain region into a plurality of sub-brain regions using an unsupervised machine learning algorithm.
4. The positioning system of claim 3, wherein the unsupervised machine learning algorithm comprises a k-means clustering method.
5. The localization system of claim 1, wherein the sub-brain region functional network construction module is further configured to extract a time series of each voxel in each of the sub-brain regions, calculate a pearson correlation coefficient between the sub-brain region mean time series of each sub-brain region in each target brain region and the mean time series of the remaining brain regions in the whole brain region, and transform the pearson correlation coefficient into a Z-value using a Fisher-Z transform, the Z-value being the second functional connection between the sub-brain region and the remaining brain region.
6. The localization system of claim 5, wherein the whole brain region comprises 116 brain regions partitioned according to an AAL template, the brain regions comprising the target brain region.
7. The localization system according to claim 1, wherein the abnormal value detection method includes a grubbs method in which a functional connection network of a sub-brain region of a normal person is taken as a normal distribution, and the abnormal value detection method calculates an abnormal degree index of the functional connection network of the sub-brain region of the subject with respect to the normal distribution.
8. The localization system according to claim 7, wherein the abnormality localization module is further configured to calculate an average value of the abnormality degree indicators of each of the sub-brain regions of the subject with respect to the remaining brain regions in the whole brain region, and to use the average value as the abnormality degree indicator of the sub-brain region.
9. The localization system of claim 7, wherein the abnormality localization module is further configured to divide each of the target brain regions into a left side and a right side, calculate left-right functional connection values between each voxel on the left side and each voxel on the right side, and compare the left-right functional connection values of the subject with the left-right functional connection values of the normal person.
10. The localization system according to claim 9, wherein the abnormality localization module further calculates abnormality degree indexes of the left side and the right side to obtain a target brain region left-right side difference, and compares the target brain region left-right side difference of the subject with the target brain region left-right side difference of the normal person.
11. The localization system of claim 1, wherein the target brain region comprises a left and right posterior combined brain region and a left and right temporal top combined brain region.
12. The localization system according to claim 1, further comprising an output module for registering the sub-brain region with the largest abnormality degree indicator and the voxel with the strongest signal of the sub-brain region to the brain space of the subject, and displaying the sub-brain region with the largest abnormality degree indicator and the voxel with the strongest signal of the sub-brain region in the image of the brain space.
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