CN106845137A - A kind of sacred disease analysis method based on brain network structure - Google Patents

A kind of sacred disease analysis method based on brain network structure Download PDF

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CN106845137A
CN106845137A CN201710107488.1A CN201710107488A CN106845137A CN 106845137 A CN106845137 A CN 106845137A CN 201710107488 A CN201710107488 A CN 201710107488A CN 106845137 A CN106845137 A CN 106845137A
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brain
shortest path
metric
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冯远静
何建忠
潘源
潘一源
吴烨
周思琪
金丽玲
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Zhejiang University of Technology ZJUT
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Abstract

A kind of sacred disease analysis method based on brain network structure, comprises the following steps：Diffusion tensor imaging data are pre-processed by step one；Step 2, node and side are two basic elements of brain network；Definition on encephalomere point, 246 cortex interested and infracortical region, a node of each Regional Representative's network are divided into according to human brain group of networks collection of illustrative plates by whole brain；For the side of network, using the fiber tracking in FSL instruments, and constructed by sparse threshold method and have no right two-value network；Step 3, calculates the whole world and Local Area Network measurement, and calculate the TG-AUC AUC of each network metric；AUC to each network metric and each network metric performs nonparametric displacement test determination in network attribute with the presence or absence of significant group difference, while by existing experimental data, coming judgement sample disease condition and information.Auxiliary diagnosis and the analysis for effectively realizing sacred disease of the invention.

Description

A kind of sacred disease analysis method based on brain network structure
Technical field
It is especially a kind of to be based on brain net the present invention relates to the medical imaging under computer graphics, Nervous System Anatomy field The sacred disease analysis method of network structure.
Background technology
With the development in epoch, the progress of Medical Imaging Technology, diffusion tensor imaging is in the research of Neuscience Increasing influence power is accounted for, it is that this epoch is indispensable to possess advanced neuroimaging technology；Diffusion tensor imaging skill Art is a kind of method of emerging description brain structure；At present, diffusion tensor imaging is just widely used in psychiatric department In disease and the supplementary means of diagnosis, it might even be possible to for the formulation of pre-operative surgical scheme, it may be said that its tribute in medical domain Offer the advantage that can not be substituted；So having great meaning for brain science to the research based on diffusion tensor imaging Justice.
Now, the brain network built with diffusion tensor imaging data has many important topological properties, such as " small generation Boundary " attribute, modular institutional framework；On the other hand, researcher has found that many mental illnesses (divide by such as stages alzheimer's disease and spirit Split disease etc.) it is relevant with the change in topology of brain structure, brain function Network Abnormal, these researchs are not only to understand neuropsychiatric disease Pathomechanism provides New Century Planned Textbook, can also be evaluated for the early prevention of disease and treatment and provide brain network video mark.
The content of the invention
In order to overcome the shortcomings of that the auxiliary diagnosis of sacred disease cannot be realized in the prior art and analyze, the present invention is combined and expanded Tensor imaging technology is dissipated, the analysis based on brain network structure of a kind of auxiliary diagnosis for effectively realizing sacred disease and analysis is proposed Method.
In order to solve the above-mentioned technical problem following technical scheme is provided：
A kind of sacred disease analysis method based on brain network structure, comprises the following steps：
Diffusion tensor imaging data are pre-processed by step one
1.1), the head in DTI data is corrected to the rigid body translation of b0 images by using each diffusion weighted images Motion and vortex distortion；
1.2), brain area is extracted using brain extracting tool in FSL；
1.3), it is registered in often with brigadier's normed space data and each participant's b0 image using the linear image of FMRIB Individual participant T1 data；
Step 2, sets up brain network
Node and side are two basic elements of brain network；Definition on encephalomere point, will according to human brain group of networks collection of illustrative plates Whole brain is divided into 246 cortex interested and infracortical region, a node of each Regional Representative's network；For net The side of network, using the fiber tracking in FSL instruments, and is constructed by sparse threshold method and haves no right two-value network；
Step 3, calculates parameters, carries out data analysis
According to the definition of brain network, the whole world and Local Area Network measurement, including cluster coefficient are calculatedShortest path length L, Worldlet degree σ, component efficiency Eloc, global efficiency Eglob, centrad Nbc, worldlet degree σ, and calculate each network metric TG-AUC AUC；AUC to each network metric and each network metric performs nonparametric displacement test determination in network Whether there is significant group difference in attribute, while by existing experimental data, coming judgement sample disease condition and information.
Further, in the step 3, various brain network parameters are calculated, including following parameter：
3.1 cluster coefficients：What cluster coefficient was weighed is the grouping of the world economy degree of network, is the important parameter for measuring network：
CiRepresent the cluster coefficient of node i, eiThe number on the side actually connected between representing the nodes neighbors,Table Show possible maximum connection side number；
Network node quantity is represented,Represent whole network；
3.2 component efficiencies：
E (i) represents the component efficiency of arbitrary node i, GiRefer to the subgraph that the neighbours of node i are constituted,Table constitutes subgraph Quantity, i, j are arbitrary node, l in networkjkRepresent node j, the shortest path length between k；
3.3 shortest path lengths：The information that shortest path features a certain node in network reaches the optimal of another node Path, by shortest path can quickly transmission information, so as to save system resource；
3.4 global efficiencies：Usual shortest path length will carry out computing in some connected graph, because if in network There is disconnected node can cause the shortest path length value between the two nodes to be infinite, therefore propose：
3.5 centrads：Centrad is a statistical indicator for portraying nodes effect and status；
NbcI () is the centrad of any point i in network, G represents whole network, and i, j, k are arbitrary nodes in network, δjkIt is the quantity of all shortest paths from node j to node k, δjkI () is the quantity by node i in these shortest paths；
3.6 worldlet degree, by worldlet degree, we can weigh the power of " worldlet " attribute, and δ, λ are calculated first；δ The ratio of real network and random network cluster coefficients is represented, λ represents the ratio of real network and random network shortest path length Value；
CrealRepresent real network cluster coefficient, CrandomThe cluster coefficient of table random network.
LrealRepresent real network cluster coefficient, LrandomThe cluster coefficient of table random network.
Worldlet degree σ is defined as follows：
Further, in the step 3, nonparametric displacement test mode：The average value of each network metric is calculated first Between group difference；For each network metric, randomly all values are re-assigned in two groups, and recalculate two Mean difference between randomization group；The randomization program repeat 10,000 time, and each distribution preceding 5% be used as have probability Error is the critical value of the one tailed test of 0.05 null hypothesis.
Beneficial effects of the present invention are：Effectively realize auxiliary diagnosis and the analysis of sacred disease.
Specific implementation process
The invention will be further described below.
A kind of sacred disease analysis method based on brain network structure, it is characterised in that：Comprise the following steps：
Diffusion tensor imaging data are pre-processed by step one:
1.1), the head in DTI data is corrected to the rigid body translation of b0 images by using each diffusion weighted images Motion and vortex distortion；
1.2), brain area is extracted using brain extracting tool in FSL；
1.3), it is registered in often with brigadier's normed space data and each participant's b0 image using the linear image of FMRIB Individual participant T1 data.
Step 2, sets up brain network:
Node and side are two basic elements of brain network；Definition on encephalomere point, will according to human brain group of networks collection of illustrative plates Whole brain is divided into 246 cortex interested and infracortical region, a node of each Regional Representative's network；For net The side of network, using the fiber tracking in FSL instruments, and constructs two-value network by sparse threshold method；
Step 3, calculates parameters, carries out data analysis
According to the definition of brain network, the whole world and Local Area Network measurement, including cluster coefficient are calculatedShortest path length L, Worldlet degree σ, component efficiency Eloc, global efficiency Eglob, centrad Nbc, worldlet degree σ, and calculate each network metric TG-AUC AUC；AUC to each network metric and each network metric performs nonparametric displacement test determination in network Whether there is significant group difference in attribute, while by existing experimental data, coming judgement sample disease condition and information.
Body calculating parameter is as follows：
3.1 cluster coefficients:What cluster coefficient was weighed is the grouping of the world economy degree of network, is the important parameter for measuring network：
CiRepresent the cluster coefficient of node i, eiThe number on the side actually connected between representing the nodes neighbors,Table Show possible maximum connection side number；
Network node quantity is represented,Represent whole network；
3.2 component efficiencies：
E (i) represents the component efficiency of arbitrary node i, GiRefer to the subgraph that the neighbours of node i are constituted,Table constitutes subgraph Quantity, i, j are arbitrary node, l in networkjkNode j is represented, (side number minimum leads to the shortest path length between k Road)；
3.3 shortest path lengths：The information that shortest path features a certain node in network reaches the optimal of another node Path, by shortest path can quickly transmission information, so as to save system resource；
3.4 global efficiencies：Usual shortest path length will carry out computing in some connected graph, because if in network There is disconnected node can cause the shortest path length value between the two nodes to be infinite, therefore propose：
3.5 centrads：Centrad is a statistical indicator for portraying nodes effect and status；
NbcI () is the centrad of any point i in network, G represents whole network, and i, j, k are arbitrary nodes in network, δjkIt is the quantity of all shortest paths from node j to node k, δjkI () is the quantity by node i in these shortest paths；
3.6 worldlet degree, by worldlet degree, we can weigh the power of " worldlet " attribute, and δ, λ are calculated first；δ The ratio of real network and random network cluster coefficients is represented, λ represents the ratio of real network and random network shortest path length Value；
CrealRepresent real network cluster coefficient, CrandomThe cluster coefficient of table random network.
LrealRepresent real network cluster coefficient, LrandomThe cluster coefficient of table random network.
Worldlet degree σ is defined as follows：
Data are analyzed：Calculate the group difference between the average value of each network metric；In order to test what is observed , for each network metric, randomly be re-assigned to all values in two groups by the possible occurrent null hypothesis of group difference, And recalculate the mean difference between two randomization groups；The randomization program is repeated 10,000 time, and before each distribution 5% critical value for being used as the one tailed test with the null hypothesis that probable error is 0.05.

Claims (3)

1. a kind of sacred disease analysis method based on brain network structure, it is characterised in that：Comprise the following steps：
Diffusion tensor imaging data are pre-processed by step one
1.1), the head movement in DTI data is corrected to the rigid body translation of b0 images by using each diffusion weighted images Distorted with vortex；
1.2), brain area is extracted using brain extracting tool in FSL；
1.3), each is registered in brigadier's normed space data and each participant's b0 image using the linear image of FMRIB to join With person's T1 data；
Step 2, sets up brain network
Node and side are two basic elements of brain network；Definition on encephalomere point, will be whole according to human brain group of networks collection of illustrative plates Brain is divided into 246 cortex interested and infracortical region, a node of each Regional Representative's network；For network Side, using the fiber tracking in FSL instruments, and is constructed by sparse threshold method and haves no right two-value network；
Step 3, calculates parameters, carries out data analysis
According to the definition of brain network, the whole world and Local Area Network measurement, including cluster coefficient C, shortest path length L, small generation are calculated Boundary degree σ, component efficiency Eloc, global efficiency Eglob, centrad Nbc, worldlet degree σ, and calculate the curve of each network metric Lower area AUC；AUC to each network metric and each network metric performs nonparametric displacement test determination in network attribute In whether there is significant group difference, while by existing experimental data, coming judgement sample disease condition and information.
2. a kind of sacred disease analysis method based on brain network structure as claimed in claim 1, it is characterised in that：The step In rapid three, various brain network parameters are calculated, including following parameter：
3.1 cluster coefficients：What cluster coefficient was weighed is the grouping of the world economy degree of network, is the important parameter for measuring network：
C i = 2 e i k i ( k i - 1 )
CiRepresent the cluster coefficient of node i, eiThe number on the side actually connected between representing the nodes neighbors,Express possibility Maximum connection side number；
C = 1 N Σ i ∈ V C i
N represents network node quantity, and V represents whole network；
3.2 component efficiencies：
E ( i ) = 1 N G i ( N G i - 1 ) Σ j ≠ k = G i 1 l j , k
E (i) represents the component efficiency of arbitrary node i, GiRefer to the subgraph that the neighbours of node i are constituted,Table constitutes the number of subgraph Amount, i, j are arbitrary node, l in networkjkRepresent node j, the shortest path length between k；
E l o c = 1 N Σ i ∈ V E ( i )
3.3 shortest path lengths：Shortest path features the optimal path of information another node of arrival of a certain node in network, By shortest path can quickly transmission information, so as to save system resource；
L = 1 N ( N - 1 ) Σ i , j ∈ V , i ≠ j l i j
3.4 global efficiencies：Usual shortest path length will carry out computing in some connected graph, because if existing in network Disconnected node can cause the shortest path length value between the two nodes to be infinite, therefore propose：
E g l o b = 1 N ( N - 1 ) Σ i , j ∈ V , i ≠ j 1 l i j
3.5 centrads：Centrad is a statistical indicator for portraying nodes effect and status；
N b c ( i ) = Σ j ≠ i ≠ k ∈ G δ j k ( i ) δ j k
NbcI () is the centrad of any point i in network, G represents whole network, and i, j, k are arbitrary node, δ in networkjkIt is The quantity of all shortest paths from node j to node k, δjkI () is the quantity by node i in these shortest paths；
3.6 worldlet degree, by worldlet degree, we can weigh the power of " worldlet " attribute, and δ, λ are calculated first；δ is represented The ratio of real network and random network cluster coefficients, λ represents the ratio of real network and random network shortest path length；
γ = C r e a l C r a n d o m
CrealRepresent real network cluster coefficient, CrandomThe cluster coefficient of table random network.
λ = L r e a l L r a n d o m
LrealRepresent real network cluster coefficient, LrandomThe cluster coefficient of table random network.
Worldlet degree σ is defined as follows：
σ = γ λ .
3. a kind of sacred disease analysis method based on brain network structure as claimed in claim 1 or 2, it is characterised in that：Institute State in step 3, nonparametric replaces test mode：The group difference between the average value of each network metric is calculated first；For every , randomly be re-assigned to all values in two groups by individual network metric, and recalculates average between two randomization groups Difference；The randomization program repeat 10,000 time, and each distribution preceding 5% be used as with the null hypothesis that probable error is 0.05 One tailed test critical value.
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107680677A (en) * 2017-10-11 2018-02-09 四川大学 Neuropsychiatric disease sorting technique based on brain network analysis
CN109920550A (en) * 2018-12-25 2019-06-21 天津大学 A method of teenager's lafora's disease is studied based on dMRI
CN110298479A (en) * 2019-05-20 2019-10-01 北京航空航天大学 A kind of brain volume atrophy prediction technique based on brain function network
CN110298479B (en) * 2019-05-20 2021-09-03 北京航空航天大学 Brain volume atrophy prediction method based on brain function network
CN112274145A (en) * 2019-07-22 2021-01-29 苏州布芮恩智能科技有限公司 Method and device for processing near-infrared brain function imaging data and storage medium
CN111627553A (en) * 2020-05-26 2020-09-04 四川大学华西医院 Method for constructing individualized prediction model of first-onset schizophrenia
CN113425312A (en) * 2021-07-30 2021-09-24 清华大学 Electroencephalogram data processing method and device
CN117690537A (en) * 2024-02-04 2024-03-12 中日友好医院(中日友好临床医学研究所) Cross-modal method and device for QSM (quad slit) and brain atrophy and brain connection group association

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Application publication date: 20170613