CN111627553A - Method for constructing individualized prediction model of first-onset schizophrenia - Google Patents
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Abstract
The invention belongs to the field of psychology, neuroimaging and artificial intelligence, and discloses a method for constructing an individualized prediction model of first-onset schizophrenia, which solves the problem of low accuracy of auxiliary diagnosis of the conventional SCH brain structure network model. The method comprises the following steps: A. acquiring a diffusion tensor image of a patient with first schizophrenia; B. preprocessing the acquired diffusion tensor image; C. constructing a sparse brain structure network based on the preprocessed image; D. constructing a multi-threshold fusion brain structure network after each tested sparse by adopting a similar network fusion method; E. extracting network topology attribute features of the multi-threshold fusion brain structure, and then performing feature screening; F. based on the screened features, performing classification training by adopting a classifier to obtain an individualized prediction model of the first schizophrenia; G. and (4) performing performance verification evaluation on the trained individualized prediction model of the first schizophrenia.
Description
Technical Field
The invention belongs to the fields of psychology, neuroimaging and artificial intelligence, and particularly relates to a method for constructing an individualized prediction model of first-onset schizophrenia.
Background
Schizophrenia (SCH) is a high disabling and lethal mental disorder, which is listed as one of ten diseases with the highest ranking list of global disease burden by the world health organization, however, the brain mechanism of the Schizophrenia is still not completely clear, the diagnosis lacks objective standards, and the cure rate is low. The clinical problem to be solved urgently is to seek an objective, effective, convenient and feasible biological marker for carrying out early individual classified diagnosis and treatment on SCH. Brain structure network changes are an important biological basis of SCH neuro-anatomical abnormalities, and a machine learning method serving as a prediction and analysis tool based on data driving can fully utilize structural information in biomarker data to construct an SCH individualized brain structure network model.
The current research situation of the SCH brain structure network model is as follows: 1) directly extracting a structural connection value in an original network as a feature, and incorporating pseudo-connection with lower weight; 2) the method for thinning the network based on the single fixed threshold has noise influence of different levels, and the selection of the single threshold has subjectivity; 3) the original structure connection value is directly applied as the information characteristic of the low-level structure, and the important network attribute with complex brain topology is ignored.
Therefore, the conventional SCH brain structure network model is very difficult to find schizophrenia sensitive biomarkers based on brain structure network characteristics, and the accuracy of the model for auxiliary diagnosis is low.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a construction method of an individual prediction model of first-onset schizophrenia is provided, and the problem that the accuracy of auxiliary diagnosis of an existing SCH brain structure network model is low is solved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the method for constructing the individualized prediction model of the first-onset schizophrenia comprises the following steps:
A. acquiring a diffusion tensor image of a patient with first schizophrenia;
B. preprocessing the acquired diffusion tensor image;
C. constructing a sparse brain structure network based on the preprocessed image;
D. constructing a multi-threshold fusion brain structure network after each tested sparse by adopting a similar network fusion method;
E. extracting network topology attribute features of the multi-threshold fusion brain structure, and then performing feature screening;
F. based on the screened features, performing classification training by adopting a classifier to obtain an individualized prediction model of the first schizophrenia;
G. and (4) performing performance verification evaluation on the trained individualized prediction model of the first schizophrenia.
As a further optimization, in step a, a nuclear magnetic resonance imaging scanner is used to scan and acquire a diffusion tensor image of the first schizophrenia patient by using a single-shot planar echo imaging (EPI) technology.
As a further optimization, in step B, the preprocessing of the obtained diffusion tensor image specifically includes:
b1, converting the dispersion tensor image of the DICOM data format into an image of NIFTI format by using MRI convert (magnetic resonance imaging conversion);
b2, performing eddy current correction and head movement correction on the diffusion tensor image after data format conversion;
b3 skull was removed by Brain Extraction Tool (FSL) and non-Brain tissue images were removed.
As a further optimization, in step C, the constructing a sparse brain structure network based on the preprocessed image specifically includes:
c1, registering the brain images after the pre-processing of each test to b0 images in a diffusion tensor space by a rotation and translation linear registration method; the registered b0 images are then registered to T1 images in standard MNI space; inverting the conversion matrix, and transforming the AAL template from the MNI space to the diffusion tensor space by using the obtained inverse matrix to obtain 90 brain area network nodes divided based on the AAL template;
c2, using a probabilistic fiber bundle imaging method, carrying out Bayesian estimation of diffusion parameters based on a BEDPOSTX tool, establishing distribution of the diffusion parameters of each voxel by using a Markov Chain Monte Carlo (MCMC) sampling method, presetting each voxel of a brain as a fiber cross model, and automatically judging how many kinds of crossed fiber bundles pass;
c3, carrying out probability tracking fiber bundle reconstruction based on a PROBTRACKX tool, repeatedly sampling the distribution of each voxel in the main diffusion direction, generating a streamline from the extracted local sample each time, and establishing a statistical graph of streamline position posterior distribution through multiple sampling to obtain the distribution situation of the structural connection probability between every two brain regions; defining the weight of each edge as the fiber bundle connection probability between every two node areas, and obtaining a symmetrical 90 x 90 fiber bundle connection probability weighting network matrix for each tested object;
and C4, setting a fiber bundle connection probability threshold, wherein structural connection exists between the two brain areas exceeding the threshold, testing the influence of different sparsity thresholds on the fusion effect, and constructing a sparse structure network by adopting a relatively narrower threshold range (5-40% and the step length of 1%).
As a further optimization, in step D, the constructing each tested sparse multi-threshold fusion brain structure network by using a similar network fusion method specifically includes:
d1, connecting the matrix W with the sparse structure to be defined as a full kernel (full kernel) matrixi jFor the ith tested jth threshold full-kernel matrix Wi jFurther constructing a corresponding sparse kernel (sparse kernel) matrix:
order touIs a full-core matrix Wi jK neighbors of the intermediate node u (including the node u itself), then the sparse kernel matrix Si jIs defined as:
d2, carrying out iterative updating of the full-kernel matrix based on the sparse kernel matrix corresponding to the full-kernel matrix:
wherein (W)i c)(m)Represents the full-kernel matrix under the ith tested c threshold value in the mth iteration, (W)i j)(m+1)Represents the m +1 thA full-kernel matrix in the secondary iteration, wherein N is the total sparse threshold number;
d3, judging whether the iteration convergence condition is met, if so, executing a step D4, otherwise, continuing the iteration;
wherein the convergence condition is: i (W)i j)(m+1)-(Wi j)(m)||≤0.01；
D4, and corresponding the updated N thresholds to the full-core matrix Wi jAveraging was performed to construct an averaged full kernel matrix for each test:
d5, mixing WiNormalized to the interval [0, 1 ]]Thus generating a final fused network for each test.
As a further optimization, in step E, the extracting the network topology attribute feature of the multi-threshold fusion brain structure specifically includes:
calculating AUC values of the fused network under all thresholds of 8 global topological attributes and 3 node topological attributes based on a graph theory analysis method as initial features of subsequent classification;
the 8 global topology attributes include network strength SpGlobal efficiency EglobLocal efficiency ElocLength of shortest path LpCoefficient of aggregation CpNormalized shortest path length λ, normalized clustering coefficient γ, small world attribute σ:
the network strength SpThe calculation formula of (2) is as follows:
wherein, S (i) is the weighted sum of edges connected with the ith node, and N is the number of brain areas in the whole brain network;
the shortest path length LpThe calculation formula of (2) is as follows:
wherein L isijRepresenting the shortest path between node i and node j, LpThe shortest path length for the entire network G;
the global efficiency EglobThe calculation formula (2) includes:
wherein E isglob_i(G) The global efficiency of the node i is the average value of the global efficiency of all nodes in the network G;
the local efficiency ElocThe calculation formula (2) includes:
wherein L isjkIs the shortest path length between region j and region k, GiIs a sub-network of nodes connected to area i, NGiIs a sub-network GiThe number of midbrain regions; eloc_i(G) Is the local efficiency of node i, the local efficiency E of network Gloc(G) The average value of the local efficiency of all nodes in the network is obtained;
the agglomeration coefficient CpThe calculation formula (2) includes:
wherein, C (i) is the aggregation coefficient of the node i, and the aggregation coefficient of the network G is the average value of the aggregation coefficients of all the nodes;
the normalized aggregation coefficient γ and the normalized shortest path length λ are calculated as follows:
wherein, Cp randAnd Lp randThe average values of the aggregation coefficients and the shortest path lengths of 100 random networks are respectively;
the formula for calculating the small world attribute sigma is as follows:
σ＝γ/λ；
the 3 kinds of node topology attributes comprise a node degree Dnodal(i) Node efficiency Enodal(i) Center degree of node betweenness Bnodal(i) Respectively defined as follows:
wherein e isstRepresenting the number of all shortest paths from node s to node t in network G, esitIs the number of passing nodes i in these shortest paths.
As a further optimization, in step E, the performing feature screening specifically includes:
a Recursive Feature Elimination (RFE) algorithm based on a support vector machine performs Feature selection by continuously training a classifier and removing Feature dimensions with smaller Feature weights, and specifically includes:
① initializes the selected feature set to contain all of the selected features,
secondly, taking the feature set as input, training a classifier, and obtaining a classification effect and the weight of each feature;
removing the features with the minimum weight to form a new feature set;
④ repeat ②, ③ until
And selecting the condition with the best classification effect.
As a further optimization, in step F, the Classifier adopts an SVM Classifier, a Logistic Regression Classifier (Logistic Regression Classifier) or a plurality of classifiers ensemble learning based on a Radial Basis Function (RBF) kernel; during classification training, a plurality of pattern recognition classification methods are applied to find an optimal classifier model and screen key multi-threshold fusion brain structure network characteristics.
And G, evaluating the performances of various prediction models of the schizophrenia training set and the test set by adopting cross validation, wherein the performances comprise accuracy, sensitivity, specificity, ROC curve, AUC value and the like, and the system optimizes brain image feature selection and individual subtype prediction models by using a replacement test through a test feature screening and classification algorithm and identifies the core brain image group features related to the subtypes so as to improve the individual prediction accuracy.
The invention has the beneficial effects that:
(1) constructing a network after multi-threshold fusion, generating a topological attribute characteristic of the fusion network independent of a single threshold as an initial characteristic of classification by integrating complementary information provided by an original network under different topological views, and performing classification training after characteristic screening, wherein the method can improve the classification accuracy, considers the component of a structural index and has interpretability;
(2) the artificial intelligence model established based on the biological data fused with the multi-threshold brain structure network can automatically acquire the data updating model and establish an objective biological marker for early diagnosis of the schizophrenia patient, and the model accurately and qualitatively displays high reliability and high stability for classification of the first schizophrenia patient.
Drawings
Fig. 1 is a flow chart of a method for constructing an individualized prediction model of first-onset schizophrenia in the invention.
Detailed Description
The invention aims to provide a construction method of an individual prediction model of first-onset schizophrenia, and solves the problem of low accuracy of auxiliary diagnosis of the conventional SCH brain structure network model. The core idea is as follows: acquiring single-shot plane echo imaging of the diffusion tensor of the first schizophrenia patient; preprocessing a diffusion tensor image; constructing a sparse brain structure network based on the preprocessed image; constructing each tested sparse multi-threshold structure network by adopting a similar network fusion method; extracting the topological attribute characteristics of the network based on the processed fusion multi-threshold brain structure, performing classification training after characteristic screening to obtain an individualized prediction model of the first schizophrenia, and finally performing performance verification evaluation on the individualized prediction model of the first schizophrenia obtained by training.
For networks under different sparsity thresholds, the networks can be regarded as different types of feature expressions of the same tested brain network, and the invention fuses the networks with multiple sparsity thresholds, so that richer topological information can be obtained, the subsequent classification work is facilitated, and the problems that the method for thinning the networks by adopting a single fixed threshold has noise influence of different levels and the selection of the single threshold has subjectivity are avoided; the network topology attributes obtained based on the graph theory analysis can reflect the high-level attributes of the network, and the attributes are used as classification features, so that better classification results can be obtained compared with the original network connection values which can only reflect low-level information as classification features.
In particular implementation, the flow of the method for constructing the individualized prediction model for first-onset schizophrenia in the invention is shown in fig. 1, and the method comprises the following implementation steps:
1. acquiring a diffusion tensor image of a patient with first schizophrenia;
in the step, Philip 3.0T and GE 3.0T nuclear magnetic resonance imaging scanners are adopted to collect data as a training module test set and a test module data set as toolsIn the volume implementation, the scanning parameters are that in 32 axial plane directions, TR is 10295ms, TE is 91ms, and FOV is 128mm × 128mm2The flip angle is 90 °, the layer thickness is 4mm, the matrix is 256 × 256, and the individual voxel size is 2 × 2 × 2mm3Acquiring 3DT1 structural image data to optimize DTI data registration, wherein TR is 8.4ms, TE is 3.8ms, and FOV is 256 × 256mm2The flip angle is 90 °, the layer thickness is lmm, the scanning is continuous without intervals, the matrix is 256 × 256, and the size of a single voxel is 1 × 1 × 1mm3The whole brain acquires 188 layers of images.
2. Preprocessing the acquired diffusion tensor image;
in this step, as a specific implementation means, the pretreatment process is as follows:
firstly, a diffusion tensor image in a DICOM data format is converted into an NIFTI format image by using MRI convert;
performing eddy current correction and head movement correction on the diffusion tensor image after data format conversion;
③ removing the skull by using Brain Extraction Tool of FSL, and removing the non-Brain tissue image.
3. Constructing a sparse brain structure network based on the preprocessed image;
in this step, as a specific implementation means, the process of constructing the sparse brain structure network is as follows:
firstly, registering the brain images preprocessed by the tests to b0 images in a diffusion tensor space by a rotation and translation linear registration method; the registered b0 images are then registered to T1 images in standard MNI space; inverting the conversion matrix, and transforming the AAL template from the MNI space to the diffusion tensor space by using the obtained inverse matrix to obtain 90 brain area network nodes divided based on the AAL template;
secondly, a probability fiber bundle imaging method is used, Bayesian estimation of dispersion parameters is performed based on a BEDPOSTX tool, distribution of the dispersion parameters of each voxel is established by a Markov chain Monte Carlo sampling method, each voxel of a brain is preset as a fiber cross model, and how many kinds of cross-passing fiber bundles are automatically judged;
thirdly, probability tracking fiber bundle reconstruction is carried out based on a PROBTRACKX tool, a streamline is generated from an extracted local sample each time by repeatedly sampling the distribution of the main dispersion direction of each voxel, and a statistical graph of the posterior distribution of the streamline position is established by sampling for many times to obtain the distribution situation of the structural connection probability between every two brain regions; defining the weight of each edge as the fiber bundle connection probability between every two node areas, and obtaining a symmetrical 90 x 90 fiber bundle connection probability weighting network matrix for each tested object;
setting a fiber bundle connection probability threshold, and testing the influence of different sparsity thresholds on the fusion effect by the structural connection of two brain areas exceeding the threshold, wherein a relatively narrower threshold range (5-40% and 1% step length) is adopted to construct a sparse structure network.
4. Constructing a multi-threshold fusion brain structure network after each tested sparse by adopting a similar network fusion method;
in this step, as a specific implementation means, the process of constructing the multi-threshold fusion brain structure network is as follows:
① the sparse post-structure connection matrix is defined as the full-core matrix Wi jFor the ith tested jth threshold full-kernel matrix Wi jAnd further constructing a corresponding sparse kernel matrix, wherein the sparse kernel matrix is used for strong connection still reserved after the coding network is sparse:
order touIs a full-core matrix Wi jK neighbors of the intermediate node u (including the node u itself), then the sparse kernel matrix Si jIs defined as:
secondly, performing iterative updating of the full-kernel matrix based on the sparse kernel matrix corresponding to the full-kernel matrix:
wherein (W)i c)(m)Represents the full-kernel matrix under the ith tested c threshold value in the mth iteration, (W)i j)(m+1)Representing a full-kernel matrix in the (m + 1) th iteration, wherein N is the total sparse threshold number;
the full-core matrix W interacts with all but one of the other threshold networksi jComplementary information provided by the original network under other topological views can be integrated. At the same time, the sparse kernel matrix Si jBy corresponding to the full-core matrix Wi jThe strongest connection in (b) leads to an iterative process and thus noise can be effectively suppressed. From the perspective of matrix multiplication, the whole iterative process is represented by the above formula, which means the full-core matrix Wi jThe magnitude of the connection value of any two nodes depends on the k neighbors of the corresponding nodes in other threshold networks at the same time. Particularly if the respective k neighbors of two nodes are the strongest connections in other thresholded networks, the connections between them will strengthen after iterative updating (although they may themselves be weak connections) and vice versa.
Judging whether the iteration convergence condition is met, if so, executing the step IV, and otherwise, continuing the iteration;
wherein the convergence condition is: i (W)i j)(m+1)-(Wi j)(m)||≤0.01；
④, and corresponding the updated N thresholds to the full-core matrix Wi jAveraging was performed to construct an averaged full kernel matrix for each test:
⑤, mixing WiNormalized to the interval [0, 1 ]]Thus generating a final fused network for each test.
5. Extracting network topology attribute features of the multi-threshold fusion brain structure, and then performing feature screening;
in this step, as a concrete implementation means, the fusion is calculated based on a graph theory analysis methodAUC values of 8 global topology attributes and 3 node topology attributes of the post-network under all thresholds serve as initial features of subsequent classification. Wherein the 8 global topology attributes include network strength (node degree) SpGlobal efficiency EglobLocal efficiency ElocLength of shortest path LpCoefficient of aggregation CpNormalized shortest path length λ, normalized clustering coefficient γ, small world attribute σ. The specific definition is as follows:
network strength (node degree) SpReflecting important network evolution characteristics. The node degree is defined as the weighted sum of edges directly connected with the node, the node is more connected when the node degree is larger, and the position of the node in the network is more important. The definition formula is:
where S (i) is the sum of the weights of the edges connected to the ith node, and N is the number of brain regions in the whole brain network. The average of the degrees of all nodes in the network is the strength of the network.
Shortest path length Lp: the average value of the shortest paths of all nodes in the network is the shortest path of the network, and the operation efficiency of the whole network is reflected. Information can be transmitted faster through the shortest path, and system resources are saved. The definition formula is:
wherein L isi,jRepresenting the shortest path between node i and node j, LpIs the shortest path length of the entire network G. It can be seen that the calculation of the shortest path length must be based on the case where the network is fully connected: l if node i cannot reach node j by any way, Li,jAbsent or infinite, LpWill also not be present.
Global efficiency Eglob: describing the information transmission efficiency in the network, the global efficiency of the node i is based on the shortest path length according to the publicThe following definitions
It can be seen from the above formula that the smaller the shortest path of a node is, the faster the information transfer between the node and other nodes is, i.e. the higher the global efficiency of the node is.
And global efficiency E of network GglobDefined as the average of the global efficiency of all nodes in the network
Local efficiency ElocThe method is an important index for measuring the compactness of a cluster (clique) formed by adjacent nodes in the network and describing the redundancy (redundancy) and the tolerance (tolerance) of the network to external attacks. The local efficiency of the node i and the local efficiency of the network G are defined according to the formula:
wherein L isjkIs the shortest path length between region j and region k, GiIs a sub-network of nodes connected to area i, NGiIs a sub-network GiThe number of midbrain regions, N is the number of nodes in G in the whole brain network.
Agglomeration coefficient CpIs an important index for measuring the degree of small group (cliquescence) and local interconnection (interconnectivity) of the network, and the class coefficient c (i) of the node i is defined as the ratio of the number of edges between "other nodes" directly connected to the node i in the network G to the maximum possible number of edges between the "other nodes" and is defined according to the following formula. Aggregation factor C of network GpIs defined as the average of all node clustering coefficients.
A network is considered to have a small-world property (small-world) if it has both a high aggregation factor and a short shortest path length. To quantitatively determine whether a network has small-world attributes, the aggregation coefficient and shortest path length of the network are typically compared to the corresponding attributes of a random network.
The normalized aggregation coefficient γ and the normalized shortest path length λ are calculated respectively according to the following equations:
wherein C isp randAnd Lp randThe aggregation coefficients and the average of the shortest path lengths are for 100 random networks, respectively.
The small world attribute σ is defined as σ ═ γ/λ. If γ >1 and λ ≈ 1, i.e., σ >1, then this network is judged to have small-world properties.
The 3 node topology attributes include a node degree Dnodal(i) Node efficiency Enodal(i) Center degree of node betweenness Bnodal(i) Respectively defined as follows:
the median center degree (median center) of the intermediate number is the center degree of the node defined from the information flow perspective, and is shown in the formula estRepresenting nodes s to nodes in the network GNumber of all shortest paths of point t, esitIs the number of passing nodes i in these shortest paths.
In this step, the performing feature screening specifically includes:
a Recursive Feature Elimination (RFE) algorithm based on a support vector machine performs Feature selection by continuously training a classifier and removing Feature dimensions with smaller Feature weights, and specifically includes:
① initializes the selected feature set to contain all of the selected features,
secondly, taking the feature set as input, training a classifier, and obtaining a classification effect and the weight of each feature;
removing the features with the minimum weight to form a new feature set;
④ repeat ②, ③ until
And selecting the condition with the best classification effect.
6. Based on the screened features, performing classification training by adopting a classifier to obtain an individualized prediction model of the first schizophrenia;
in this step, as a specific implementation means, the Classifier adopts an SVM Classifier, a Logistic Regression Classifier (Logistic Regression Classifier) or a plurality of classifiers integrated learning based on a Radial Basis Function (RBF) kernel; during classification training, a plurality of pattern recognition classification methods are applied to find an optimal classifier model and screen key multi-threshold fusion brain structure network characteristics.
7. And (4) performing performance verification evaluation on the trained individualized prediction model of the first schizophrenia.
In the step, as a specific implementation means, cross validation is adopted to evaluate the performances of various prediction models of a schizophrenia training set and a testing set, including accuracy, sensitivity, specificity, ROC curve, AUC value and the like, and the system optimizes brain image feature selection and individual subtype prediction models and identifies subtype-related core brain image group features through testing feature screening and classification algorithms by applying displacement inspection so as to improve the individual prediction accuracy.
In conclusion, the invention adopts the technologies of artificial intelligence and machine learning, and constructs an individualized prediction model of the first-onset schizophrenia with good robustness by analyzing and mining the magnetic resonance imaging data of the brain structure of the first-onset schizophrenia, so as to realize accurate and objective auxiliary diagnosis for the early diagnosis and identification of the schizophrenia and improve the curative effect.
Claims (9)
1. The method for constructing the individualized prediction model of the first-onset schizophrenia is characterized by comprising the following steps of:
A. acquiring a diffusion tensor image of a patient with first schizophrenia;
B. preprocessing the acquired diffusion tensor image;
C. constructing a sparse brain structure network based on the preprocessed image;
D. constructing a multi-threshold fusion brain structure network after each tested sparse by adopting a similar network fusion method;
E. extracting network topology attribute features of the multi-threshold fusion brain structure, and then performing feature screening;
F. based on the screened features, performing classification training by adopting a classifier to obtain an individualized prediction model of the first schizophrenia;
G. and (4) performing performance verification evaluation on the trained individualized prediction model of the first schizophrenia.
2. The method for constructing an individualized model for predicting first-onset schizophrenia according to claim 1, wherein the first-onset schizophrenia is a single-dose schizophrenia,
in the step A, a nuclear magnetic resonance imaging scanner is used for scanning and obtaining a diffusion tensor image of the first schizophrenia patient by adopting a single-shot plane echo imaging technology.
3. The method for constructing an individualized model for predicting first-onset schizophrenia according to claim 1, wherein the first-onset schizophrenia is a single-dose schizophrenia,
in step B, the preprocessing of the acquired diffusion tensor image specifically includes:
b1, converting the dispersion tensor image in the DICOM data format into an image in an NIFTI format by using MRI convert;
b2, performing eddy current correction and head movement correction on the diffusion tensor image after data format conversion;
b3, removing skull by Brain Extraction Tool of FSL, and removing non-Brain tissue image.
4. The method for constructing an individualized model for predicting first-onset schizophrenia according to claim 1, wherein the first-onset schizophrenia is a single-dose schizophrenia,
in step C, the constructing a sparse brain structure network based on the preprocessed image specifically includes:
c1, registering the brain images after the pre-processing of each test to b0 images in a diffusion tensor space by a rotation and translation linear registration method; the registered b0 images are then registered to T1 images in standard MNI space; inverting the conversion matrix, and transforming the AAL template from the MNI space to the diffusion tensor space by using the obtained inverse matrix to obtain 90 brain area network nodes divided based on the AAL template;
c2, using a probabilistic fiber bundle imaging method, carrying out Bayesian estimation of dispersion parameters based on a BEDPOSTX tool, establishing distribution of the dispersion parameters of each voxel by using a Markov chain Monte Carlo sampling method, presetting each voxel of a brain as a fiber cross model, and automatically judging how many kinds of cross-passing fiber bundles pass;
c3, carrying out probability tracking fiber bundle reconstruction based on a PROBTRACKX tool, repeatedly sampling the distribution of each voxel in the main diffusion direction, generating a streamline from the extracted local sample each time, and establishing a statistical graph of streamline position posterior distribution through multiple sampling to obtain the distribution situation of the structural connection probability between every two brain regions; defining the weight of each edge as the fiber bundle connection probability between every two node areas, and obtaining a symmetrical 90 x 90 fiber bundle connection probability weighting network matrix for each tested object;
and C4, setting a fiber bundle connection probability threshold, wherein structural connection exists between the two brain areas exceeding the threshold, testing the influence of different sparsity thresholds on the fusion effect, and constructing a sparse structure network by adopting a relatively narrower threshold range.
5. The method for constructing an individualized model for predicting first-onset schizophrenia according to claim 1, wherein the first-onset schizophrenia is a single-dose schizophrenia,
in step D, the constructing of the multi-threshold fusion brain structure network after each tested sparse by using the similar network fusion method specifically includes:
d1, defining the sparse structure connection matrix as a full-core matrix
For the ith tested full-core matrix under the jth threshold
Further constructing a corresponding sparse kernel matrix:
order touIs a full-core matrix
K adjacent to the middle node u, then sparse kernel matrix
Is defined as:
d2, carrying out iterative updating of the full-kernel matrix based on the sparse kernel matrix corresponding to the full-kernel matrix:
wherein (W)i c)(m)Represents the full-kernel matrix under the ith tested c threshold value in the mth iteration, (W)i j)(m+1)Representing a full-kernel matrix in the (m + 1) th iteration, wherein N is the total sparse threshold number;
d3, judging whether the iteration convergence condition is met, if so, executing a step D4, otherwise, continuing the iteration;
wherein the convergence condition is: i (W)i j)(m+1)-(Wi j)(m)||≤0.01；
D4, and corresponding the updated N thresholds to the full-core matrix
Averaging was performed to construct an averaged full kernel matrix for each test:
d5, mixing WiNormalized to the interval [0, 1 ]]Thus generating a final fused network for each test.
6. The method for constructing an individualized model for predicting first-onset schizophrenia according to claim 1, wherein the first-onset schizophrenia is a single-dose schizophrenia,
in step E, the extracting of the multi-threshold fusion brain structure network topology attribute feature specifically includes:
calculating AUC values of the fused network under all thresholds of 8 global topological attributes and 3 node topological attributes based on a graph theory analysis method as initial features of subsequent classification;
the 8 global topology attributes include network strength SpGlobal efficiency EglobLocal efficiency ElocLength of shortest path LpCoefficient of aggregation CpNormalized shortest path length λ, normalized clustering coefficient γ, small world attribute σ:
the network strength SpThe calculation formula of (2) is as follows:
wherein, S (i) is the weighted sum of edges connected with the ith node, and N is the number of brain areas in the whole brain network;
the shortest path length LpThe calculation formula of (2) is as follows:
wherein L isijRepresenting the shortest path between node i and node j, LpThe shortest path length for the entire network G;
the global efficiency EglobThe calculation formula (2) includes:
wherein E isglob_i(G) The global efficiency of the node i is the average value of the global efficiency of all nodes in the network G;
the local efficiency ElocThe calculation formula (2) includes:
wherein L isjkIs the shortest path length between region j and region k, GiIs a sub-network of nodes connected to area i, NGiIs a sub-network GiThe number of midbrain regions; eloc_i(G) Is the local efficiency of node i, the local efficiency E of network Gloc(G) The average value of the local efficiency of all nodes in the network is obtained;
the agglomeration coefficient CpThe calculation formula (2) includes:
wherein, C (i) is the aggregation coefficient of the node i, and the aggregation coefficient of the network G is the average value of the aggregation coefficients of all the nodes;
the normalized aggregation coefficient γ and the normalized shortest path length λ are calculated as follows:
wherein, Cp randAnd Lp randThe average values of the aggregation coefficients and the shortest path lengths of 100 random networks are respectively;
the formula for calculating the small world attribute sigma is as follows:
σ＝γ/λ；
the 3 kinds of node topology attributes comprise a node degree Dnodal(i) Node efficiency Enodal(i) Center degree of node betweenness Bnodal(i) Respectively defined as follows:
wherein e isstRepresenting the number of all shortest paths from node s to node t in network G, esitIs the number of passing nodes i in these shortest paths.
7. The method for constructing an individualized model for predicting first-onset schizophrenia according to claim 1, wherein the first-onset schizophrenia is a single-dose schizophrenia,
in step E, the performing feature screening specifically includes:
the recursive feature elimination algorithm based on the support vector machine performs feature selection by continuously training a classifier and removing feature dimensions with smaller feature weights, and specifically comprises the following steps:
① initializes the selected feature set to contain all of the selected features,
secondly, taking the feature set as input, training a classifier, and obtaining a classification effect and the weight of each feature;
removing the features with the minimum weight to form a new feature set;
④ repeat ②, ③ until
And selecting the condition with the best classification effect.
8. The method for constructing an individualized model for predicting first-onset schizophrenia according to claim 1, wherein the first-onset schizophrenia is a single-dose schizophrenia,
in the step F, the classifier adopts an SVM classifier, a logistic regression classifier or a plurality of classifiers integrated learning based on a radial basis function kernel; during classification training, a plurality of pattern recognition classification methods are applied to find an optimal classifier model and screen key multi-threshold fusion brain structure network characteristics.
9. The method for constructing the individualized prediction model for the first-onset schizophrenia according to any one of claims 1 to 8, wherein in the step G, cross validation is adopted to evaluate the performances of various prediction models of the training set and the test set of schizophrenia, including accuracy, sensitivity, specificity, ROC curve, AUC value and the like, and the system optimizes the brain image feature selection and the individual subtype prediction models by using the test feature screening and classification algorithm and applies the permutation test to identify the core brain image group features related to the subtypes so as to improve the individualized prediction accuracy.
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