CN106021949B - A kind of function connects analysis method of brain default network - Google Patents
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Abstract
The invention discloses a kind of function connects analysis methods of brain default network.Functional MRI is pre-processed, and chooses default mode brain area；Brain area is defined as node, the connection between brain area is defined as side, constitutes the default mode network for containing several nodes；It analyzes the node degree of default mode network, gather coefficient, shortest path length, the average distance and Hamilton path distance for calculating default mode network using Dijkstra's algorithm and improvement ant group algorithm respectively, study the connection features between default mode network node；The default mode network average distance whether having the same and Hamilton path for finding out cerebral disease patient and normal person, both judge at identical Hamilton path direction whether path length having the same.The present invention explores the difference of normal person and cerebral disease brain in patients function by the function connects of analysis default mode network, has certain application value in terms of Cognitive Function Research, mental disease.
Description
Technical field
The present invention relates to a kind of brain default network analysis method based on medical image, specifically a kind of brain network function
The analysis method that can be connected, belongs to biomedical information processing technology field, is project of national nature science fund project
(51307010) interim research achievement.
Background technique
Default mode refers to the state that baseline is revert to when brain does not process external task.The brain area of this function is supported to exist
Activity under the conditions of peace and quiet is higher than under the conditions of active tasks, is tested when carrying out Cognitive task, these brain areas always show to bear
Activation.These are collectively referred to as default mode net with the specific brain area that spontaneous activation, synchronousness and built-in function connect
Network (Default Mode Network, DMN) or default network.Greicius etc. is analyzed using tranquillization state function connects silent for the first time
Recognize network, discovery posterior cingutate (PCC)/Precuneus and other brain areas illustrate that the activity of these brain areas has there are function connects
Synchronism, to confirm the presence of default network.Default mode network possesses its specific space and constitutes, and is different from other
The mechanics of tranquillization state network provides a new starting point to study the cognition of human spontaneous.
In recent years, brain default mode network has become the research hotspot in Cognitive Neuroscience field.People are using quiet
State functional mri (functional Magnetic Resonance Imaging, the fMRI) technology of breath is to psychoneural
The default mode network of class disease has conducted extensive research, discovery it is some spirit and neurogenic diseases (such as Alzheimer's disease,
Schizophrenia, epilepsy etc.) in brain in patients, the activation degree and function connects of default network related brain areas, which have occurred, significantly to be changed
Become.
The research method of many brain default mode networks is only limited to medical means, for example, task induce activation or
Negative activation, tranquillization state function connects, diffusion tensor, low frequency amplitude etc., however using network-based analysis method come pair
The document and patent that the connection of default mode network function is studied are relatively fewer, study the function connects side of default mode network
The document and patent in face are just more rare.Complex brain network analytical technology based on graph theory is primarily upon brain structure or function
Network, main indexes have node degree, cluster coefficients, shortest path length etc..The present invention is research pair with fMRI data
As studying the nodal properties of default mode network first, then being calculated using Dijkstra's algorithm and improved self-adapting ant colony
Method studies it and is connected to the network characteristic.Invention explores normal person and brain disease by the function connects characteristic of analysis default mode network
The otherness of patient's cerebral function has very important reason to the function connects research of cerebral disease patient's default mode network
By and application value.
Summary of the invention
The needs of place and practical application in view of the shortcomings of the prior art, the problem to be solved in the present invention is:
A kind of brain default network function connects analysis method is provided, is realized special between the function connects brain default network
Sign is analyzed and researched.In order to achieve the above object, the present invention takes following technical scheme:
Step 1: carrying out magnetic resonance imaging to subject's brain, the cerebral function magnetic resonance image of subject is obtained.Divide again
Are as follows:
Situation one: to magnetic resonance imaging is carried out under Normal Subjects state, the magnetic resonance image under subject's tranquillization state is obtained.
Situation two: magnetic resonance imaging is carried out to the subject under certain stimulation or disease, obtains subject in stimulation or disease
Magnetic resonance image under diseased state.
Step 2: the functional MRI of acquisition is pre-processed, pretreatment include format conversion, time adjustment,
Head move correction, registration, Spatial normalization, smoothly, remove linear drift, filtering.
Step 3: dividing cerebral function magnetic resonance image into several brain regions using standardization Partition Mask, choose silent
To recognize mode brain area, and each brain area is defined as a node, contacting between brain area and brain area is defined as the side of connecting node,
Each node is interconnected to constitute default mode network by side.
Step 4: calculating node degree, cluster coefficients and the shortest path length of default mode network.The cluster system of node i
Number is defined as
In formula: the number of N expression nodes；EiThe number of edges of physical presence between node i and adjacent node；If ki< 2,
Ci=0.
The shortest path length L of node iiIt is defined as the average shortest path length of all other node in node i to network:
In formula: Li,jRepresent the shortest path length between node i and node j.
Step 5: the average departure of default mode network is calculated using Dijkstra's algorithm and improved ant group algorithm respectively
From with a distance from Hamilton path.Dijkstra's algorithm description are as follows: setting G={ V, E } is the figure with n vertex, and V is institute in figure
There is the set on vertex, E is the set on all sides in figure.Steps are as follows for algorithm execution:
(1) S={ V is enabled0, T=V-S={ remaining vertex }, if in T vertex correspondence distance value < V0,Vi> exist, then d
(V0,Vi) it is < V0,ViWeight on>arc, if it does not exist<V0,Vi>, then d (V0,Vi) it is ∞.
(2) it from a side relevant with vertex in S and the smallest vertex W of weight is chosen in T, is added in S.
(3) it modifies to the distance value on remaining vertex in T.If adding W makees intermediate vertex, so that from V0To ViDistance
Value shortens, then modifies this distance value.
(4) repeat the above steps (2), (3), until including all vertex, i.e. W=V in SiUntil.
Improved ant group algorithm description are as follows: set bi(t) indicate that t moment is located at the ant number of node i, τijIt (t) is t moment path
Information content on (i, j), n indicate the scale of network node, and m is the total number of ant in ant colony, then It is the set of residual risk amount on all paths in t moment node set C.The road Ke Getiao at the beginning
Information content is equal on diameter, and sets τij(0)=const.
Ant individual k (k=1,2 ... m) in search process, according to the inspiration of information content and path on each paths
Information calculates state transition probability.Indicate that t moment ant k is transferred to the adaptive of element (node) j by element (node) i
Answer state transition probability.
In formula: allowedk={ C-tabukIndicate the node for allowing ant k to select in next step；tabukFor taboo set；
α is information heuristic greedy method, indicates the relative importance of track；β is expected heuristic value, indicates the relatively important of visibility
Property；ηij(t) it is heuristic function, expression formula is as follows:
In formula: dijIndicate the distance between two adjacent nodes.
In order to avoid residual risk element excessively residual risk is caused to flood heuristic information, every ant cover a step or
After completing to the traversal of all n nodes, need to be updated residual risk processing.Information content more new formula is as follows:
τij(t+n)=(1- ρ) τij(t)+ρΔτij(t) (5)
In formula: ρ indicates that pheromones volatilization factor, 1- ρ indicate pheromones residual factor；The unlimited product of information in order to prevent
Tired, the value range of ρ isΔτij(t) the pheromones increment in this circulation on path (i, j), initial time are indicated
Δτij(0)=0；Δτk ij(t) indicate that kth ant stays in the information content on path (i, j), expression formula in this circulation
Are as follows:
In formula: Q is constant, LkIt is the path length that k-th of ant is traveled round.
Step 6: compare the node degree of normal person and cerebral disease patient's default mode network, gather coefficient, average path,
Characteristic path length, Hamilton path.
Step 7: analyzing the degree of the node of the default mode network of normal person and cerebral disease patient, gathering coefficient, feature road
Electrical path length judges whether these values are identical；Average path and the Kazakhstan of its network are analyzed on the basis of above three nodal community
Simultaneously draw its brain function connection figure in close path.
After adopting the above technical scheme, the method have the benefit that:
(1) can compare tranquillization state with different activity or stimulation under default mode brain area node degree, gather coefficient, spy
Nodes and the network characteristic such as path length are levied, default mode brain under different activities, stimulation or disease event is studied with this
The connection features in area.
(2) by comparing the default mode brain zone function connection under different situations, such as by normal condition and disease event
Under the function connects of default mode brain area compare, help to infer that the function connects of default mode brain area under corresponding disease are special
Sign.
The present invention has certain in fields such as brain function linking parsing, Cognitive Function Research, mental disease Clinics and Practices
Application value.
Detailed description of the invention
Fig. 1 is the flow chart of brain default network function connects analysis method.
Fig. 2 is patients with cerebral apoplexy default mode network function connection figure.
Fig. 3 is normal person's default mode network function connection figure.
Fig. 4 is patients with cerebral apoplexy default mode network Hamilton path connection figure.
Fig. 5 is normal subject default mode network Hamilton path connection figure.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings and specific examples.
As shown in Figure 1, a kind of for analyzing the analysis side of normal person and patients with cerebral apoplexy brain default network function connects
Method specific implementation the following steps are included:
(1) two groups of experiments are carried out in the present embodiment respectively:
Experiment one: magnetic resonance under tranquillization state is carried out to 30 normal person subjects (15 male, 15 female, 20~40 years old age) and is swept
It retouches, obtains the magnetic resonance image under subject's tranquillization state.
Experiment two: it allows 20 (10 male, 10 female, 65~75 years old age) patients with cerebral apoplexy to carry out magnetic resonance under quiescent condition and sweeps
It retouches, obtains the magnetic resonance image under subject's tranquillization state.
(2) magnetic resonance image under normal person and patients with cerebral apoplexy tranquillization state is formatted respectively, by DICOM lattice
Formula data are converted to NIFTI format.The image after conversion is pre-processed again, pretreatment include format conversion, time adjustment,
Head move correction, registration, Spatial normalization, smoothly, go linear drift, filtering etc..In the present embodiment, low frequency filtering range is taken
0.01Hz~0.08Hz standardizes Bounding Box:[-90-126-72；90 90 108],Voxel Size:[3 3 3].
(3) it is matched using the brain function MRI obtained after standardization Partition Mask and pretreatment, by brain
Functional MRI divides 90 brain regions into.Each brain function region corresponds to a node in cerebral function network.This
In embodiment, brain is divided by 90 brain areas by AAL template, this 90 brain areas correspond respectively to 90 sections in brain network
Point；Default mode brain area is chosen, and each brain area is defined as a node, contacting between brain area and brain area is defined as connecting
The side of node, each default brain area are interconnected to constitute default mode network；Cerebral apoplexy and normal person's default mode network function
Connection is as shown in Figures 2 and 3 respectively.
(4) node degree, cluster coefficients and the shortest path length of default mode network are calculated.The cluster coefficients of node i are fixed
Justice is
In formula: the number of N expression nodes；EiThe number of edges of physical presence between node i and adjacent node；If ki< 2,
Ci=0.
The shortest path length L of node iiIt is defined as the average shortest path length of all other node in node i to network:
In formula: Li,jRepresent the shortest path length between node i and node j.
(5) in the present embodiment, default mode net is calculated using Dijkstra's algorithm and improved ant group algorithm respectively
The average distance and Hamilton path distance of network.Dijkstra's algorithm description are as follows: setting G={ V, E } has n vertex
Figure, V are the set on all vertex in figure, and E is the set on all sides in figure.Steps are as follows for algorithm execution:
1. enabling S={ V0, T=V-S={ remaining vertex }, if in T vertex correspondence distance value < V0,Vi> exist, then d
(V0,Vi) it is < V0,ViWeight on>arc, if it does not exist<V0,Vi>, then d (V0,Vi) it is ∞.
2. being added in S from a side relevant with vertex in S and the smallest vertex W of weight is chosen in T.
3. modifying to the distance value on remaining vertex in T.If adding W makees intermediate vertex, so that from V0To ViDistance value
Shorten, then modifies this distance value.
4. repeating the above steps 2., 3., until including all vertex, i.e. W=V in SiUntil.
Improved ant group algorithm description are as follows: set bi(t) indicate that t moment is located at the ant number of node i, τijIt (t) is t moment path
Information content on (i, j), n indicate the scale of network node, and m is the total number of ant in ant colony, then It is the set of residual risk amount on all paths in t moment node set C.The road Ke Getiao at the beginning
Information content is equal on diameter, and sets τij(0)=const.
Ant individual k (k=1,2 ... m) in search process, according to the inspiration of information content and path on each paths
Information calculates state transition probability.Indicate that t moment ant k is transferred to the adaptive of element (node) j by element (node) i
Answer state transition probability.
In formula: allowedk={ C-tabukIndicate the node for allowing ant k to select in next step；tabukFor taboo set；
α is information heuristic greedy method, indicates the relative importance of track；β is expected heuristic value, indicates the relatively important of visibility
Property；ηij(t) it is heuristic function, expression formula is as follows:
In formula: dijIndicate the distance between two adjacent nodes.
In order to avoid residual risk element excessively residual risk is caused to flood heuristic information, every ant cover a step or
After completing to the traversal of all n nodes, need to be updated residual risk processing.Information content more new formula is as follows:
τij(t+n)=(1- ρ) τij(t)+ρΔτij(t) (5)
In formula: ρ indicates that pheromones volatilization factor, 1- ρ indicate pheromones residual factor；The unlimited product of information in order to prevent
Tired, the value range of ρ isΔτij(t) the pheromones increment in this circulation on path (i, j) is indicated, when initial
Carve Δ τij(0)=0；Δτk ij(t) it indicates that kth ant stays in the information content on path (i, j) in this circulation, expresses
Formula are as follows:
In formula: Q is constant, LkIt is the path length that k-th of ant is traveled round.
Parameter setting in the present embodiment are as follows: ant number m=120, the number of iterations N=200, information heuristic greedy method α=1,
Expected heuristic value β=5, pheromones volatility coefficient ρ=0.9, constant Q=100, the information content on each paths of initial time
C=3.
Normal person obtained in the present embodiment connects respectively such as with the Hamilton path of patients with cerebral apoplexy default mode network
Shown in Fig. 4 and Fig. 5.
(6) compare the node degree of normal person and patients with cerebral apoplexy default mode network, gather coefficient, average path, feature
Path length, Hamilton path.
(7) it analyzes the degree of the node of the default mode network of normal person and cerebral apoplexy in the present embodiment, gather coefficient, feature
Path length finds that the above three characteristic of normal person and cerebral apoplexy has apparent difference.Analyze the average path of its network
Graph discovery is connected with Hamilton path and in conjunction with brain function, normal person and patients with cerebral apoplexy have different average path and difference
Hamilton path connection.
Claims (2)
1. a kind of function connects analysis method of brain default network, which comprises the following steps:
(1) magnetic resonance imaging is carried out to several subject's brains, obtains the tranquillization state cerebral function magnetic resonance image of subject；
(2) functional MRI of acquisition is pre-processed, pretreatment include format conversion, time adjustment, head it is dynamic correct,
Registration, Spatial normalization, it is smooth, remove linear drift, filtering；
(3) several brain regions into are divided the cerebral function magnetic resonance image of each subject using standardization Partition Mask, selected
Default mode brain area is taken, and each brain area is defined as a node, contacting between brain area and brain area is defined as connecting node
Side, each node is interconnected to constitute default mode network by side；
(4) it calculates the node degree of each subject default mode network, gather coefficient and shortest path length；Gathering for node i be
Number is defined as
In formula: the number of N expression nodes；EiThe number of edges of physical presence between node i and adjacent node；If ki< 2, Ci=
0；
The shortest path length L of node iiIt is defined as the shortest path length average value of all other node in node i to network:
In formula: Li,jRepresent the shortest path length between node i and node j；
(5) average distance and Hamilton path distance of default mode network are calculated；
(6) compare the node degree of normal person and cerebral disease patient's default mode network, gather coefficient, average path, shortest path
Length, Hamilton path；
(7) it analyzes the node degree of the default mode network of normal person and cerebral disease patient, gather coefficient, shortest path length, sentence
Whether these values of breaking are identical；Brain disease is analyzed on the basis of node degree, gathering three nodal communities of coefficient and shortest path length
The average path and Hamilton path of the network of patient and the brain function connection figure for drawing cerebral disease patient；
Wherein, the Hamilton path distance that default mode network is calculated in the step (5) is carried out using improved ant group algorithm
It solves, algorithm steps are as follows:
S1: Hamilton path is defined；It is primary merely through other nodes from a certain node in network, it finally returns back to out
Send out the path distance of point；The self-adaptive genetic operator of application enhancements solves Hamilton path distance；
S2: setting ant group algorithm initializaing variable；If bi(t) indicate that t moment is located at the ant number of node i, τijIt (t) is t moment road
Information content on diameter (i, j), n indicate the scale of network node, and m is the total number of ant in ant colony, then It is the set of residual risk amount on all paths in t moment node set C；Each item is carved at the beginning
Information content is equal on path, and sets τij(0)=const；
S3: setting state transition probability function；Ant individual k, k=1,2 ... m；In search process, according on each paths
Information content and the heuristic information in path calculate state transition probability；Indicate that t moment ant k is transferred to section by node i
The adaptive state transition probability of point j；
In formula: allowedk={ C-tabukIndicate the node for allowing ant k to select in next step；tabukFor taboo set；α is
Information heuristic greedy method indicates the relative importance of track；β is expected heuristic value, indicates the relative importance of visibility；
ηij(t) it is heuristic function, expression formula is as follows:
In formula: dijIndicate the distance between two adjacent nodes；
S4: pheromones are updated；In order to avoid residual risk element excessively causes residual risk to flood heuristic information, walked in every ant
A complete step or complete to the traversal of all n nodes after, need to be updated residual risk processing；Information content more new formula
It is as follows:
τij(t+n)=(1- ρ) τij(t)+ρΔτij(t) (5)
In formula: ρ indicates that pheromones volatilization factor, 1- ρ indicate pheromones residual factor；The unlimited accumulation of information in order to prevent, ρ's
Value range isΔτij(t) the pheromones increment in this circulation on path (i, j), initial time Δ τ are indicatedij
(0)=0；Δτk ij(t) indicate that kth ant stays in the information content on path (i, j), expression formula in this circulation are as follows:
In formula: Q is constant, LkIt is the path length that k-th of ant is traveled round.
2. a kind of function connects analysis method of brain default network according to claim 1, which is characterized in that the step
Suddenly the shortest path length L of default mode network is calculated in (4)i,jIt is solved using Dijkstra's algorithm, algorithm executes step
It is rapid as follows:
If G={ V, E } is the figure with n vertex, V is the set on all vertex in figure, and E is the set on all sides in figure；
(1) enable S={ V0 }, T=V-S, if in T vertex correspondence distance value<V0, Vi>exist, then d (V0, Vi) be<V0, Vi>
Weight on arc, if it does not exist<V0, Vi>, then d (V0, Vi) is ∞；
(2) it from a side relevant with vertex in S and the smallest vertex W of weight is chosen in T, is added in S；
(3) it modifies to the distance value on remaining vertex in T；If adding W makees intermediate vertex, so that the distance value from V0 to Vi contracts
It is short, then modify this distance value；
(4) repeat the above steps (2), (3), until including all vertex in S.
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| CN107358022A (en) * | 2017-06-02 | 2017-11-17 | 常州大学 | A kind of Modularity analysis method of cerebral function network |
| CN107242873B (en) * | 2017-07-05 | 2020-06-09 | 成都信息工程大学 | Brain network construction method based on functional magnetic resonance psychophysiological interaction |
| CN109034360A (en) * | 2018-07-13 | 2018-12-18 | 北京工业大学 | A kind of ant colony method constructing brain effective connectivity network from fMRI and DTI data |
| CN109589113B (en) * | 2018-10-26 | 2021-04-20 | 天津大学 | Time-space network construction method of multi-electrode array neuron discharge sequence |
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| CN113902732A (en) * | 2019-05-07 | 2022-01-07 | 上海联影智能医疗科技有限公司 | Brain image analysis method, computer device, and readable storage medium |
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