CN105912837A - Method of building individual brain effect connection network based on functional magnetic resonance data - Google Patents

Abstract

The invention relates to a method of building an individual brain effect connection network based on functional magnetic resonance data, comprising the following steps: matching a preprocessed individual brain with a Brodmann standard template, wherein the individual brain is divided into a plurality of brain regions corresponding to Brodmann regions in a one-to-one way, and each brain region is called a BA region; taking any BA region as a seed point of entropy connection to build a mathematical model for solving entropy connection; and building an individual brain effect connection network based on the mathematical model of the entropy of entropy connection. The individual brain effect connection network built using entropy connection is simple, quick, economic and effective, and is more suitable for studying the neuro-imaging mechanism of diseases causing change in the brain nerve system.

Description

The construction method of individual brain effective connectivity net based on functional MRI data

Technical field

The invention belongs to functional MRI processing technology field, be specifically related to a kind of based on functional MRI The construction method of the individual brain effective connectivity net of data.

Background technology

Research shows, multiple disease can cause cerebral nervous system to change, and verifies and causes nervous system to change The neuroimaging mechanism of disease has great importance for clinical rehabilitation treatment.People often utilize function The mechanism of this respect studied by magnetic resonance brain effective connectivity net.

At present, the study hotspot during the construction method of brain effective connectivity net is functional MRI data post processing. The construction method of conventional brain effective connectivity net has DCM (Dynamic Causal Modeling, dynamic cause and effect Model) method and Granger cause-effect method.Both approaches is all based on the construction method of model, is suitable for Organize the group data such as data or matched group data based on patient and carry out the structure of brain effective connectivity net.

The brain effective connectivity net ratio that DCM method builds is more suitable for the causal difference of data between analysis group, I.e. analyze patient's group and the significant difference of matched group effective connectivity net midbrain interval Causal Strength.Because utilizing During DCM method, need to introduce a kind of external stimulus signal, the intensity of brain interval effective connectivity could be analyzed, In this way be not suitable for the change of network topology attribute between analysis group.Neuroimaging at analysis of disease During mechanism, there is certain limitation.The brain effective connectivity net that Granger cause-effect method builds is more applicable Analyze the topological attribute of network.Such as, worldlet attribute, input and output degree, shortest path length, mould Massing and centrad.This method can not build individual brain effective connectivity net, it is impossible to analysis group diencephalon effect is even Connect the topological attribute change of net, be unfavorable for exploring the neuroimaging mechanism of disease.

Summary of the invention

In order to solve the problems referred to above that prior art exists, the invention provides a kind of based on functional MRI number According to the construction method of individual brain effective connectivity net.

For achieving the above object, the present invention takes techniques below scheme: a kind of based on functional MRI data The construction method of individual brain effective connectivity net, it comprises the following steps:

Pretreated individual brain is registrated with Bu Ludeman standard form, if then individual capsules of brain is divided into Doing and brain district one to one of Bu Ludeman district, each brain district is referred to as a BA district；

The seed points connected as entropy in arbitrary BA district, builds the mathematical model solving the entropy that entropy connects；

Mathematical model based on the entropy that entropy connects, builds individual brain effective connectivity net.

Further, the entropy that described entropy connects includes synchronizing the entropy of input entropy connection, synchronism output entropy connects Entropy, asynchronous input entropy connect entropy and be output asynchronously entropy connect entropy.

Further, the building process of the mathematical model of the entropy that described synchronization entropy connects is:

The seed points connected as entropy by brain district BAX, builds and solves synchronization between brain district BAX and BAY The mathematical model of the entropy that entropy connects, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t； Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was fixed at the increment of moment t Justice is Δ x_t=x_t+1-x_t；

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t； Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was at the increment of moment t+1 It is defined as Δ y_t+1=y_t+2-y_t+1；Wherein, t=1,2 ..., N-2；

Default n be average time sequence x in increment Delta x of moment t_tWith sequences y average time moment t+1's Increment Delta y_t+1Synchronize the total step number of change, then increment Delta x_tWith Δ y_t+1The Bayesian probability synchronizing change is:

P_s(y/x)=n/ (N-2)；

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAX Liang Genao district BAX and BAY between synchronize the entropy that entropy connects Mathematical model be:

${\mathrm{TS}}_{x\→y}=\left\{\begin{array}{cc}{r}_{xy}\×P_s(y/x)\log \frac{(1+r_{xy}\×P_s(y/x\left)\right)}{(1-r_{xy}\×P_s(y/x\left)\right)}& P_s(y/x)>0.5\\ 0& P_s(y/x)\≤0.5\end{array}\right.;$

The seed points connected as entropy by brain district BAY, builds and solves synchronization entropy between brain district BAX and BAY The mathematical model of the entropy connected, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t； Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was at the increment of moment t+1 It is defined as Δ x_t+1=x_t+2-x_t+1；

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t； Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was fixed at the increment of moment t Justice is Δ y_t=y_t+1-y_t；Wherein, t=1,2 ..., N-2；

Preset n₁For sequences y average time in increment Delta y of moment t_tWith sequence x average time moment t+1's Increment Delta x_t+1Synchronize the total step number of change, then increment Delta y_tWith Δ x_t+1The Bayesian probability synchronizing change is:

P_s(x/y)=n₁/(N-2)；

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAY Liang Genao district BAX and BAY between synchronize the entropy that entropy connects Mathematical model be:

${\mathrm{TS}}_{y\→x}=\left\{\begin{array}{cc}-r_{xy}\×P_s(x/y)\log \frac{(1+r_{xy}\×P_s(x/y\left)\right)}{(1-r_{xy}\×P_s(x/y\left)\right)}& P_s(x/y)>0.5\\ 0& P_s(x/y)\≤0.5\end{array}\right.;$

TS_x→ySynchronize between Liang Genao district BAX and BAY of the seed points that expression connects using brain district BAX as entropy The entropy that entropy connects, TS_y→xRepresent Liang Genao district BAX and BAY of the seed points connected using brain district BAY as entropy Between synchronize entropy connect entropy；

If met | TS_x→y|≥|TS_y→x| and TS_x→y>=0, then show to synchronize Shi Congnao district, the direction BAX that entropy connects To brain district BAY；For brain district BAY, it is to synchronize input entropy to connect that this synchronization entropy connects, this synchronization The entropy that input entropy connects is TS_x→y；And for brain district BAX, it is synchronism output entropy that this synchronization entropy connects Connecting, the entropy that this synchronism output entropy connects is TS_x→y；

If met | TS_x→y| ＜ | TS_y→x| and TS_y→x＜ 0, then show to synchronize Shi Congnao district, the direction BAY that entropy connects To brain district BAX；For brain district BAX, it is to synchronize input entropy to connect that this synchronization entropy connects, this synchronization Input entropy connect entropy be | TS_y→x|；And for brain district BAY, it is synchronism output entropy that this synchronization entropy connects Connect, this synchronism output entropy connect entropy be | TS_y→x|。

Further, individual brain effective connectivity net is built based on the mathematical model synchronizing the entropy that input entropy connects Process be:

Based on the mathematical model synchronizing the entropy that input entropy connects, it is thus achieved that the arbitrarily synchronization input entropy between Liang Nao district Connect；

Calculate the entropy that any brain district connects with the synchronization input entropy that other brain is interval, it is thus achieved that each brain district is with it The synchronization input entropy connection that its all brain is interval；

Brain district regards as node, and the input entropy that synchronizes in brain interval connects as the limit between each node, synchronizes defeated Enter entropy connect entropy as the weighted value on limit, build a directive network, this directive network I.e. based on synchronizing the individual brain effective connectivity net that input entropy connection builds.

Further, the mathematical model of the entropy connected based on synchronism output entropy builds individual brain effective connectivity net Process be:

The mathematical model connected based on synchronism output entropy, it is thus achieved that the arbitrarily synchronism output entropy between Liang Nao district connects；

Calculate the entropy that any brain district connects with the synchronism output entropy that other brain is interval, it is thus achieved that each brain district is with it The synchronism output entropy connection that its all brain is interval；

Brain district regards as node, and the synchronism output entropy in brain interval connects as the limit between each node, synchronizes defeated Go out entropy connect entropy as the weighted value on limit, build a directive network, this directive network The individual brain effective connectivity net built i.e. is connected based on synchronism output entropy.

Further, the building process of the mathematical model of the entropy that described asynchronous entropy connects is:

The seed points connected as entropy by brain district BAX, structure solves between brain district BAX and BAY asynchronous The mathematical model of the entropy that entropy connects, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t； Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was fixed at the increment of moment t Justice is Δ x_t=x_t+1-x_t；

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t； Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was at the increment of moment t+1 It is defined as Δ y_t+1=y_t+2-y_t+1；Wherein, t=1,2 ..., N-2；

Default m be average time sequence x in increment Delta x of moment t_tWith sequences y average time moment t+1's Increment Delta y_t+1The total step number of asynchronous change, then increment Delta x_tWith Δ y_t+1The Bayesian probability of asynchronous change is:

P_a(y/x)=m/ (N-2)；

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAX Liang Genao district BAX and BAY between the entropy that connects of asynchronous entropy Mathematical model be:

${\mathrm{TA}}_{x\→y}=\left\{\begin{array}{cc}{r}_{xy}\×P_a(y/x)\log \frac{(1+r_{xy}\×P_a(y/x\left)\right)}{(1-r_{xy}\×P_a(y/x\left)\right)}& P_a(y/x)>0.5\\ 0& P_a(y/x)\≤0.5\end{array}\right.;$

The seed points connected as entropy by brain district BAY, builds and solves asynchronous entropy between brain district BAX and BAY The mathematical model of the entropy connected, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t； Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was at the increment of moment t+1 It is defined as Δ x_t+1=x_t+2-x_t+1；

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t； Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was fixed at the increment of moment t Justice is Δ y_t=y_t+1-y_t；Wherein, t=1,2 ..., N-2；

Preset m₁For sequences y average time in increment Delta y of moment t_tWith sequence x average time moment t+1's Increment Delta x_t+1The total step number of asynchronous change, then increment Delta y_tWith Δ x_t+1The Bayesian probability of asynchronous change is:

P_a(x/y)=m₁/(N-2)；

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAY Liang Genao district BAX and BAY between the entropy that connects of asynchronous entropy Mathematical model be:

${\mathrm{TA}}_{y\→x}=\left\{\begin{array}{cc}-r_{xy}\×P_a(x/y)\log \frac{(1+r_{xy}\×P_a(x/y\left)\right)}{(1-r_{xy}\×P_a(x/y\left)\right)}& P_a(x/y)>0.5\\ 0& P_a(x/y)\≤0.5\end{array}\right.;$

TA_x→yRepresent asynchronous between Liang Genao district BAX and BAY of the seed points connected as entropy using brain district BAX The entropy that entropy connects, TA_y→xRepresent Liang Genao district BAX and BAY of the seed points connected using brain district BAY as entropy Between asynchronous entropy connect entropy；

If met | TA_x→y|≥|TA_y→x| and TA_x→y>=0, then show Shi Congnao district, the direction BAX that asynchronous entropy connects To brain district BAY；For brain district BAY, it is that asynchronous input entropy connects that this asynchronous entropy connects, and this is asynchronous The entropy that input entropy connects is TA_x→y；And for brain district BAX, it is to be output asynchronously entropy that this asynchronous entropy connects Connecting, this entropy being output asynchronously entropy connection is TA_x→y；

If met | TA_x→y| ＜ | TA_y→x| and TA_y→x＜ 0, then show Shi Congnao district, the direction BAY that asynchronous entropy connects To brain district BAX；For brain district BAX, it is that asynchronous input entropy connects that this asynchronous entropy connects, and this is asynchronous Input entropy connect entropy be | TA_y→x|；And for brain district BAY, it is to be output asynchronously entropy that this asynchronous entropy connects Connect, this be output asynchronously entropy connect entropy be | TA_y→x|。

Further, the mathematical model of the entropy connected based on asynchronous input entropy builds individual brain effective connectivity net Process be:

Mathematical model based on the entropy that asynchronous input entropy connects, it is thus achieved that the arbitrarily asynchronous input entropy between Liang Nao district Connect；

Calculate the entropy that any brain district connects with the asynchronous input entropy that other brain is interval, it is thus achieved that each brain district is with it The asynchronous input entropy connection that its all brain is interval；

Brain district regards as node, and the asynchronous input entropy in brain interval connects as the limit between each node, asynchronous defeated Enter entropy connect entropy as the weighted value on limit, build a directive network, this directive network The individual brain effective connectivity net built i.e. is connected based on asynchronous input entropy.

Further, individual brain effective connectivity net is built based on the mathematical model being output asynchronously the entropy that entropy connects Process be:

Based on being output asynchronously the entropy mathematical model that entropy connects, it is thus achieved that the arbitrarily entropy that is output asynchronously between Liang Nao district connects Connect；

Calculate any brain district entropy being output asynchronously entropy connection with other brain interval, it is thus achieved that each brain district is with it What its all brain was interval is output asynchronously entropy connection；

Brain district regards as node, and the entropy that is output asynchronously in brain interval connects as the limit between each node, asynchronous defeated Go out entropy connect entropy as the weighted value on limit, build a directive network, this directive network I.e. based on being output asynchronously the individual brain effective connectivity net that entropy connection builds.

Due to use above technical scheme, the invention have the benefit that with existing utilize DCM method and The brain effective connectivity net that Granger cause-effect method builds is compared, and the present invention utilizes entropy to connect the individual brain built Effective connectivity net has simple, quick and cost-effective feature, and it is particularly suited for research and causes human brain god Neuroimaging mechanism through the disease that system changes.

Accompanying drawing explanation

Fig. 1 is the flow process of the construction method of the present invention individual brain effective connectivity net based on functional MRI data Figure；

Fig. 2 is the flow process building individual brain effective connectivity net based on the mathematical model synchronizing the entropy that input entropy connects Figure；

Fig. 3 is the flow process of the individual brain effective connectivity net of mathematical model structure of entropy based on the connection of synchronism output entropy Figure；

Fig. 4 is the flow process of the individual brain effective connectivity net of mathematical model structure of entropy based on the connection of asynchronous input entropy Figure；

Fig. 5 is the flow process building individual brain effective connectivity net based on the mathematical model being output asynchronously the entropy that entropy connects Figure.

Detailed description of the invention

With embodiment, technical scheme is described in detail below in conjunction with the accompanying drawings.

Conceptual illustration:

1, human brain Bu Ludeman district

1909, Germany neurosurgeon Bu Ludeman (Brodmann) was according to Cerebral Cortex Neuronal Cells structure Difference cerebral cortex is divided into 52 Ge Nao districts, the structure of neurons in same brain district is identical, and different brain The structure of neurons in district differs.Each brain district is carried out a brain function, and Different brain region performs difference Brain function.Therefore, human brain be divide into different brain districts by this 52 Ge Nao district, and Bu Lude is also in these brain districts Man Qu, is called for short BA district, the corresponding coding in each BA district.Such as, BA41 is the primary of human brain Auditory cortex, is responsible for perception and the process of audible signal.BA district as node, is built individuality by the present invention Brain effective connectivity net.

2, entropy connects

In order to build individual brain effective connectivity net, need to define entropy and connect this concept.In theory of information, entropy For metric amount.In recent years in Neuroscience Research, the information exchange interval in order to describe brain, research Person defines transformation entropy, and is used for measuring the intensity of brain interval effective connectivity.In order to make transformation entropy to be more suitable for In building individual brain effective connectivity net, the present invention is based on Markov law, Bayesian probability and Pearson's phase Close coefficient transformation entropy is improved, and utilize the transformation entropy of improvement to measure the connection of directive function (also Claim effective connectivity or cause and effect to connect) intensity.In order to be different from the effective connectivity that existing algorithm builds, this Bright this kind of directive function connection is defined as entropy connection.The transformation entropy improved is referred to as the entropy that entropy connects, and uses Measure the intensity that entropy connects.Entropy connects cause effect relation and the information flow direction describing brain interval.

Whether consistent according to brain interval nerve signal activity change, entropy connect can be subdivided into again synchronization entropy connect and Asynchronous entropy connects.

(1) synchronize entropy to connect

If a Ge Nao district neururgic state change can cause the neural activity that another brain district acts in agreement State changes, then the entropy connection in the two brain interval is known as synchronizing entropy and connects.The synchronization entropy in brain interval is even Connect the difference according to causal difference, i.e. closure, synchronization input entropy can be subdivided into again and connect and same Step output entropy connects.

Another brain district is always pointed to from a Ge Nao district, then relative to quilt in the direction of the synchronization entropy connection that brain is interval The brain district pointed to, the connection of this synchronization entropy is referred to as synchronizing input entropy and connects, and relative to another brain district, this is same Step entropy connects and is referred to as the connection of synchronism output entropy.

(2) asynchronous entropy connects

If a Ge Nao district neururgic state change can cause another brain district to be out of step (i.e. step Neural activity state change on the contrary), then the entropy connection in the two brain interval is known as asynchronous entropy and connects. The asynchronous entropy in brain interval connects the difference according to causal difference, i.e. closure, can be subdivided into again different Step input entropy connects and is output asynchronously entropy and connects.

Another brain district is always pointed to from a Ge Nao district, then relative to quilt in the direction of the asynchronous entropy connection that brain is interval The brain district pointed to, this asynchronous entropy connects and is referred to as the connection of asynchronous input entropy, and relative to another brain district, this is different Step entropy connects and is referred to as being output asynchronously entropy connection.

3, individual brain effective connectivity net

Individual brain effective connectivity net is each brain of connection human brain built based on single human brain function MR data There is direction brain network in district.This closure having direction brain network midbrain interval can present the letter that brain is interval Breath transmission direction, shows the direction of brain interval causal connection.By the causal connection that research brain is interval, permissible Illustrate the mechanism that some nervous system disease occurs.Additionally, observe the node topology of individual brain effective connectivity net The change of attribute, such as, the change of shortest path length, the image of some nervous system disorders can be explained Learning mechanism, the rehabilitation for patient provides theory support.

Utilize entropy to connect the individual brain effective connectivity net built and compare the neuroimaging being adapted to verify disease Mechanism.Such as, the individual brain effective connectivity net of congenital deafness patient's group and Normal group is carried out between group Statistical analysis and inspection, it is found that primary auditory cortex in the brain effective connectivity net of congenital deafness patient Synchronize input node degree to dramatically increase, input entropy bonding strength with the synchronization from visual cortex notable simultaneously Strengthen.Because it is that a kind of cause and effect connects that entropy connects, so the vision of these changes explanation congenital deafness patient The change of cortex causes the response of auditory cortex, say, that the auditory cortex of deaf patient is by plastic Property change can process visual signal, the primary auditory cortex of prompting congenital deafness patient there occurs crossed module (i.e. primary auditory cortex does not process audible signal to formula function integrity, but is used for processing by Changes of Plasticity Visual signal).It is visible by the individual brain effective connectivity net of congenital deafness patient's group and Normal group is entered Statistical analysis and inspection between row group, can reveal that what congenital deafness patient's primary auditory function of cortex was recombinated Neuroimaging mechanism.

As it is shown in figure 1, the invention provides a kind of individual brain effective connectivity net based on functional MRI data Construction method, it comprises the following steps:

S1, pretreated individual brain is registrated, then with Bu Ludeman (Brodmann) standard form Individual capsules of brain is divided into some and brain district one to one of Bu Ludeman district, and each brain district is referred to as a BA district.

S2, the seed points connected as entropy in arbitrary BA district, build the mathematical modulo solving the entropy that entropy connects Type.

The present invention constructs four types entropy connect: synchronize input entropy connect, synchronism output entropy connect, different Step input entropy connects, is output asynchronously entropy connection.The connection of each entropy all has certain bonding strength, uses entropy The entropy connected measures the intensity that entropy connects, and obtains, by the mathematical model built, the entropy that entropy connects.

As a example by the solving of entropy that entropy connects between any two brain district BAX and BAY, build and solve entropy and connect The mathematical model of entropy.The seed points connected with brain district BAX with BAY for entropy separately below builds different numbers Learn model.

(1) mathematical model of the entropy solving synchronization entropy connection is built

The seed points connected as entropy by brain district BAX, builds and solves synchronization between brain district BAX and BAY The mathematical model of the entropy that entropy connects, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t. Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was fixed at the increment of moment t Justice is Δ x_t=x_t+1-x_t, wherein, t=1,2 ..., N-2.

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t. Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was at the increment of moment t+1 It is defined as Δ y_t+1=y_t+2-y_t+1, wherein, t=1,2 ..., N-2.

Default n be average time sequence x in increment Delta x of moment t_tWith sequences y average time moment t+1's Increment △ y_t+1Synchronize the total step number of change, then increment Delta x_tWith Δ y_t+1The Bayesian probability synchronizing change is:

P_s(y/x)=n/ (N-2).

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAX Liang Genao district BAX and BAY between synchronize the entropy that entropy connects Mathematical model be:

${\mathrm{TS}}_{x\→y}=\left\{\begin{array}{cc}{r}_{xy}\×P_s(y/x)\log \frac{(1+r_{xy}\×P_s(y/x\left)\right)}{(1-r_{xy}\×P_s(y/x\left)\right)}& P_s(y/x)>0.5\\ 0& P_s(y/x)\≤0.5\end{array}\right.---\left(1\right)$

The seed points connected as entropy by brain district BAY, builds and solves synchronization entropy between brain district BAX and BAY The mathematical model of the entropy connected, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t. Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was at the increment of moment t+1 It is defined as Δ x_t+1=x_t+2-x_t+1, wherein, t=1,2 ..., N-2.

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t. Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was fixed at the increment of moment t Justice is Δ y_t=y_t+1-y_t, wherein, t=1,2 ..., N-2.

Preset n₁For sequences y average time in increment Delta y of moment t_tWith sequence x average time moment t+1's Increment Delta x_t+1Synchronize the total step number of change, then increment Delta y_tWith Δ x_t+1The Bayesian probability synchronizing change is:

P_s(x/y)=n₁/(N-2)。

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAY Liang Genao district BAX and BAY between synchronize the entropy that entropy connects Mathematical model be:

${\mathrm{TS}}_{y\→x}=\left\{\begin{array}{cc}-r_{xy}\×P_s(x/y)\log \frac{(1+r_{xy}\×P_s(x/y\left)\right)}{(1-r_{xy}\×P_s(x/y\left)\right)}& P_s(x/y)>0.5\\ 0& P_s(x/y)\≤0.5\end{array}\right.---\left(2\right)$

TS_x→ySynchronize between Liang Genao district BAX and BAY of the seed points that expression connects using brain district BAX as entropy The entropy that entropy connects, TS_y→xRepresent Liang Genao district BAX and BAY of the seed points connected using brain district BAY as entropy Between synchronize entropy connect entropy.

If met | TS_x→y|≥|TS_y→x| and TS_x→y>=0, then show to synchronize Shi Congnao district, the direction BAX that entropy connects To brain district BAY；For brain district BAY, it is to synchronize input entropy to connect that this synchronization entropy connects, this synchronization The entropy that input entropy connects is TS_x→y；And for brain district BAX, it is synchronism output entropy that this synchronization entropy connects Connecting, the entropy that this synchronism output entropy connects is TS_x→y；

If met | TS_x→y| ＜ | TS_y→x| and TS_y→x＜ 0, then show to synchronize Shi Congnao district, the direction BAY that entropy connects To brain district BAX；For brain district BAX, it is to synchronize input entropy to connect that this synchronization entropy connects, this synchronization Input entropy connect entropy be | TS_y→x|；And for brain district BAY, it is synchronism output entropy that this synchronization entropy connects Connect, this synchronism output entropy connect entropy be | TS_y→x|。

(2) mathematical model of the entropy solving the connection of asynchronous entropy is built

The seed points connected as entropy by brain district BAX, structure solves between brain district BAX and BAY asynchronous The mathematical model of the entropy that entropy connects, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t. Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was fixed at the increment of moment t Justice is Δ x_t=x_t+1-x_t, wherein, t=1,2 ..., N-2.

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t. Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was at the increment of moment t+1 It is defined as Δ y_t+1=y_t+2-y_t+1, wherein, t=1,2 ..., N-2.

Default m be average time sequence x in increment Delta x of moment t_tWith sequences y average time moment t+1's Increment Delta y_t+1The total step number of asynchronous change, then increment Delta x_tWith Δ y_t+1The Bayesian probability of asynchronous change is:

P_a(y/x)=m/ (N-2).

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAX Liang Genao district BAX and BAY between the entropy that connects of asynchronous entropy Mathematical model be:

${\mathrm{TA}}_{x\→y}=\left\{\begin{array}{cc}{r}_{xy}\×P_a(y/x)\log \frac{(1+r_{xy}\×P_a(y/x\left)\right)}{(1-r_{xy}\×P_a(y/x\left)\right)}& P_a(y/x)>0.5\\ 0& P_a(y/x)\≤0.5\end{array}\right.---\left(3\right)$

The seed points connected as entropy by brain district BAY, builds and solves asynchronous entropy between brain district BAX and BAY The mathematical model of the entropy connected, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t. Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was at the increment of moment t+1 It is defined as Δ x_t+1=x_t+2-x_t+1, wherein, t=1,2 ..., N-2.

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t. Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was fixed at the increment of moment t Justice is Δ y_t=y_t+1-y_t, wherein, t=1,2 ..., N-2.

Preset m₁For sequences y average time in increment Delta y of moment t_tWith sequence x average time moment t+1's Increment Delta x_t+1The total step number of asynchronous change, then increment Delta y_tWith Δ x_t+1The Bayesian probability of asynchronous change is:

P_a(x/y)=m₁/(N-2)。

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAY Liang Genao district BAX and BAY between the entropy that connects of asynchronous entropy Mathematical model be:

${\mathrm{TA}}_{y\→x}=\left\{\begin{array}{cc}-r_{xy}\×P_a(x/y)\log \frac{(1+r_{xy}\×P_a(x/y\left)\right)}{(1-r_{xy}\×P_a(x/y\left)\right)}& P_a(x/y)>0.5\\ 0& P_a(x/y)\≤0.5\end{array}\right.---\left(4\right)$

TA_x→yRepresent asynchronous between Liang Genao district BAX and BAY of the seed points connected as entropy using brain district BAX The entropy that entropy connects, TA_y→xRepresent Liang Genao district BAX and BAY of the seed points connected using brain district BAY as entropy Between asynchronous entropy connect entropy；

If met | TA_x→y|≥|TA_y→x| and TA_x→y>=0, then show Shi Congnao district, the direction BAX that asynchronous entropy connects To brain district BAY；For brain district BAY, it is that asynchronous input entropy connects that this asynchronous entropy connects, and this is asynchronous The entropy that input entropy connects is TA_x→y；And for brain district BAX, it is to be output asynchronously entropy that this asynchronous entropy connects Connecting, this entropy being output asynchronously entropy connection is TA_x→y；

If met | TA_x→y| ＜ | TA_y→x| and TA_y→x＜ 0, then show Shi Congnao district, the direction BAY that asynchronous entropy connects To brain district BAX；For brain district BAX, it is that asynchronous input entropy connects that this asynchronous entropy connects, and this is asynchronous Input entropy connect entropy be | TA_y→x|；And for brain district BAY, it is to be output asynchronously entropy that this asynchronous entropy connects Connect, this be output asynchronously entropy connect entropy be | TA_y→x|。

S3, the mathematical model of the entropy connected based on entropy, build individual brain effective connectivity net.

Mathematical model based on the entropy synchronizing the mathematical model of entropy of input entropy connection, the connection of synchronism output entropy, The mathematical model of the entropy that asynchronous input entropy connects and the mathematical model being output asynchronously the entropy that entropy connects build four respectively The individual brain effective connectivity net of type, its detailed process is:

As in figure 2 it is shown, build individual brain effective connectivity net based on the mathematical model synchronizing the entropy that input entropy connects Process be:

Based on the mathematical model synchronizing the entropy that input entropy connects, it is thus achieved that the arbitrarily synchronization input entropy between Liang Nao district Connect, such as, Liang Nao district BA41R and BA41L.

Calculate the entropy that any brain district connects with the synchronization input entropy that other brain is interval, it is thus achieved that each brain district is with it The synchronization input entropy connection that its all brain is interval.Such as, the seed points connected for entropy with brain district BA41L, Obtain the entropy that Tong Bu input entropy interval with other brain for brain district BA41L connects.

Brain district regards as node, and the input entropy that synchronizes in brain interval connects as the limit between each node, synchronizes defeated Enter entropy connect entropy as the weighted value on limit, build a directive network, this directive network I.e. based on synchronizing the individual brain effective connectivity net that input entropy connection builds.

As it is shown on figure 3, the individual brain effective connectivity net of mathematical model structure of the entropy connected based on synchronism output entropy Process be:

The mathematical model connected based on synchronism output entropy, it is thus achieved that the arbitrarily synchronism output entropy between Liang Nao district connects, Such as, Liang Nao district BA41L and BA41R.

Calculate the entropy that any brain district connects with the synchronism output entropy that other brain is interval, it is thus achieved that each brain district is with it The synchronism output entropy connection that its all brain is interval；Such as, the seed points connected for entropy with brain district BA41L, Obtain the entropy that synchronism output entropy interval with other brain for brain district BA41L is connected.

Brain district regards as node, and the synchronism output entropy in brain interval connects as the limit between each node, synchronizes defeated Go out entropy connect entropy as the weighted value on limit, build a directive network, this directive network The individual brain effective connectivity net built i.e. is connected based on synchronism output entropy.

As shown in Figure 4, the mathematical model of the entropy connected based on asynchronous input entropy builds individual brain effective connectivity net Process be:

Mathematical model based on the entropy that asynchronous input entropy connects, it is thus achieved that the arbitrarily asynchronous input entropy between Liang Nao district Connect.Such as, Liang Nao district BA41R and BA41L.

Calculate the entropy that any brain district connects with the asynchronous input entropy that other brain is interval, it is thus achieved that each brain district is with it The asynchronous input entropy connection that its all brain is interval.Such as, the seed points connected for entropy with brain district BA41L, Obtain the entropy that asynchronous input entropy interval with other brain for brain district BA41L is connected.

Brain district regards as node, and the asynchronous input entropy in brain interval connects as the limit between each node, asynchronous defeated Enter entropy connect entropy as the weighted value on limit, build a directive network, this directive network The individual brain effective connectivity net built i.e. is connected based on asynchronous input entropy.

As it is shown in figure 5, build individual brain effective connectivity net based on the mathematical model being output asynchronously the entropy that entropy connects Process be:

Based on being output asynchronously the entropy mathematical model that entropy connects, it is thus achieved that the arbitrarily entropy that is output asynchronously between Liang Nao district connects Connect.Such as, Liang Nao district BA41L and BA41R.

Calculate any brain district entropy being output asynchronously entropy connection with other brain interval, it is thus achieved that each brain district is with it What its all brain was interval is output asynchronously entropy connection.Such as, the seed points connected for entropy with brain district BA41L, What acquisition brain district BA41L was interval with other brain is output asynchronously the entropy that entropy is connected.

Brain district regards as node, and the entropy that is output asynchronously in brain interval connects as the limit between each node, asynchronous defeated Go out entropy connect entropy as the weighted value on limit, build a directive network, this directive network I.e. based on being output asynchronously the individual brain effective connectivity net that entropy connection builds.

The present invention is not limited to above-mentioned preferred forms, those skilled in the art under the enlightenment of the present invention all Other various forms of products can be drawn, no matter but in its shape or structure, make any change, every have Same as the present application or akin technical scheme, within all falling within protection scope of the present invention.

Claims (8)

1. a construction method for individual brain effective connectivity net based on functional MRI data, it includes following Step:

Pretreated individual brain is registrated with Bu Ludeman standard form, if then individual capsules of brain is divided into Doing and brain district one to one of Bu Ludeman district, each brain district is referred to as a BA district；

The seed points connected as entropy in arbitrary BA district, builds the mathematical model solving the entropy that entropy connects；

Mathematical model based on the entropy that entropy connects, builds individual brain effective connectivity net.

2. the structure side of individual brain effective connectivity net based on functional MRI data as claimed in claim 1 Method, it is characterised in that: the entropy that described entropy connects includes synchronizing entropy, the connection of synchronism output entropy that input entropy connects Entropy, asynchronous input entropy connect entropy and be output asynchronously entropy connect entropy.

3. the structure of individual brain effective connectivity net based on functional MRI data as claimed in claim 1 or 2 Construction method, it is characterised in that: the building process of the mathematical model of the entropy that described synchronization entropy connects is:

The seed points connected as entropy by brain district BAX, builds and solves synchronization between brain district BAX and BAY The mathematical model of the entropy that entropy connects, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t； Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was fixed at the increment of moment t Justice is Δ x_t=x_t+1-x_t；

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t； Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was at the increment of moment t+1 It is defined as Δ y_t+1=y_t+2-y_t+1；Wherein, t=1,2 ..., N-2；

Default n be average time sequence x in increment Delta x of moment t_tWith sequences y average time moment t+1's Increment Delta_yt+1Synchronize the total step number of change, then increment Delta x_tWith Δ y_t+1The Bayesian probability synchronizing change is:

P_s(y/x)=n/ (N-2)；

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAX Liang Genao district BAX and BAY between synchronize the entropy that entropy connects Mathematical model be:

${\mathrm{TS}}_{x\→y}=\left\{\begin{array}{cc}{r}_{xy}\×P_s(y/x)log\frac{(1+r_{xy}\×P_s(y/x\left)\right)}{(1-r_{xy}\×P_s(y/x\left)\right)}& P_s(y/x)>0.5\\ 0& P_s(y/x)\≤0.5\end{array}\right.;$

The seed points connected as entropy by brain district BAY, builds and solves synchronization entropy between brain district BAX and BAY The mathematical model of the entropy connected, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t； Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was at the increment of moment t+1 It is defined as Δ x_t+1=x_t+2-x_t+1；

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t； Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was fixed at the increment of moment t Justice is Δ y_t=y_t+1-y_t；Wherein, t=1,2 ..., N-2；

Preset n₁For sequences y average time in increment Delta y of moment t_tWith sequence x average time moment t+1's Increment Delta x_t+1Synchronize the total step number of change, then increment Delta y_tWith Δ x_t+1The Bayesian probability synchronizing change is:

P_s(x/y)=n₁/(N-2)；

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAY Liang Genao district BAX and BAY between synchronize the entropy that entropy connects Mathematical model be:

${\mathrm{TS}}_{y\→x}=\left\{\begin{array}{cc}-r_{xy}\×P_s(x/y)log\frac{(1+r_{xy}\×P_s(x/y\left)\right)}{(1-r_{xy}\×P_s(x/y\left)\right)}& P_s(x/y)>0.5\\ 0& P_s(x/y)\≤0.5\end{array}\right.;$

TS_x→ySynchronize between Liang Genao district BAX and BAY of the seed points that expression connects using brain district BAX as entropy The entropy that entropy connects, TS_y→xRepresent Liang Genao district BAX and BAY of the seed points connected using brain district BAY as entropy Between synchronize entropy connect entropy；

If met | TS_x→y|≥|TS_y→x| and TS_x→y>=0, then show to synchronize Shi Congnao district, the direction BAX that entropy connects To brain district BAY；For brain district BAY, it is to synchronize input entropy to connect that this synchronization entropy connects, this synchronization The entropy that input entropy connects is TS_x→y；And for brain district BAX, it is synchronism output entropy that this synchronization entropy connects Connecting, the entropy that this synchronism output entropy connects is TS_x→y；

If met | TS_x→y| ＜ | TS_y→x| and TS_y→x＜ 0, then show to synchronize Shi Congnao district, the direction BAY that entropy connects To brain district BAX；For brain district BAX, it is to synchronize input entropy to connect that this synchronization entropy connects, this synchronization Input entropy connect entropy be | TS_y→x|；And for brain district BAY, it is synchronism output entropy that this synchronization entropy connects Connect, this synchronism output entropy connect entropy be | TS_y→x|。

4. the structure side of individual brain effective connectivity net based on functional MRI data as claimed in claim 3 Method, it is characterised in that: build individual brain effective connectivity net based on the mathematical model synchronizing the entropy that input entropy connects Process be:

Based on the mathematical model synchronizing the entropy that input entropy connects, it is thus achieved that the arbitrarily synchronization input entropy between Liang Nao district Connect；

Calculate the entropy that any brain district connects with the synchronization input entropy that other brain is interval, it is thus achieved that each brain district is with it The synchronization input entropy connection that its all brain is interval；

Brain district regards as node, and the input entropy that synchronizes in brain interval connects as the limit between each node, synchronizes defeated Enter entropy connect entropy as the weighted value on limit, build a directive network, this directive network I.e. based on synchronizing the individual brain effective connectivity net that input entropy connection builds.

5. the structure side of individual brain effective connectivity net based on functional MRI data as claimed in claim 3 Method, it is characterised in that: the individual brain effective connectivity net of mathematical model structure based on the entropy that synchronism output entropy connects Process be:

The mathematical model connected based on synchronism output entropy, it is thus achieved that the arbitrarily synchronism output entropy between Liang Nao district connects；

Calculate the entropy that any brain district connects with the synchronism output entropy that other brain is interval, it is thus achieved that each brain district is with it The synchronism output entropy connection that its all brain is interval；

Brain district regards as node, and the synchronism output entropy in brain interval connects as the limit between each node, synchronizes defeated Go out entropy connect entropy as the weighted value on limit, build a directive network, this directive network The individual brain effective connectivity net built i.e. is connected based on synchronism output entropy.

6. the structure of individual brain effective connectivity net based on functional MRI data as claimed in claim 1 or 2 Construction method, it is characterised in that: the building process of the mathematical model of the entropy that described asynchronous entropy connects is:

The seed points connected as entropy by brain district BAX, structure solves between brain district BAX and BAY asynchronous The mathematical model of the entropy that entropy connects, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t； Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was fixed at the increment of moment t Justice is Δ x_t=x_t+1-x_t；

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t； Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was at the increment of moment t+1 It is defined as Δ y_t+1=y_t+2-y_t+1；Wherein, t=1,2 ..., N-2；

Default m be average time sequence x in increment Delta x of moment t_tWith sequences y average time moment t+1's Increment Delta y_t+1The total step number of asynchronous change, then increment Delta x_tWith Δ y_t+1The Bayesian probability of asynchronous change is:

P_a(y/x)=m/ (N-2)；

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAX Liang Genao district BAX and BAY between the entropy that connects of asynchronous entropy Mathematical model be:

${\mathrm{TA}}_{x\→y}=\left\{\begin{array}{cc}{r}_{xy}\×P_a(y/x)log\frac{(1+r_{xy}\×P_a(y/x\left)\right)}{(1-r_{xy}\×P_a(y/x\left)\right)}& P_a(y/x)>0.5\\ 0& P_a(y/x)\≤0.5\end{array}\right.;$

The seed points connected as entropy by brain district BAY, builds and solves asynchronous entropy between brain district BAX and BAY The mathematical model of the entropy connected, detailed process is:

Default x be brain district BAX one average time sequence, x_tFor sequence x average time in the value of moment t； Obtaining N number of sampled point from sequence x equal intervals average time, average time, sequence x was at the increment of moment t+1 It is defined as Δ x_t+1=x_t+2-x_t+1；

Default y be brain district BAY one average time sequence, y_tFor sequences y average time in the value of moment t； Obtaining N number of sampled point from sequences y equal intervals average time, average time, sequences y was fixed at the increment of moment t Justice is Δ y_t=y_t+1-y_t；Wherein, t=1,2 ..., N-2；

Preset m₁For sequences y average time in increment Delta y of moment t_tWith sequence x average time moment t+1's Increment Delta x_t+1The total step number of asynchronous change, then increment Delta y_tWith Δ x_t+1The Bayesian probability of asynchronous change is:

P_a(x/y)=m₁/(N-2)；

Preset r_xyFor the Pearson correlation coefficients between sequence x average time and average time sequences y, then build The seed points connected as entropy using brain district BAY Liang Genao district BAX and BAY between the entropy that connects of asynchronous entropy Mathematical model be:

${\mathrm{TA}}_{y\→x}=\left\{\begin{array}{cc}-r_{xy}\×P_a(x/y)log\frac{(1+r_{xy}\×P_a(x/y\left)\right)}{(1-r_{xy}\×P_a(x/y\left)\right)}& P_a(x/y)>0.5\\ 0& P_a(x/y)\≤0.5\end{array}\right.;$

TA_x→yRepresent asynchronous between Liang Genao district BAX and BAY of the seed points connected as entropy using brain district BAX The entropy that entropy connects, TA_y→xRepresent Liang Genao district BAX and BAY of the seed points connected using brain district BAY as entropy Between asynchronous entropy connect entropy；

If met | TA_x→y|≥|TA_y→x| and TA_x→y>=0, then show Shi Congnao district, the direction BAX that asynchronous entropy connects To brain district BAY；For brain district BAY, it is that asynchronous input entropy connects that this asynchronous entropy connects, and this is asynchronous The entropy that input entropy connects is TA_x→y；And for brain district BAX, it is to be output asynchronously entropy that this asynchronous entropy connects Connecting, this entropy being output asynchronously entropy connection is TA_x→y；

If met | TA_x→y| ＜ | TA_y→x| and TA_y→x＜ 0, then show Shi Congnao district, the direction BAY that asynchronous entropy connects To brain district BAX；For brain district BAX, it is that asynchronous input entropy connects that this asynchronous entropy connects, and this is asynchronous Input entropy connect entropy be | TA_y→x|；And for brain district BAY, it is to be output asynchronously entropy that this asynchronous entropy connects Connect, this be output asynchronously entropy connect entropy be | TA_y→x|。

7. the structure side of individual brain effective connectivity net based on functional MRI data as claimed in claim 6 Method, it is characterised in that: the individual brain effective connectivity net of mathematical model structure based on the entropy that asynchronous input entropy connects Process be:

Mathematical model based on the entropy that asynchronous input entropy connects, it is thus achieved that the arbitrarily asynchronous input entropy between Liang Nao district Connect；

Calculate the entropy that any brain district connects with the asynchronous input entropy that other brain is interval, it is thus achieved that each brain district is with it The asynchronous input entropy connection that its all brain is interval；

Brain district regards as node, and the asynchronous input entropy in brain interval connects as the limit between each node, asynchronous defeated Enter entropy connect entropy as the weighted value on limit, build a directive network, this directive network The individual brain effective connectivity net built i.e. is connected based on asynchronous input entropy.

8. the structure side of individual brain effective connectivity net based on functional MRI data as claimed in claim 6 Method, it is characterised in that: build individual brain effective connectivity net based on the mathematical model being output asynchronously the entropy that entropy connects Process be:

Based on being output asynchronously the entropy mathematical model that entropy connects, it is thus achieved that the arbitrarily entropy that is output asynchronously between Liang Nao district connects Connect；

Calculate any brain district entropy being output asynchronously entropy connection with other brain interval, it is thus achieved that each brain district is with it What its all brain was interval is output asynchronously entropy connection；

Brain district regards as node, and the entropy that is output asynchronously in brain interval connects as the limit between each node, asynchronous defeated Go out entropy connect entropy as the weighted value on limit, build a directive network, this directive network I.e. based on being output asynchronously the individual brain effective connectivity net that entropy connection builds.