CN105931281A - Method for quantitatively describing cerebral function network based on network characteristic entropy - Google Patents

Abstract

The invention discloses a method for quantitatively describing the cerebral function network based on network characteristic entropy. The method comprises the steps that fMRI data in the resting state is collected and preprocessed; a cerebral template including a functional zone of the cerebral cortex is selected; a single cerebral region in the cerebral template is regarded as a node in the cerebral function network, a average time sequence signal at each node is extracted from the fMRI data, functional connection among the nodes is calculated, and a functional connection matrix is obtained; the functional connection matrix is binarized to obtain an adjacent matrix, and the cerebral function network is formed; the characteristic vector centrality and the energy concentration degree of each node in the cerebral function network are calculated; and the cerebral network characteristic entropy is calculated according to the energy concentration degree of each node in the cerebral function network. According to the invention, the cerebral network characteristic entropy is used to describe the cerebral function network quantitatively to reveal growth and aging rules of the brain, and the method has the advantages of being simple in principles, convenient to realize, stable in results of testees, and high in sensitivity to different types of samples.

Description

The method quantitatively portraying brain function network based on network characterization entropy

Technical field

The present invention relates to brain network research field based on functional mri (fMRI) data, be specifically related to one The method quantitatively portraying brain function network based on network characterization entropy.

Background technology

The principle of functional mri is to measure the hemodynamic change that causes of neuron activity by the magnetic radiography that shakes. Such as: when stimulating some sense organ, some brain domain activation that cerebral cortex is concrete, these brain districts activated send neural letter Number, utilize magnetic shake radiography obtain nerve signal, obtain a series of images.It is various multiple that people perform motion, sensation, higher cognitive etc. During miscellaneous task, some nerve signal in brain can be evoked, this physiological cerebral activity causes local cerebral blood flow, brain blood to hold, The change of energy metabolism, thus cause the change of tissue magnetic.FMRI is as a kind of live body cerebral function imaging technology, because it has Noninvasive, the advantage such as preferable spatial and temporal resolution, rapidly become the brain imaging technique that cranial nerve scientific research is the most frequently used.

No matter it is that field of biology, technical field, social field or other scientific domain all create phase similarly Mutual associated data set.In the past few decades, in order to portray these data sets, Complex Networks Analysis is arisen at the historic moment.Complicated The topological property of given network mainly quantitatively portrayed by analysis of network by graph theory.Brain connects data and has referred to that function connects or knot Structure is followed the trail of and is connected the network formed, and it has common feature with biological or physical system, and researcher finds that brain is from micro-simultaneously Little neuron could set up network model on the different levels such as brain district, so utilizes Complex Networks Analysis technique study Brain network attribute is that people recognize on the whole and study brain function activity and provide a kind of brand-new Research Thinking.

2006, Achard Sophi et al. utilized discrete wavelet change to convert fMRI data estimation and portrays what function connected Frequency dependence matrix, is concluded that fMRI data medium and low frequency oscillation data collection has worldlet (small world) character. Van den Heuvel MP in 2008 et al. according to tranquillization function nuclear magnetic resonance, NMR (rs-fMRI) data based on voxel (voxel- Wise) method, builds the function between voxel and connects, and the statistical attribute then calculating network finds the cluster of brain function network Wanting of coefficient ratio correspondence random network is big, and average path length is shorter than random network, indicates the little of brain function network World's characteristic；It addition, the power rate exponential approximation power law distribution equal to 2, table are obeyed in the distribution that between voxel with voxel, function is connected The uncalibrated visual servo characteristic (scale free network) of bright brain function network.But, these topologicaies property derived above are only carved Having drawn a feature of brain function network, these features are isolated, and its application is limited to.Such as we explore by very The brain development of multifactor impact, it is necessary to these characteristics comprehensively analyzed, extracts the structure trend feature of network, how to lead to Cross comprehensive analysis and quantitatively portray brain function network to explore network characterization entropy and brain development, aging rule, have become as one Key technical problem urgently to be resolved hurrily.

Summary of the invention

The technical problem to be solved in the present invention: for the problems referred to above of prior art, it is provided that by brain network Characteristic Entropy Disclose brain development and aging rule, have that principle is simple, it is high, different classes of sensitive to realize interior result stability easy, tested The method quantitatively portraying brain function network based on network characterization entropy that property is good.

In order to solve above-mentioned technical problem, the technical solution used in the present invention is:

A kind of method quantitatively portraying brain function network based on network characterization entropy, step includes:

1) fMRI data under quiescent condition is gathered；

2) pretreatment is carried out for the fMRI data collected；

3) the brain template comprising cerebral cortex function division is selected；

4) for pretreated fMRI data, the Different brain region in brain template is regarded as brain function network In node, thus extract sequence signal average time of each node, calculate the correlation coefficient between node, obtain function even Connect matrix, described function connection matrix is carried out binaryzation and obtains adjacency matrix, thus form brain function network, described adjacent square The brain function network that formation becomes features node interphase interaction relation, reflects the topological attribute of brain function network；

5) eigenvector centrality degree and the encircled energy of each node in brain function network are calculated；

6) brain network Characteristic Entropy is calculated according to the encircled energy of each node in brain function network.

Preferably, described step 4) detailed step include:

4.1) for pretreated fMRI data, the Different brain region in brain template is regarded as brain function net Node in network, extracts sequence signal average time of each node；

4.2) using formula (1) to calculate the correlation coefficient between node, obtaining a symmetrical size is the time series of n*n Correlation matrix A (a_ij), wherein n represents brain function network node number, a_ijRepresent sequence signal x average time of node i_iWith joint Sequence signal x average time of some j_jBetween correlation coefficient, represent that function between node connects with correlation coefficient, therefore institute State time series correlation matrix A (a_ij) illustrate in brain function network function between each node and connect, referred to as function connection matrix；

$a_{ij}=\frac{E\left(x_i{x}_j\right)-E\left(x_i\right)E\left(x_j\right)}{\sqrt{E\left(x_i^2\right)-E^2\left(x_i\right)}\sqrt{E\left(x_j^2\right)-E^2\left(x_j\right)}}---\left(1\right)$

In formula (1), E (x_ix_j) represent x_ix_jMathematic expectaion, E (x_i) represent x_iMathematic expectaion, E (x_j) represent x_jMathematics Expect,Represent x_i ²Mathematic expectaion, E²(x_i) represent x_iMathematic expectaion square,Represent x_j ²Mathematic expectaion, E²(x_j) represent x_jMathematic expectaion square；

4.3) for described function connection matrix, first corresponding statistical value p is connected according to function between any two nodes, Corrected by Bonferroni, by corresponding statistical value p more than preset the node of statistical value between function connect and be set to 0, will Corresponding statistical value p less than or equal to preset the node of statistical value between function connect and be set to 1, secondly described function is connected In matrix, the element less than 0 is set to 0, thus described function connection matrix binaryzation obtains adjacency matrix and forms brain function net Network, described brain function network features node interphase interaction relation, reflects the topological attribute of brain function network.

Preferably, described step 4.3) in default statistical value be 0.05.

Preferably, described step 5) detailed step include:

5.1) the i-th component conduct of the eigenvalue of maximum character pair vector of described adjacency matrix is calculated according to formula (2) The eigenvector centrality degree of i-th node in adjacency matrix；

$e_i=\frac{1}{\mathrm{\λ}}\underset{j=1,j\≠i}{\overset{n}{\Σ}}{a}_{ij}{e}_j---\left(2\right)$

In formula (2), e_iRepresent the eigenvector centrality degree of i-th node, a in adjacency matrix_ijRepresent in adjacency matrix i-th Function between individual node with jth node is connected, and λ represents the eigenvalue of maximum that adjacency matrix is corresponding, e_jRepresent spy corresponding to λ Levying vector, n represents brain function network node number；

5.2) encircled energy of each node in adjacency matrix is calculated according to formula (3)；

$I_i=\frac{e_i}{\underset{i=1}{\overset{n}{\Σ}}{e}_i}---\left(3\right)$

In formula (3), I_iRepresent the encircled energy of i-th node, e in adjacency matrix_iRepresent i-th joint in adjacency matrix The eigenvector centrality degree of point, n represents brain function network node number.

Preferably, described step 6) in calculate brain network Characteristic Entropy function expression such as formula (4) shown in；

$E=-\underset{i=1}{\overset{n}{\Σ}}{I}_i{\mathrm{lnI}}_i---\left(4\right)$

In formula (4), E represents brain network Characteristic Entropy, I_iRepresent the encircled energy of i-th node, n table in adjacency matrix Show brain function network node number.

Preferably, described step 2) detailed step include:

2.1) image of the front specified number of frames of fMRI data is removed；

2.2) removing function magnetic resonance imaging data exceedes the tested of appointment threshold value in the head amplitude of moving；

2.3) interlayer time adjustment is carried out for fMRI data；

2.4) dynamic(al) correction is carried out for fMRI data；

2.5) Spatial normalization process is carried out for fMRI data；

2.6) utilize the gaussian kernel function specifying size halfwidth that fMRI data is carried out at space smoothing Reason；

2.7) utilize assigned frequency interval that fMRI data is carried out time-domain filtering；

2.8) for fMRI data carry out returning full brain average signal, white matter, hydrocrania average signal and Dynamic parameter.

Preferably, described step 2.6) to specifically refer to the half-breadth of 6mm high for middle finger sizing halfwidth.

Preferably, described step 2.7) in specify frequency separation specifically refer to 0.01～0.08Hz.

The method that the present invention quantitatively portrays brain function network based on network characterization entropy has an advantage that

1, the present invention regards the single brain district in brain template as node in network, by function magnetic after extracting pretreatment altogether Shake imaging data sequence signal average time at each node, calculates the function between node and connects, and obtains function and connects square Battle array, carries out function connection matrix binaryzation and obtains adjacency matrix, form brain function network, for the adjacent square of brain function network Battle array, calculates eigenvector centrality degree and the encircled energy of each node respectively, has excavated and network essence topological attribute Relevant feature.

2, the present invention is theoretical by existing shannon entropy, calculates brain net according to the encircled energy of each node Network Characteristic Entropy, thus can explore brain development and aging rule by comparing the brain network calculating different developmental phases, logical Cross repetition experimental verification network characterization entropy stability on individuality, for utilizing network characterization entropy to explore brain development, aging Rule provides method and thinking, has that principle is simple, it is good to realize interior result stability susceptiveness high, different classes of easy, tested Advantage.

Accompanying drawing explanation

Fig. 1 is the basic procedure schematic diagram of embodiment of the present invention method.

Fig. 2 is tested age distribution figure in the embodiment of the present invention.

Detailed description of the invention

As it is shown in figure 1, the step of the present embodiment method of quantitatively portraying brain function network based on network characterization entropy includes:

1) fMRI data under quiescent condition is gathered；

2) pretreatment is carried out for the fMRI data collected；

3) the brain template comprising cerebral cortex function division is selected；

4) for pretreated fMRI data, the Different brain region in brain template is regarded as brain function network In node, thus extract sequence signal average time of each node, calculate the correlation coefficient between node, obtain function even Connect matrix, described function connection matrix is carried out binaryzation and obtains adjacency matrix, thus form brain function network, described adjacent square The brain function network that formation becomes features node interphase interaction relation and reflects the topological attribute of brain function network；

5) eigenvector centrality degree and the encircled energy of each node in brain function network are calculated；

6) brain network Characteristic Entropy is calculated according to the encircled energy of each node in brain function network.

The present embodiment step 1) in, gather the functional mri under quiescent condition (resting state) During (functional magnetic resonance imaging is called for short fMRI) data, the equipment of use is that nuclear magnetic resonance, NMR is swept Retouch instrument, and select fast gradient echo mode, before scanning, need setting scan period, thickness, the number of plies, scan matrix, scanning to regard The sweep parameters such as open country, require tested holding head still, close eyes and loosen in scanning process, but keep clear-headed.

In the present embodiment, step 2) detailed step include:

2.1) image of the front specified number of frames of fMRI data is removed, for saturated to number to eliminate magnetic field According to the inaccurate impact brought, the present embodiment specially removes front 5 two field pictures；

2.2) removing function magnetic resonance imaging data exceedes the tested of appointment threshold value in the head amplitude of moving；

2.3) interlayer time adjustment is carried out for fMRI data；

2.4) dynamic(al) correction is carried out for fMRI data；

2.5) Spatial normalization process is carried out for fMRI data；

2.6) utilize the gaussian kernel function specifying size halfwidth that fMRI data is carried out at space smoothing Reason；In the present embodiment, step 2.6) to specifically refer to the half-breadth of 6mm high for middle finger sizing halfwidth；

2.7) utilize assigned frequency interval that fMRI data is carried out time-domain filtering；In the present embodiment, step 2.6) frequency separation is specified to specifically refer to 0.01～0.08Hz in；

2.8) for fMRI data carry out returning full brain average signal, white matter, hydrocrania average signal and Dynamic parameter, to reduce hardware noise.

In the present embodiment, step 2.2)～2.8) specifically use Statistical Parametric Mapping software (SPM8) realize under Matlab runs, by above-mentioned process, it is possible to increase the accuracy of fMRI data.

Brain template is the cerebral cortex function division that forefathers are determined by methods such as task state active region detections, the present embodiment Step 3) in, select the brain template comprising cerebral cortex function division to be specially Montreal Neurological Anatomical Automatic Labeling (AAL) template that Institute (MNI) mechanism provides, it comprises altogether 116 Ge Nao district；In addition it is also possible to select other to generally acknowledge more stable brain template as required.

In the present embodiment, step 4) detailed step include:

Described step 4) detailed step include:

4.1) for pretreated fMRI data, the Different brain region in brain template is regarded as brain function net Node in network, extracts sequence signal average time of each node；

4.2) using formula (1) to calculate the correlation coefficient between node, obtaining a symmetrical size is the time series of n*n Correlation matrix A (a_ij), wherein n represents brain function network node number, a_ijRepresent sequence signal x average time of node i_iWith joint Sequence signal x average time of some j_jBetween correlation coefficient, represent that function between node connects with correlation coefficient, therefore institute State time series correlation matrix A (a_ij) illustrate in brain function network function between each node and connect, referred to as function connection matrix；

$a_{ij}=\frac{E\left(x_i{x}_j\right)-E\left(x_i\right)E\left(x_j\right)}{\sqrt{E\left(x_i^2\right)-E^2\left(x_i\right)}\sqrt{E\left(x_j^2\right)-E^2\left(x_j\right)}}---\left(1\right)$

In formula (1), E (x_ix_j) represent x_ix_jMathematic expectaion, E (x_i) represent x_iMathematic expectaion, E (x_j) represent x_jMathematics Expect,Represent x_i ²Mathematic expectaion, E²(x_i) represent x_iMathematic expectaion square,Represent x_j ²Mathematic expectaion, E²(x_j) represent x_jMathematic expectaion square；

4.3) for described function connection matrix, first corresponding statistical value p is connected according to function between any two nodes, Corrected by Bonferroni, by corresponding statistical value p more than preset the node of statistical value between function connect and be set to 0, will Corresponding statistical value p less than or equal to preset the node of statistical value between function connect and be set to 1, secondly described function is connected In matrix, the element less than 0 is set to 0, thus described function connection matrix binaryzation obtains adjacency matrix and forms brain function net Network, described brain function network features node interphase interaction relation, reflects the topological attribute of brain function network.

In the present embodiment, step 4.3) in default statistical value be 0.05.

In the present embodiment, step 5) detailed step include:

5.1) the i-th component conduct of the eigenvalue of maximum character pair vector of described adjacency matrix is calculated according to formula (2) The eigenvector centrality degree (eigenvector centrality) of i-th node in adjacency matrix；

$e_i=\frac{1}{\mathrm{\λ}}\underset{j=1,j\≠i}{\overset{n}{\Σ}}{a}_{ij}{e}_j---\left(2\right)$

In formula (2), e_iRepresent the eigenvector centrality degree of i-th node, a in adjacency matrix_ijRepresent in adjacency matrix i-th Function between individual node with jth node is connected, and λ represents the eigenvalue of maximum that adjacency matrix is corresponding, e_jRepresent spy corresponding to λ Levying vector, n represents brain function network node number；

5.2) encircled energy of each node in adjacency matrix is calculated according to formula (3)；

$I_i=\frac{e_i}{\underset{i=1}{\overset{n}{\Σ}}{e}_i}---\left(3\right)$

In formula (3), I_iRepresent the encircled energy of i-th node, e in adjacency matrix_iRepresent i-th joint in adjacency matrix The eigenvector centrality degree of point, n represents brain function network node number；

In the present embodiment, step 6) in calculate shown in the function expression such as formula (4) of brain network Characteristic Entropy；

$E=-\underset{i=1}{\overset{n}{\Σ}}{I}_i{\mathrm{lnI}}_i---\left(4\right)$

In formula (4), E represents brain network Characteristic Entropy, I_iRepresent the encircled energy of i-th node, n table in adjacency matrix Show brain function network node number.Function expression shown in formula (4) is the information entropy theory brain net by existing Shannon The entropy of network, quantitatively portrays brain function network based on network characterization entropy, may be used for exploring brain development, aging rule.

In order to assess the brain network Characteristic Entropy performance in brain function network application, two factors must be considered: 1, weight Survey reliability (test-retest reliability): it is big that the height of test retest reliability reflects tested interior network Characteristic Entropy transmutability Little, weigh network characterization entropy stability in time domain, 2, susceptiveness: checked by double sample t or non parametric tests inspection Difference between different classes of (such as: between men and women) is tested is the most notable.The present embodiment is for gender matched totally 97 people (male 54, women 43) same a collection of tested, by two Scan fMRI data collecting in the case of obtaining different time (Scan it Between be separated by one week), calculate the test retest reliability of network characterization entropy and susceptiveness.The tested mean age is 27.83 years old, and variance is 5.09 In year, age distribution is as shown in Figure 2.

Test retest reliability refers to that same tranquillization state functional MRI is tested in the different time the testing of same tested colony The degree of consistency of obtained measurement result.In function connection group, conventional interclass correlation coefficient intra-class The test retest reliability of correlation (ICC) quantitative predication one tolerance.Within 2014, survey article is delivered with reference to Zuo Xi nian test-retest reliabilities of resting-state FMRI measurements inhuman brain Test retest reliability is divided into five by functional connectomics:A systems neuroscience perspective Individual grade: relatively low: 0 < ICC≤0.2, general: 0.2 < ICC≤0.4, medium: 0.4 < ICC≤0.6, notable: 0.6 < ICC≤0.8, Higher: 0.8 < ICC≤1.In the present embodiment, utilize the step of the test retest reliability of interclass correlation coefficient calculating brain network Characteristic Entropy Including:

A1) two Scan fMRI data (Scan1 and Scan2) are respectively adopted the present embodiment fixed based on network characterization entropy Amount is portrayed the method for brain function network and is calculated brain network Characteristic Entropy, tested number n=97, pendulous frequency d=2, network characterization Entropy is E, E_ij(i=1 ... d；J=1 ..., n) refer to the tested network characterization entropy obtained when i & lt is measured of jth.To calculate The brain network Characteristic Entropy obtained utilizes linear mixed model Linear mixed models (LMM) to model, linear mixed model Expression formula be E_ij=λ_0j+e_ij,λ_0j=μ₀₀+p_0j, wherein μ₀₀Being a fixing parameter, it refers to all tested d time measure The average of network characterization entropy, λ_0jFor intermediate parameters, p_0jAnd e_ijRespectively be obey average be that 0 variance isWithIndependent the most just State distribution random error, p_0jRefer to the error of tested, e_ijRefer to measurement error.If μ₀₀Notable is not 0, then brain In the group of network characterization entropy, mean coefficient ICC (E) defines as shown in formula (5).

$ICC\left(E\right)=\frac{{\mathrm{MS}}_b\left(E\right)-{\mathrm{MS}}_w\left(E\right)}{{\mathrm{MS}}_b\left(E\right)+(2-1){\mathrm{MS}}_w\left(E\right)}=\frac{{\mathrm{\&sigma;}}_p^2}{{\mathrm{\&sigma;}}_p^2+{\mathrm{\&sigma;}}_e^2}---\left(5\right)$

In formula (5), MS_b(E) it is the summation of variance, MS between sample two-by-two_w(E) it is all tested interior mean square errors,WithFor variance.

A2) SS is calculated_pAnd SS_e。

According to linear mixed model E_ij, in order to estimate interclass correlation coefficient ICC, calculate SS according to formula (6) and (7)_pWith SS_e。

${\mathrm{SS}}_p=2\underset{i=1}{\overset{n}{\&Sigma;}}{(\stackrel{\&OverBar;}{Y_{i.}}-\stackrel{\&OverBar;}{Y_{\mathrm{..}}})}^2---\left(6\right)$

${\mathrm{SS}}_e=\underset{i=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{2}{\&Sigma;}}{(\stackrel{\&OverBar;}{Y_{ij}}-\stackrel{\&OverBar;}{Y_{i.}}-\stackrel{\&OverBar;}{Y_{.j}}+\stackrel{\&OverBar;}{Y_{\mathrm{..}}})}^2---\left(7\right)$

In formula (6) and (7), SS_pFor difference quadratic sum, SS between group_eFor total error quadratic sum,For jth sample in i & lt The network characterization entropy obtained during measurement, n is number of samples, whereinSample average between i-th sample group,Sample in jth group group This average,The expression formula of total sample average three refers to formula (8).

$\left\{\begin{array}{c}\stackrel{\&OverBar;}{Y_{i.}}=\frac{\underset{j=1}{\overset{n}{\&Sigma;}}{Y}_{ij}}{n}\\ \stackrel{\&OverBar;}{Y_{.j}}=\frac{\underset{i=1}{\overset{d}{\&Sigma;}}{Y}_{ij}}{d}\\ \stackrel{\&OverBar;}{Y_{\mathrm{..}}}=\frac{\underset{i=1}{\overset{d}{\&Sigma;}}\underset{j=1}{\overset{n}{\&Sigma;}}{Y}_{ij}}{nd}\end{array}\right.---\left(8\right)$

In formula (8), d is pendulous frequency, and remaining meaning of parameters is identical with formula (6) and (7), does not repeats them here.

A3) ICC (E) is estimated.

MS_b=SS_p/ (n-1) and MS_w=SS_eThe mathematic expectaion of/(n-1) (d-1) is respectively

Therefore,WithUnbiased esti-mator such as formula (9) shown in；

$\left\{\begin{array}{c}{\widehat{\mathrm{\&sigma;}}}_p^2=\frac{{\mathrm{MS}}_b-{\mathrm{MS}}_w}{2}\\ {\widehat{\mathrm{\&sigma;}}}_e^2={\mathrm{MS}}_w\end{array}\right.---\left(9\right)$

In formula (9),For varianceUnbiased esti-mator,For varianceUnbiased esti-mator.

Shown in the estimation such as formula (10) of so ICC (E)；

$ICC\left(E\right)=\frac{{\widehat{\mathrm{\&sigma;}}}_p^2}{{\widehat{\mathrm{\&sigma;}}}_p^2+{\widehat{\mathrm{\&sigma;}}}_e^2}=\frac{{\mathrm{MS}}_b-{\mathrm{MS}}_w}{{\mathrm{MS}}_b+(d-1){\mathrm{MS}}_w}---\left(10\right)$

In formula (10),For varianceUnbiased esti-mator,For varianceUnbiased esti-mator.In the present embodiment, finally count The estimated value calculating the test retest reliability ICC (E) obtaining brain network Characteristic Entropy is 0.96, and the present embodiment is quantitative based on network characterization entropy The test retest reliability value of the method portraying brain function network belongs to higher level, and the present embodiment quantitatively portrays brain based on network characterization entropy This tested interior transmutability of tolerance of brain network Characteristic Entropy in the method for functional network is little, and time-domain stability is high.

Obtained by susceptiveness refers to that the experiment of same tranquillization state functional MRI is to different classes of tested colony testing The diversity degree of measurement result.In statistics, we are frequently with the difference of double sample t inspection two groups of different groups of inspection Property, significance p value is set to 0.05, if p < 0.05, then two groups of different groups have significant difference under this tolerance, and otherwise two Group different groups are not significantly different under this tolerance.In the present embodiment, determine that the step of susceptiveness includes:

B1) same tested scan1 and scan2 time series is combined, and calculates each tested network characterization entropy.

B2) linear regression model (LRM) is set up, E=β₀+β₁× age, wherein E represents that observable variable refers specifically to network Characteristic Entropy, the residual error obtained for subsequent analysis, wherein β₀、β₁Representing regression coefficient, age is independent variable.

B3) residual error obtained is divided into two groups according to sex,

B4) double sample t is utilized to check, the difference of the inspection tested network characterization entropy of men and women two groups, it is calculated p=0.024, P < 0.05 is known according to significance level p=0.05.

In sum, the research for brain network at present mostly is qualitative analysis, and the present embodiment is fixed based on network characterization entropy Amount portrays the definition of the method combining information entropy of brain function network, probes into the definition of network characterization entropy, is given and can portray brain net The entropy definition of network complexity, and measure intraindividual stability (test retest reliability) and the checking of region class other susceptiveness from investigating The reliability of network characterization entropy, and then comparative development, the network characterization entropy of aging different times brain, seek network characterization entropy And the relation existed between brain development, aging rule, and growth, rate of ageing, thus study brain development, aging rule Rule.

The above is only the preferred embodiment of the present invention, and protection scope of the present invention is not limited merely to above-mentioned enforcement Example, all technical schemes belonged under thinking of the present invention belong to protection scope of the present invention.It should be pointed out that, for the art Those of ordinary skill for, some improvements and modifications without departing from the principles of the present invention, these improvements and modifications are also Should be regarded as protection scope of the present invention.

Claims (8)

1. the method quantitatively portraying brain function network based on network characterization entropy, it is characterised in that step includes:

1) fMRI data under quiescent condition is gathered；

2) pretreatment is carried out for the fMRI data collected；

3) the brain template comprising cerebral cortex function division is selected；

4) for pretreated fMRI data, the Different brain region in brain template is regarded as in brain function network Node, thus extract sequence signal average time of each node, calculate the correlation coefficient between node, obtain function and connect square Battle array, carries out described function connection matrix binaryzation and obtains adjacency matrix, thus form brain function network, described adjacency matrix shape The brain function network become features node interphase interaction relation, reflects the topological attribute of brain function network；

5) eigenvector centrality degree and the encircled energy of each node in brain function network are calculated；

6) brain network Characteristic Entropy is calculated according to the encircled energy of each node in brain function network.

The method quantitatively portraying brain function network based on network characterization entropy the most according to claim 1, it is characterised in that institute State step 4) detailed step include:

4.1) for pretreated fMRI data, the Different brain region in brain template is regarded as in brain function network Node, extract sequence signal average time of each node；

4.2) use formula (1) to calculate the correlation coefficient between node, obtain the time series that symmetrical size is n*n and be correlated with Matrix A (a_ij), wherein n represents brain function network node number, a_ijRepresent sequence signal x average time of node i_iWith node j Sequence signal x average time_jBetween correlation coefficient, represent that function between node connects with correlation coefficient, therefore described Time series correlation matrix A (a_ij) illustrate in brain function network function between each node and connect, referred to as function connection matrix；

$a_{ij}=\frac{E\left(x_i{x}_j\right)-E\left(x_i\right)E\left(x_j\right)}{\sqrt{E\left(x_i^2\right)-E^2\left(x_i\right)}\sqrt{E\left(x_j^2\right)-E^2\left(x_j\right)}}---\left(1\right)$

In formula (1), E (x_ix_j) represent x_ix_jMathematic expectaion, E (x_i) represent x_iMathematic expectaion, E (x_j) represent x_jMathematic expectaion,Represent x_i ²Mathematic expectaion, E²(x_i) represent x_iMathematic expectaion square,Represent x_j ²Mathematic expectaion, E²(x_j) Represent x_jMathematic expectaion square；

4.3) for described function connection matrix, first connect corresponding statistical value p according to function between any two nodes, pass through Bonferroni corrects, by corresponding statistical value p more than preset the node of statistical value between function connect and be set to 0, by correspondence Statistical value p less than or equal to preset the node of statistical value between function secondly connect and be set to 1, by described function connection matrix In element less than 0 be set to 0, thus described function connection matrix binaryzation is obtained adjacency matrix and forms brain function network, institute State brain function network and feature node interphase interaction relation, reflect the topological attribute of brain function network.

The method quantitatively portraying brain function network based on network characterization entropy the most according to claim 2, it is characterised in that institute State step 4.3) in default statistical value be 0.05.

The method quantitatively portraying brain function network based on network characterization entropy the most according to claim 1, it is characterised in that institute State step 5) detailed step include:

5.1) the i-th component of eigenvalue of maximum character pair vector of described adjacency matrix is calculated according to formula (2) as adjacent The eigenvector centrality degree of i-th node in matrix；

$e_i=\frac{1}{\mathrm{\&lambda;}}\underset{j=1,j\&NotEqual;i}{\overset{n}{\mathrm{\&Sigma;}}}{a}_{ij}{e}_j---\left(2\right)$

In formula (2), e_iRepresent the eigenvector centrality degree of i-th node, a in adjacency matrix_ijRepresent i-th joint in adjacency matrix Point is connected with the function between jth node, and λ represents the eigenvalue of maximum that adjacency matrix is corresponding, e_jRepresent λ characteristic of correspondence to Amount, n represents brain function network node number；

5.2) encircled energy of each node in adjacency matrix is calculated according to formula (3)；

$I_i=\frac{e_i}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{e}_i}---\left(3\right)$

In formula (3), I_iRepresent the encircled energy of i-th node, e in adjacency matrix_iRepresent i-th node in adjacency matrix Eigenvector centrality degree, n represents brain function network node number.

The method quantitatively portraying brain function network based on network characterization entropy the most according to claim 1, it is characterised in that institute State step 6) in calculate brain network Characteristic Entropy function expression such as formula (4) shown in；

$E=-\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{I}_i\ln I_i---\left(4\right)$

In formula (4), E represents brain network Characteristic Entropy, I_iRepresenting the encircled energy of i-th node in adjacency matrix, n represents brain Functional network node number.

The method quantitatively portraying brain function network based on network characterization entropy the most according to claim 1, it is characterised in that institute State step 2) detailed step include:

2.1) image of the front specified number of frames of fMRI data is removed；

2.2) in removing function magnetic resonance imaging data, the head amplitude of moving exceedes the tested of appointment threshold value；

2.3) interlayer time adjustment is carried out for fMRI data；

2.4) dynamic(al) correction is carried out for fMRI data；

2.5) Spatial normalization process is carried out for fMRI data；

2.6) utilize the gaussian kernel function specifying size halfwidth that fMRI data is carried out space smoothing process；

2.7) utilize assigned frequency interval that fMRI data is carried out time-domain filtering；

2.8) carry out returning full brain average signal, white matter, hydrocrania average signal and head for fMRI data dynamic Parameter.

The method quantitatively portraying brain function network based on network characterization entropy the most according to claim 6, it is characterised in that institute State step 2.6) to specifically refer to the half-breadth of 6mm high for middle finger sizing halfwidth.

The method quantitatively portraying brain function network based on network characterization entropy the most according to claim 6, it is characterised in that institute State step 2.7) in specify frequency separation specifically refer to 0.01～0.08Hz.