CN1628608A - Functional magnetic resonance data processing method utilizing partial uniformity method - Google Patents
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Abstract
The invention relates to a functional magnetic resonance data processing method utilizing partial uniformity method which comprises, (1) acquiring and preprocessing brain function magnetic resonant data, (2) sorting the time sequence, (3) local consistency measurement. reading, etc. }, but also for but also for data analysis of non-task condition (such as resting) and brain function magnetic resonance data analysis for other non-linear conditions.
Description
Technical field
The present invention relates to the mr techniques field, particularly a kind of functional MRI data processing method of utilizing the locally coherence method.
Background technology
The history of the existing more than ten years of functional MRI (being designated hereinafter simply as functional MRI) that change based on blood oxygen, this technology is widely used in the research of live body brain, as the Premium Features of research human brain, be used to assist the preoperative functional localization of brain etc.With what functional MRI collected is the four-dimension (three dimensions+time) data of brain.The traditional data processing method generally requires in data acquisition, and the experimenter need be interrupted and finishes certain task or different task, refers to 30 seconds as the continuously active right hand, has a rest then 30 seconds or finishes another task (referring to as the continuously active left hand).In addition, present analytical method also requires these tasks will repeat repeatedly, like this with the difference between relatively the caused signal of different task changes, or when relatively finishing the work with resting state under different.
Discover that in recent years even under resting state, a kind of slow wave fluctuation also can appear in live body brain inner blood kinetics, this slow wave fluctuation has reflected the self-organization of brain.Analysis to this slow wave fluctuation has very important meaning, and for example, patient need not finish complicated task, lies in the magnetic resonance examination instrument interior several minutes with only needing peace and quiet, promptly might check out the situation of change of some function in its brain.But because this slow wave fluctuation does not meet the requirement of above-mentioned repeatedly repeated events, the analysis of this slow wave fluctuation is not still had effective way, the method that adopts usually is at present: allow the experimenter finish certain task earlier, obtain the location, brain district of mission-enabling.Then, be foundation with the brain district that obtains under the task status, study the slow wave fluctuation under the resting state again.Localized method can not reflect the cerebration situation under the resting state under the obvious this employing task status.And required finishing of task can be described as unlimited manyly in reality, and standard also just is difficult to unified.It is the problem of avoiding using task that researcher is also arranged, with the artificial method of determining region of interest, such as, (Functional connectivity in the resting brain:a network analysis of thedefault mode hypothesis.PNAS such as Greiciusi, 2003,100:253-8) under with function MR investigation resting state during functional network, determine in advance that according to previous experience cingule gyrus rear portion and cingule gyrus are anterior as with reference to the zone, study the dependency in these two zones and other brain district.Obviously, this method need be determined the good brain district of being studied according to previous knowledge frequently, but in the face of the so complicated system of human brain, determines it is very difficult by experience merely.
Summary of the invention
For this reason, we have proposed to utilize the functional MRI data processing method of locally coherence method, and use it for functional MRI data and handle.The theory hypothesis of this method is: be similar in time between the time series of each voxel in (1) brain domain; (2) this similarity can change under the different task state in these functional areas.This method not with the generation of repeated events as prerequisite, be very suitable for handling the date processing under the resting state, and this method is applicable to the various situations of executing the task too.In addition, this method need not be determined region of interest in advance.Functional MRI data to human brain is handled.After collecting functional MRI data, choose a voxel and 3 or above neighbour's voxel, calculate seasonal effect in time series Ken Deer (Kendall) the coefficient of concordance W of these voxels, again the W value that obtains is invested selected voxel, to represent the locally coherence of this voxel.All voxels to full brain calculate one by one, so just obtain the locally coherence of complete all voxels of brain.
Below step by step and introduce the implementation process of this method in conjunction with practical application.
One, obtaining and pretreatment of brain function MR data:
Being captured on the magnetic resonance scanner that possesses plane echo-wave imaging (EPI) sequence of brain function MR data finished.The concrete parameter of imaging does not have specific (special) requirements, but generally is no less than 3 layers, and the sampling time point is generally dozens of or more, and spatial resolution is generally several millimeters, as 3 * 3mm
2Generally need carry out conventional pretreatment after data acquisition finishes, comprise that head moving corrects, goes linear drift, space filtering (being also referred to as space smoothing), space criteriaization etc., but these processes to decide according to application target, and nonessential process.
Two, time series is sorted:
To all time points on the time series of each voxel, numerical values recited on this time series is ascending is numbered by it, value as the 56th time point is 456, this value is minimum on this time series, and then it is numbered 1, if the value of some time point equates, then its numbering is averaged, value as the 5th, 7,30,50 time point is 457, and the numbering that obtains should be 2,3,4,5, then each gets average 3.5 and is separately numbering.Like this, each time point of each voxel all obtains a numbering, thereby each voxel forms a new time series.Then these new time serieses being carried out locally coherence measures.
Three, locally coherence is measured:
Arbitrary voxel A in the selected brain, and the selected individual voxel of its nearest K (K is at least 3), for example, selected its 26 nearest voxels (for convenience of description, be example all below) with selected its 26 neighbour's voxels, add selected this voxel A totally 27 voxels itself, and the seasonal effect in time series of supposing each voxel is counted and is n (here, with n=70 being example).Calculate the concordance of these 27 voxels.The measurement of the locally coherence harmonious coefficient of Kendall:
In the formula, W represents the Kendall coefficient of concordance, and its value is the no concordance of 0～1,0 expression, 1 expression concordance maximum; R
iProgression summation in the express time sequence after certain time point ordering;
Expression R
iAll can be worth; K represents the number of voxel; The number of n express time point.
The harmonious coefficient W of resulting Kendall is invested selected voxel A, and then selected next voxel, draws the locally coherence of complete all voxels of brain successively.
Illustrate the application of locally coherence method under different situations below.
Have 6 experimenters and participate in the experiment, male 2 people, women 4 people, in 23-40 year, 6 people have all finished the scanning of left and right finger button task.In addition, wherein 5 people add and sweep a sequence under the resting state, require the experimenter to close order during scanning, do not go to think anything as far as possible, particularly systematically do not think deeply a certain problem, and be 7 minutes sweep time.
The finger motion task adopts incident relevant design method.In the middle of the screen be a vertical line as point of fixation, briefing is an annulus that flashes, annulus flashes 4 times as once " incident " in 2 seconds, require the experimenter by the frequency keying that flashes.Annulus comes across the left side or the right side of point of fixation with the form of pseudo-random, requires the experimenter with the melon response key of respective side thumb by respective side.Be not wait in 10 to 16 seconds the blanking time of incident and incident.
The preprocessing process of the functional MRI data that collects comprises the moving rectification of head, removes linear drift, space criteriaization, space filtering.Then left hand is referred to that button, the right hand refer under button and the resting state that the data of totally three kinds of states carry out locally coherence respectively and measure, and obtain the locally coherence figure of three kinds of states.Then three kinds of states are carried out statistical.Less because of the sample (number) of experiment, and the number of samples of three kinds of states is inconsistent, therefore to the relatively employing pairing T check of three kinds of states, and does not adopt variance analysis.
Kinestate and resting state find that relatively when left side finger and right side finger motion, the primary motor area locally coherence of bilateral all increases (accompanying drawing 2), and this result supports the bilateral primary motor area to participate in the conclusion of one-sided limb motion.At cingule gyrus rear portion and prefrontal lobe medial surface, the locally coherence under the resting state but is higher than task status, and this result then supports relevant this viewpoint of vigilance that these zones may be higher with maintenance under the resting state.
From these results as can be seen, the locally coherence method both can detect the active region under the task status effectively, and the functional MRI data that also can be used under the resting state is handled.
Functional MRI scanning has very application prospects under the resting state.Compare with the situation of executing the task, the scanning of resting state has following major advantage: (1) different places of checking check that condition is unified easily, so just may obtain big-sample data, thereby clinical diagnosis is offered help, and functional MRI is various owing to adopting of task at present, constituent parts does not have unified standard, and the data comparability is not poor between commensurate; (2) patient cooperates inspection easily: the functional MRI under the situation of executing the task requires the experimenter to finish the task of different difficulty, concerning some patient, cooperate difficulty bigger, scanning only requires that then the experimenter is the same with general magnetic resonance examination under the resting state, lies in motionless getting final product in the somascope; (3) also have sleeping eyes, stupor or oral drugs with the similar state of resting state after (latter continue in review time of several minutes be a kind of steady statue), the research of these states is also had very important physiology and clinical meaning.
Description of drawings
Fig. 1 is the flow chart that utilizes the functional MRI data processing method of locally coherence method of the present invention;
Fig. 2 is the comparison test figure as a result of brain function locally coherence under finger motion state and the resting state.
The specific embodiment
The flow chart of Fig. 1, locally coherence method.Step 1: the obtaining and pretreatment of brain function MR data:
Being captured on the magnetic resonance scanner that possesses plane echo-wave imaging sequence of brain function MR data carried out;
Step 2:, be numbered ordering by its numerical values recited on this time series is ascending to all time points on the time series of each voxel;
Time series to voxel sorts;
Step 3: the arbitrary voxel A in the selected brain, and selected its nearest K voxel, to each voxel and on every side voxel carry out locally coherence and measure, calculate each voxel and its concordance of voxel on every side one by one.
The ratio of brain function locally coherence is than the t assay under Fig. 2, finger motion state and the resting state.A: the left hand comparison with resting state that refers to move; B: the right hand comparison with resting state that refers to move; L: left side; R: right side; MFC: medial prefrontal cortex; PPC: cingule gyrus posterior cortex; M1: primary motor area.
Method of the present invention is handled the functional MRI data of human brain.After collecting functional MRI data, choose a voxel and 3 or above neighbour's voxel, calculate the seasonal effect in time series Kendall coefficient of concordance W of these voxels, again the W value that obtains is invested selected voxel, to represent the locally coherence of this voxel.All voxels to full brain calculate one by one, so just obtain the locally coherence of complete all voxels of brain.This method can be used for analyzing cognitive task status (as finger motion, reading etc.), the more important thing is that this method can be used for the data analysis and the analysis of the brain function MR data under other nonlinear state situations such as (as the influence of medicinal application to cerebral blood flow) of non task state (as resting state).This method computational process is quick, can finish in 1 minute disposing preferably on the computer less than at present.This method can be finished on the work station of magnetic resonance scanner, also can finish on common microcomputer.This method can be widely used in the clinical and basic research of brain function magnetic resonance, and can have a tremendous social and economic benefits.
Claims (2)
1, a kind of method that the functional MRI data of brain is handled, it is characterized in that utilizing the measurement of locally coherence, to understand under the different task state or brain function situation under the different steady statues, the locally coherence measuring method, be by determining arbitrary voxel and 3 or above voxel on every side, calculate the harmonious coefficient W of seasonal effect in time series Ken Deer of these voxels, and this coefficient is invested that selected earlier voxel, repeat successively, thereby obtain each regional locally coherence of full brain.
2, a kind of functional MRI data processing method of utilizing the locally coherence method comprises following step:
One, obtaining and pretreatment of brain function MR data:
Being captured on the magnetic resonance scanner that possesses plane echo-wave imaging sequence of brain function MR data carried out;
Two, time series is sorted:
To all time points on the time series of each voxel, be numbered ordering by its numerical values recited on this time series is ascending;
Three, locally coherence is measured:
Arbitrary voxel A in the selected brain, and selected its nearest K voxel, to each voxel and on every side voxel carry out locally coherence and measure, calculate each voxel and its concordance of voxel on every side one by one.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100423691C (en) * | 2005-11-23 | 2008-10-08 | 中国科学院自动化研究所 | Method for analyzing functional MRI data by integration of time domain and space domain information |
| CN101912263A (en) * | 2010-09-14 | 2010-12-15 | 北京师范大学 | Real-time functional magnetic resonance data processing system based on brain functional network component detection |
| CN102508184A (en) * | 2011-10-26 | 2012-06-20 | 中国科学院自动化研究所 | Brain function active region detection method based on moving average time series models |
| CN110263791A (en) * | 2019-05-31 | 2019-09-20 | 京东城市(北京)数字科技有限公司 | A kind of method and apparatus in identification function area |
| CN113100780A (en) * | 2021-03-04 | 2021-07-13 | 北京大学 | Automatic processing method for synchronous brain electricity-function magnetic resonance data |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100423691C (en) * | 2005-11-23 | 2008-10-08 | 中国科学院自动化研究所 | Method for analyzing functional MRI data by integration of time domain and space domain information |
| CN101912263A (en) * | 2010-09-14 | 2010-12-15 | 北京师范大学 | Real-time functional magnetic resonance data processing system based on brain functional network component detection |
| CN102508184A (en) * | 2011-10-26 | 2012-06-20 | 中国科学院自动化研究所 | Brain function active region detection method based on moving average time series models |
| CN102508184B (en) * | 2011-10-26 | 2015-04-08 | 中国科学院自动化研究所 | Brain function active region detection method based on moving average time series models |
| CN110263791A (en) * | 2019-05-31 | 2019-09-20 | 京东城市(北京)数字科技有限公司 | A kind of method and apparatus in identification function area |
| CN110263791B (en) * | 2019-05-31 | 2021-11-09 | 北京京东智能城市大数据研究院 | Method and device for identifying functional area |
| CN113100780A (en) * | 2021-03-04 | 2021-07-13 | 北京大学 | Automatic processing method for synchronous brain electricity-function magnetic resonance data |
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