CN102266223A - Pain evaluation system based on magnetic resonance resting state functional imaging - Google Patents
Pain evaluation system based on magnetic resonance resting state functional imaging Download PDFInfo
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Abstract
The invention discloses a pain evaluation system based on magnetic resonance resting state functional imaging, and the pain evaluation system comprises a pain characteristic specimen library for storing history resting state functional imaging attributes of specimens of patients with different pain levels and pain causes and control normal persons; a functional imaging unit for obtaining resting state functional imaging data from brains of new individual patients suffering from chronic pain and transmitting to an attribute extraction unit; the attribute extraction unit for extracting resting state functional image attributes of new individuals from the resting state functional imaging data; and a pain evaluation unit for comparing the resting state functional image attributes of new individuals and the history resting state functional image attributes in the pain characteristic specimen library, and evaluating pain levels and/or pain causes of new individuals. Hereby, the pain evaluation system disclosed by the invention can objectively and accurately evaluate pain levels and/or pain causes through objective image data of human brains.
Description
Technical field
The present invention relates to a kind ofly evaluate the pain grade of chronic pain patient and/or the iconography system of the pain origin cause of formation based on magnetic resonance tranquillization attitude functional imaging data.
Background technology
Pain is one of human modal misery, also is patient's one of the most insufferable symptom.IASP (IASP) is defined as pain " discomfort and emotional experience that tissue injury necessary being or potential or analogue are brought ".This definition has emphasized that pain is not only a kind of sentience, and is a kind of various dimensions phenomenon, comprises sensation, emotion, motivation, environment and cognitive component.IASP is defined as chronic pain " pain of paranormal organization healing time (being generally 3 months) ".
Chronic pain has not only brought misery to the individual, and because the occurrence rate of chronic pain among the crowd is higher, entire society has also been caused serious financial burden.Data shows, have 35% to suffer from chronic pain among the U.S. common people, wherein surpassing 500,000 Americans because chronic pain becomes partially or completely disabled can't operate as normal, and annual because the total output value loss that chronic pain causes is 65,000,000,000 dollars, the medical treatment cost is 75,000,000,000 dollars; China expert in July, 2003 China's chronic pain survey result is shown: the chronic pain patient of going to see a doctor in six cities in month reaches more than 130,000 people; The pain grade is distributed as and slightly accounts for 9%, moderate accounts for 68.1%, severe accounts for 22.9% (data from Tianjin medical association pain credit meeting website).Because chronic pain lacks objectively diagnosis and treatment back evaluation criteria, the selection of its therapeutic scheme is mainly based on patient's subjectivity statement and doctor's individual clinical experience, under the situation of chronic pain, the pathological changes that continues: as arthritis or nerve injury, nonvolatil damage: as amputation, the capital causes lasting pain, and may not use usually feasible method to go to treat this pain.More difficult is, under many circumstances, can not determine to cause the pathology of constant pain, and in this case, the side effect that analgesic brings may surpass its curative effect.
For the evaluation of chronic pain, be subjected to its character and existing diagnostic means (in addition the information processing technology is not auxiliary with diagnostic techniques merely) limitation all can't its pain essence of objective quantification and the related neural basis change.
The pathomechanism of pain comprises mental mechanism and physiological mechanism.The persistent period of chronic pain may with nocuity perception that continues and the nervous system that brings out thereof change relevant, but most of pain researcheres consistent think psychological factor or Nervous and Mental Factors generation, the development of chronic pain, continue or increase the weight of in play key effect.In the research of pain, find between the noxious stimulation and the pain sensation to be not simple response relation already, stimulation degree and pain grade are also not the same, and pain still can come from non-noxious stimulation, and these phenomenons show that pain and psychological process have substantial connection.Psychological factor all exerts an influence to pain character, degree, time and spatial perception, resolution and the extent of reaction, and is reflected on each link of pain." gate control theory " that Melzack proposes helps further to understand the effect of psychological factor in pain.The physiological mechanism of chronic pain is very complicated, relates to each nervous system, neurotransmitter and biochemical substances.Through years of researches, chronic pain generally is considered to be made of three distinguishing but overlapped origin causes of formation now: nociception, neuropathic and psychogenic, these all factors can be to people's pain perception and performance generation influence in various degree.Nociceptive pain is the most general types of pain, and it is considered to the signal by the bodily tissue generation of pathological changes.Therefore as arthritis, calculus and tumor all can cause the signal input from the increase of damaged tissues, and this is perceived as pain.Although these input signals may be revised (raising or reduction) by the physiological process of brain and spinal cord, pain still arrives the central nervous system owing to the signal input that increases basically.Be different from nociceptive pain, second kind of pain origin cause of formation, neuropathic pain be by periphery or central nervous system impaired due to.In a narrow sense, when this pain occurs in the appearance of nerve disorder feature, impaired as sense organ or nervus motorius, this class factor takes on a different character, and it is described to a prickling sensation, electric-shock feeling usually, burn feeling or tingling, present imaging technique is difficult to detect its pathological changes.The third pain origin cause of formation, the psychological origin cause of formation, it acts in people's the pain experience widely, and psychological process can influence the impression of pain and the expression of pain (afunction) in the mode that increases or alleviate.In football match, many sportsmen can note less than or ignore unexpected seriously injuredly, in this case, although damaged tissues has a large amount of signals inputs, pain and afunction all are minimum.Cause the process of pain though people have been familiar with these widely, still be difficult to distinguish at present each role in three kinds of pain origin causes of formation especially.And treatment means and mode that the dissimilar pain origin causes of formation is taked also are not quite similar, and some means and medicine do not use under correct situation then can produce curative effect hardly.Therefore, the best therapeutic scheme of pain is depended on the different origins of accurate differentiation pain.Present subjective diagnostic techniques (for example estimating scale, pain questionnaire table) can only be evaluated the grade of pain singlely, can not draw the character of pain; And this method is restricted for the subjective patient who expresses of forfeiture.Present objective evaluation method (for example behavior determination method, physiology's algoscopy) all is from different angles pain to be carried out indirect evaluation, and is reliable inadequately and can only provide finite information, and the quantification of pain also is restricted.
Therefore, a kind of energy is evaluated the pain grade objective and accurately and is detected the system of various origin cause of formation effects in the pain, and very big social need is arranged.Present pain evaluation equipment depends on patient's subjectivity statement to a great extent, and a kind of system that can reliably evaluate patient's pain grade and can distinguish the different origins in the pain bring strong impetus will for Pain assessment and treatment.
The research of cerebral function imaging has in recent years made people that the understanding of pain central mechanism has been produced revolutionary variation.In fact, as if people have been in by FMRI (Functional Magnetic ResonanceImaging, magnetic resonance functional imaging) and have obtained the especially edge of the brand-new clinical information of chronic pain of pain.It is a kind of degenerative disease that functional imaging has redefined chronic pain, and has brought hope for some complex diseases such as the research of these diseases of fibromyalgia.Tranquillization attitude functional mri be a kind of to patient and imaging operation personnel require less, repeatable high, can reflect a kind of new functional imaging means of brain spontaneous activity.Because the reaction of brain is the final co-channel of behavior reaction (consciously or be not intended to spontaneous) to pain, the utilization of functional imaging will make the people can be with a kind of objective method understanding pain, and understand potential loop better, distinguish the different origins of pain.
Yet, the subjective assessment method of existing evaluation pain is subjected to many influences that adds factor easily, thereby the influence evaluation is objective and accurate, existing objective evaluation method only relies on single index to judge, the grade that can not accurately reflect pain objectively, and existing assessment method mainly pays close attention to pain impression, can not distinguish each origin cause of formation in the pain.In summary, existing evaluation pain technology obviously exists inconvenience and defective, so be necessary to be improved on reality is used.
Summary of the invention
At above-mentioned defective, the object of the present invention is to provide a kind of pain assessment system based on magnetic resonance tranquillization attitude functional imaging, it can evaluate the pain grade and/or the pain origin cause of formation of chronic pain patient objective and accurately.
To achieve these goals, the invention provides a kind of pain assessment system, comprising based on magnetic resonance tranquillization attitude functional imaging:
Pain feature samples storehouse, be used to store the different pain grades and the pain origin cause of formation the patient and with the historical tranquillization attitude functional image attribute of its contrast normal person sample;
The functional imaging unit is used for obtaining tranquillization attitude functional imaging data from the new individual patient brain that bears chronic pain, and is transferred to the attributes extraction unit;
The attributes extraction unit is used for from the new individual tranquillization attitude functional image attribute of described tranquillization attitude functional imaging extracting data;
The pain evaluation unit is used for the corresponding historical tranquillization attitude functional image attribute of tranquillization attitude functional image attribute and described pain feature samples storehouse of described new individuality is compared, and assesses the pain grade and/or the pain origin cause of formation of described new individual patient.
According to pain assessment system of the present invention, described tranquillization attitude functional image attribute comprises: the subnet/component attribute of nodal community, reflection brain district's modularity or the different component of the function connection attribute of cooperative ability, reflection brain district power of influence between the full brain network attribute of reflection brain overall situation integration ability, the local attribute of the local integration ability of reflection brain, each brain district of reflection brain.
According to pain assessment system of the present invention, described pain evaluation unit comprises a pain ranking subelement, be used for the tranquillization attitude functional image attribute and the historical tranquillization attitude functional image attribute of described new individuality are compared, to utilize must the make new advances fuzzy membership of the historical tranquillization attitude functional image attribute of different pain grade colony in individual tranquillization attitude functional image attribute and the described pain feature samples storehouse of fuzzy cluster analysis with generic attribute, give full brain network attribute, subnet/component attribute, function connection attribute gained goes out the different weights of fuzzy membership, as final degree of membership; In described local attribute and the described pain feature samples storehouse with the similarity of generic attribute confidence level as this degree of membership; Determine personalized pain added value according to the variation of Different Individual key node attribute; The pain ranking value of described new individual patient=a certain pain grade degree of membership+confidence level+personalized pain added value.
According to pain assessment system of the present invention, described pain evaluation unit also comprises pain origin cause of formation evaluation subelement, be used for between the new individual subnet/component attribute self of described new individual patient and with described pain feature samples storehouse different pain origin cause of formation colony historical subnet/component attribute compare, by carrying out must the make new advances pain origin cause of formation of individual patient of lateral comparison between new individual subnet/component attribute self; By must the make new advances weight of the various pain origin causes of formation of individual patient of the longitudinal comparison between the same subnet/component attribute of new individual subnet/component attribute and different historical genesis samples.
According to pain assessment system of the present invention, subnet/the component that reflects the different pain origin causes of formation is isolated by the mode identification technology with priori in described attributes extraction unit from full brain network, calculate isolated subnet/component attribute, function connection attribute and nodal community respectively;
The described different sub-network of described pain origin cause of formation evaluation subelement lateral comparison self/component attribute is determined the pain origin cause of formation of new individual patient, and subnet/component attribute, function connection attribute and the nodal community of different historical genesis samples between the subnet/component attribute that represent this kind pain origin cause of formation are somebody's turn to do in longitudinal comparison, draw the weight of the various pain origin causes of formation.
According to pain assessment system of the present invention, the described pain origin cause of formation comprises the nociception origin cause of formation, the nerve origin cause of formation and the psychological origin cause of formation.
According to pain assessment system of the present invention, described attributes extraction unit at first is divided into several different brain districts to full brain according to dissecting template, utilize described dissection template to cut apart the resulting described tranquillization attitude functional imaging data in described functional imaging unit then, with described brain district is the time series that unit extracts reflection tranquillization attitude cerebration, makes up full brain network and subnet net with described brain district and time series.
According to pain assessment system of the present invention, the brain district of described subnet/component comprises: thalamus, corpus amygdaloideum, island leaf, assisted movement cortex, posterior parietal cortex, prefrontal cortex, cingulum cortex, periaqueductal gray, Basal ganglia, cerebellum, primary and secondary sensory cortex.
According to pain assessment system of the present invention, described pain evaluation unit also is used to obtain the clinical pain information of new individual patient, in conjunction with the tranquillization attitude functional image attribute of described new individuality and the optimization of described clinical pain informix analyze the pain grade and/or the pain origin cause of formation of the individual patient that makes new advances; And/or
Described pain evaluation unit also is used for finely tuning by the self adaptation feedback learning method in described pain feature samples storehouse the pain grade and/or the pain origin cause of formation of described new individual patient.
According to pain assessment system of the present invention, described pain feature samples storehouse also be used to store be used to make up full brain network, subnet net, function connect and during node by intermediate value, pain grade/origin cause of formation evaluation result, the clinical pain information of described tranquillization attitude functional imaging data by calculating, the trim values after the study of self adaptation feedback algorithm.
The present invention is based on the objective image data of magnetic resonance tranquillization attitude functional imaging gained, adopt the information processing technology therefrom to extract the tranquillization attitude functional image attribute of new individuality and comparing of the historical tranquillization attitude functional image attribute in the pain feature samples storehouse, preferably by fuzzy clustering, methods such as pattern recognition and self adaptation feedback learning are classified to image data eigenvalue and clinical pain information, extract, optimize, the comprehensive assessment system that becomes to reflect the objective pain grade and the origin cause of formation, and it is objective finally to obtain quantizating index, evaluate the pain grade and/or the pain origin cause of formation of chronic pain patient exactly, this pain evaluation result can be used for instructing medical personnel that the patient is carried out the science diagnosis, management and treatment.
Description of drawings
Fig. 1 is the structure chart that the present invention is based on the pain assessment system of magnetic resonance tranquillization attitude functional imaging;
Fig. 2 is the flow chart that pain assessment system of the present invention carries out the pain ranking;
Fig. 3 is the flow chart that pain assessment system of the present invention carries out the evaluation of the pain origin cause of formation; And
Fig. 4 is the component identification process figure of pain assessment system of the present invention.
The specific embodiment
In order to make purpose of the present invention, technical scheme and advantage clearer,, the present invention is further elaborated below in conjunction with drawings and Examples.Should be appreciated that specific embodiment described herein only in order to explanation the present invention, and be not used in qualification the present invention.
By magnetic resonance functional imaging technology studies have shown that human brain: many brain area (being called for short brain district or node) are relevant with the pain process, and these brain districts comprise the contained brain of pain network district, endogenous the ease pain contained brain of loop district, somatic cortex, front and back cingule gyrus, prefrontal cortex, cerveau isole cortex and thalamus.Chronic pain may change relevant with function connection, structure and the biochemistry in these brain districts.Human brain tranquillization attitude functional imaging data and can detect the patient based on the subsequent analysis of dissecting template and whether how standing pain and pain grade; Point out to increase that the pain grade is connected variation with the function that causes pain, possible structural change and biochemistry changes; Central nervous system's mechanism that identification is relevant with the pain origin cause of formation with pain and sense pain sensation mechanism.
The invention provides a kind of pain assessment system based on magnetic resonance tranquillization attitude functional imaging, as shown in Figure 1, this pain assessment system 100 comprises functional imaging unit 10, attributes extraction unit 20, pain evaluation unit 30 and pain feature samples storehouse 40, wherein:
Pain feature samples storehouse 40, be used to store the different pain grades and the pain origin cause of formation the patient and with the historical tranquillization attitude functional image attribute of its contrast normal person sample.The tranquillization attitude functional image attribute of indication of the present invention comprises: between the local attribute of the full brain network attribute of reflection brain overall situation integration ability, the local integration ability of reflection brain, each brain district of reflection brain the function connection attribute of cooperative ability, reflection brain district power of influence determine grade nodal community, reflect that brain district's modularity or different component determine the subnet/component attribute of the pain origin cause of formation.Described function connection attribute comprises: function connection degree, dependency, mutual information etc.Described nodal community comprises: the degree (degree) in some brains district (node), efficient (Efficiency) and connectedness (Betweenness), the amplitude index of physiology frequency range between the brain district voxel.Described subnet/component attribute comprises: the importance ranking of the origin cause of formation (ranking value), each stability of dividing, the significance level of the origin cause of formation etc.This pain feature samples storehouse 40 also is used for carrying out when pain is evaluated with reference to comparison and necessary information feedback.Described subnet is meant the combination in several brains district, but its function is indeterminate; And described component also is the combination in several brains district, but has clear and definite function.
Functional imaging unit 10, be used for from bear chronic pain new individual patient brain obtain tranquillization attitude functional imaging data, and be transferred to attributes extraction unit 20.Functional imaging unit 10 can adopt existing FMRI equipment to realize, also can be used for obtaining the pain patients of different aspects and normal person's tranquillization attitude functional imaging data.
Attributes extraction unit 20 is used for the new individual tranquillization attitude functional image attribute of tranquillization attitude functional imaging extracting data from new individual patient.In fact, the historical tranquillization attitude functional image attribute in the pain feature samples storehouse 40 is also obtained and is preserved by attributes extraction unit 20.Particularly, attributes extraction unit 20 at first is divided into several different brain districts to full brain according to dissecting template, utilize then and dissect template dividing function image-generating unit 10 resulting tranquillization attitude functional imaging data, the Yi Nao district is for unit extracts the time series that reflects tranquillization attitude cerebration, and require mental skill district and time series make up full brain network and subnet net.Mode comprises: with different brain districts as node, the seasonal effect in time series relation is as the limit between the Yi Nao district, be built with to connection layout (concerning orderliness and sensing between the brain district) and undirected connection layout (the out of order and sensing of relation between the brain district), the various attributes of the full brain of calculation chart, subnet and node aspect; The calculating that function connects comprises: determine brain interested district to dissect template or self-defined mode, extract the time series in brain interested district, calculate the dependency relation between the brain interested district.Pain network main reference (Moissset et al., 2007) this piece paper, the brain district that attributes extraction unit 20 makes up subnet/component comprises: thalamus, corpus amygdaloideum, island leaf, assisted movement cortex, posterior parietal cortex, prefrontal cortex, cingulum cortex, periaqueductal gray, Basal ganglia, cerebellum, primary and secondary sensory cortex.
Pain evaluation unit 30 is used for the tranquillization attitude functional image attribute and the pain feature samples storehouse 40 corresponding historical tranquillization attitude functional image attributes of the new individuality of new individual patient are compared, evaluation make new advances the pain grade and/or the pain origin cause of formation of individual patient.Pain evaluation unit 30 of the present invention mainly is divided into three kinds with the pain origin cause of formation: the nociception origin cause of formation, the nerve origin cause of formation and the psychological origin cause of formation.
Preferably, pain evaluation unit 30 comprises a pain ranking subelement 31, this pain ranking subelement 31 is used for the tranquillization attitude functional image attribute of new individuality and historical tranquillization attitude functional image attribute are compared, to utilize must the make new advances fuzzy membership of the historical tranquillization attitude functional image attribute of different pain grade colony in individual tranquillization attitude functional image attribute and the pain feature samples storehouse 40 of fuzzy cluster analysis with generic attribute, give full brain network attribute respectively according to priori, subnet/component attribute, function connection attribute gained goes out heavy (for example full brain network attribute 50% of the different weights of fuzzy membership, subnet/component attribute 30%, function connection attribute 20%), as final degree of membership D (KP); In local attribute and the pain feature samples storehouse 40 with the similarity of generic attribute confidence level C (C%) as this degree of membership D (KP); Determine personalized pain added value according to the variation of Different Individual key node attribute.Finally, pain ranking value=a certain pain grade degree of membership D (KP)+confidence level (the C%)+personalized pain added value of new individual patient.
Studies show that: the different pain origin cause of formation may be corresponding different subnet/components, detects different subnets/component activation degree and may determine the pain origin cause of formation.At the tranquillization attitude functional imaging data of pain patients, adopt component analysis isotype recognition technology to detect different sub-network/component in conjunction with priori and activate degree; Difference between more same individual different sub-network/component can be determined the pain origin cause of formation; And, can determine the shared proportion of the various pain origin causes of formation by comparing the difference between respective subnet/component in different sub-network/component and the pain feature samples storehouse 40.
In view of the above, pain evaluation unit 30 also comprises pain origin cause of formation evaluation subelement 32, this pain origin cause of formation evaluation subelement 32 be used for between the new individual subnet/component attribute self of new individual patient and the colony of the different pain origin cause of formation with pain feature samples storehouse 40 historical subnet/component attribute compare, by carrying out must the make new advances pain origin cause of formation of individual patient of lateral comparison between new intrasubject different sub-network/component attribute; Must the make new advances weight of the various pain origin causes of formation of individual patient of longitudinal comparison by different historical genesis samples between new individual subnet/component attribute and the same historical subnet/component attribute.Subnet/the component that reflects the different pain origin causes of formation is isolated by the mode identification technology with priori in attributes extraction unit 20 from full brain network, calculate isolated subnet/component attribute, function connection attribute and nodal community respectively; The described different sub-network of pain origin cause of formation evaluation subelement 32 lateral comparisons self/component attribute is determined the pain origin cause of formation of new individual patient, and subnet/component attribute, function connection attribute and the nodal community of different historical genesis samples between the subnet/component attribute that represent this kind pain origin cause of formation are somebody's turn to do in longitudinal comparison, draw the weight of the various pain origin causes of formation.
Pain assessment system 100 of the present invention also relates to consideration and the correction for individual difference in the pain evaluation, evaluate employed individual tranquillization attitude functional imaging data for pain, at first give the benchmark weight of full brain network attribute maximum, reduce the pain evaluation result deviation that causes based on certain brain district image feature value that causes because of individual configurations difference as far as possible; Simultaneously, pain assessment system 100 is also admitted individual variation, with reflecting that the nodal community of individual variation adjusts individual pain added value.
Pain evaluation unit 30 also is used to obtain the clinical pain information of new individual patient, clinical pain information comprises: VAS (VisualAnalogue Scale, visual analogue scales) scale and McGill pain questionnaire (McGillpain questionnaire, MPQ).By the VAS scale, the patient can evaluate according to language or visible sensation method according to the pain perception of oneself.McGill pain questionnaire is from physiological sensation, emotional factor and be familiar with to become to grade and many-sided the pain grade more comprehensively evaluated.Pain evaluation unit 30 in conjunction with new individual tranquillization attitude functional image attribute and the optimization of clinical pain informix analyze the pain grade and/or the pain origin cause of formation of the individual patient that makes new advances.
Pain evaluation unit 30 also is used for the pain grade and/or the pain origin cause of formation by the new individual patient of self adaptation feedback learning method (for example artificial neural network) fine setting in pain feature samples storehouse 40.Pain feature samples storehouse 40 also be used to store be used to make up that full brain network is connected with subnet net, function, during node by intermediate value, pain grade/origin cause of formation evaluation result, the clinical pain information of tranquillization attitude functional imaging data by calculating, trim values after the study of self adaptation feedback algorithm is as new sample data.To the sample in pain feature samples storehouse 40 through after the study repeatedly, finally make each evaluation result all reach steady statue, thereby guarantee the stability of pain evaluation result.
Specifically for instance, pain origin cause of formation evaluation subelement 32 also is used to obtain patient's clinical pain information, the correction weight that goes out patient's the pain origin cause of formation in conjunction with the benchmark weight and the clinical pain information analysis of the described pain origin cause of formation; And pain origin cause of formation evaluation subelement 32 is also finely tuned the fine setting weight of the described pain origin cause of formation, the weight after being optimized by the self adaptation feedback learning method in pain feature samples storehouse 40.Correction and fine setting when below also being applicable to pain ranking subelement 31 evaluation pain grades.
Show the flow process that pain assessment system 100 carries out the pain ranking as Fig. 2: the 10 pairs of chronic pain patient in functional imaging unit are carried out the data acquisition of tranquillization attitude functional imaging, the tranquillization attitude functional imaging data of being gathered are carried out pretreatment, and attributes extraction unit 20 extracts the full brain network attribute of patients etc. and the full brain network attribute of normal person etc.Choose pain network (and subnet) according to types of pain (being the origin cause of formation), be aided with clinical other information of amount of pain harmony in the exterior, calculate pain network and node image feature, determining the pain calibration algorithm by algorithms library according to types of pain, clinical pain information, basic image feature.Demarcate the pain grade point according to image feature.Pain feature samples storehouse 40 establishment stage pain determining values, pain feature samples storehouse 40 establishment stage evaluation values and expert determination value compare, and both match condition and pain ranking value all deposit pain feature samples storehouse 40 in.In addition, in sample process multi-level artificial neuron learning network correction and the fine setting to pain feature samples storehouse 40, the pain ranking value of finally being measured.
Show the flow process that pain assessment system 100 carries out pain origin cause of formation evaluation as Fig. 3: at first carry out after the pretreatment to obtaining tranquillization attitude functional imaging data, extract image features such as full brain network attribute, extract nociception component, nerve component, psychological component respectively in conjunction with priori, subnet/component attribute that calculating image feature is worth coming out compares through after the normalization, be aided with clinical pain information, draw the component of the pain origin cause of formation (type) that can represent this individual patient.Consider difference of specific gravity between the different pain origin causes of formation, the difference of more a certain component and pain feature samples storehouse 40 similar component different brackets samples draws the weight of every kind of origin cause of formation at last.Carry out according to the pain origin cause of formation that subnet/component is divided and node calculates, same individuality is carried out lateral comparison between different sub-network/component attribute, carry out longitudinal comparison between same subnet/component attribute for Different Individual; Determine the leading origin cause of formation (nociception, nerve or psychological) and the different origins proportion of this individuality pain more respectively twice.
The instantiation of a pain grade classification is below described:
The pain grade classification is at first determined several classification standard groups, calculates each criterion group fuzzy relation matrix numerical value, calculates and each criterion group each individual degree of membership relatively by membership function then, judges thereby make grade.The border that is divided in of pain grade shows stronger ambiguity, directivity index measure value shows intensive range attenuation rule, stable more the closer to the center, its membership function can be expressed as the Gauss distribution form, following (foundation of membership function is determined in clinical pain information and the pain ranking value conduct of reserving before):
C
Ij, σ
IjRepresent the center and the width of membership function respectively.Many index such as be connected with function for full brain network, subnet/component, its comprehensive membership function can draw by the linear weighted function method:
U＝∑ai*μ(x) (12)
In the formula, ai is the weight of i item image index.
Can represent the image feature vector U of individual pain feature according to formula (12)
U＝(μ(1)，μ(2)，μ(3)) (13)
Because each origin cause of formation has different stressing among the U, need give different weights to each origin cause of formation, wherein the weight of full brain network attribute a1 is 50%, the weight of subnet/component attribute a2 is 30%, the weight of function connection attribute a3 is 20%.Characteristic vector U has reflected the degree of membership of tested individual to pain grade samples at different levels.In order to judge the pain grade of individual specimen, characteristic vector U formation fuzzy relationship matrix r and fuzzy subset W are carried out the Fuzzy Compound computing.This paper adopts " " and "+" fuzzy operator, is designated as model M (,+).If the result of compound operation is E, then the element among the E is:
Ab=ab in the formula is product operator (algebraic product); A+b=(a+b) ∧ 1 is closed addition operator (algebraical sum); ∑ is represented the k number in "+" summation down.
Finally obtain the degree of membership that this individuality pain characteristic vector belongs to certain pain sample criterion group, equally, carry out the degree of membership of component efficiency attribute and this attribute of pain sample criterion group again and calculate, the degree of membership that draws belongs to the confidence level of certain pain sample criterion group as this individuality.And nodal community is as personalized pain value, when nodal community forward during greater than this criterion group meansigma methods, and this individuality pain grade+1 minute; And when nodal community negative sense during less than this criterion group meansigma methods, this individuality pain grade-1 minute.
Be input as example with two dimension, the model input is respectively " the activation degree of feature brain district K " and the pain grade of VAS evaluation after the obfuscation, and model is output as the pain grade.Pain grade obfuscation class set is [severe pain high, normal, basic zero], and each fuzzy subset's membership function is a Gauss distribution, and according to the activated situation in brain district, its pain grade can be from " zero pain " until change between " having an intense pain ".Clinical and image information adjustment function makes the sensitivity between the pain grade pitch different by study pain feature samples storehouse, and is the highest to its sensitivity between the low pain in zero pain, makes early diagnosis and intervenes more pregnancy.Stick with paste process through reverse, can obtain the pain grade of a non-zero at last, this moment, the mapping of pain grade was output as: pain grade D (KP)+confidence level (C%)+individual character pain added value.
The instantiation of a pattern recognition is below described:
Suppose to exist in the full brain network attribute n separate component x (t)=[x
1(t), x
2(t) ..., x
n(t)]
T, the component identification of full brain in fact also is categorizing process, purpose is labeled as a new individual D predefined N kind component type C={C exactly
1, C
2.。。C
NIn a kind of, training algorithm is exactly to form in part set in one of the good type of labelling to obtain an effective classification function F:D → C, and new individuality is mapped to type of coding.
We use d and c to represent individual respectively and two variablees of component type, and what the application Bayesian formula calculated individual d is that the probability of c is as follows in component sign indicating number classification:
The posterior probability of individual d depends on prior probability and class conditional probability, and prior probability and class conditional probability all can obtain in training before.
If in the full brain network attribute N kind component is arranged, then obtain the distribution matrix of a N*P (the brain district number of P) for determining according to the dissection masterplate.Choose activation degree (spontaneous activity level) as recognition feature, according to independence assumption in the model-naive Bayesian, each characteristic quantity is independently to the contribution of classification, does not have influence to each other.So when characteristic point was M, formula (21) can be reduced to:
Can obtain the coding classification of probability maximum according to the posterior probability maximization principle:
The individual number of each pain origin cause of formation is roughly the same in training, and the principle of identification is exactly to calculate the probability size of every kind of component in the full brain network attribute according to formula (24), relatively selects probability maximum and satisfy the component classification of threshold range.
At first in full brain network attribute, find M characteristic point representing the pain general character in the identifying, (following the example of of M can exert an influence to recognition effect) according to priori.Certainly, the big more selected characteristic of M value is many more, and recognition accuracy is also high more, but the also corresponding increase of computation complexity.In actual identifying, M comes value according to priori (the brain district number of pain network) and empirical equation, (from 10 to 30 incremental variations are observed the influence of M value to recognition result stability, employed M value scope when selected stability is the highest).
Can obtaining by the random function that structure is dissected in the template brain district number at random of M characteristic point, construct relatively difficulty of a good random function, the more difficult true random of accomplishing when especially being confined to dissect the masterplate number, so the recognition strategy that M is ordered in the actual moving process is to choose non-conterminous M point as characteristic point in full brain scope and subnet/component, such M point is dispersed in each position, all the probability of the same feature of representative is very little, and because the distribution probability difference of different characteristic in the various pain origin causes of formation, so have the difference degree for different pain components.
Fig. 4 shows the flow process of discerning whole process from training to: choose M characteristic point from individuality to be identified; The characteristic frequency table of concentrating according to feature database component training sample calculates the different component product of probability; Select component of candidate; According to each component threshold values that feature database component training sample set provides, judge that component whether in the threshold values scope, if then export component, otherwise is considered to unknown component.
By repeatedly component identification, finally obtain the importance ranking of (1) origin cause of formation: utilize the dependency repeatedly move between the same origin cause of formation that component analysis obtains, try to achieve the stability indicator of the variance of every kind of origin cause of formation with the tolerance different origins, utilize this index and the distribution on Different Individual, calculate the general stability index of every kind of origin cause of formation, draw the ordering curve chart of all origin causes of formation on Different Individual and overall ordering curve chart; (2) each stability of dividing: take turns component identification more and obtain a plurality of origin causes of formation, utilize the correlation coefficient between the origin cause of formation and carry out the multi-dimentional scale analysis and show similarity between the same origin cause of formation that repeatedly operation obtains; (3) significance level of the origin cause of formation: obtain all origin causes of formation by cartogram (mean chart, T check figure, standard deviation figure) or individual origin cause of formation scattergram, calculate the significant indexes in the population sample composition of repeatedly dividing certain individual origin cause of formation of back.
The present invention has carried out two researchs, first research is about judging whether the tranquillization attitude can discern the pain grade, research contents comprises that the cerebral cortex fMRI to suffering from chronic pain patient and pairing healthy volunteer after the amputation compares, and finds that there is the cortex hormone function recombination zone in the phantom pain patient; The phantom pain patient further again by taking place and phantom pain patient cerebral cortex fMRI not taking place relatively in us after the amputation, the generation of finding cerebral cortex function reorganization phenomenon and phantom pain is closely related, thereby confirms: the generation development of cerebral cortex function reorganization phenomenon and phantom pain has substantial connection.This research and previous research conclusion: degree relevant (Flor, etal., 1995 of the reorganization of the grade of chronic pain and function of cortex after the amputation; Saadahet al., 1994) unanimity as a result, result of study is to have set up to utilize functional image to evaluate the pattern of different pain grades.In conjunction with former studies, can draw: after the amputation reorganization of the generation of chronic pain and patient's cerebral cortex function in close relations, the degree of cerebral cortex function reorganization may with phantom limb pain grade positive correlation.An other research is at morphology algoscopy (the Voxel-based morphometry of high resolution structures picture use based on voxel, VBM) find: grey matter volume relative normal person in the outside has significantly and reduces behind the thalamus in patients with amputation amputation offside brain district, the time relation of being proportionate after degree that the grey matter volume reduces and the amputation, but there is no obviously relevant with degree with the occurrence frequency of phantom pain.In addition, research is proof also: though that phantom pain and thalamus grey matter change in volume do not have is relevant, the volume that phantom pain is positively correlated with brain pain management zone reduces.
Have the function MR investigation to show: the different pain origin cause of formation may be corresponding different function subnets, detects different function subnet state of activation (specifically threshold value) and may determine the shared proportion of this kind origin cause of formation in the origin cause of formation of pain or the pain.These researchs are about judging whether the imaging of magnetic resonance tranquillization attitude can be used to discern the different pain origin causes of formation, and research contents comprises patient's pain loop/network (Painmatrix), endogenous analgesia loop, anterior cingutate, posterior cingutate, prefrontal cortex, preceding colour band cortex and the thalamus of patient's phantom pain after the amputation.Result of study is to have set up the pattern that can distinguish the different pain origin causes of formation.
Second research is about judging the image degree of each image index to disease with fuzzy comprehensive evaluation method, three image index: 1-Cost, 2-Eglobal as shown in the table, 3-Elocal.
Divide group image 1-Cost image 2-Eglobal image 3-Elocal
Organize 1 329.5359 158.1113 264.7445
Organize 1 326.4346 158.3553 265.6859
Organize 1 326.5555 158.3569 265.5657
Organize 2 322.1001 158.4251 266.0616
Organize 2 326.9431 158.4602 265.5657
Organize 2 326.4387 158.3185 265.4383
Organize 3 330.8502 158.1233 264.1331
Organize 3 331.2514 158.1714 264.5345
Organize 3 327.2779 158.2419 265.4953
2947.3874 1424.5639 2387.2246
(2) calculate every group average Ridit value
Result of calculation,
Be worth littler, big more to the influence degree of disease; Otherwise,
Value is bigger, and is more little to the influence of disease.Research conclusion:
Illustrate that image index 3-Elocal is bigger to the influence degree of disease.
In addition, there is function MR investigation (Chicago Northwest University Feenberg medical college) to carry out the comparative study discovery: not have a group of pain by the patient that the patient that experiences the lower back portion chronic pain and one group of pain are controlled fully, the brain position presents poised state: when enlivened in a zone, other zones just quieted down.In the patient of experience chronic pain, mainly follow the relevant cerebral cortex proparea of emotion but " peace and quiet are not forever got off ".This zone keeps highly active state always, neurocyte is strained, and changed the connection line between the neurocyte.This studies show that low back pain patient emotion and psychological factor may account for mastery reaction, and the poised state of whole brain is destroyed.
In sum, the present invention is based on the objective image data of magnetic resonance tranquillization attitude functional imaging gained, adopt the information processing technology therefrom to extract the tranquillization attitude functional image attribute of new individuality and comparing of the historical tranquillization attitude functional image attribute in the pain feature samples storehouse, preferably by fuzzy clustering, methods such as pattern recognition and self adaptation feedback learning are classified to image data eigenvalue and clinical pain information, extract, optimize, the comprehensive assessment system that becomes to reflect the objective pain grade and the origin cause of formation, and it is objective finally to obtain quantizating index, evaluate the pain grade and/or the pain origin cause of formation of chronic pain patient exactly, this pain evaluation result can be used for instructing medical personnel that the patient is carried out the science diagnosis, management and treatment.The present invention compared with the prior art has the noinvasive of pain information, dynamic and instantaneous collection, by the sample size characteristic optimization, can accurate, objective, stably evaluate pain grade and the origin cause of formation situation of patient in a certain period.
Certainly; the present invention also can have other various embodiments; under the situation that does not deviate from spirit of the present invention and essence thereof; those of ordinary skill in the art work as can make various corresponding changes and distortion according to the present invention, but these corresponding changes and distortion all should belong to the protection domain of the appended claim of the present invention.
Claims (10)
1. the pain assessment system based on magnetic resonance tranquillization attitude functional imaging is characterized in that, comprising:
Pain feature samples storehouse, be used to store the different pain grades and the pain origin cause of formation the patient and with the historical tranquillization attitude functional image attribute of its contrast normal person sample;
The functional imaging unit is used for obtaining tranquillization attitude functional imaging data from the new individual patient brain that bears chronic pain, and is transferred to the attributes extraction unit;
The attributes extraction unit is used for from the new individual tranquillization attitude functional image attribute of described tranquillization attitude functional imaging extracting data;
The pain evaluation unit is used for the corresponding historical tranquillization attitude functional image attribute of tranquillization attitude functional image attribute and described pain feature samples storehouse of described new individuality is compared, and assesses the pain grade and/or the pain origin cause of formation of described new individual patient.
2. pain assessment system according to claim 1, it is characterized in that described tranquillization attitude functional image attribute comprises: between the local attribute of the full brain network attribute of reflection brain overall situation integration ability, the local integration ability of reflection brain, each brain district of reflection brain the function connection attribute of cooperative ability, reflection brain district power of influence determine grade nodal community, reflect that brain district's modularity or different component determine the subnet/component attribute of the pain origin cause of formation.
3. pain assessment system according to claim 2, it is characterized in that, described pain evaluation unit comprises a pain ranking subelement, be used for the tranquillization attitude functional image attribute and the historical tranquillization attitude functional image attribute of described new individuality are compared, to utilize must the make new advances fuzzy membership of the historical tranquillization attitude functional image attribute of different pain grade colony in individual tranquillization attitude functional image attribute and the described pain feature samples storehouse of fuzzy cluster analysis with generic attribute, give full brain network attribute, subnet/component attribute, function connection attribute gained goes out the different weights of fuzzy membership, as final degree of membership; In described local attribute and the described pain feature samples storehouse with the similarity of generic attribute confidence level as this degree of membership; Determine personalized pain added value according to the variation of Different Individual key node attribute; The pain ranking value of described new individual patient=a certain pain grade degree of membership+confidence level+personalized pain added value.
4. pain assessment system according to claim 2, it is characterized in that, described pain evaluation unit also comprises pain origin cause of formation evaluation subelement, be used for between the new individual subnet/component attribute self of described new individual patient and with described pain feature samples storehouse different pain origin cause of formation colony historical subnet/component attribute compare, by carrying out must the make new advances pain origin cause of formation of individual patient of lateral comparison between new individual subnet/component attribute self; By must the make new advances weight of the various pain origin causes of formation of individual patient of the longitudinal comparison between the same subnet/component attribute of new individual subnet/component attribute and different historical genesis samples.
5. pain assessment system according to claim 4, it is characterized in that, subnet/the component that reflects the different pain origin causes of formation is isolated by the mode identification technology with priori in described attributes extraction unit from full brain network, calculate isolated subnet/component attribute, function connection attribute and nodal community respectively;
The described different sub-network of described pain origin cause of formation evaluation subelement lateral comparison new individual patient self/component attribute is determined the pain origin cause of formation of new individual patient, and subnet/component attribute, function connection attribute and the nodal community of different historical genesis samples between the subnet/component attribute that represent this kind pain origin cause of formation are somebody's turn to do in longitudinal comparison, draw the weight of the various pain origin causes of formation.
6. pain assessment system according to claim 4 is characterized in that, the described pain origin cause of formation comprises the nociception origin cause of formation, the nerve origin cause of formation and the psychological origin cause of formation.
7. pain assessment system according to claim 2, it is characterized in that, described attributes extraction unit at first is divided into several different brain districts to full brain according to dissecting template, utilize described dissection template to cut apart the resulting described tranquillization attitude functional imaging data in described functional imaging unit then, with described brain district is the time series that unit extracts reflection tranquillization attitude cerebration, makes up full brain network and subnet net with described brain district and time series.
8. pain assessment system according to claim 2, it is characterized in that the brain district of described subnet/component comprises: thalamus, corpus amygdaloideum, island leaf, assisted movement cortex, posterior parietal cortex, prefrontal cortex, cingulum cortex, periaqueductal gray, Basal ganglia, cerebellum, primary and secondary sensory cortex.
9. according to each described pain assessment system of claim 1～8, it is characterized in that, described pain evaluation unit also is used to obtain the clinical pain information of new individual patient, in conjunction with the tranquillization attitude functional image attribute of described new individuality and the optimization of described clinical pain informix analyze the pain grade and/or the pain origin cause of formation of the individual patient that makes new advances; And/or
Described pain evaluation unit also is used for finely tuning by the self adaptation feedback learning method in described pain feature samples storehouse the pain grade and/or the pain origin cause of formation of described new individual patient.
10. pain assessment system according to claim 9, it is characterized in that, described pain feature samples storehouse also be used to store be used to make up full brain network, subnet net, function connect and during node by intermediate value, pain grade/origin cause of formation evaluation result, the clinical pain information of described tranquillization attitude functional imaging data by calculating, the trim values after the study of self adaptation feedback algorithm.
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