EP1456798A2 - Evaluation d'images du cerveau obtenues par tomographie par resonance magnetique fonctionnelle - Google Patents

Evaluation d'images du cerveau obtenues par tomographie par resonance magnetique fonctionnelle

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Publication number
EP1456798A2
EP1456798A2 EP02791617A EP02791617A EP1456798A2 EP 1456798 A2 EP1456798 A2 EP 1456798A2 EP 02791617 A EP02791617 A EP 02791617A EP 02791617 A EP02791617 A EP 02791617A EP 1456798 A2 EP1456798 A2 EP 1456798A2
Authority
EP
European Patent Office
Prior art keywords
brain
activity
neural network
image
pools
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP02791617A
Other languages
German (de)
English (en)
Inventor
Bernd SCHÜRMANN
Gustavo Deco
Martin Stetter
Jan Storck
Silvia Corchs
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
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Siemens AG
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Publication date
Application filed by Siemens AG filed Critical Siemens AG
Publication of EP1456798A2 publication Critical patent/EP1456798A2/fr
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
    • G16Z99/00—Subject matter not provided for in other main groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G06V2201/031—Recognition of patterns in medical or anatomical images of internal organs

Definitions

  • the main reason for this discrepancy lies in the complexity of the brain:
  • the human cerebral cortex alone can be divided into around 200 functional units, the brain areas, between which there are around 10,000 closed, dense network paths.
  • the network paths each consist of several synaptic connections, ie nerve strands.
  • cerebral dysfunction usually manifests itself in a change in the interaction of the areas compared to healthy people, which in turn modifies the activity state of the entire brain in a complex manner.
  • the object of the invention is to make fMRI images more usable for diagnosis.
  • a method for evaluating an image (fMRI image) of the brain obtained by means of functional magnetic resonance tomography is specified.
  • a neural network is used to simulate the activities of the brain.
  • Suspected disturbances in the brain are simulated in the neural network (disturbed neural network).
  • the determined activities in the disturbed neural network are compared with the activities observed in the fMRI image. Brain disorders are deduced from the comparison.
  • the other two classes of fault causes, (b) and (c), can have a very hidden effect.
  • the dense networking of the brain areas means that the fully constant interruption of the connection between two areas does not only affect the two affected areas.
  • the interruption of the connection changes the overall signal propagation through the brain and thus indirectly causes disturbances in other brain functions that appear to be independent of the areas under consideration.
  • the doctor is provided with a tool with the aid of which the complex interaction of several brain areas can be simulated by the brain while solving defined tasks.
  • the simulator is based on the simulation of the dynamics of coupled populations of neurons, i.e. on a neural network.
  • the dynamics simulate the time course of the activities of the neurons.
  • the failure of any substructure in the brain can be artificially simulated in the model and its impact on the complex interplay of the areas of the brain can be quantified.
  • the comparison with the fMRI image or activity pattern measured on the patient leads to the localization of the cause of the fault and thus to a successful diagnosis.
  • a quantitative relationship is thus established between the measured spatial distribution of brain activity on the one hand and the medically relevant functional brain state on the other.
  • the neural network can also have a structure that is based on the structure of the brain in its division into areas and their connections. On the one hand, this leads to a reduction in the complexity of the neural network. On the other hand, the structure of the neural network corresponds to that of the brain.
  • a third-generation neurosimulator (neurocognition) is therefore advantageously used for the quantitative interpretation and thus diagnosis of fMRI images.
  • a neuro- First generation simulators are called models of networks of neurons on a more or less static basis, the classic neural networks.
  • Second generation neurosimulators are models of the dynamic behavior of the neurons, in particular the pulses they generate.
  • Third-generation neurosimulators are hierarchical models of the organization of neurons in pools and pools in areas. A pool contains thousands of neurons.
  • the method according to the invention can be integrated as an evaluation tool in the operating software of a computer which controls an fMRI tomograph, can be integrated, or can work in the form of an independent diagnostic support device.
  • the object is further achieved by a computer program which executes the method according to the invention when it is running on a computer, and by a computer program with program code means to carry out all of the steps according to the invention when the program is executed on a computer.
  • the object is achieved by a computer program with the program code means mentioned, which are stored on a computer-readable data carrier. Furthermore by a
  • Fig. 1 shows an example of an fMRI image
  • the local changes in the oxygen content in the blood during activation processes can be displayed with high spatial resolution using fMRI and precisely assigned to the individual anatomical structures of the brain.
  • the aim of the modeling is a detailed neural network model of the areas of the brain, which reflects the real conditions in the brain during activation processes, especially with regard to visual attention control, and thus provides an explanation of these services through physiological mechanisms.
  • top-down approach better reflects the realities of the visual cortex.
  • intermediate results at a higher processing level are used by feedback to meaningfully reevaluate lower processing levels. What is important is the moment of feedback between the individual levels. In the model to be described in more detail below, this feedback is realized by the interaction of the individual areas.
  • the feedback leads to a shift in the balance in the attention competition of the individual neurons or groups of neurons. This leads to an uneven situation
  • the "where” question is traditionally answered by searching the entire image using a given attention window.
  • the "what” question is answered by comparing the known patterns with the predefined image section or by searching the predefined image section for features on which attention is concentrated in order to recognize the image features.
  • Third generation neurosimulators are hierarchical models of the organization of neurons in pools and pools in areas, corresponding to the areas of the brain, as described below using the example of the visual cortex is described.
  • a pool contains thousands of neurons.
  • the 2 shows in simplified form the essential areas of the visual cortex of the brain 10.
  • the cerebrum 16 and the cerebellum 18 are shown.
  • the cerebrum 16 contains, among others, the areas VI, V4, PP and IT shown and explained in more detail below in the visual cortex , There are many-stranded synaptic connections 20 between these reales.
  • the IT area (inferotemporal) is used for image recognition or
  • the area PP serves to localize known patterns ("where" question).
  • the area PP in the present model therefore contains as many pools 24 as there are pixels in the image to be recognized.
  • the concentration of neuronal activity in a small number of neighboring pools in PP corresponds to a localization of the object.
  • the areas VI and V4 are combined in the present model to form the A-real V1-V4, which is also referred to as V4.
  • This area is generally responsible for the extraction of features. It contains approximately 1 million pools 24, one pool for each characteristic. The pools 24 speak to individual features of the
  • a characteristic is thus defined by a certain size or spatial frequency, a spatial orientation and a certain position in the x and y directions (see below). All captured image data initially go to area V1-V4.
  • each area there is at least one inhibitory pool 22, that is to say a pool which has an inhibitory effect on the activity of other pools.
  • the inhibitory pools are coupled to the stimulable pools 24 with bidirectional connections 26.
  • the inhibitory pools 22 result in competitive interaction or competition between the pools.
  • the competition in V1-V4 is carried out with pools 24, which encode both location and object information.
  • PP abstracts location information and mediates a competition on the spatial level.
  • IT abstracts information from classes of objects and mediates competition at the level of classes of objects.
  • synaptic connections 20 between the areas, through which the pools 24 can be stimulated to activity There are synaptic connections 20 between the areas, through which the pools 24 can be stimulated to activity.
  • the IT area is connected to the V4 area.
  • Area PP is connected to area V4.
  • the synaptic connections 20 between the areas simulated in the model reflect that
  • the activities of the neural pools are modeled using the mean field approximation.
  • Many areas of the brain organize groups of neurons with similar properties in columns or field assemblies, such as orientation columns, in the primary visual cortex and in the somatosensory cortex.
  • These groups of neurons, the pools are composed of a large and homogeneous population of neurons that receive a similar external input, are mutually coupled, and are likely to function together as a unit.
  • These pools can form a more robust processing and coding unit because their current population mean response, in contrast to the time average of a relatively stochastic neuron in a large time window, is better adapted to the analysis of rapid changes in the real world.
  • T refractory is the dead time of a neuron after sending out a
  • Pulses indicates (about 1 ms) and ⁇ the latency of the membrane of the neuron, ie the time between an external input and the complete polarization of the membrane (Usher, M. and Niebur, E .: “Modeling the temporal dynamics of IT neurons in visual search: A mechanism of top-down selective attention ", Journal of Cognitive Neuroscience, pages 311-327 (1996)).
  • ⁇ is 7 ms.
  • the immediately recorded images are encoded in a gray value image which is described by an nxn matrix T "" 8 .
  • a non-square matrix is also possible.
  • the indices i and j denote the spatial one Position of the pixel.
  • the gray value T TM 8 within each pixel is preferably encoded by 8 bits. Bit value 0 corresponds to black and bit value 255 corresponds to white.
  • the nxn image matrix T y is obtained by subtracting the mean:
  • features are extracted from the image by the pools in area V4 in such a way that the pools carry out a Gabor wavelet transformation of the image, more precisely that the activity of the pools corresponds to the coefficients of a Gabor wavelet transformation ,
  • the functions G kqpl used for the Gabor wavelet transformation are functions of the location x and y or of the discrete ones
  • G kpql (x, y) a- k ⁇ ⁇ (a ⁇ k (x - 2p) - a- k (y - 2q))
  • k corresponds to the size of the feature, expressed by the octave k, ie the spatial frequency, determined by 2 A k times the basic frequency, which is scaled by the parameter a;
  • the value 2 is usually chosen for a.
  • ⁇ 0 ⁇ / L, i.e. the orientation resolution. Values from 2 to 10 are preferably chosen for L.
  • the activity l pql of a pool in area V4 which responds to the spatial frequency at the octave k, the spatial orientation with the index 1 and an incentive whose center is determined by p and q, is stimulated by
  • this corresponds to the coefficients of the Gabor wavelet transformation.
  • the respective behavior of the pools is determined by previous training (see below).
  • Equation (10) The third term on the right-hand side of equation (10), bE (/ 4 ' 7 ), describes the above-mentioned inhibitory effect of the inhibitory pool 22, which is described in more detail below.
  • the parameter b on the right side of equation (10) scales the strength of the inhibition. A typical value for b is 0.8.
  • I p V q PP r describes the attention control for a feature with the spatial position corresponding to p and q, ie the emphasis on the "where" question, as explained in more detail below becomes.
  • inhibitory pool 22 Effect of inhibitory pool 22 on area V4.
  • the pools 24 within an area are in competition with one another, which is conveyed by an inhibitory pool 22, which receives the exciting input 27 from all excitable pools 24 and directs a uniform inhibitory feedback 28 to all excitable pools 24.
  • This inhibiting feed-back 28 has a stronger effect on less active pools than on more active ones. This allows more active pools to prevail over less active pools.
  • bias an external input current 30 (bias) is shown in Fig. 4, which can excite one or more pools.
  • the exact function of the bias 30 is described below in connection with equation (15).
  • the first term on the right side of equation (11) again describes the decay of the inhibitory pool 22.
  • the second term describes the input current from V4 into the inhibitory pool 22 belonging to V4 with the index k, scaled by the parameter c.
  • a typical value for c is 0.1.
  • the third term represents a self-locking of the inhibitory pool 22 belonging to V4 with the index k.
  • a typical value for d is 0.1.
  • equation (11) already described. There is only one uniform inhibitory effect for the PP area.
  • equation (15) again describes the attention-controlling feedback from V4 to PP and is given by
  • the fifth term 1 u on the right-hand side of equation (15) is an external top-down bias that draws attention to a specific location (i, j). This is represented by arrow 30 in FIG. 4. If the bias is preset, an object is expected at the preset location. A typical value for this external bias is 0.07 for the expected location and 0 for all other locations.
  • I kp ⁇ , IT describes - as mentioned - the attention control in V4 for certain patterns from IT, ie the emphasis on the "what" question. Attention control takes place by feedback of an activity i 'of the pools, which stand for pattern c, from the area IT to associated pools in area V4. This feedback is modeled by
  • I C JT is the activity of a pool, which stands for pattern c, in the IT area.
  • the temporal development of I 1 follows the differential equation:
  • the weights w ck the synaptic connections between V4 and IT are through Hebbian learning (Hebbian Training) (Deco, G. and Obradovic, D.: "An Information-theoretical Approach to Neurocomputing", Springer Verlag (1996)) with known objects educated. To put it simply, pattern c is presented to the neural network one after the other and the weights w ckpql are varied until the grandmother pools c recognize pattern c in IT, ie show maximum activity. In a first approximation, the weights w ckpql result from the above-described Gabor wavelet transformation of the pattern c stored in IT.
  • simulations can be carried out with the neural network.
  • the evaluation of an fMRI image is basically an inverse problem: the cause (the activity of certain areas) should be used to determine the cause. Due to the complexity of the networking, the effect cannot be deductively deduced from the cause. It is only possible to reproduce the effects by varying a variety of causes.
  • the effect of such assumptions on the neural network is calculated by solving the differential equations given above and compared with the measured fMRI images.
  • the system of the given differential equations is highly parallel. It consists of approximately 1.2 million coupled differential equations. These are solved numerically, preferably by discretization with the aid of the Eu-1 or Runge-Kutta method.
  • the time increment chosen is preferably 1 ms, ie approximately T refraclo ⁇ y according to equation (2).
  • Ungerleider, L. "Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI”; Science 282 (1998) 108-111. Kaster, S .; Pinsk, M .; De Weerd, P .; Desimone, R. and Ungerleider, L.: "Increased activity in human visual cortex during directed attention in the absence of visual stimulation”; Neuron 22 (1999) 751-761.). The dynamics of the activity of the pools in V4 with significant changes on the scale below one second could be tracked. Likewise, attention control through expectation and the inhibitory effect of simultaneous or neighboring stimuli.
  • Visual neglect is the fading out of half of the visual field from the

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Abstract

L'invention concerne un procédé d'évaluation d'une image du cerveau obtenue par tomographie par résonance magnétique fonctionnelle. Selon ce procédé, on utilise un réseau neuronal pour stimuler les activités du cerveau. Des troubles supposés du cerveau sont simulés dans le réseau neuronal (réseau neuronal perturbé). Les activités déterminées dans le réseau neuronal perturbé sont comparées aux activités observées dans l'image du cerveau obtenue par tomographie par résonance magnétique fonctionnelle. La perte de sous-structures quelconques du cerveau peut être simulée artificiellement dans le modèle et son effet sur la synergie complexe des zones du cerveau peut être quantifié. La comparaison avec l'image ou la représentation d'activités obtenue par tomographie par résonance magnétique fonctionnelle chez le patient permet de localiser la cause des troubles et ainsi d'effectuer un bon diagnostic.
EP02791617A 2001-12-20 2002-12-09 Evaluation d'images du cerveau obtenues par tomographie par resonance magnetique fonctionnelle Ceased EP1456798A2 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
DE10162927A DE10162927A1 (de) 2001-12-20 2001-12-20 Auswerten von mittels funktionaler Magnet-Resonanz-Tomographie gewonnenen Bildern des Gehirns
DE10162927 2001-12-20
PCT/DE2002/004517 WO2003054794A2 (fr) 2001-12-20 2002-12-09 Evaluation d'images du cerveau obtenues par tomographie par resonance magnetique fonctionnelle

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US (1) US7349728B2 (fr)
EP (1) EP1456798A2 (fr)
CN (1) CN1620666A (fr)
DE (1) DE10162927A1 (fr)
WO (1) WO2003054794A2 (fr)

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US20050107682A1 (en) * 2003-10-21 2005-05-19 Rao Stephen M. fMRI system for use in assessing the efficacy of therapies in treating CNS disorders
US20050085705A1 (en) * 2003-10-21 2005-04-21 Rao Stephen M. fMRI system for use in detecting neural abnormalities associated with CNS disorders and assessing the staging of such disorders
DE102005046747B3 (de) * 2005-09-29 2007-03-01 Siemens Ag Verfahren zum rechnergestützten Lernen eines neuronalen Netzes und neuronales Netz
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EP2718864A4 (fr) * 2011-06-09 2016-06-29 Univ Wake Forest Health Sciences Modèle de cerveau à base d'agents et procédés associés
CA2880758C (fr) * 2012-08-02 2024-01-23 Max-Planck-Gesellschaft Zur Forderung Der Wissenschaften E.V. Procede et systeme de calcul pour modeliser un cerveau de primate
US9092692B2 (en) * 2012-09-13 2015-07-28 Los Alamos National Security, Llc Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection
CN103279644B (zh) * 2013-04-27 2016-04-06 中国人民解放军信息工程大学 fMRI头动的实时监测与反馈方法
US9739856B2 (en) 2013-06-20 2017-08-22 Siemens Aktiengesellschaft Magnetic resonance imaging method and apparatus with interleaved resting state functional magnetic resonance imaging sequences and morphological magnetic resonance imaging sequences
CN105022934B (zh) * 2015-06-29 2018-03-09 北京工业大学 一种用于从fMRI数据中构建脑效应连接网络的人工免疫方法
US20170071470A1 (en) * 2015-09-15 2017-03-16 Siemens Healthcare Gmbh Framework for Abnormality Detection in Multi-Contrast Brain Magnetic Resonance Data
JP6687940B2 (ja) * 2016-01-18 2020-04-28 国立研究開発法人情報通信研究機構 素材評価方法、及び素材評価装置
CN105763864A (zh) * 2016-02-23 2016-07-13 北京理工大学 立体视觉成像装置和立体视觉刺激设备
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DE10162927A1 (de) 2003-07-17
US20050119558A1 (en) 2005-06-02
US7349728B2 (en) 2008-03-25
WO2003054794A2 (fr) 2003-07-03
WO2003054794A3 (fr) 2004-06-17
CN1620666A (zh) 2005-05-25

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