WO2004021244A2 - Procede, dispositif et programme informatique avec codes de programme et produit de programme pour analyser des activites neuronales dans des zones neuronales - Google Patents

Procede, dispositif et programme informatique avec codes de programme et produit de programme pour analyser des activites neuronales dans des zones neuronales Download PDF

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Publication number
WO2004021244A2
WO2004021244A2 PCT/DE2003/002643 DE0302643W WO2004021244A2 WO 2004021244 A2 WO2004021244 A2 WO 2004021244A2 DE 0302643 W DE0302643 W DE 0302643W WO 2004021244 A2 WO2004021244 A2 WO 2004021244A2
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WO
WIPO (PCT)
Prior art keywords
signals
neural
determined
statistical distribution
areas
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PCT/DE2003/002643
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German (de)
English (en)
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WO2004021244A3 (fr
Inventor
Gustavo Deco
Norbert Galm
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Siemens Aktiengesellschaft
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Application filed by Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Priority to AU2003260263A priority Critical patent/AU2003260263A1/en
Publication of WO2004021244A2 publication Critical patent/WO2004021244A2/fr
Publication of WO2004021244A3 publication Critical patent/WO2004021244A3/fr

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    • 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

Definitions

  • the invention relates to an analysis of neuronal activities in neuronal areas, for example of nerve structures in the brain areas of a patient.
  • the previously known magnetic resonance imaging (also magnetic resonance imaging, abbreviated: MR) is an imaging method that generates sectional images of the human body without the use of stressful X-rays.
  • the MR makes use of the behavior of the body tissue in a strong magnetic field. Pathological changes in the body tissue, for example in the brain or spinal cord, can be identified.
  • BOLD signal Blood Oxygenation Level Dependent
  • the result of the fMRI measurements shows the course of the activity of the individual areas over a certain period of time, for example during cognitive processes as a result of certain perceptual processes or motor tasks.
  • Previously known methods such as the analysis method known from [6] are limited to the detection of functional relationships between different brain areas in certain, predetermined tasks, such as perceived processes or motor tasks (functional connectivity). These functional relationships are referred to as functional connectivity.
  • functional connectivity In contrast to functional connectivity, the determination of a true physical connectivity, ie the determination of actually existing connection structures (of brain areas) independently of certain, predetermined tasks, is not possible with these known methods.
  • the aim of this known analysis method is to identify the functional relationships between different brain areas in certain perceptual processes or motor tasks described above.
  • This known analysis method is based on a predefined model of a brain, i.e. a predefined brain architecture.
  • This brain architecture which is predefined from prior knowledge, defines general functional and / or spatial dependencies between certain brain areas
  • the coupling matrix S has a (column / row) shape or structure which is defined in accordance with the predefined brain architecture and is accordingly specific
  • Coupling strengths Si a probability of the measured data occurring, i.e. the fMRI measurement or the BOLD signals can be maximized.
  • the fMRI measurement comprises a large number of such data points for possibly different perception processes and / or motor tasks for which the corresponding BOLD signals were measured.
  • the individual data points sl, s2, ..., sT are not evaluated directly, but rather statistical parameters which result from these.
  • a statistical distribution of the data points sl, s2, ..., sT it is assumed that it is completely described by a multivariant normal distribution, ie a statistical distribution of the first order, with an average value ⁇ and a covariance ⁇ :
  • the unknown quantities, the mean ⁇ and the covariance ⁇ depend exclusively on a (brain) model that describes the measurement data.
  • ⁇ Ss + ⁇ (3)
  • ⁇ describes the external influence on the individual BOLD signals, such as sensory input from sensory cells on the examined areas of the brain.
  • the influencing variables ⁇ i and ⁇ j on various examined areas i and j can certainly be correlated.
  • the model parameters to be set are therefore the
  • the known analysis method or the known analysis methods have the disadvantage that the measured fMRI signals can only be explained inadequately accurately or that the model can only be inadequately adapted to the measured fMRI signals and thus the mode of operation or the interaction of neuronal areas can only be insufficiently reproduced. This deficiency may lead to incorrect conclusions regarding the connectivity functionality.
  • a software tool for an fMRI analysis method is known from [4].
  • a device for carrying out the fMRI technique is known from [5].
  • the invention is therefore based on the object of specifying an improved analysis method for analyzing neuronal activities.
  • the improved analytical method is intended to explain measured fMRI signals better and thus to better describe the functioning and interaction of neuronal areas than in the known analytical method described above.
  • the signals are determined, one signal each describing the neural activity in one of the neuronal areas,
  • the signals are based on a linear statistical relationship between the signals, which is described using coupling variables, each of which describes the linear statistical relationship between the signals,
  • Probabilities are determined for the occurrence of the signals, the occurrence of the signals being based on a statistical distribution
  • the coupling sizes are determined by optimizing the probabilities and
  • the statistical distribution on which the signals are based when determining the probabilities is such a higher order than only first order.
  • Signals describing activities have functional units in contact with one another, which are set up in such a way that the signals can be determined, one signal each describing the neuronal activity in one of the neuronal areas,
  • the signals can be based on a linear statistical relationship between the signals, which is described using coupling variables, each of which describes the linear statistical relationship between the signals,
  • Probabilities for the occurrence of the signals can be determined, the occurrence of the signals being based on a statistical distribution
  • the coupling sizes can be determined by optimizing the probabilities and
  • the neural activities can be analyzed using the coupling sizes.
  • the invention is based on the knowledge that a weak point in the previously known (previously described) analysis method for analyzing neuronal activities is the use of a multivariate normal distribution, that is to say a first order, to describe the statistical distribution of the measurement data .
  • the invention is based on the assumption that the statistical distribution of the measurement data can be described much better using a higher-order statistical distribution, such as an Edgeworth development [2] or a sum of normal distributions.
  • the computer program according to the invention with program code means is set up to carry out all steps according to the analysis method according to the invention when the program is executed on a computer.
  • the computer program product with program code means stored on a machine-readable carrier is set up to carry out all steps according to the analysis method according to the invention when the program is executed on a computer.
  • Carrier-stored program code means set up to carry out all steps according to the inventive analysis method when the program is executed on a computer, are particularly suitable for carrying out the analysis method according to the invention or one of its further developments explained below.
  • the invention or any further development described below can also be implemented by a computer program product which has a storage medium on which the computer program with program code means which carries out the invention or further development is stored.
  • the statistical distribution of higher order can be used using an Edgeworth development [2] or a sum of normal distributions, which of the statistical
  • Distribution of the signals is used as a basis.
  • the optimization can also be carried out using a method of maximum likelihood estimation [1].
  • the signals in the invention for example BOLD signals, can be determined by measuring signals or by transmitting and / or reading in already existing signals.
  • the invention and further development described are particularly suitable for use with an fMRI technique, which is considerably improved and more efficient as a result.
  • the neuronal areas are brain areas with corresponding nerve structures of the patients to be examined and diagnosed.
  • BOLD signals are measured in different brain areas of a patient for defined perception or motor tasks performed by the patient, which BOLD signals describe or represent the neuronal activities in the respective brain areas. These are evaluated or analyzed, the signal coupling quantities being determined.
  • the signal coupling quantities, functional as well as physical dependencies between brain areas can be recognized and determined. These can be used further for a diagnosis of a functional disorder in a patient's brain area, for example by
  • FIG. 1 sketch with method steps in an analysis of BOLD signals according to an embodiment.
  • Exemplary embodiment functional magnetic resonance imaging (fMRI)
  • FIG. 1 shows a device 100 for performing a functional magnetic resonance imaging
  • Magnetic resonance imaging short: fMRI
  • a functional magnetic resonance scanner or magnetic resonance imaging scanner 100 Magnetic resonance imaging (short: fMRI), a functional magnetic resonance scanner or magnetic resonance imaging scanner 100.
  • the magnetic resonance tomograph 100 has a closed tube 110, which is embedded in a magnet 120 such that it generates a strong magnetic field in the tube 110.
  • the magnetic resonance tomograph 100 has a patient table 130 which can be moved into the tube 110 and on which a patient is supported during an examination.
  • the magnetic resonance tomograph 100 has a control device 131 which controls and controls the patient table 130 during the examination, for example a controlled retraction of the
  • the magnetic resonance tomograph 100 has a further component
  • Measuring device 140 for measuring BOLD signals (Blood
  • Evaluation device 141 for evaluating the measured BOLD signals in this case a high-performance computer, and one
  • the components of the magnetic resonance tomograph 100 are functionally connected to one another, for example via signal or data lines 150, via which data and signals can be transmitted.
  • Magnetic resonance tomograph 100 can use the fMRI technique to measure, analyze and analyze the neuronal activity in areas of a patient's brain, and a diagnosis can be derived therefrom.
  • the measuring device 140 measures the BOLD signal (Blood Oxygenation Level Dependent) in individual, selected areas of the patient's brain, which is related to the neuronal activity in the respective area. '
  • BOLD signal Bit Oxygenation Level Dependent
  • fMRI measurements shows the course of the activity of the individual areas over a certain period of time, for example during cognitive processes as a result of certain perceptual processes or motor tasks which are to be carried out by the patient during an examination. Functional disorders in the patient's brain are therefore implicit in the measured fMRI signals.
  • the fMRI measurements i.e. the BOLD signals measured in individual areas of the brain are analyzed.
  • This new analysis method represents an improved further development of the known analysis method described above (relationships (1) to (4)).
  • brain activity is determined in the form of corresponding activation patterns in the areas examined in the brain and / or relationships between modes of action of activation patterns in the areas examined, and direct conclusions can be drawn about functional disorders in the brain and their causes.
  • the new analysis method provided by the evaluation device 140 is based on a model of the brain, the neuron structures in the brain and their behavior, in particular their interaction, on the basis of which the measured BOLD signal is analyzed and evaluated.
  • results or the conclusions of an examination are shown on the display device 143 and can be processed further by means of the operating and interaction device 142 in connection with the evaluation device 141 become. They also serve as the basis for a medical
  • the fMRI measurements i.e. the BOLD signals in the examined brain areas of a patient, analyzed (210 to 250) and / or compared with reference fMRI measurements. This enables immediate conclusions to be drawn about functional disorders in the brain examined and their causes.
  • the new analysis method 200 which generates statistical parameters, such as statistical correlations between fMRI measurements in different areas of the brain, is based on the known mathematical model of the brain from (3).
  • the coupling strengths Si are determined in such a way that statistical parameters derived from the fMRI Measurements are determined, can best be explained
  • the fMRI measurement comprises a large number of such data points sl, s2, ..., sT, the indices 1, 2 to T each characterizing T different perception processes and / or motor tasks for which the corresponding BOLD signals were measured.
  • the weighted sum of normal distributions is assumed in the new analysis method 200 for the statistical distribution (230).
  • ⁇ ⁇ (C l , ...., C L , ⁇ l , ..., ⁇ L , ⁇ l , ..., ⁇ L )
  • the optimal model parameters are determined using the maximum likelihood estimation [1] by optimizing or maximizing the probabilities (5) (240).
  • the parameters to be taken into account for the optimization process are the parameters of the selected higher-order statistical distribution, in this case the weighted sum of the normal distributions, the model parameters sought and the statistical variables, in this case the mean ⁇ and the covariance ⁇ from (6) which established the relationships between the model parameters and the statistical distribution (5).

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  • Medical Informatics (AREA)
  • Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Biomedical Technology (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Complex Calculations (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

L'invention concerne l'analyse d'activités neuronales dans des zones neuronales. Des signaux sont déterminés, chaque signal décrivant l'activité neuronale dans l'une des zones neuronales. Ces signaux sont basés sur un rapport statistique linéaire déterminé par l'application de grandeurs de couplage, lesquelles décrivent le rapport statistique linéaire entre les signaux. On calcule des probabilités d'apparition des signaux pour lesquels on applique une répartition statistique d'ordre supérieur. Les grandeurs de couplage sont déterminées et analysées par optimisation des probabilités.
PCT/DE2003/002643 2002-08-09 2003-08-06 Procede, dispositif et programme informatique avec codes de programme et produit de programme pour analyser des activites neuronales dans des zones neuronales WO2004021244A2 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2003260263A AU2003260263A1 (en) 2002-08-09 2003-08-06 Method, system, computer program with program code means, and computer program product for analyzing neuronal activities in neuronal areas

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE10236643 2002-08-09
DE10236643.8 2002-08-09

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WO2004021244A2 true WO2004021244A2 (fr) 2004-03-11
WO2004021244A3 WO2004021244A3 (fr) 2004-10-28

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AU (1) AU2003260263A1 (fr)
WO (1) WO2004021244A2 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SG129315A1 (en) * 2005-07-28 2007-02-26 Oculus Ltd Contact lens that makes the eye appear larger

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HALEY R W ET AL: "Use of structural equation modeling to test the construct validity of a case definition of Gulf War syndrome: invariance over developmental and validation samples, service branches and publicity." PSYCHIATRY RESEARCH. 1 JUN 2001, Bd. 102, Nr. 2, 1. Juni 2001 (2001-06-01), Seiten 175-200, XP002294940 ISSN: 0165-1781 *
MCINTOSH A R ET AL: "STRUCTURAL EQUATION MODELING AND ITS APPLICATION TO NETWORK ANALYSIS IN FUNCTIONAL BRAIN IMAGING" HUMAN BRAIN MAPPING, WILEY-LISS, NEW YORK, NY, US, Bd. 2, Nr. 1-2, 1994, Seiten 2-22, XP009023530 ISSN: 1065-9471 in der Anmeldung erw{hnt *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SG129315A1 (en) * 2005-07-28 2007-02-26 Oculus Ltd Contact lens that makes the eye appear larger

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AU2003260263A1 (en) 2004-03-19
WO2004021244A3 (fr) 2004-10-28

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