EP1527406A2 - Procede, dispositif, programme informatique comprenant un systeme de code de programmation et produit de programme informatique pour analyser des activites neuronales dans des zones neuronales - Google Patents

Procede, dispositif, programme informatique comprenant un systeme de code de programmation et produit de programme informatique pour analyser des activites neuronales dans des zones neuronales

Info

Publication number
EP1527406A2
EP1527406A2 EP03747800A EP03747800A EP1527406A2 EP 1527406 A2 EP1527406 A2 EP 1527406A2 EP 03747800 A EP03747800 A EP 03747800A EP 03747800 A EP03747800 A EP 03747800A EP 1527406 A2 EP1527406 A2 EP 1527406A2
Authority
EP
European Patent Office
Prior art keywords
signals
adaptable
coupling
determined
neural
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.)
Withdrawn
Application number
EP03747800A
Other languages
German (de)
English (en)
Inventor
Gustavo Deco
Norbert Galm
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
Siemens Corp
Original Assignee
Siemens AG
Siemens Corp
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Siemens AG, Siemens Corp filed Critical Siemens AG
Publication of EP1527406A2 publication Critical patent/EP1527406A2/fr
Withdrawn legal-status Critical Current

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Classifications

    • 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

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 in individual areas of the brain, which is related to the neuronal activity in the respective areas.
  • 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 and subsequently described analysis method is the above-described recognition of functional relationships between different brain areas in certain perception processes or motor tasks.
  • This known analysis method is based on a predefined model of a brain, i.e. a predefined brain architecture.
  • This brain architecture predefined from a priori, defines a priori general functional and / or spatial dependencies between certain brain areas in the form of a so-called coupling matrix S.
  • the coupling matrix S has a (column / row) shape or structure defined according to the given brain architecture and is accordingly occupied at certain, but not at all (matrix) locations with so-called, variable coupling strengths Si. This can be changed and are adjusted as part of the analysis process.
  • the vacant (matrix) positions are filled with fixed, unchangeable values, namely zero.
  • 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.
  • 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 determined are accordingly the coupling strengths Si of the underlying coupling matrix S, the mean value ⁇ of the external influence ⁇ and the covariance ⁇ of ⁇ .
  • the known analysis method described in the above has 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 functioning or the interaction of neural ones Areas can only be insufficiently replicated. 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.
  • This object is achieved by the method and the arrangement as well as by the computer program with program code means and the computer program product for analyzing neural activities in neuronal areas with the features according to the respective independent patent claim.
  • the signals are determined, each signal describing the neural activity in one of the neuronal areas.
  • Probabilities for the occurrence of the signals are determined, the occurrence of the signals being based on a statistical distribution.
  • the neural activities are analyzed using the adjustable coupling sizes
  • the arrangement for analyzing neural activities in neural areas using signals describing the neural activities has functional ones 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,
  • All signals can be based on an adaptable coupling, which is described using adaptable coupling variables that describe a statistical relationship between the adaptably coupled signals.
  • Probabilities for the occurrence of the signals can be determined, the occurrence of the signals being based on a statistical distribution
  • the neural activities can be analyzed using the adjustable coupling sizes.
  • the invention thus differs from the known analysis method described above in that in the known analysis method only a part of the signals are based on adaptable statistical coupling. Only these can be determined by optimizing the probabilities and thus adapted. Seen clearly, the known analysis method thus sets a known, predetermined and defined neuronal
  • both the most likely coupling structure on the basis of the signals is determined and a coupling strength of the specific couplings.
  • 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.
  • the arrangement and the computer program with program code means, set up to carry out all steps according to the inventive analysis method when the program is executed on a computer, and the computer program product with program code means stored on a machine-readable medium, set up all steps according to the Carrying out analysis methods according to the invention 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.
  • Training can be realized by a computer program product which has a storage medium on which the
  • Computer program with program code means is stored, which carries out the invention or further development.
  • the statistical distribution on which the occurrence of the signals is based can be of first order as well as higher order.
  • the higher order can be realized using an Edgeworth expansion [2] or a sum of normal distributions.
  • the individual normal distributions and thus indirectly the neuronal activities can be weighted in the sum of normal distributions.
  • the optimization can also be carried out using a method of maximum likelihood estimation [1].
  • a relationship between the linear statistical relationship and the statistical distribution can be considered as a secondary condition.
  • external influences on the signals can be, for example, sensory inputs from sensory cells to the areas examined.
  • 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 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. Using the analysis results, especially 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 device for carrying out an fMRI according to an exemplary embodiment
  • 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
  • Control of the patient table 130 during the examination for example a controlled insertion of the patient table 130 into the tube 120, is made possible.
  • the magnetic resonance tomograph 100 has a measuring device 140 for measuring BOLD signals (Blood Oxygenation Level Dependent), an associated evaluation device 141 for evaluating the measured BOLD signals, in this case a high-performance computer, and an operating or interaction device 142 for on BOLD signals (Blood Oxygenation Level Dependent), an associated evaluation device 141 for evaluating the measured BOLD signals, in this case a high-performance computer, and an operating or interaction device 142 for on BOLD signals (Blood Oxygenation Level Dependent), an associated evaluation device 141 for evaluating the measured BOLD signals, in this case a high-performance computer, and an operating or interaction device 142 for on BOLD signals (Blood Oxygenation Level Dependent), an associated evaluation device 141 for evaluating the measured BOLD signals, in this case a high-performance computer, and an operating or interaction device 142 for on BOLD signals (Blood Oxygenation Level Dependent), an associated evaluation device 141 for evaluating the measured BOLD signals
  • 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.
  • the neuronal activity in areas of the brain of a patient can be measured, analyzed and a diagnosis can be derived therefrom on the basis of the fMRI technique.
  • 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.
  • the result of such 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.
  • 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 in the above.
  • the new analysis method brain activity is determined in the form of corresponding activation patterns in the areas examined in the brain and / or relationships between 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 an expanded and more flexible one
  • 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. They also serve as the basis for a medical diagnosis for a patient to be examined and diagnosed.
  • the fMRI measurements ie the BOLD signals in the examined brain areas of a patient, are 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 brain areas, is based on an expanded and more flexible mathematical model of the brain based on the known mathematical model according to (3) (220).
  • the fMRI measurement comprises a large number of such data points sl, s2, ..., sT for different perception processes and / or motor tasks for which the corresponding BOLD signals were measured.
  • a weighted sum of normal distributions is assumed for the statistical distribution
  • the chosen statistical distribution and thus the correspondence of the probabilities depend ..., CL, ⁇ l, ..., ⁇ L, ⁇ l, ..., ⁇ L) (230) (cf. (2)) for the occurrence of the measured data points sl, s2, ..., sT from more or different parameters than the mean ⁇ and the covariance ⁇ of the old known analysis method.
  • ⁇ ⁇ (C 1 , C, ⁇ 1 , ..., ⁇ , ⁇ 1 , ..., ⁇ )
  • ⁇ ⁇ (C 1 , ...., C, 1 , ...,, ⁇ 1 , ..., ⁇ )
  • 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). These relationships from (6) are constraints on the

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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)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

La présente invention concerne l'analyse d'activités neuronales dans des zones neuronales. Selon l'invention, des signaux sont détectés, un signal décrivant respectivement l'activité neuronale de l'une des zones neuronales. Un couplage adaptable constitue la base de tous les signaux, ledit couplage étant décrit par utilisation de paramètres de couplage adaptables qui décrivent le rapport statistique entre les signaux. Des probabilités d'occurrence des signaux sont déterminées, les signaux se basant sur une répartition statistique. Les paramètres de couplage adaptables sont déterminés par optimisation des probabilités, et ainsi adaptés et analysés.
EP03747800A 2002-08-09 2003-08-07 Procede, dispositif, programme informatique comprenant un systeme de code de programmation et produit de programme informatique pour analyser des activites neuronales dans des zones neuronales Withdrawn EP1527406A2 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
DE10236641 2002-08-09
DE10236641 2002-08-09
PCT/DE2003/002658 WO2004021245A2 (fr) 2002-08-09 2003-08-07 Procede, dispositif, programme informatique comprenant un systeme de code de programmation et produit de programme informatique pour analyser des activites neuronales dans des zones neuronales

Publications (1)

Publication Number Publication Date
EP1527406A2 true EP1527406A2 (fr) 2005-05-04

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EP03747800A Withdrawn EP1527406A2 (fr) 2002-08-09 2003-08-07 Procede, dispositif, programme informatique comprenant un systeme de code de programmation et produit de programme informatique pour analyser des activites neuronales dans des zones neuronales

Country Status (4)

Country Link
US (1) US7398120B2 (fr)
EP (1) EP1527406A2 (fr)
AU (1) AU2003266912A1 (fr)
WO (1) WO2004021245A2 (fr)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
ITMI20012828A1 (it) * 2001-12-28 2003-06-28 Gambro Dasco Spa Dispositivo non invasivo per il rilevamento della temperatura ematicain un circuito per la circolazione extracorporea del sangue e apparato
US9367288B2 (en) * 2006-11-22 2016-06-14 Psigenics Corporation Device and method responsive to influences of mind
US7983740B2 (en) * 2006-12-22 2011-07-19 Washington University High performance imaging system for diffuse optical tomography and associated method of use
US9480425B2 (en) * 2008-04-17 2016-11-01 Washington University Task-less optical mapping of dynamic brain function using resting state functional connectivity

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Publication number Priority date Publication date Assignee Title
WO2001039664A1 (fr) * 1999-12-02 2001-06-07 The General Hospital Corporation Procede et appareil permettant de mesurer des indices d'activite cerebrale
US20010056231A1 (en) * 2000-03-13 2001-12-27 Andrzej Jesmanowicz High resolution MRI imaging of brain functions
US7136696B2 (en) * 2002-04-05 2006-11-14 The Cleveland Clinic Foundation Neuron signal analysis system and method

Non-Patent Citations (2)

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See also references of WO2004021245A3 *

Also Published As

Publication number Publication date
AU2003266912A1 (en) 2004-03-19
US20050261874A1 (en) 2005-11-24
WO2004021245A3 (fr) 2004-11-04
US7398120B2 (en) 2008-07-08
WO2004021245A2 (fr) 2004-03-11

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