Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/242—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
  • A61B5/245—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetoencephalographic [MEG] signals
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/369—Electroencephalography [EEG]
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/369—Electroencephalography [EEG]
  • A61B5/372—Analysis of electroencephalograms

Definitions

• the invention relates to a method and a non-invasive system for detecting and evaluating neuronal electrophysiological sources by exploring a multiplicity of points belonging to an area of interest.
• the present invention relates to the field of acquisition and treatment, by non-invasive brain imaging, of electrophysiological signals, particularly for the aid to therapeutic decision and medical diagnosis. More particularly, the invention relates to a method and a non-invasive system for detecting and evaluating neuronal activity in patients with neurological diseases with clear electrophysiological signature, such as epilepsy, neurodegenerative diseases , Alzheimer's, Parkinson's, etc.
• the purpose of the analysis of cerebral electrophysiological signals is to identify brain regions involved in normal or pathological electrophysiological activity. It is known from the state of the art, various tools for collecting and analyzing electrical signals corresponding to the neuronal electrophysiological activity of a patient.
• the first of these consists in the invasive implantation of intracranial electrodes, for example in regions suspected of epileptogenesis.
• This type of implantation requires a delicate, risky and potentially traumatic surgical procedure for patients. It involves placing electrodes in the brain very precisely to record the activity of regions suspected to be the site of pathological electrophysiological activity.
• the risk of implantation-related infections and subdural hematomas is important. The patient stays implanted for long periods observation periods in specialized clinical services and the economic costs of this type of protocol are considerable.
• the imaging technique requires the registration of the MEG or EEG recordings with a structural image of the cortical anatomy that can be obtained in a second step by means of a Magnetic Resonance Imaging (MRI) examination.
• MRI Magnetic Resonance Imaging
• This operation has many sources of errors and inaccuracies (of the order of a centimeter maximum). Or 1 small variations in the relative position of measurement points vis-à-vis anatomical area of interest lead to large variations in the estimate of the neural currents to be matched.
• the present invention aims at overcoming the drawbacks of the state of the art by proposing a method and a non-invasive system for detecting and evaluating the neuronal electrophysiological activity which are at the fast, complete and accurate.
• Another objective is to provide the clinician with reliable and representative information of brain activity in the environment of a region of interest in order to integrate the variability of the results in his final diagnosis.
• the invention proposes a step of estimating the electrophysiological potentials in a region of interest located around a predefined anatomical target so as to integrate the uncertainty on the measurements due to the repositioning errors. relative cortical anatomy and MEG or EEG surface recordings.
• the subject of the invention is a non-invasive method for detecting and evaluating neuronal electrophysiological activity comprising a step of non-invasive acquisition of anatomical and electrophysiological data in a region of analysis, a step of identifying at least one electrophysiological source and a step of selecting at least one main measurement point.
• This method further comprises a step of estimating electrical potentials at a plurality of measurement points secondary areas belonging to an area of interest around the main measurement point.
• the instability of the measurements related to the positioning of the main measuring point is compensated by the plurality of secondary measurement points which make it possible to obtain an estimate of the deep signals that is representative of the environment in the area of the earth. interest.
• the selection step consists in choosing the implantation of virtual electrodes, defining the main measurement points, as a function of the electro-physiological data acquired during the preceding steps;
• the estimation step includes a classification phase of the secondary measurement points, in particular as a function of the electrophysiological data acquired during the preceding steps.
• This classification is advantageous because it provides the user with two different signals and therefore representative of the environment in the area of interest;
• the classification is furthermore carried out by a decomposition into singular values.
• the classification is carried out by a classification to the nearest neighbor in the sense of the K-average algorithm;
• the method comprises a phase of calculating the electrophysiological potentials representative of each of the classes
• the area of interest corresponds substantially to a cube of 1 cm 3 centered on the main measurement point.
• the invention also relates to a non-invasive system for detecting and evaluating neuronal electrophysiological activity comprising apparatus for acquiring anatomical and electrophysiological data. in an analysis region, a module for identifying at least one electrophysiological source and a module for selecting at least one main measurement point, further comprising a module for estimating electrical potentials in a plurality of secondary measurement points belonging to an area of interest located around the main measurement point.
• the electrical potential estimation module comprises means for classifying the secondary measurement points (52) according to two classes; the module for estimating electrical potentials comprises means for calculating the electrophysiological potentials representative of each of the classes.
• FIG. 1 a diagrammatic representation of an exemplary embodiment of a system for implementing the method for detecting and evaluating the neuronal electrophysiological activity according to the invention
• FIG. 2 a flowchart of the method according to the invention.
• FIGS. 4a, 4b, 4c and 4d four graphs representing the electrophysiological signals measured by intracranial electrodes or estimated by means of the method according to the invention.
• the system comprises a magnetic resonance imaging apparatus 2a, hereinafter MRI, and a magnetoencephalograph 2b, hereinafter MEG, for acquiring electrophysiological data.
• MRI magnetic resonance imaging apparatus
• MEG magnetoencephalograph
• These two devices 2a, 2b are connected to a processing unit 3 consisting of a direct problem solving module 4, a problem solving module 5 on the entire mesh of the cortex and an estimation module 6 of the Electro-physiological potentials within an area of interest 8.
• the processing unit is furthermore advantageously connected to a display screen 9 for the representation of the electrophysiological signals obtained by means of the method according to the invention.
• the method for detecting and evaluating the neuronal electrophysiological activity illustrated in FIG. 2 comprises a first step 10 for acquiring physiological data making it possible to model an analysis region 12, for example the entire cortex. brain of a patient. This modeling step is performed using the T1-weighted anatomical MRI 2a. The data is stored and the MRI of the patient is segmented so as to perform a surface mesh of the cerebral cortex.
• three first markers such as vitamin A pellets, are placed on the patient's skull before the MRI to allow the registration of the head with the MEG 2b system for further processing.
• the second step 20 of the method according to the invention is to perform a magnetoencephalographic examination of the patient.
• This MEG examination consists of acquiring and digitizing surface electromagnetic data collected using an MEG 2a device composed of a plurality of sensors positioned on the cortical surface of the patient.
• the magnetoencephalographic examination is carried out using a MEG CTF ⁇ / SM MedTech system, the number of MEG sensors is equal to 151 and the sampling frequency of 1250 Hz.
• any recording made on an equivalent MEG instrument, or even by an EEG system incorporating a plurality of scalp electrodes may be suitable for the analysis proposed by the invention.
• the MEG or EEG examination consists of recording the brain activity of the subject either at rest, with eyes open or closed, or during an experimental paradigm aimed at exploring certain specific functions of the brain such as perception, the language , memory, attention, etc.
• the duration of the recording must be sufficient to ensure the acquisition of at least one electrophysiological event of interest for the study, in this case at least one epileptic point.
• Three second markers such as coils, located at the same positions as for the MRI, for example on the nasion, the left ear and the right ear, can mark the position of the MEG sensors relative to the anatomy of the patient.
• the third step of the method according to the invention consists in identifying the electrophysiological sources of the analyzed region.
• a first phase 30a of the method consists in performing a registration of the data from the two MRI 2a and MEG 2b measurement systems. This registration is performed by superposition of the first and second markers. Alternatively, there are registration systems with a greater number of reference points using a complete scanning of the scalp by a three-dimensional locating system of Isotrak / Polhemus type or equivalent.
• the electrophysiological data recorded during the first 10 and the second 20 step are used during a resolution phase of the direct problem 30b.
• the direct problem solving module 4 makes it possible to model the magnetic and potential fields collected on the scalp and generated by a configuration of known sources.
• a third phase 30c of the step of estimating the position 30 of the electro-physiological sources consists, in agreement with the direct model, of reconstructing and identifying in time and space the generators, or electronic sources. physiological, at the origin of the electrophysiological signals collected on the surface by the MEG 2b system.
• This step 30c identifies the electrophysiological sources of the signals recorded outside the head by the MEG sensors.
• This reverse problem solving technique is described in particular in the document “S. Baillet, J. C. Mosher, R. M. Leahy,” Electromagnetic brain mapping “, IEEE Signal Proc. Mag., 18 (6), 14-30, Nov. 2001 ".
• This problem can advantageously be posed when the sources are to be detected on the surface of the cortex obtained by treatment of the MRI examination of the subject according to step 10 and after relative repositioning of the MRI and functional MEG or EEG anatomical information according to the invention. step 30a.
• - B is the data matrix containing the MEG or EEG surface measurements whose number of lines corresponds to the number of sensors and whose number of columns corresponds to the number of temporal samples of the recordings;
• G is the gain matrix that is given by the direct problem according to the procedure of step 30b;
• J is the unknown matrix of cortical sources whose respective amplitudes are to be estimated.
• ⁇ represents the noise present in the recordings.
• ⁇ is a parameter that weights the weight of the regularizing term in relation to the fit of the model to the data.
• the treatment unit identified the electrophysiological sources of cortical origin of the MEG or EEG recordings.
• the method according to the invention then consists, during a fourth step 40, in allowing the investigator to select the position of the main measurement points 42 whose electrical potentials created by the corresponding electro-physiological neuronal sources are estimated.
• this technical aspect allows the investigator to access a virtual electrodes implantation scheme 44 of depth comprising at least one virtual sensor corresponding to a main measurement point 42.
• the method proposes to visualize on the display screen 9, the electro-physiological data acquired during the steps 10, 20 and 30 and to visualize the electrophysiological activities collected according to the implantation of virtual depth electrodes 44.
• the position of the main measurement points 42 can be determined according to the usual clinical assessment of the patient which leads to the development of a pattern of implantation of depth electrodes.
• the regions likely to be at the origin of a pathological brain activity are targeted by the clinician as a priority and will be subject to a virtual implantation of electrodes according to the principles of the invention.
• the investigator can determine the anatomical location of regions of pre-established interest in the context of the object of the experimental study (occipital cortex and vision, hippocampus and memory, etc. .).
• the process according to the invention therefore provides a step of estimating the electrophysiological potentials 50 in a multitude of secondary measurement points 52 covering an area of interest 8 around the main measurement point. 42 and whose dimensions cover the uncertainties relating to the geometric registration between the MEG / EEG and MRI examinations.
• the area of interest 8 corresponds to a cube of 1 cm side centered at the main measurement point 42 and the interior volume of this area of interest 8 is sampled in 1000 Secondary measurement points 52.
• the dimensions of the area of interest 8 and sampling in this area of interest 8 can be defined directly by the investigator.
• the dimensions of the area of interest 8 are related to both the repositioning uncertainty between the MRI and functional MEG / EEG anatomical examinations and the distance between two consecutive measurement points as defined by the investigator .
• the volume of the region of interest can be limited by the distance separating two consecutive electrodes for the material that will be used in fine by the neuro-chirugian during the operation.
• a region that is too small will not make it possible to correctly grasp the measurement of the uncertainties linked to a particular estimate of the neural currents.
• the dimensions of the area of interest result from a tradeoff between the relevance of the measurements and the level of measurement of the uncertainties on the particular estimate.
• the area of interest 8 can advantageously be represented by a cube of about 1 cm side, which easily encompasses the geometric registration uncertainties already mentioned.
• the method comprises a first estimation phase 50a of the electrophysiological potentials at each of the secondary measurement points 52 and a second phase 50b consisting in distributing the estimated electrophysiological potentials within the zone of interest 8 according to two distinct and antagonistic classes so as to provide clinicians with two different signals that are representative of the environment within area of interest 8 and that incorporate the variability of results inherent in the experimental context of the measurements.
• the method according to the invention thus makes it possible to establish an estimate having a high degree of reliability compared to a method which would present only a single signal.
• the classification is performed according to a singular value decomposition, and a classification to the nearest neighbors by the K-average method, called kmeans.
• the singular value decomposition is a mathematical method which consists of the decomposition of a matrix of measurements M on orthonormal vector bases, said singular, left U and right V weighted by singular values arranged on the diagonal of a singular matrix S, such that M ⁇ Us V, where V is the transposed matrix of V.
• the singular value decomposition is used here for the purpose of updating. trends in the spatial distribution of the electrophysiological potentials in each of the secondary measurement points 52 in the area of interest 8. If this area of interest 8 is a cube of one centimeter on the side, it can then be decomposed into 1000 points of Secondary measures 52. Each row of the measurement matrix M is thus made up of time steps of one of these secondary potentials 52. The number of columns of the measurement matrix M corresponds to the number of temporal samples specific to the data collected.
• the singular vectors in the matrix U represent a base of orthonormal time series, and thus decorrelated.
• the corresponding singular values therefore indicate the contributions in terms of relative power in the set of original measurements.
• the method then consists in recovering the first two components of the matrix U having the highest relative powers and multiplying them by the first two respective singular values S, in order to extract the two measurements from the matrix of measurements M. the most representative.
• the method then consists in calculating the time correlation ratio between:
• the same method is applied for the second component of the matrix U.
• the two time series of the matrix of measurements M having the maximum correlation rate with the first and second components are then extracted. These two time series are used to initialize a classification step 52b of the time series of the measurement matrix M according to two classes in order to provide a compact representation of the variability of the measurements within the predefined area of interest 8.
• the classification of the time series is carried out according to the principle of K-means, or k-means, preferably for K equal to two classes.
• the classification of the time series could alternatively be carried out by any time series classification approach.
• the measurement used to classify the time series of the measurement matrix M is based on the temporal correlation between the series of measurements and those of the two seeds.
• the method then provides, in a step 60, to represent the two signals corresponding to the representative electrophysiological potentials of each class on the display screen, so that the investigator can take into account instability and variability of results in the analysis of experimental measurements.
• FIG. 3 represents a parcel of cortex and a zone of interest 8, in this case a cube of one centimeter of side centered at a main measurement point 42 defined by a virtual electrode 44 of depth. . It is interesting to observe the correlation of the potentials estimated in this area of interest 8 with respect to the original deep signal measured with a real intracranial electrode. Two distinct areas of color appear clearly: a first zone whose activity is not very correlated with the actual measurement (dark colors) and a second zone strongly correlated (light colors).
• FIG. 4a represents the electrophysiological potential measured 62 by an invasive intracranial electrode at a main measurement point
• FIGS. 4b and 4c represent the estimated electrophysiological potentials 64 and 66 in a zone of interest 8.
• Figure 4d showing a superposition of the measured signal 62 of Figure 4a with the estimated signal 64 of Figure 4b, that the significant events are still detected and that the amplitude of the invasive and estimated signals are consistent.
• the invention is not limited to the embodiments described and shown. It is also possible to provide several steps of acquisition of electrophysiological data before the registration of these data. In addition, the geometry of the area of interest 8 may be different from that presented.
• this zone of interest 8 could possibly take into account the physiological data acquired during the first MRI step.

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