Patent No. 5307807 Method and system for three dimensional tomography of activity and connectivity of brain and heart electromagnetic waves generators

 

Patent No. 5307807

Method and system for three dimensional tomography of activity and connectivity of brain and heart electromagnetic waves generators (Valdes Sosa, et al., May 3, 1994)

Abstract

A method and system for the localization and characterization of the generators of human brain electromagnetic physiological activity includes a set of bioelectromagnetic amplifiers, sensorial stimulators, and a computer based system for signal analog to digital conversion and recording. Sufficient statistics, including higher order statistical moments, for event related components are computed from the recorded signals, either in the time, frequency, or time-frequency domain, retaining stationary, non-stationary, linear, and non-linear information. The localizations, orientations, activities, and connectivities of the generators are obtained by solving the inverse problem using sufficient statistics under anatomical and functional constraints. Realistic head geometry and conductivity profiles are used to transform the measurements into infinite homogeneous medium measurements, through use of ananatomical deconvolution operator, thus simplifying optimally inverse solution computations. Goodness of fit tests for the inverse solution are provided. Generator characteristics are visually displayed in the form of three and two dimensional head images, and optionally include probability scaled images obtained by comparing estimated generator characteristics with those of a normal population sampled and stored in a normative data base.

Notes:

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to electronic computerized medical instruments and more particularly to the localization and characterization of the generators of brain and heart electric and magnetic activity by a non-invasive computerized method and system.

2. Description of Related Art

The determination of the three dimensional localization and of the temporal activity of the neuronal generators which give place to waveshapes, in an electroencephalogram (EEG) and an magnetoencephalogram (MEG) related to pathologies of the central nervous system (CNS), constitutes an important medical problem. Such knowledge can be helpful in producing more precise diagnostics in diverse neuropsychiatric pathologies and in determining more efficient treatments. A typical example is the study of the focus location followed by its sequential propagation in epilepsies that are being evaluated for surgical treatment.

The EEG and the MEG both have their common origin in the ionic currents produced by the cellular elements (the neurons) composing the CNS. The total current density vector field is determined by the vectorial additive combination of all of the elementary currents. The simultaneous activation of a large number of such elements, together with an adequate geometrical distribution, produces resulting electric potentials and magnetic fields which can be measured outside the head. In the transformation process from total current density to measurable external fields, the effects of the volume conductor properties of the different tissues composing the head must be taken into account: brain, meninges, cerebral spinal fluid, skull, and scalp.

The resulting measured fields have the characteristics of a stochastic process, which can be described either in the frequency domain or in the temporal domain, as a function of the statistical moments. In the case of a Gaussian process, first and second order moments give an exhaustive description.

The neural elements which generate a given EEG or MEG component may be localized on a small cortical area ("concentrated generator") or may, on the other hand, be widely distributed in different parts of the CNS ("diffuse generator"). The determination of the spatial distribution of the generators and of the multivariate statistical moments describing their interactions is very important.

For a number of decades electric potential measurements of the CNS have been performed by means of electrodes placed on the scalp. Much experience has accumulated on the practical utility of the visual inspection of the EEG in the diagnostics and treatment of patients with neuropsychiatric diseases. More recently, brain magnetic fields have been measured (U.S. Pat. No. 4,591,787), offering complementary information to that obtained from the EEG.

The current state of the art, as reflected in U.S. Pat. Nos. 4,201,224; 4,408,616; and 4,417,592 is summarized as follows. Quantitative analysis of brain electric activity by means of digital signal processing methods (QEEG) allows an objective evaluation of the functioning of the CNS. The signal recorded at each electrode is summarized by means of a set of descriptive parameters (DPs), based on stochastic process modeling. The DPs reflect the normal and pathological functioning of the CNS. Topographic maps based on the DPs are clinically useful, and even more so when statistically compared to a normative data base.

However, this analysis method generates an excessively large number of DPs, thus making quite difficult the evaluation of a particular patient. Moreover, the method does not attempt to localize the generators responsible for the measured DPs, thus limiting the clinical usefulness and contributing to the excessive redundancy of the DPs due to volume conduction effects. Finally, EEG is limited to the study of second order moments in the frequency domain, which means that the EEG has been implicitly assumed to be a Gaussian stochastic process, despite evidence revealing the non-linear nature of such signals.

In U.S. Pat. No. 4,913,160 a method for the reduction of the dimensionality of the DPs is proposed based on principal components (PCs) computation. This procedure produces minimum sets of linear combinations of the original DPs, with optimum descriptive properties, but which are meaningless in terms of the underlying neuronal generators and their localization. Furthermore, this method does not take into account the non-linear nature of the original signals.

An improvement in the usefulness of QEEG has been achieved by means of biophysical models which take into account the behavior of the electromagnetic fields produced by current sources in a complex volume conductor such as the human head. In this sense, U.S. Pat. Nos. 4,416,288; 4,753,246; and 4,736,751 propose procedures for eliminating the distortion effects due to the volume conductor. However, they do not deal with the spatial characterization of the generators.

Several attempts have been made to fit equivalent dipoles to measured fields in order to represent, albeit approximately, concentrated generators, either in the time domain or in the frequency domain. These procedures are based on the minimization of a certain distance criterion between the measurements and the theoretical field values due to a current dipole inside a volume conductor model of the head.

This type of procedure for source localization, based on first order moment data, does not take into account the existence of diffuse generators, nor the existence of other sources of "spatial noise". Furthermore, a statistical method for testing the goodness of fit of the source model is not provided. On the other hand, there is a fundamental limit on the number of dipoles that can be estimated, the maximum number being roughly equal to the number of electric or magnetic signals divided by six.

In French patent 2,622,990, several improvements are achieved by using frequency domain second order moment data, in the form of coherence matrices. An estimation method for the cross spectral spatial noise matrix is proposed, under the assumption of interelectrode independence, the method thus being statistically equivalent to the classical factor analysis model. The eigenvectors of the common factor space are then used for determining the concentrated generators (as many as the number of common factors).

However, empirical and theoretical evidence points towards a diffuse generator model for spatial noise, producing a structured cross spectral noise matrix for EEG and MEG. This explains why the proposed noise elimination method under the interelectrode independence assumption gives incorrect results. In such a case computations based on coherence matrices are not justified. Furthermore, dipole fitting methods applied to second order moment data or to eigenvector data are not equivalent. Finally, interactions between generators, neither linear nor non-linear, are taken into account in the eigenvector dipole fitting approach.

SUMMARY OF THE INVENTION

The objective of the present invention is a method and system for the characterization of both concentrated and diffuse generators of the EEG and MEG, based on all the statistical information available in these signals, in the form of statistical moments of all orders, in the time or frequency domain. The invention will allow the detection and estimation of the effect of the diffuse generators on the EEG and MEG. Also, it will allow the estimation of an increased number of concentrated generators, together with their linear and non-linear interactions.

In accordance with a first aspect of the invention there is provided a method for the three dimensional tomography of activity and connectivity of brain electromagnetic waves generators, said method including:

a) Attaching or approximating a set of electrodes and magnetic sensors to the scalp of an experimental subject to detect brain electromagnetic physiological activity in the form of an electroencephalogram (EEG) and a magnetoencephalogram (MEG), and measuring the exact positions of the electrodes and sensors with respect to a reference coordinate system determined by certain anatomical landmarks of the subject's head;

b) Amplifying the said electromagnetic signals detected at each electrode and sensor;

c) Obtaining on-line digital spatio-temporal signals, consisting of the EEG and MEG, by connecting analog-digital converters to each amplifier, and digitizing all data as it is gathered, under the control of a central experimental program;

d) Optional presentation of visual, auditory, and somato-sensorial stimulation to the experimental subject during EEG and MEG recording, carried out under the control of the central experimental program;

e) Optional recording and identification of responses produced by the experimental subject during EEG and MEG recording, for the inclusion of fiducial markers in the recording, and for the modification of the central experimental program;

f) Optional real-time detection of spontaneous events in the EEG and MEG produced by the experimental subject during recording, for the inclusion of fiducial markers in the recording, and for the modification of the experimental program;

g) Determination of a parametric description for the anatomy of the experimental subject's head (parametric geometry), by means of: i) exact computations based on anatomical or functional image processing of the subject's head, or ii) approximate computations based on a small set of anatomical measurements and comparison with a data base of normal and abnormal variability;

h) Using the parametric geometry for constructing a head phantom with all the volume conductor properties of the real head;

i) Performing EEG and MEG measurements on the head phantom due to known current dipoles located in the corresponding neural tissue volume, for determining the linear operator which transforms original EEG and MEG measurements into equivalent infinite homogeneous medium measurements (anatomical deconvolution);

j) Using the parametric geometry for determining anatomical and functional constraints for the localizations, orientations, activities, and connectivities of the brain electromagnetic waves generators (generator constraints);

k) Digital preprocessing of the EEG and MEG for artifact and noise elimination, and for the separation of EEG and MEG samples related to the fiducial markers, for obtaining event related components (ERCs);

l) Statistical analysis of the ERCs for determining the most adequate numerical description of the spatio-temporal properties in terms of sufficient statistics;

m) Computation of the activities and connectivities of the ERCs generators, based on the static solution to the inverse electromagnetic problem, under the above-mentioned generator constraints, using said sufficient statistics for the ERCs transformed to an infinite homogeneous medium by means of the anatomical deconvolution;

n) in case the generator constraints do not allow a unique solution to the inverse problem, the number of ERCs generators should be decreased sufficiently to allow for the proper identifiability of the inverse problem;

o) Statistical evaluation of the goodness of fit of the inverse solution, taking into account the existence of colored spatial and temporal noise, and including statistical hypotheses testing on the absence of activity and connectivity of the ERCs generators;

p) Optional computation of multivariate distances between ERCs generators characteristics (localizations, orientations, activities, and connectivities) of the experimental subject and of a normal population as determined from a normative data-base;

q) Visual display of three dimensional and two dimensional images corresponding to the localizations, orientations, activities, and connectivities of the ERCs generators, and the optional display of the multivariate distances.

In accordance with a second aspect of the invention there is provided a system for the three dimensional tomography of activity and connectivity of brain electromagnetic waves generators, including:

a) A set of electrodes and magnetic sensors adapted to be attached or approximated to the scalp of an experimental subject for the detection of brain electromagnetic physiological activity in the form of electroencephalogram (EEG) and magnetoencephalogram (MEG) electromagnetic signals, and means for measuring the exact positions of the electrodes and sensors with respect to a reference coordinate system determined by certain anatomical landmarks of the subject's head;

b) Means for the amplification electromagnetic signals detected at each electrode and sensor;

c) Means for obtaining on-line digital spatio-temporal signals consisting of EEG and MEG signals;

d) Means for the presentation of visual, auditory, and somato-sensorial stimulation to the experimental subject during EEG and MEG recording;

e) Means for recording the vocal or movement responses produced by the experimental subject during EEG and MEG recording;

A central digital computer subsystem, consisting of a multitasking processor or a set of distributed processors, that includes:

Means for reading the experimental subject's image data in the form of CAT scan images, NMR images, or in the form of a small set of anatomical measurements, and means for computing and storing the descriptive parametric geometry, the anatomical deconvolution operator, and the generator constraints;

Means for constructing a head phantom based on the descriptive parametric geometry, and means for the implantation of current dipoles in the corresponding neural tissue volume of the phantom;

Means for programming and for the control of experiments that comprise stimulation of the experimental subject, recording of the subject's responses, detection and recording of special EEG and MEG events, and simultaneous recording of the digitized electromagnetic signals;

Means for pre-processing the recorded electromagnetic signals for artifact and noise elimination;

Means for estimating event related components (ERCs);

Means for computing the ERCs sufficient statistics;

Means for estimating the additive non-white spatio-temporal noise due to diffuse generators;

Means for performing tests of hypotheses about the goodness of fit of the estimated inverse solution;

Means for estimating the localizations, orientations, activities, and connectivities of the ERCs generators;

Means for comparing the ERCs generators characteristics with a normative data base and means for computing multivariate metrics;

Means for the visual display of ERCs generators characteristics and of the multivariate metrics.