Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2000-07-20
2002-04-09
Lateef, Marvin M. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
C382S128000, C324S301000, C324S302000, C324S244000, C324S260000, C324S248000
Reexamination Certificate
active
06370414
ABSTRACT:
TECHNICAL FILED
The present invention relates to the measurement and analysis of bioelectro-magnetic activity in electrically active organs. More specifically, the present invention relates to a method of transforming the measurements into a corresponding current distribution map estimating the original sources and combining current distribution maps of two or more states of organ activity into difference maps that reveal regions of the organ in which activity differs for the different states.
BACKGROUND
The ion currents of electrically active organs such as the brain and heart can generate magnetic fields that can be measured outside the surface of the body. Further, the corresponding electrical potentials in the organs themselves, when conducted through the body, can be measured on the skin, using surface electrodes or in the interior of the body by means of invasive depth electrodes. The process of computing the biological source current or currents giving rise to the observed magnetic and electrical measurements is generally referred to as “the bioelectromagnetic inverse problem”. The importance of having a biomagnetic or bioelectric inverse solution is that it can be used to correlate electrophysiological function with a particular coordinate within the body. This, in turn, can be used to associate normal function and dysfunction with specific anatomic structures. It can be shown that, in three dimensions, there can be no unique bioelectromagnetic inverse solution without applying constraints to the solution, such as assuming the number and configuration of possible sources. Notwithstanding this, it is possible to calculate useful estimates or approximations of the distribution and intensity of source activity from electrophysiological measurements.
In the present art, Magnetoencephalographic (MEG) and Electroencephalographic (EEG) signals may be examined for waveform morphology in independent channels, characterized, for example, by their frequency and amplitude. In addition, MEG and/or EEG measurements, recorded from a plurality of sites, are often represented as topographic distributions of either spontaneous or evoked signals in the form of signal intensity maps about the head. Such topographic maps are also commonly presented for MEG/EEG in distinct frequency bands.
It is also known to average MEG and EEG signals synchronously with a stimulus presented to the subject or to a voluntary motor movement from the subject. Signal averaging can improve the signal-to-noise ratio (SNR) of the brain activity underlying a particular sensory or motor event. The resulting averaged signal is conventionally known as the event-related potential (ERP) or the event-related field (ERF). The averaged evoked response is most useful for improving the SNR or activity in the primary cerebral cortex, in which the time delay between stimulus and response has low variability. However, the evoked response relating to higher cognitive functions, which involve associative cerebral cortex, can be more variable in time delay and duration relative to the driving stimulus. Thus, signal averaging is less useful for evaluating higher cognitive functions. The application of signal averaging to EEG and MEG brain signals is predicated upon the notion that the underlying neural events are identical with each and every stimulus event. Common sense and personal experience dictate that this is not necessarily the case for higher levels of brain functioning. The delay between external events and related thought processes is known to vary greatly. Brain activity associated with critical higher mental processes, such as the production and understanding of language, are therefore not adequately represented by the averaged evoked response.
Another known representation of the MEG and EEG signals is the Equivalent Current Dipole (ECD). The ECD can be computed by fitting a simplified model of a current dipole (or multiple dipoles), each characterized by a location, current vector, and magnitude, to the MEG and/or EEG measurements at some selected instant of time, usually in the least squares sense. In the Minimum Norm Current Distribution method, a more complex, often under-determined model is fitted to the measurements at some instant of time by a least squares method. Both the ECD and Minimum Norm methods can yield erroneous results (e.g., inaccurate localization and magnitude of cortical generators) when noise is present in the EEG or MEG signal. When the Minimum Norm solution is underdetermined (and it almost always is), the non-uniqueness of the inverse problem implies that the result is only one of many possible source configurations that can explain the measurements. Thus, only spontaneous EEG or MEG signals having high signal-to-noise ratio and a source characterized by few parameters, such as epileptic spikes and abnormal high-amplitude “slow waves” (a sign of closed-head injury) can be localized accurately by these two methods. Normal (non-pathological) events within the brain are of much lower amplitude; when possible, signal averaging is conventionally used to improve the signal-to-noise ratio of such events.
Much of the above-mentioned prior art is described in SQUID-Based Measuring Techniques by Manfried Hoke in THE ART OF MEASUREMENT METROLOGY IN FUNDAMENTAL AND APPLIED PHYSICS, edited by B. Kramer (1988).
The activity of electrically active organs, such as the brain, may also be monitored and imaged using Positron Emission Tomography (PET) and fictional magnetic resonance imaging (fMRI). Neither of these imaging modalities are direct measures of the electrochemical events that comprise neural activity. Instead, they detect local changes in metabolism, metabolic products or blood flow within the brain. These changes are consequent to the energy requirements of the electrochemical events. Although electrochemical events can occur in less than one millisecond, corresponding local changes in metabolism and blood flow are much slower, having time constants of several seconds. Hence, PET and fMRI lack the time resolution of EEG and MEG, as they are indirect measures of brain activity.
Lead Field Synthesis (LFS) departs from previous methods for analyzing bioelectromagnetic measurements. LFS is disclosed by S. E. Robinson and W. C. Black in U.S. Pat. No. 4,977,896 (Robinson et al.) and U.S. Pat. No. 5,269,325 (Robinson et al.) entitled “Analysis of Biological Signals using Data from Arrays of Sensors”. Instead of localizing brain activity, LFS increases the spatial selectivity of an array of MEG sensors by summing the weighted observations. The weights are selected to impart higher spatial selectivity to a specified coordinate in the head. The sum of products of the measured signal and these weights results in a “virtual sensor” that estimates electrical activity as a function of time at the selected location.
It is also known that the bioelectromagnetic inverse solution can be improved by constraining the location of source currents to the cortex of the brain, since it is the electrical currents flowing between the dendrites and cell bodies of the neurons that are the primary contributors to the measured magnetic fields and electrical potentials. Furthermore, the source current is known to flow in a direction approximately normal to each point on the cortical surface which provides an additional constraint for the inverse solution. The coordinates and vectors describing the cortical surface can be extracted from anatomical images of the brain. These images can be obtained, for example, using magnetic resonance imaging (MRI) or computed tomography (CT) scanning of the head.
While certain advances have been made in this art, there is still much room for improvement. For example, heretofore, the prior art approach has been unable to localize brain activity in a manner which can adequately represent spontaneous (unaveraged) activity (e.g., brain activity), particularly that of normal higher cognitive functions.
Specifically, certain prior art approaches (e.g., the ECD and Minimum Norm methods discussed above)
CTF Systems Inc.
Lateef Marvin M.
Lin Jeoyuh
Merchant & Gould P,C,
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