Clinical Work: Diagnosis of Neurological Disorders using EEG
Quantitative EEG
From the patient's perspective, Quantitative EEG, or qEEG, is similar to regular EEG, but with about 50% more electrodes. In addition to the usual visual interpretation of the results, however, qEEG subjects the measured electrical potentials to a complex series of mathematical calculations. The result is a three dimensional map of the brain, often allowing neurophysiologists to pinpoint the source of epileptic seizures or improve their ability to diagnose subtly different conditions.
To understand qEEG one must first understand EEG. qEEG, or quantitative EEG, began in the 1970s and early 80s as an attempt to extract from brain electrical activity more than what could be readily appreciated by simple, unaided visual inspection of EEG. In that sense qEEG should be viewed as an extension of and not a replacement for traditional EEG. Clinically, as now used, qEEG should always follow the preparation and analysis of the classic EEG (or dEEG). The human eye is still superior to the computer in many aspects of brain signal analysis. Pioneers in qEEG include names such as Bickford, Duffy, Harner, John, Lehmann, Ueno, and many others. Quantitative EEG adds modern computer and statistical analyses to traditional EEG and EP studies. Because results may be graphically displayed on a schematic map of the head, the procedure is often called Brain Electrical Activity Mapping (BEAM) or simply "mapping." The clinical use of qEEG was pioneered at the Children's Hospital Boston (the paediatric teaching hospital of Harvard Medical School), by the current laboratory director, Dr. Frank H. Duffy. Each year 350-400 clinical qEEG studies are performed, totaling over 3,000 studies done altogether. From the patient's perspective, the procedure is nearly identical to the EEG and EP. About 50 percent more electrodes are applied to the scalp for better definition and about twice as much data is collected to facilitate rigorous analyses. Both resting (EEG) and stimulated (EP) data is collected and are first read by traditional visual inspection before being statistically compared to our unique data base of brain electrical activity. Reference data is available on over 2,000 normal healthy subjects ranging in age from newborn infants to adults in their 70s.
In general, patients are referred for BEAM when it is believed that brain function is abnormal but traditional techniques have failed either to demonstrate or adequately define electrical abnormalities. Typical referrals include patients of all ages (newborns to adults) with questioned but undefined seizures; intractable epilepsy (especially when better determination of the epileptic source or sources is needed); Landau-Kleffner Syndrome (when treatment is being considered); unexplained developmental delay or intellectual decline (suspected dementia); learning disability and/or attentional deficit disorder where the causes for this are in question; behavioral dysfunction or emotional illness (including unremitting headaches) when organic etiology is suspected; chronic fatigue syndrome; and whenever the EEG would be expected to show something but does not.
Special EP activation stimuli are available to suit special issues (e.g., dyslexia or reading disability, Landau-Kleffner Syndrome). Special analyses are available (e.g., three dimensional source analysis to locate the epileptic focus, or foci, within the brain itself using just scalp recorded measures). qEEG techniques are also used on data recorded from our inpatient Long-term Monitoring service.
Spectral Analysis and Mapping
To assist in the estimation of EEG spectral content (one of the most difficult tasks by visual inspection), EEG data are entered into a computer, as for dEEG, and spectral content is rigorously determined by the use of techniques of mathematical signal analysis (typically by the FFT or Fast Fourier Transform algorithm). One of the first problems was how to visualize results since qEEG typically uses more channels than EEG. The solution was to map the results using colored grey scaling on schematic maps of the head. To some, such brain electrical activity mapping or simply "mapping" is taken as synonymous with qEEG. However mapping is only a display technique and only the first step. The heart of qEEG lies with the underlying computerized analytic and statistical techniques.
Spectral Coherence
A special result of spectral analysis is the measure of coherence between two electrodes. It assesses the similarity of spectral content of two electrodes over time and is usually taken to reflect a measure of "coupling" between brain regions. It is virtually impossible to estimate coherence by visual EEG inspection. Some illnesses may begin with abnormalities of cortical coupling. Leuchter has reported such abnormalities in Alzheimer's disease and Thatcher found abnormality of coherence as the best discriminator of mild closed head injury.
Statistical Probability Maps
Spectral maps provide excellent displays of the spatial distribution of EEG spectral content and are clinically useful as such. However, it soon became evident that in some way it would be necessary to estimate when such data were outside of normal bounds for a patients age. This lead, first, to the need for and the development of normative data bases of brain electrical activity at all ages. Second, it lead to the development of the technique of mapping not just a patient's brain activity but also the degree of statistical deviancy of the patient from the normal data base (in units of standard deviation of Z-scores). Such images of deviancy are referred to as SPM (statistical or significance probability maps). Thus a neurophysiologist may look at a SPM and locate regions of possible clinical abnormality by deviant regions on the SPM. The term "encephalopathic" often refers to brains with excessive EEG slowing. A typical application would be to determine whether behavioral disturbance in an adult is due to early dementia (increased slowing) or otherwise uncomplicated depression (no increase of slowing). QEEG techniques add significant power to the search for subtle encephalopathic change. Although developed first for qEEG analyses, the SPM technique has been widely adapted for use with other neuroimaging techniques.
Event-Related Evoked Potentials
Another area where qEEG techniques have been applied is to the long latency sensory evoked potentials. EEG represents the brain's ambient, spontaneously ongoing electrical activity. Evoked potentials (EPs) are the brain's transient response to externally applied stimuli - such as light flashes, auditory clicks, and mild electrical shocks. These stimuli form, respectively, the visual evoked response or potential (VEP), auditory EP (AEP) and somatosensory EP (SEP). Since the EEG is much higher amplitude than the EP, it is necessary to apply a stimulus repetitively at random times and average the result so as to effectively remove the random background EEG and visualize the EP. This computerized technique is often referred to as signal averaging. Classic neurophysiology employs a few EP channels and evaluates the short latency response (e.g., under 30 msec). When obtained these signals are seen to arise from specific deep brain structures and allow for assessment of structures within the brain stem and thalamus. When longer latencies (longer times from stimulation) are evaluated, signals appear to be coming from the cortical mantle. Unfortunately the complex waveform morphologies from a large set of such long latency EPs can be very difficult to analyze by unaided visual inspection. However with the use of normative databases and the SPM technique, regions of clinically important abnormality can be delineated within the complex combined spatial-temporal information within long latency EP data sets. Many qEEG laboratories incorporate the long latency EP along with spectral analyzed EEG signals and traditional EEG as part of their routine clinical studies. Such EP data tend to be sensitive to clinical conditions where cortical dysfunction is hypothesized (e.g., dyslexia, schizophrenia, Alzheimer's disease) although they are also often found to be abnormal in epilepsy.
Discriminant Analysis
Discriminant analysis refers to the established "multivariate" statistical technique whereby a multiplicity of gathered data (multiple variables) are combined into a single number (the discriminant function) in such a way that this new variable (the discriminant) maximally separates two patient populations. John, Duffy, and Thatcher have all demonstrated that when discriminant analysis is applied to qEEG data, resulting discriminant functions are accurate in classifying individual subjects into clinically relevant diagnostic groups (e.g., head injured or not, dyslexia or not, bipolar vs. monopolar depression, etc). Such discriminants are more widely used for psychiatric than neurologic issues.
Epileptic Source Analysis
A major goal in the neurophysiologic investigation of patients with epilepsy is to locate the epileptic focus. This involves determining where inside the three dimensional brain, the abnormal signals are generated using only data gathered from the intact scalp. This is a key prelude to removal of the epileptic focus by neurosurgical procedure. Considerable progress has been made in our ability to calculate, from simple scalp recorded segments containing epileptic spikes, where these signals arise. Scherg has been a leader in the development of brain electrical source analysis or besa. It involves calculation of a source assuming a multi-sphere brain model. Other techniques (using boundary or finite element analyses) such as that pioneered by Fuchs use MRI constructed realistic head models. Multisphere calculations permit better separation of multiple epileptic sources, whereas, realistic head models allow for better representation of results with the patient's own brain structure. This technology is rapidly improving and it is likely to shown increasing use and value in the combined neurological and neurosurgical investigation of epileptic patients.
If you are a patient who would like learn more about the diagnosis of neurophysiological dysfunctions using qEEG, please email Dr Shankardass
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