Basic Principles of Electroencephalography Research Paper

Zero-phase synchronization of high frequency oscillations between anterior and posterior brain regions takes place shortly after presentation of sensory stimuli. Energetic contemporary research suggests that synchronization of high-frequency oscillatory activity among distant brain regions may play an important role in ‘binding’—the integration of features of a complex stimuli, extracted by spatially separated ‘feature detectors,’ into a perceptual whole.

These aspects of brain organization and information processing are accessible from quantitative analyses of EEG recordings from the scalp. Usually, such recordings are obtained from an array of electrodes placed in accordance with the internationally standardized 10 20 Electrode Placement System and affixed to the scalp with paste. Data can be recorded from different numbers of electrode positions varying with the purpose of the study, but 19 channel recordings most commonly are used. Numerous economical and reliable EEG recording instruments are commercially available. While EEG machines initially recorded ink traces upon moving paper, present technology utilizes desktop computers for digital collection and storage of EEG data, so-called digital EEG or DEEG. Analysis usually is performed by computer after visual selection of artifact-free data.

These analyses usually utilize the Fast Fourier Transform (FFT) to quantify the amount of power at selected frequency intervals in the EEG from every recording channel to compute the power spectrum. Normative QEEG databases have been constructed for the power spectrum and the covariance matrix of spontaneous brain electrical activity. QEEG methods provide sensitive and precise measures for study of normal as well as abnormal brain functions.

2. Neurophysiological Basis Of The EEG

An anatomically complex homeostatic system regulates the EEG power spectrum. Brainstem, thalamic, and cortical processes involving large neuronal populations mediate this regulation, utilizing all neurotransmitters. Some of the neuroanatomical interactions and neurochemical transmitters comprising this control system have been clarified by recent neurophysiological and neuropharmacological research (Llinas 1988, Lopes Da Silva 1996, McCormick 1992, Steriade et al. 1990). On the basis of such studies, it is believed that pacemaker neurons distributed throughout the thalamus normally oscillate synchronously. Efferent thalamo-cortical projections globally distributed across the cortex receiving cortico-thalamic feedback produce the rhythmic electrical activity known as the alpha rhythm (7.5–12.5 Hz), which dominates the EEG of an alert healthy person at rest and can be seen readily as the most prominent peak in the power spectrum. Nucleus reticularis can hyperpolarize the cell membranes of thalamic neurons by gamma-amino-butyric acid (GABA) release, slowing the dominant alpha rhythm into the lower theta range (3.5–7.5 Hz) and diminishing sensory throughput to the cortex. Slow delta activity (1.5–3.5 Hz) is believed to originate in oscillator neurons in deep cortical layers and in the thalamus, normally inhibited by input from the ascending reticular activating system in the midbrain. Activity in the beta band (12.5–20 Hz) is believed to reflect cortico-cortical and thalamo-cortical transactions related to specific information processing. Activity in the 20–60 Hz band is referred to as gamma, and recent studies have included so-called ‘higher frequency’ activity as high as 600 Hz. These very fast oscillations appear to arise from corticocortico transactions and geniculo-cortical projections.

Evidence is reviewed below to support the belief that a complex homeostatic system regulates the stability and predictability of the normal EEG and may be genetically specified. A circuit summarizing the hypothetical neuroanatomy and neurochemistry of the homeostatic system postulated to achieve regulation of the EEG power spectrum has been published (Hughes and John 1999), in accordance with the insights provided by the research cited above. Activation of the mesencephalic reticular formation (MRF) causes inhibition of nucleus reticularis by cholinergic and serotonergic mediation which releases the thalamic cells from inhibition by nucleus reticularis. The dominant activity of the EEG power spectrum becomes more rapid, with return of alpha activity and the higher frequency beta activity (12– 20 Hz), and the flow of information through the thalamus to the cortex is facilitated. The cortex can activate n. reticularis directly by glutamatergic pathways to suppress the arrival of information to the cortical level and, by dopaminergic projections, the striatum can inhibit the MRF. Such inhibition of the MRF enables inhibition of thalamic neurons to occur and blocks the flow of sensory information through the thalamus to the cortex.

This model suggests that deficiencies or excesses of any of the neurotransmitters should produce marked departure from the homeostatically regulated normative EEG spectrum. Such neurotransmitter perturbations are widely believed to make decisive contributions to much psychiatric pathophysiology. Abundant documentation exists in the literature that demonstrates the sensitivity of QEEG to psychiatric disorders (for a review, see Hughes and John 1999).

Since this regulatory neurophysiological system is common to all humans, the EEG power spectrum can reasonably be expected to be stable and characteristic for all healthy individuals, with high specificity reflecting their common genetic heritage. These measures can also be expected to be sensitive to many modulations of the resting brain state as well as to brain dysfunctions present in some developmental, neurological and psychiatric disorders. The data supports both of these expectations. A thorough review of the abundant such evidence is beyond the scope of this research paper; only illustrative examples will be cited.

3. Principles Of QEEG

3.1 Normative Databases

The QEEG has been found to display remarkable stability and reproducibility among healthy, normally functioning individuals of any age, ethnic or cultural background (John and Prichep 1993). After proper editing to remove artifacts and transformations to achieve normal distributions and appropriate ageregression, the values of extracted QEEG measures referred to normative databases possess high predictability (Fein et al. 1983, Oken and Chiappa 1988). Test–retest replicability, both short and long-term, has been shown to be extremely high (Kondacs and Szabo 1999).

3.2 Data Acquisition And Analysis

In QEEG, multichannel recordings (usually 19 electrodes at standardized positions) of eyes closed resting EEG are edited visually. An artifact-free sample, usually 1–2 min long, is analyzed using the FFT. The power at each frequency of the EEG is averaged across the entire sample, providing a Power Spectrum with very high test–retest replicability.

The bandwidth of interest has long been considered to extend from about 1–20 Hz. traditionally separated into four wide frequency bands, typically defined as Delta (1.5–3.5 Hz), Theta (3.5–7.5 Hz), Alpha (7.5– 12.5 Hz), and Beta (12.5–20 Hz). More recently, the region of interest has been extended to include Gamma or ‘high frequency activity,’ with bands up to 100 Hz and beyond. Intracerebral as well as scalp recordings have related oscillatory activity at these high frequencies to cognition and information processing (Curio 2000).

4. Stability And Specificity Of The EEG Power Spectrum—The Basis Of QEEG

Confirmed reference databases provide reliable ‘population norms,’ thereby minimizing the need to collect control data. Comparison of individual QEEG results to such norms enables sensitive detection of developmental, neurological or psychiatric dysfunctions. Further, QEEG stability facilitates identification of transient brain activity related to temporary changes of brain state due to cognitive activity or other influences, by comparing sequential QEEG samples from the same person to each other or to a preceding ‘self-norm.’ QEEG studies using statistical evaluation of computer extracted features relative to population or self-reference norms are often referred to as ‘neurometric’ studies (John et al. 1977, 1988). A major advantage of QEEG vs. all other contemporary brain imaging technologies is the unique availability of confirmed, ethnic fair, normative data that reduces or eliminates the need for separate control studies. The high specificity of QEEG is beyond the confidence level achieved by many routinely used clinical tests, such as mammograms, cervical screenings, or CT brain scans (Swets 1988). Further, the demonstrated high test–retest replicability provides potential simplification in repeated measure experimental designs.

Using analog methods, the eyes closed, resting EEG power spectra of large samples of healthy normally functioning individuals were found to show systematic changes, initially from ages 17–64 (Matousek et al. 1967), and then from ages 1–21 (Matousek and Petersen 1973). Using general purpose digital computers, not only were the systematic changes with age soon confirmed, but no significant differences were found between the age-regressed EEGs of normallyfunctioning Swedish children and white or black US children (John et al. 1980).

Normative data now exist from 1 to 95 years of age, for each electrode in the standardized International 10/20 System, for absolute power, relative power, mean frequency, coherence, and symmetry, as well as for covariance matrices which quantify inter-relationships among brain regions (John et al. 1988, John and Prichep 1993). These multivariate composite measures are unique to QEEG. Since cognitive processes as well as psychiatric disorders rarely engage focally localized brain activity, QEEG measures are, therefore, well suited to studies in these areas.

4.1 Independence From Cultural And Ethnic Influences

Soon after the initial demonstration of cross-cultural equivalence of normative data, normally functioning black children in Barbados were found to display the same values of the EEG power spectrum as the US and Swedish groups (Ahn et al. 1980). The independence of the normative QEEG descriptors from cultural and ethnic factors enables objective assessment of functional as well as anatomical brain integrity in persons of any age, origin, or background, repeatedly confirmed in studies from Barbados, China, Cuba, Germany, Holland, Japan, Korea, Mexico, Netherlands, Sweden, US, and Venezuela (Ahn et al. 1980, Alvarez et al. 1987, Gasser et al. 1982, Harmony et al. 1987, Jonkman et al. 1985, Yingling et al. 1986, Diaz de Leon et al. 1988, Duffy et al. 1993, 1995, Matsuura et al. 1993, Oken and Chiappa 1988, Ritchlin et al. 1992, Valdes-Sosa et al. 1992, Veldhuizen et al. 1993).

5. QEEG Sensitivity To Changes In Brain State

The temporal resolution of state changes detectable by QEEG is far superior to that of any other modality except the magnetoencephalogram, or MEG. The very high short-term replicability provides excellent sensitivity to transient changes in brain state. Such changes can reflect allocation of brain resources to various cognitive, motor or perceptual tasks, or functional alterations imposed by some internal or external influences.

5.1 Detection Of Brain Regions Engaged By Mental Activity

QEEG has been used extensively to detect brain regions engaged during performance of various activities. Studies performed shortly after discovery of the EEG (Berger 1937) found that during mental activity, dominant alpha waves were replaced by low voltage fast beta activity, termed ‘desynchronization.’ This phenomenon has been exploited in numerous studies of ‘event-related desynchronization,’ ERD, or event-related synchronization, ERS (Petsche 1990, Pfurtscheller 1992). Very narrow band QEEG analyses compare the power spectrum in a reference state to the power spectrum immediately after presentation of a sensory stimulus, imposition of a cognitive task, or onset of a change in the environment (e.g., hearing a musical composition). Differential ERD or ERS in various brain regions enables topographic maps to display the loci of significant state alterations correlated with the experimental procedures. The detected topography has, in some cases, been confirmed by other functional imaging techniques such as regional cerebral blood flow (rCBF). Other QEEG studies of cognitive processes have focused upon changes during cognitive tasks relative to the profile before initiating the task (Fernandez et al. 1995, Harmony et al. 1996).

One variation on this theme uses the strategy of labeling several attributes of a stimulus by presenting each aspect at a distinctive repetition or ‘tracer’ rate. By very narrow band analysis of the power spectrum of every recording derivation, the modulation of power at the tracer frequency in different brain regions provides a functional image of the particular brain regions engaged in the sequential steps required to process the stimulus dimensions labeled by each frequency (John et al. 1997). This technique recently has been adapted to MEG studies of binocular rivalry (Tononi and Edelman 1998).

Another productive variation has been to examine the changes in phase angles of oscillatory rhythms between brain regions, rather than changes in local power, as cognitive tasks are performed (Gevins et al. 1981). The logic of this approach is based upon the expectation that transmission from one cortical region to another will be reflected in the phase angle of some critical frequency between the interacting regions. Topographic maps based upon cortico-cortical delays among brain regions allow inferential construction of the sequence of steps in the information processing system used in a given task.

5.2 Adaptive Segmentation Of Brain Fields

Evidence has been provided that the topographic fluctuation of brain voltage fields in real time is quantized into intervals during which the field is stable in one topography followed abruptly by a period of stability with a different topography, etc. These brief stable periods have been called microstates (Lehmann 1971). Computer classification methods have been applied to the field contours manifested during a prolonged observation and four prototypic field patterns have been identified (Pascual-Marqui et al. 1995). Evidence has been published that different modes of ideation characterize these different microstates (Strik and Lehmann 1993).

Normative data have been computed from large groups of healthy individuals from age 6 to 90 and show that the same four basic microstate modes exist across this representative age span, with average duration of about 80 milliseconds (Koenig et al. in press). This duration is approximately the same as that of a ‘perceptual frame’ (Efron 1967), which is the period during which sequential stimuli will be perceived as simultaneous. This similarity suggests that microstates may reflect the parsing of time into discontinuous brief samples which characterize subjective perception. Evidence has been provided that microstate duration is altered from this stable value in abnormal conditions such as ADHD, Alzheimer’s disease, and schizophrenia (Lehmann 1994).

Using the mathematically very different approach of spatial principal component analysis (SPCA) based upon the covariance matrices across the 10 20 scalp array of electrodes, it has been found that over 90 percent of the variance of the spontaneous EEG and event related potentials can be accounted for by 4–5 SPCs in normally functioning individuals (Duffy et al. 1995, Easton and John in press). It is noteworthy that the topography of these SPCs closely corresponds to that of the prototypic microstates. These stable patterns of spatial covariance in the scalp EEG may reflect the organization of the major functional neuroanatomical systems which represent global resources allocated as needed by the brain.

5.3 QEEG Assessment Of The Depth Of Anesthesia

Studying several hundred actual surgical procedures, a set of invariant reversible QEEG changes during loss and return of consciousness were found which were independent of the particular anesthetic agents used (John et al. 2001). These invariant changes were translated into multiple stepwise discriminant functions and embedded in a real-time monitor of the depth of anesthesia, called the Patient State Analyzer, or PSA (Physiometrix, Inc.). The PSA has received approval from the Food and Drug Administration (FDA) for routine use as an intraoperative monitor of the depth of anesthesia.

The successful development of a quantitative index for the level of consciousness gives evidence that QEEG is a technology with the potential of making powerful contributions to basic and applied psychological research into the relationships between brain and behavior.

6. Three-Dimensional QEEG Source Localization Images

In the 1990s, mathematicians, physicists, and neurophysiologists devised numerous approaches to the problem of localizing the sources of scalp voltages within the depths of the brain. Some of these methods are now used to pinpoint neurosurgical targets in patients with intractable seizures. Perhaps the most promising approach to the inverse solution of distributed generator sources is low-resolution electromagnetic tomography (LORETA) (Pascual-Marqui et al. 1994). This method has been applied to the localization of intra-cranial epileptic foci and space-occupying lesions. Locations identified by LORETA correspond well to the actual location of the abnormalities as confirmed by subdural electrodes or during neurosurgery.

Topographic mapping of QEEG data has been extended to QEEG tomography, or QEEG-T, using variable resolution electromagnetic tomography (VARETA) (Valdes-Sosa 1997). A standard probabilistic MRI atlas in Talairach space, constructed from more than 300 normal MRIs (Evans et al. 1993), was evaluated to constrain the hypothetical generators of the distributed inverse solution to voxels composed of gray matter. For 3500 voxels, age regressed voxelnormative data were calculated across the age range 5 to 97 years over spectra ranging from 0.39 to 19 Hz in 0.39 Hz intervals. Statistical probability tomographic images (SPTI) were then obtained by Z-transformation of each voxel of the raw VARETA image at each frequency, obtained from any individual subject, relative to the corresponding voxel-normative data. Spatial statistical methods evaluate deviations from the norms of anatomical regions of interest. Results are displayed on slices from the probabilistic MRI atlas with each voxel color-coded for statistical significance. A complete description of the VARETA method and the underlying mathematical basis has very recently been published (Bosch-Bayard et al. 2001).

The accuracy of this QEEG-T method has received excellent confirmation in MRI and neurosurgical studies of space-occupying lesions (Fernandez-Bouzas et al. 1999) and is routinely in use in our laboratories to study brain dysfunctions in psychiatric and neurological patients. It has been used recently to localize invariant QEEG correlates of the loss and return of consciousness during anesthesia (John et al. 2001) QEEG-T is an economical brain imaging technology which provides very high resolution of brain electrical activity in space and time, yielding statistically interpretable images. These features make the method an attractive competitor to functional brain imaging alternatives such as PET, fMRI and MEG, which are not only more expensive (all), lack normative data (all), require substantial time for image acquisition and therefore lack temporal resolution (PET, fMRI), or impose a radiation burden which limits repeated testing for safety reasons.

7. QEEG Sensitivity To Brain Dysfunctions In Psychopathology

A voluminous literature attests to the utility of the conventional EEG in neurological and psychiatric disorders, but test-retest reliability between as well as within clinical EEG practitioners is poor. QEEG supplements the EEGer’s trained eye with literally many hundreds of reliable quantitative measures which can be computed automatically and can generate precise statistical comparisons of each of these numerous measures from an individual relative to databases of age-matched normal subjects or of patients having different diagnoses. A broad consensus exists across hundreds of EEG and QEEG studies on the high incidence and particular types of abnormalities found in different psychiatric disorders.

Replicated findings of QEEG abnormalities have been reported in cerebrovascular disease, degenerative dementias, major affective disorders, mood disorders, obsessive-compulsive disorder, schizophrenia, learning disabilities, attention deficit disorder with and without hyperactivity, and mild head injury. Discriminant functions have been developed and independently replicated which accurately classify patients into DSM categories, using QEEG profiles. Using cluster analysis based upon QEEG measures, in several DSM categories subtypes have been identified within samples of patients homogeneous by diagnosis. Subtype membership correlated well with response to treatment or evolution of illness.

Space precludes detailed descriptions here of the abnormal QEEG features distinctive for each of these disorders. More complete details have been published elsewhere (Hughes and John 1999). The evidence overwhelmingly shows that QEEG assessment provides a valuable adjunct to clinical diagnosis and treatment selection in behavioral, developmental, neurological, and psychiatric disorders.

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