Brain Electrical activity: A historical perspective
As early as 1848, researchers reported the observation of electrical signals from the nervous system. Animal studies in 1875 proposed similar findings of brain wave activity. Then, however, the notion that features of measurable electrical activity could describe brain functions remained relatively obscure for nearly 50 years. Hans Berger then published an article in 1929 describing a pattern of oscillating electrical activity recorded from the human scalp.
In the 1930s and 1940s the Electroencephalograph (EEG) became the object of much interest in the realm of psychiatric and neurological sciences. Those early studies suggested a preponderance of certain EEG features in clinical populations compared to normal individuals. Jasper, et al. (American Journal of Psychiatry, 1938) reported on an electroencephalographic analysis of behaviour problems in children that still holds up to scientific scrutiny. Dawson et al (Journal of Neurosurgery, 1951) reported associations between EEG and head trauma that are similarly robust in their longevity. As digital computer technology developed in the 1960s and 1970s, it became feasible to statistically assess and precisely quantify many more EEG parameters than is possible through human visual inspection of raw EEG waveforms. Studies reported in this era, indicating various relationships between brain waves and mental conditions laid groundwork for the many confirmatory studies that followed.
|Hirshberg, Chui, and Frazier edited a special edition of the prestigious journal: Child and Adolescent Psychiatric Clinics of North America. In this edition the journal reviewed QEEG and Neurotherapy for ADHD and Neurodevelopmental disorders. Click here to read the preface to this edition.|
Werry et al (1964) reported that EEG data suggest a significant excess of minor dysthymia and a deficit of centrencephalic and epileptiform pattern among hyperactives as compared with controls. Klingerfuss et al (Neurology, 1965) reported on electroenphalographic abnormalities of children with hyper-kinetic behaviour. Satterfield et al (1973) reported EEG aspects of minimal brain dysfunction. In an early study using computerised EEG, Mechelse et al (Electroencephalography and Clinical Neurophysiology , 1975), reported on both visual and quantitative analysis of the brain wave activity of children with and without specific reading disability. In that same journal (1977), Fuller found that attenuation of the parieto-occipital alpha rhythm is correlated with attention deficit. Grunewald-Zuberbier et al (1975) found that, in periods free from stimulation, hyperactive children have higher alpha and beta amplitudes, more alpha waves and fewer beta waves, interpreted as a lower state of EEG arousal in the hyperactives.
Over the past two decades, neuroscientists have conducted an intense study of the brain's electrical functioning. With the dawning of the "Digital Age", the "art" of medical specialists who visually inspect EEG records has been enhanced by the "pattern recognition" capabilities of a computer technology called "QEEG" (quantitative electroencephalography).
In the late eighties, Dr. E. Roy John and his research team at the Brain Laboratories at New York University Medical Center published what has proven to be the seminal work in computer-assisted differential diagnosis of brain dysfunctions. (Science, 1988). Building on that work researchers, using QEEG and powerful computer analysis, have created an objective evaluation that is highly sensitive and specific for assessment and interpretation of human electroencephalography.
Frank H. Duffy, M.D. of Harvard Medical School is thought of as the father of QEEG. In 1994, Dr. Duffy and other prominent researchers and clinicians in QEEG prepared a position paper of the American Medical EEG Association (AMEEGA) presenting the current status of QEEG in clinical practice. They reported three broad uses of QEEG in clinical practice, "the first often broadly termed ‘organicity detection,’ the second involving more specific diagnoses using Discriminant functions, and the third epileptic source localization via [Dipole Localization Method]."
Selected diagnoses where the authors suggested QEEG is of use included Attention Deficit Disorder, Learning Disability, and Traumatic Brain Injury, including mild head injuries and post concussion syndrome. They went on to say that QEEG studies are most helpful in supplementing traditional neuropsychological evaluations of children with learning disabilities where diagnosis or aetiology remain in doubt.
In cases of head injury, which results in predictable QEEG abnormality, the QEEG is often redundant to traditional electroencephalographic, neurological, and radiographic evaluations. However, when the head injury is mild, the patient may continue to be symptomatic, despite normal results of traditional tests. In such cases, the authors indicated that the outcome of a QEEG study might greatly assist patient management.
In order to be of trustworthy value, a test (any test) must possess two critical characteristics: Validity and Reliability. A test is "reliable" if it reports the same result upon testing and retesting. For example, a ruler is a reliable test that yields the same result repeatedly, or, "reliably." A test is "valid" if it measures what it sets out to measure. However, the ruler, a reliable measure of length, would not be considered to be a "valid" test of weight, as it purports to measure length not weight.
It would appear, then, that QEEG has been established as a valid test of brain function. Research also indicates that a highly reliable, precise test can be developed if brain waves of an individual are compared to well-constructed statistical databases. This diagnostic procedure yields:
" . . . a level of specificity and sensitivity that is comparable to sonograms, blood tests, MRIs and other diagnostic measures commonly used in clinical practice."
Robert Thatcher, PhD, Norman Moore, M.D., E. R. John, PhD, F. Duffy, M.D., et. al. Clinical Electroencephalography, 1999
The above authors are scientists responsible for the key databases used by Psychophysiologists and Clinical Neuroscientists. They are eminent researchers in their field and conduct their research at Harvard University, New York University, Veterans' Administration Hospitals, and other leading medical institutions.
Over the last few years, there has been wider acceptance of brain wave analysis as a diagnostic technology in clinical applications. The FDA (US Federal Drug Administration) regulates the medical devices and methods involved. In addition, many major insurance carriers in the US now routinely agree to pay for the procedure.
In 1999, two prominent QEEG scientists, John R. Hughes, M.D., Ph.D. and E. Roy John, Ph.D., published a comprehensive review of the scientific literature relating to the use of QEEG in psychiatry. The article (The Journal of Neuropsychiatry and Clinical Neurosciences) reviewed over 500 scholarly papers published just in the last decade, relating specific patterns of abnormality to particular diagnoses.
This article indicates the clinical utility of QEEG as a diagnostic tool in many mental illness categories. Among these categories, the literature specifically documents reliable EEG differences between ADHD and non-ADHD children and shows QEEG Discriminant analysis to be sensitive and specific to sub-types of ADHD, ADHD versus normal subjects, and ADHD versus specific developmental learning disorders (SDLD) individuals. The authors summarised their review of this voluminous body of literature by stating:
"Evidence has unequivocally established that 'mental illness' has definite correlates with brain dysfunction . . . QEEG promises to have greater expanded use as psychiatrists become more familiar with its many applications."
"In view of the accumulation of positive findings surveyed in this article, more psychiatrists may wish to explore the utility of these methods for themselves and begin to apply them in their clinical practices."
At the same time, in 1999, the American Psychiatric Press published Psychiatry in the New Millennium in which it was contended that, although new methods for imaging the function of the brain make headlines and "in spite of their unquestioned potential, these methods are not adequately represented in daily psychiatric practice".
Brain wave analysis appears to be the diagnostic tool of the future for mental health. Nonetheless, it is clear that what is true of all emerging science will prove to have been true of QEEG. Early adopters, who are the first to see the advantages of new technology, will recognise that the technology of brain wave analysis is a science whose time has come; other, more conservative, health care practitioners will follow, but more slowly.
Current Physiological Measurements
A number of valuable tests of the brain are currently available: eg, PET scans, Functional MRI, SPECT Scans, and MEG. Unfortunately, these tests are costly and, in some cases, there are risk factors associated with the procedures. For example, consider the PET scan. A PET scan measures the metabolic activity of the brain and is a good test for localising regions of altered activity within the brain. However, PET scans involve the injection of radioactive labels and are considered unsafe for repeated use over a short time period or with pregnant women and small children.
Similar radiation risk, pricing, and limited availability are also true of other imaging techniques such as rCBF (which measures regional cerebral blood flow), MEG (which assesses brain electromagnetic activity), and MRS (magnetic resonance spectroscopy).
Importantly, recent studies have concluded that QEEG findings are highly correlated with other types of brain analysis. Moreover, with brain wave analysis, subtle brain dysfunction can be detected that is not discernible at all with other methods.
It is important to make a clear differentiation between QEEG tests of the brain and other commonly used, and perhaps more familiar, imaging techniques in medicine. For example, x-rays, CAT scans and MRIs are all used to measure brain anatomy, or structure. The QEEG, on the other hand, measures brain physiology, or function.
It is also important to understand that a QEEG is not the same as a "clinical EEG," which is used in medical practice to evaluate epilepsy or to determine if there is serious brain pathology, such as a tumor. By contrast, the QEEG does not assess the structure of the brain, but rather, evaluates the manner in which a particular person’s brain functions. It is not designed to diagnose tumors, epilepsy, or other structural medical conditions.
The report of brain wave analysis is a diagnostic aid that gives health care practitioners an objective scientific tool for use in assessing mental health. The report provides information about exactly what is functionally out of balance in a given patient’s brain, and, in turn, may be causing that individual's symptoms. With this more precise understanding of the underlying physiology, the clinician can make a more confident diagnosis and determination of the most appropriate treatment.
Diagnosing ADHD in Children
Prominent in headlines today are discussions of the mental health of children. It appears that we are in the midst of an epidemic of Attention Deficit/Hyperactivity Disorder (ADHD). The National Association of School Psychologists (N.A.S.P.), and the National institute of Mental Health (NIH) have warned practitioners of the wide variety of psycho-medical and bio-medical problems that can be mistaken for ADHD, or that may co-exist with ADHD, stressing that it is always essential for a child to be carefully evaluated.
According to N.A.S.P., ADHD "Look-alikes" include depression, stress-induced anxiety states, biologically-based anxiety disorders, bipolar disorders, schizophrenia, and other medical disorders. While children with look-alike disorders may fulfil the diagnostic criteria for ADHD, they may have an entirely different underlying physiological condition and, therefore, might more appropriately receive a different diagnosis, resulting in different treatment. Moreover, if a medical or other psychiatric disorder is presenting as ADHD, a treatment that merely improves the ADHD symptoms may leave a residue of untreated behavioural problems, mood abnormalities or disorders of physiology. In such cases, even if stimulants – which are commonly prescribed for ADHD – are helpful, or if environmental changes improve the child’s self-control, it is critical to insure that other, perhaps more serious, problems are not left to smoulder.
The N.A.S.P. guidelines conclude that ADHD needs
"a psycho-medical evaluation that matches our growing awareness of the complexity that goes by the simple name of ADHD ."
The QEEG holds particular promise in answering that call for the more accurate diagnosis of childhood mental health disorders. Dr. Joel Lubar, a pioneer in the use of QEEG in the diagnosis of ADHD, was the principal researcher in a landmark 1985 study (Journal of Learning Disabilities) that reported spectral analyses of EEG differences between children with and without learning disabilities.
Others have added to this body of research linking ADHD and brain wave dysfunction. Colby, in the Journal of Child Neurology (1991) reported on the neuroanatomy and neurophysiology of attention. Benson, in the same journal, (1991) discussed the role of frontal dysfunction in attention deficit hyperactivity disorder. Chabot et al (Clinical Electroencephalography, 1995) found QEEG to be a useful adjunct to behavioural testing and clinical evaluation in the differential diagnosis of children with SDLD (specific developmental learning disorders) and those with ADHD. Discriminant functions that use combinations of QEEG features were found to distinguish these two types of developmental disorders from each other and from normal development with accuracy levels between 85 and 95%.
Moreover, Chabot et al reported that, within the ADHD population, QEEG could be used to distinguish those children that respond favourably to dexamphetamine from those that respond to methyphenidate. The authors also present preliminary evidence that the QEEG can help identify children who respond to Thioridazine and not to stimulants.
The research concluded that QEEG could differentiate children with ADHD from children without ADHD and from others with look-alike disorders.
Within the catch-all category of ADHD, other researchers, notably Daniel Amen, who uses Single Photon Emission Computerised Tomography (SPECT) to aid in the diagnosis, and Joel Lubar are among others have used QEEG to discern up to seven sub-types of ADHD.
There is much research defining specific brain function abnormalities that have been linked to behavioural and mental dysfunction through the use of QEEG. It would appear the clinical question is not whether QEEG is the appropriate tool for diagnosing childhood mental health, but how and where best to implement this promising technology.
About Brain Waves
Brain wave analysis assesses mental functioning through the use of measurements of brain wave activity. Brain waves are the fluctuations, usually rhythmical, of electric impulses produced by large group of neurons firing in the brain. These electrical signals are the results of an elaborate electro-chemical chain of events. EEG, the recording of the electrical activity of the brain, is a standard medical diagnostic procedure used in hospitals everywhere.
Brain wave activity reflects one’s level of arousal and shows decidedly different patterns, depending on whether the individual being measured is awake, asleep, or engaged in a cognitive task. Scientific evidence, growing out of a great deal of research and clinical study, now documents that these brain signals are related not only to level of arousal, but also to many aspects of cognition (thinking) and affect (emotion).
Nerve Cell Anatomy
Neurons (also called brain cells or nerve cells) are the basic working units of the brain that generate the electrical activity. Large assemblies of neurons in turn behave as electrical generators responsible for the brain waves measured on the scalp. Neurons need both electrical and chemical stimulation in order to operate. Neurons function in both the initiation and the conduction of electrical impulses in our nervous system. In order to produce an electrical impulse, neurons must be triggered by a "stimulus," which can be anything inside or outside the body that evokes a physical or psychological response.
When triggered, the electrical impulse travels along the neuron’s outgoing branch, called the axon. The axon is surrounded by a myelin sheath (a fatty covering) that acts as an electrical insulator. The myelin sheath also assists the speed of the travelling electrical impulse down the axon to the point at which it meets the next neuron at a synapse.
The incoming branches are called dendrites. The space where the axon of one nerve cell connects with the dendrite of the next nerve cell is called the synapse, or synaptic gap on account of the 1/1,000,000 inch gap at this point.
Nerve signals jump the synaptic gap with the help of specialised chemicals called neurotransmitters that are released from the tips of the dendrite. These neurotransmitters carry messages from neuron to neuron to neuron. Depending on the stimulus, these neurotransmitter chemicals will act either to excite or inhibit a response depending upon which chemical (e.g., norepinephrine, dopamine, serotonin, acetylcholine) are released into the synaptic gap.
There may be several different neurotransmitters at a synapse simultaneously, causing a communication "debate" resulting in an electrochemical resolution, a message causing either the firing or in the inhibition of firing or the neuron. The chemical messages exchanged at the synapses are susceptible to such things as fatigue, Essential Fatty Acid and micronutrient deficits, oxygen deprivation, toxic chemicals, and drugs. Psychoactive drugs are pharmaceutical agents that work by either imitating or interfering with the chemistry of the neurotransmitters, thereby influencing the message received at the nerve synapse. Hence the firing of neurons reflects the chemical activity occurring at the synapses.
Brain Wave Clinical Bands
Quantitative EEG topography, sometimes referred to as "Topometric Brain Mapping" measures the electrical patterns present on the surface of the scalp. Accessed and analysed through digital technology, these measurements primarily reflect cortical electrical activity or "brainwaves." Some brainwaves occur at faster frequencies, or wave speeds; some are quite slow. The classic names of these EEG bands are delta, theta, alpha, SMR and beta and are identified according to their frequency, which is measured in terms of repetitions (or "cycles") per second ("cps") or "Hertz" (Hz). There are some variations in opinion as to the best way to group brain waves into clinical bands, but generally, it is agreed that, from slow to fast, brain waves should be grouped as follows:
- Delta brain waves (1-3 Hz) are the slowest, highest amplitude brainwaves, and are present primarily during sleep or when in an empathetic state. Excess delta activity in the awake state is usually indicative of dysfunction.
- Theta waves (4-8 Hz) are present when daydreaming or fantasising. At the same time, creativity and intuition are also associated with theta waves. This contrast occurs because theta waves occur at two levels: The lower range of theta (4-5 Hz) basically represents the twilight zone between waking and sleep. It is a profoundly calm, serene, floaty, drifty state. In this range, conscious intellectual activity is not occurring. It is also the range of frequencies produced in excess by children and adults with ADHD.
By contrast the higher range of theta (6-8 Hz), when present at midline frontal sites is associated with a state of highly inwardly focused attention. This is where the mind goes when we are engaging in complex, inwardly focused problem solving, such as mental arithmetic. This is also the level people enter when they go into in a deep hypnotic or meditative state. Persons experienced in self-hypnosis and highly hypnotizable persons (as well as very proficient meditators) produce more 6-8Hz theta brainwaves in both a waking state and in hypnosis.
- Alpha waves (8-11 Hz) are slower and larger. They are associated with a state of relaxation and basically represent the brain shifting into idling gear, relaxed and disengaged, waiting to respond when needed. If one merely closes his or her eyes and begins picturing something peaceful, in less than half a minute there will be an increase in alpha brainwaves. Alpha is present typically when one feels at ease and calm or in a position to change one's mind efficiently and effectively in order to accomplish a task.
- Sensory Motor Rhythm (12-15 Hz) measured over the sensorimotor cortex are brain waves associated with mental alertness and readiness for action, combined with behavioural stillness.
- Beta waves (16 Hz and above) are small, faster brainwaves associated with a state of mental or intellectual activity and outwardly focused concentration. Beta waves are present when one is thinking, problem solving, processing information, or anxious.
Having made these differentiations among the various brain wave bands, it must also be pointed out that at any one time everyone has a mix of all those brainwave frequencies present in different parts of the brain. However research has shown that:
- In the awake state, delta brainwaves also occur when areas of the brain go "off line" to take up nourishment.
- If one is becoming drowsy, there are more delta and slow theta brainwaves present. If someone is inattentive to external events and daydreaming (internalising), there is more low frequency 4-5 Hz theta present.
- If individuals are exceptionally anxious and tense, excess high frequency beta activity is present.
- Persons with ADHD, learning disabilities, and head injuries tend to have excess slow waves (usually delta, slow theta, and sometimes excess alpha). When excess slow wave activity is present in the executive (frontal) part of the brain, it is difficult to control attention, behaviour, and emotions. Such persons may have serious problems with concentration, memory, controlling impulses and moods, or with hyperactivity. They can’t focus well and exhibit diminished intellectual efficiency.
While all types of brain waves are always present regardless of the state of mind, the dominant frequency generally describes an individual’s state of consciousness. No frequency is "better" or "worse" than any other. In fact, each is essential to healthy mental functioning. Problems arise when humans have the improper mix of frequencies for dealing with the task at hand.
Brain Wave analysis or Topometric brainmapping
Generally speaking, QEEG is an assessment tool used to aid in identifying mental health conditions by means of statistical evaluations of the EEG. The QEEG is useful to the clinician as an adjunct to traditional clinical assessment, as it provides a sensitive and specific method to detect subtle variations in the activity of the brain. Subtle brain wave variations might otherwise go unnoticed by the clinician, even with a traditional, visually inspected EEG. But the computer-based QEEG may provide evidence of an underlying dysfunction that needs to be recognised, evaluated and treated.
QEEG is the only widely available technology that can be used for this purpose today.
In the past QEEG reports have not been readily understandable to the average clinician, who does not have extensive training in QEEG physiological correlates or clinical neuroscience. Up until now, these factors have been barriers to the broader acceptance of QEEG as a clinical science. Nowadays however, the use of a scientifically validated QEEG database (John and Prichep, 1988) whose establishment and validation has been extensively peer reviewed in the literature, provides well normed data sets, standardisation of processes and clarification of information reported. This makes the resulting reports and interpretations are more readily understood and more useable by healthcare practitioners.
High Specificity of QEEG
Research has found that QEEG has a high level of reliability that is equal or superior to routinely used clinical tests such as mammograms, cervical screenings, blood tests, MRI and CAT scans. A comprehensive literature review (Hughes & John, 1999) in the Journal of Neuropsychiatry and Clinical Neurosciences reported,
"Of all the imaging modalities, the greatest body of replicated evidence regarding pathophysiological concomitants of psychiatric and developmental disorders has been provided by EEG and QEEG studies."
QEEG measures the minute electrical activity of a person's brain and then, using proprietary software, compares that unique pattern to known databases of "normal" and "abnormal" patterns. This type of computer-driven statistical analysis is particularly useful in evaluating difficult and borderline cases.
The Patient’s Data Collection Experience
The data collection procedure begins with a simple, non-invasive procedure in which a clinician or qualified technician captures a sample of the raw electrical activity of the patient’s brain, using an "electrode cap" with 10/20 electrode placement (fig. 1).
Sensors in the cap are electrically connected to the scalp by means of a gel that can be simply washed off with water after the data recording. The cap is connected to specialised medical equipment that amplifies the microscopic electrical signals that the patient’s brain produces and sends those signals to a computer.
A syringe with a blunt needle is used to squirt conductive gel into the sensors.
Fig 1. Electrocap used in data collection procedure
Fig 2. Topometric QEEG maps expressed in Z scores (standard deviations from the norm).
The colour black and two colour bands on either side (dark red and dark blue) are considered to be within the normal range. The hotter colours indicate excesses and the colder colours indicate deficits.
There are hundreds of studies using QEEG to investigate patterns of brain activity and their associated behaviours and psychiatric presentations.
Knowledge of these patterns helps us determine what brain electrical patterns are associated with ADHD and Learning Difficulties, enabling us to formulate more targeted treatment strategies
A key feature of the report is the probability statement, a highly distilled reduction of an extremely complex process involving thousands of data sets. The report also highlights those characteristics found to be the most significant statistical contributors to the discriminant findings.
Further, the report includes full colour topographic maps (fig. 2) and tables (fig. 3) of data that can be used for more detailed analysis, should the referring clinician choose to forward the patient data and report to a specialist for additional interpretation.
Fig 3. Z score tables. The boxed figures are those that exceed 1.96 Standard Deviations
Ease and Safety
The only electricity involved in the cap comes from electrical activity produced by the patients own brain. No electricity flows toward the patient. In fact, there is no possibility of the patient being shocked by the electrical connections as the patient and the cap are specifically isolated from all of the other equipment. Once the electrode cap has been put on the patient will need to sit quietly for a few minutes with eyes closed while the computer collects a sample of brain wave activity. During data collection, the patient is asked to minimise movements of the head, blinking, and clenching of the jaw muscles. Data may also be collected while the patient gazes softly at a focal point or is engaged in specific tasks such as reading or while performing a visuo-spatial task on a computer. The entire recording process usually takes less than an hour.
Standardised data analysis insures reliability and validity
Once a sample of the electrical activity data of the patient’s brain has been collected, the proprietary software of the acquisition device performs a computerised transformation of the raw, analog brain waves into digital form, which can be analysed by the computer. The first phase of analysis is designed to identify, characterise and highlight brainwave activity on the basis of its prominent features. Next, the analysis uses computerised statistical procedures to make over a thousand statistical analyses to compare patient EEG patterns with those of a scientific normative database that have been developed over the past twenty years in major research laboratories and hospitals.
This procedure provides a sensitive and specific method to detect subtle variations in the activity of the brain and quantifies the likelihood that a particular patients profile is consistent with one or more of the clinical groups in the original database study.
The results of a brain wave analysis allow the health care practitioner to determine, in a highly scientific, objective manner whether, and how, the patients brainwave patterns are significantly different from what is considered normal for the patients age group.
In the first phase of analysis, the patient’s brain wave data are used to measure the efficiency of electrical interaction between the brain regions (frontal, temporal, central, parietal, and occipital) and correlate these findings with the patient’s symptoms and history. In this first stage of the analysis, computerised statistical procedures are used to compare patient EEG patterns with those of a normative, gender and age-matched database. This statistical process is designed to identify, characterise and highlight the brainwave activity on the basis of its prominent features.
Next, the individual patient QEEG data are compared to a large database of patients with known disorders. The patients QEEG data are tested against "discriminant equations," a set of multi variant, derived measures that facilitate a "pattern recognition" process.
Using the discriminant equations, it is possible, statistically, to compare the EEG profile of a patient with the profiles found in a variety of clinical conditions. The discriminant functions quantify the likelihood (i.e., probability) that a particular patients profile is consistent with one or more of the clinical groups in the original database studies. The likelihood of group membership is then presented as a statistical probability statement.
The diagram on the left is of a Discriminant function results for a mild head injury patient indicating that there was a high degree of probability that the patients EEG still reflects a head injury.
The John’s database can determine with close to 95% accuracy the following discriminants:
- normal vs. abnormal
- normal vs. depressed
- normal vs. primary degenerative dementia
- normal vs. schizophrenia
- normal vs. mild head injury
- normal vs. learning disability
- normal vs. ADHD
ADHD vs. Learning Disability
dementia vs. depression
- unipolar vs. bipolar depression
- To detect subtleties of other conditions that need to be recognized, evaluated, and treated.
The clinic uses the Lexicor Neurosearch NRS24, a 24 channel data acquisition system approved for medical and research applications. It is regulated by the FDA (US Federal Drug Administration) and NxLink, a proprietary QEEG software product also FDA approved containing the John’s QEEG database and developed by the New York University Medical Centre Brain Research Laboratories. We also use Neuroguide another FDA approved QEEG system
Diagnostic Use of the QEEG
Some practitioners associate the science of EEG quantification exclusively with neurotherapy. This is far from the truth. Aside from its use as a roadmap to guide EEG operant training, the quantification of brain wave activity is a powerful diagnostic aid that transcends treatment modality.
At this time in the history of psychiatric disorders, a specific strength of QEEG that is particularly relevant, is its use in guiding decisions about the use of pharmaceuticals. The past decade has witnessed the launch of new generations of psychoactive drugs for use with less severe mental health problems. These drugs have been prescribed with what some have claimed to be increasing casualness, raising concerns in the literature about the diagnostic measures on which these prescriptions have been based.
There is a pressing need for a tool, such as the QEEG Neurometric report, that offers a powerful adjunct leading to more accurate and more scientifically measurable mental health information based on QEEG analysis. As we emerge from the "Decade of the brain" we have the opportunity of obtaining QEEG Neurometric reports that may help answer several implicit questions relating to the underlying neurophysiology:
- Which patients should receive drug treatment?
- Which patients should not?
- What class of drugs might most benefit the patient?
- What changes occur in the patient as the result of taking the medication?
- Whether global abnormalities may relate to intestinal dysbiosis and cortical toxicity.
- When the patient’s QEEG is consistent with HPA axis dysregulation. The QEEG in conjunction with patient history can provide valuable clues as to whether the aetiology is due to chronic toxicity from intestinal dysbiosis or food allergies or from trauma-related HPA hyperactivation.
- Such information guides further investigations and suggests which neurotransmitter system, serotonergic as opposed to dopaminergic/noradrenergic, should be targeted with either medication and/ or nutrient supplementation.
- What changes have taken place as a result of nutrient supplementation.
- Whether focal abnormalities are best redressed with neurothearapy.
Through the use of normative and discriminant databases, the quantification of EEG should prove to be of significant value in helping clinicians determine the underlying neurophysiology of the mental health problems of their patients and guide treatment more effectively.
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