Brain waves are oscillations in electrical activity thought to be produced by the multitude of synapses within the cerebral cortex, influenced also by regulatory relay sites in the brainstem. Sterman and Lubar’s seminal research begun in the 1960s and 70s showed that the motor cortex of alert and motionless cats produces a 12-15 Hz sensorimotor rhythm (SMR) which decreases skeletal muscle tone and inhibits drug-induced (monomethylhydrazine) seizures. This neuropsychological model was soon applied to treating epilepsy in humans by increasing the SMR voltage using neurofeedback behavioral training, thereby creating a need for quantitative electroencephalography (qEEG). During the past several decades, the advent of qEEG has made it possible to easily measure the magnitude, dominant frequency, asymmetry, and coherence of brain waves and to compare the values to those in normative databases. Quantitative changes in EEG can be correlated to brain activity for evaluating their relevance to virtually all aspects of cognition and behavior. Several vendors of qEEG instrumentation have developed and maintained fMRI databases with their corresponding qEEG data to provide a statistical method for evaluating interindividual and behavioral state variabilities. Heretofore, the usefulness of such quantitative, statistical analyses of EEG information has been limited. One of these limitations, pointed out by the author, involves a relative insensitivity to the qualitative texture of a client’s/patient’s symptoms. However, this impediment has been effectively circumvented by the development of the ClinicalQ, which purports to take advantage of data obscured by the full qEEG normative databases. Using case histories, throughout the book, the author presents numerous examples demonstrating how, after showing clients/patients the neuro-electrical bases for their signs and symptoms, they were motivated and empowered to make changes in their behaviors.
The ability of a clinician to link characteristics of the qEEG with behavioral signs and symptoms is a daunting challenge. The author’s explanation of the ClinicalQ, based on observing the electrical patterns at five key qEEG electrode sites, provides immediate, logical insight; and, it motivates the reader to want to learn more about this method and how it differs from conventional full EEG brain mapping. The author compares and contrasts the behavioral signs and symptoms associated with remarkable versus unremarkable qEEG patterns at each of the aforementioned five key sites. The discussions of “braindriving” and other neurofeedback-based therapies, as well as heart rate variability and other peripheral biofeedback modalities as treatments, are very useful adjuncts to the author’s protocols for motivating and empowering clients/patients to change their brain wave patterns and, hence, their behaviors.
Of special interest is the Chapter 3 discussion of conditions such as anxiety, depression, bipolar, and the attention deficit hyperactivity disorders for which traditional medicine and psychology have not produced good clinical outcomes because of “trial-and-error” approaches based on “labeling” conditions rather than addressing the putative causes of a client’s/patient’s complaints. The discussion of brain efficiency and alpha peak frequency is useful in that it uses examples and data on senior (elderly) subjects to demonstrate the relationship between alpha peak frequency slowing and problems with sleep architecture, which would also apply to most behavioral disorders. Indeed, many neuropsychiatric conditions, including schizophrenia, chronic fatigue syndrome, hemispheric stroke, and cognitive decline from cancer chemotherapy, are associated with decreased alpha peak frequency. The contribution of qEEG to the practice of personalized medicine is elucidated with respect to measuring alpha peak frequency (Arns, 2012). This well-conceived book includes a collection of historical references and appendices that contain a wealth of information covering a wide range of relevant topics such as tabulations of qEEG characteristics for probing the brain, what to tell the client about his brain wave patterns, how to develop rapport and motivate children, as well as how marijuana and classes of prescription drugs may produce effects which can be thought of as both therapeutic and confounding influences on the qEEG. Usage of the SMR frequency (Lubar & Bahler, 1976; Sterman & Egner, 2006) and several other protocols for “quieting-down” the brain’s electrical activity in highly stressed clients/patients may be applicable to virtually every neuropsychiatric disorder and emotional state. The concept of asymmetry is covered with respect to qEEG markers for several behavioral conditions associated with frontal imbalances such as those associated with attention deficit hyperactivity disorder and depression, involving the processing of negative emotions and the creation of pessimistic thoughts. The neurophysiological correlations are clarified with respect to cerebral hemispheric specialization and the clinical significance of lateralized brain functions. It is noteworthy that the author’s experience is that the main marker for depression in right- handed individuals, high beta activity in the right relative to the left hemisphere, may not be as reliable in left-handed clients/patients because they may experience anxiety rather than depression with this imbalance. The discussions of alpha peak frequency were effectively incorporated, but they could have been made more useful by including more information about the significances of coherence and the shifting of dominant delta, theta, and beta EEG frequencies.
Adding Neurotherapy to Your Practice delivers what the author promised in the subtitle: It provides a clinician’s guide to the applications of ClinicalQ, neurofeedback, and braindriving techniques to the behavioral sciences. This book will likely be an invaluable reference for any licensed healthcare professional who wants to use neurotherapeutic assessment and treatment in his or her clinical practice. Adding Neurotherapy to Your Practice also functions as a motivational tool for clinicians who want to employ the principles of personalized medicine for improving the medical and behavioral outcomes of their clients and patients.
References
- Arns, M. (2012). EEG-based personalized medicine in ADHD: Individual alpha peak frequency as an endophenotype associated with nonresponse. Journal of Neurotherapy, 16, 123-141. http://dx.doi.org/10.1080/10874208.2012.677664
- Lubar, J. F., & Bahler, W. W. (1976). Behavioral management of epileptic seizures following EEG biofeedback training of the sensorimotor rhythm. Biofeedback and Self-Regulation, 1, 77-104. http://dx.doi.org/10.1007/BF00998692
- Sterman, M. B., & Egner, T. (2006). Foundation and practice of neurofeedback for the treatment of epilepsy. Applied Psychophysiology and Biofeedback, 31, 21-35. http://dx.doi.org/10.1007/s10484-006-9002-x
**Disclosure – Dr. Yulia A. Volkova is The Dean of Basic Science at Metropolitan University College of Medicine as of May 2025, and this publication is posted with her consent.
