The Midwest Society for Behavioral Medicine and Biofeedback (MSBMB) DSM-5 and Biomarkers/Phenotypes Jay Gunkelman, QEEG-Diplomate CSO, Brain Science International Jay@brainsinternational.com
The modern dilemma in psychology: Diagnosis, when the DSM is being abandoned by the author.
The Research Domain Criteria (RDoC) project transforms diagnosis by incorporating genetics, imaging, cognitive science, and other levels of information, forming a foundation for a new classification system. “That is why NIMH will be re-orienting its research away from DSM categories. Going forward, we will be supporting research projects that look across current categories – or sub-divide current categories – to begin to develop a better system.”
“RDoC, for now, is a research framework, not a clinical tool. This is a decade-long project that is just beginning. … RDoC is nothing less than a plan to transform clinical practice by bringing a new generation of research to inform how we diagnose and treat mental disorders.”
The DSM is based on behavior • The linkage from genetics to behavior is indirect • The intermediate step between genetics and behavior is the phenotype • Behaviorally driven therapy can succeed, but is it optimized?
Prior studies using EEG have documented “clusters” of EEG/qEEG features within psychiatric populations John ER, et al. Subtyping of Psychiatric Patients by Cluster Analysis of QEEG. Brain Topography 1992; 4:321-326). Chabot, R. J., & Serfontein G. (1996). Quantitative electroencephalographic profiles of children with attention deficit disorder. Biological Psychiatry, 40, 951-963. Prichep LS, Mas F, Hollander E, Liebowitz M, John ER, Almas M, De Caria CM, & Levine RH. (1993) Quantitative electroencephalographic subtyping of obsessive-compulsive disorder. Psychiatry Research, 50(1), 25-32. Prichep LS, & John ER. (1992). QEEG profiles of psychiatric disorders. Brain Topography, 4(4), 249-257. Suffin SC, & Emory WH. (1995). Neurometric subgroups in attentional and affective disorders and their association with pharmacotherapeutic outcome. Clinical Electroencephalography, 26, 76-83.
The underlying assumption in the DSM is: “diagnosis, thus treatment” • The DSM does not yield optimal treatment efficacy following diagnosis • Diagnoses have multiple “EEG subtypes”, but they ARE NOT SPECIFICTO THE DIAGNOSIS • Frontal alpha in ADD • Frontal alpha in depression • Frontal alpha in early dementia • Frontal alpha in anxiety • Frontal alpha in OCD
Phenotypic patterns are not isomorphic with the DSM categories Phenotypes have powerful implications for both medication, and Neurofeedback. Wright, C, & Gunkelman J. (1998). QEEG evaluation doubles the rate of clinical success. Abstracts of the 6th Annual Conference, Society for the Study of Neuronal Regulation, Austin, TX. Suffin SC, & Emory WH. (1995). Neurometric subgroups in attentional and affective disorders and their association with pharmacotherapeutic outcome. Clinical Electroencephalography, 26, 76-83. Johnstone, J., Gunkelman, J.,& Lunt, J.. (2005). Clinical database development: Characterization of EEG Phenotypes. Clinical EEG and Neuroscience, 2, 99-107.
Phenotypes have led to enhanced outcomes clinically • S. Suffin and H. Emory (attentional and affective, medication) • L. Prichep (OCD, medication) • Chabot, et al. (ADD/ADHD, medication) • C. Wright (ADD/ADHD, Neurofeedback) • M.I.N.D. (U.C. Davis, Autism) Databases for EEG are starting to include the genetic component (Brain Resource Company, Australia)
Some EEG patterns have strong genetics links • Low voltage EEG Enoch, MA, et al. (2002). The relationship between two intermediate phenotypes for alcoholism: Low voltage alpha EEG and low P300 ERP amplitude. Journal of Studies on Alcohol, 63(5), 509-17. Porjesz B, et al. (2002). Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus. Proceedings of the National Academy of Sciences of the United States of America; 99(6): 3729-3733. Gunkelman J. (2001). Low voltage or absolute power. Journal of Neurotherapy; 5:1-2. • Epileptiform bursts Kaneko S., Iwasa H., & Okada M. (2002). Genetic identifiers of epilepsy. Epilepsia, 43 (Suppl 9), 16-20. Huag K, et al. (2003) Mutations in CLCN2 encoding a voltage-gated chloride channel are associated with idiopathic generalized epilepsies. Nature Genetics; 33:527-532.
A limited set of phenotypic divergence patterns can characterize the bulk of the variance of the EEGThese EEG phenotypes predict effective therapy (medication and NF)The following table is not a substitute for professional evaluation and consultation
Epileptiform Transient spike/wave, sharp waves, paroxysmal EEG Anticonvulsant medication Inhibit low and high frequencies over affected regions, reward SCP and/or SMR. Generally low magnitudes (fast or slow) Metabolic support (LVS), nutraceuticals Reward alpha activity posteriorly. (Penniston protocol for LVF) Faster alpha variants, not low voltage Alpha frequency greater than 12 Hz over posterior cortex. GABA related medication (slightly slows the EEG frequencies) Reward 9-10Hz alpha at Pz, shift alpha frequency lower with alpha/theta protocol Spindling excessive beta High frequency beta with a spindle morphology, often with an anterior emphasis. Anticonvulsants Inhibit beta frequencies, wide band inhibit, possibly Penniston if alpha levels are depressed. Persistent alpha with eyes open Lack of appreciable alpha blocking with eye opening, generally this is slower alpha SNRI or amphetamine Reward beta frequencies, inhibit alpha. Reward higher frequency alpha.
Diffuse slow activity, with or without low frequency alpha. Increased delta and theta (1-7 Hz) with or without slow posterior dominant rhythm StimulantInhibit midline frontocentral activity below 10 Hz., add reward anterior beta frequencies for increased effect Focal abnormalities, not epileptiform. Focal slow activity or focal lack of activity. Inhibit slow activity (<10 Hz) and reward higher frequencies(> 12 Hz). Mixed fast and slow Increased activity below 8 Hz., lack of alpha, increased beta frequency activity Combine across classes, e.g. stimulant + anticonvulsant Inhibit slow frequencies, reward SMR. Frontally dominant excess theta or alpha frequency activity Antidepressant, stimulant Inhibit midline frontocentral activity below 10 Hz., add reward anterior beta frequencies for increased effect Frontal asymmetries Variable asymmetry L>R or R>L, primarily at F3, F4. Antidepressant Reward F3 beta, inhibit F3 theta and alpha frequencies. Excess temporal lobe alpha Increased alpha activity generated in temporal lobe Stimulant Inhibit 9-12 Hz activity over affected temporal region(s), + inhibit frontal slow activity.
Addiction outcomes are about more than sobriety… a dry drunk is not “cured”… we look for “optimal function” when the “brain works better”.
REFERENCES • John ER, Prichep LS, Almas M. Sub typing of Psychiatric Patients by Cluster Analysis of QEEG. Brain Topography 1992; 4:321-326. • Nelson LA. Neurotherapy and the challenge of empirical support: A call for a neurotherapy practice research network. Journal of Neurotherapy; 2003:7(2) 53-67. • Yucha C, Gilbert C. Evidence-based practice in biofeedback and neurofeedback. Association of Applied Psychophysiology and Biofeedback (www.aapb.org) • Suffin SC, Emory WH. Neurometric subgroups in attentional and affective disorders and their association with pharmacotherapeutic outcome. Clinical Electroencephalography 1995; 26:76-83. • Leuchter AF, Cook IA, Morgan ML, Witte EA, Abrams M. Changes in brain function of depressed subjects during treatment with placebo. American Journal of Psychiatry 2002; 159(1):122-129. • Porjesz B, Almasy L, Edenberg HJ, Wang K, Chorlian DB, Foroud T, Goate A, Rice JP, O'Connor SJ, Rohrbaugh J, Kuperman S, Bauer LO, Crowe RR, Schuckit MA, Hesselbrock V, Conneally PM, Tischfield JA, Li T-K, Reich T, Begleiter H.S. Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus. Proceedings of the National Academy of Sciences of the United States of America 2002; 99(6): 3729-3733. • Enoch MA, White KV, Harris CR, Rohrbaugh JW, Goldman D. The relationship between two intermediate phenotypes for alcoholism: low voltage alpha EEG and low P300 ERP amplitude. Journal of Study of Alcohol 2002; 63(5):509-517. • Huag K, Warnstedt M, Alekov AK, Sander T, Ramirez A, Poser B, Maljevic S, Hebeisen S, Kubisch C, Rebstock J, Horvath S, Hallman K, Dullinger JS, Rau B, Haverkamp F, Beyenburg S, Schulz H, Janz D, Giese B, Muller-Newen G, Propping P, Elger CE, Fahlke C, Lerche H, Heils A. Mutations in CLCN2 encoding a voltage-gated chloride channel are associated with idiopathic generalized epilepsies. Nature Genetics 2003; 33:527-532.
Phillips C. Electrophysiology in the study of developmental language impairments: prospects and challenges for a top-down approach. Applied Psycholinguistics (in press) • Light GA, Braff DL. Human and animal studies of schizophrenia-related gating deficits. Current Psychiatry Reports 1999; 1:31-40. • Johnstone J, Gunkelman J. Use of databases in QEEG evaluation. Journal of Neurotherapy 2003; 7(3/4):31-52. • Lorensen TD, Dickson P. Quantitative EEG databases: A comparative investigation. Journal of Neurotherapy 2003; 7(3/4):53-68. • Chabot RJ, Serfontein G. Quantitative electroencephalographic profiles of children with attention deficit disorder. Biological Psychiatry 1996; 40:951-963. • Chabot RJ, Merkin H, Wood LM, Davenport TL, Serfontein G. Sensitivity and specificity of QEEG in children with attention deficit or specific developmental learning disorders. Clinical EEG 1996; 27:26-34. • Chabot RJ, Merkin H, Wood L, Davenport T, Serfontein G. Quantitative EEG profiles of children with attention and learning disorders and the role of QEEG in predicting medication response and outcome. Society for Neuronal Regulation, 5th Annual Meeting, September 18-21, 1997 Aspen, CO. • Chabot RJ, di Michele F, Prichep L, John ER. The clinical role of computerized EEG in the evaluation and treatment of learning and attention disorders in children and adolescents. Neuropsychiatry and Clinical Neuroscience 2001; 13(2):171-186. • Fein G, Galin D, Yingling CD, Johnstone J, Nelson MA. EEG spectra in 9-13-year-old boys are stable over 1-3 years. Electroencephalography and Clinical Neurophysiology 1984; 58(6):517-518. • Niedermeyer E, Lopes Da Silva F (Eds). EEG patterns and genetics, In: Electroencephalography: basic principles, clinical applications and related fields, 3rd edition, Baltimore: Lippincott, Williams & Wilkins, 1993, 192-195. • Vogel F. Genetics and the Electroencephalogram. New York: Springer-Verlag Telos, 2000.
Vogel F. Genetics and the Electroencephalogram. New York: Springer-Verlag Telos, 2000. • Van Beijsterveldt CE, Van Baal GC. Twin and family studies of the human electroencephalogram: a review and a meta-analysis. Biological Psychology 2002; 61(1-2):111-38. • Gunkelman J. Evaluating the frontal lobes in affective and attentional disorders with QEEG and EP - the electrophysiology of frontal lobe disconnection syndrome: implications for neurotherapy. Journal of Neurotherapy 1998. • Cook IA, O'Hara R, Uijtdehaage SH, Mandelkern M, Leuchter AF. Assessing the accuracy of topographic EEG mapping for determining local brain function. Electroencephalography and Clinical Neurophysiology 1998; 107(6):408-414. • Prichep LS, Mas F, Hollander E, Liebowitz M, John ER, Almas M, De Caria CM, Levine RH. Quantitative electroencephalographic subtyping of obsessive-compulsive disorder. Psychiatry Research 1993; 50(1):25-32. • Wright C, Gunkelman J. QEEG evaluation doubles the rate of clinical success. Series data and case studies. Abstracts 6th Annual Conference, Society for the Study of Neuronal Regulation, September 10-13, 1998, Austin, TX. • Budzynski TH. Reversing age-related cognitive decline: Use of neurofeedback and audio-visual stimulation. Biofeedback 2000; 28:19-21. • Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research and Brain Research Review 1999; 29(2-3), 169-195. • Losifescu D, Greenwald S, Devlin P, Alpert J, Hamill S, Fava M. Frontal EEG Predicts Clinical Response to SSRI Treatment in MDD. 44th Annual NCDEU, June, 2004, Phoenix, AZ • Rosenfeld JP. EEG biofeedback of frontal alpha asymmetry in affective disorders. Biofeedback 1997; 5(1) 8-25. • Lawson R. Different measure of anterior EEG asymmetry and depression severity: continuous performance task, grade point average, and self-report scales. Society for Neuronal Regulation, 2000. • Perrin F, Bertrand O, Pernier J. Scalp current density mapping: value and estimation from potential data. IEEE Transactions on Biomedical Engineering 1987; 34(4):283-288. • Srinivasan R, Nunez PL, Tucker DM, Silberstein RB, Caducsh PJ. Spatial sampling and filtering of EEG with spline Laplacians to estimate cortical potentials. Brain Topography 1996; 8(4):355-366.
Sterman MB. Basic concepts and clinical findings in the treatment of seizure disorders with EEG operant conditioning. Clinical Electroencephalography 2000; 31(1):45-55. • Lantz D, Sterman MB. Neuropsychological assessment of subjects with uncontrolled epilepsy: effects of EEG biofeedback training. Epilepsia 1998; 29(2):163-171. • Birbaumer N, Elbert T, Rockstroh B et al. Biofeedback of event-related slow potentials of the brain. International Journal of Psychology 1981; 16:389-415. • Gibbs FA, Gibbs EL. Atlas of electroencephalography. Reading MA: Addison-Wesley, 1950. • Niedermeyer E, Lopes Da Silva F (Eds). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 4th edition, Baltimore: Lippincott, Williams & Wilkins, 1999. • Prichep LS, John ER. QEEG profiles of psychiatric disorders. Brain Topography 1992; 4(4):249-257. • Donaldson S. Society for Neuronal Regulation Annual Meeting, Scottsdale, AZ., 2002. • Gunkelman J. Low voltage or absolute power. Journal of Neurotherapy 2001; 5:1-2. • Kolb, B. and Whishaw, I. Fundamentals of Human Neuropsychology, 5th Edition, New York, NY: W.H.