1 / 34

(1) Faculty of Medicine, University of Skopje, Macedonia

Conjunct COST B27 and SAN Scientific Meeting, Swansea, UK, 16-18 September 2006. BRAIN-RATE AS A COMPLEMENTARY DIAGNOSTIC INDICATOR AND BIOFEEDBACK PARAMETER Nada Pop-Jordanova, MD, DSc (1) and Jordan Pop-Jordanov, DSc (2). (1) Faculty of Medicine, University of Skopje, Macedonia

honey
Download Presentation

(1) Faculty of Medicine, University of Skopje, Macedonia

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Conjunct COST B27 and SAN Scientific Meeting, Swansea, UK, 16-18 September 2006 BRAIN-RATE AS A COMPLEMENTARY DIAGNOSTIC INDICATOR AND BIOFEEDBACK PARAMETERNada Pop-Jordanova, MD, DSc (1) and Jordan Pop-Jordanov, DSc (2) (1) Faculty of Medicine, University of Skopje, Macedonia (2) Macedonian Academy of Sciences and Arts

  2. Contents: • Empirical EEG-arousal correlation • Theoretical consideration • Brain-rate as arousal indicator • Some clinical results • Conclusions

  3. 1. Empirical EEG–arousal correlation 1.1 Textbook classification of EEG activity and the level of arousal (Pritchard, Alloway, 1999)

  4. 1.2 Extended classification of EEG activity and the mental states (Bendorfer, 2001)

  5. 1.3 Clinical EEG-arousal correlation Slow waves – Underarousal (depression, autism, etc.) Fast waves – Overarousal (caffeine, anxiety, alcoholism, etc.) “Mixed”: Slow or fast waves – UA or OA (ADHD, OCD, headache, etc.) → subgroups (clusters)

  6. 1.4 From “how” to “why” questions Why is the arousal correlated with EEG frequency? Why in this pattern? Why in this frequency interval? Why alpha band corresponds to “relaxed” state?

  7. 2. Theoretical consideration 2.1 Basic mental-neural mechanism Hypotheses: Eccles (1986) - microsite probabilities Jibu and Yashue (1995) - photon-corticon dynamics Penrose/Hameroff (1998) - microtubular proteins Romijn (2002) - virtual photons

  8. General scheme Synaptic activity e/m fields mental activity Solutions: conceptual, lacking analytical expressions and numerical results. Common elements: electric field and dipole molecules on nano level (nanomedicine).

  9. Basic “nanometric” scale New domain BMBS includes nanomedicine

  10. 2.2 Field – dipole interaction J. Pop-Jordanov, E. Solov’ev, N. Pop-Jordanova, N. Markovska, D. Dimitrovski (1998) D. Dimitrovski, J. Pop-Jordanov, N. Pop-Jordanova, E. Solov’ev (2004) Analytical solution for transition probabilities: N = 1012 dipole molecules per neuron

  11. 2.3 Transition probabilities and mental arousal J. Pop-Jordanov and N. Pop-Jordanova (2004) Readiness to change the state → mental arousal (alertness) (S = Smax for f = fe) A = is capacity, not entity!

  12. 2.4 A = A (f) empirical results Peak perf. Anxiety Alert Still Relax Drowsy Deep sleep 20 16 30 12 50 Delta Theta High Beta Gama Mid Beta SMR Alpha EEG activity and the mental states i.e. arousal (adapted from Tables 1.1&1.2).

  13. 2.5 A = A (f) theoretical formula ch ad Derived theoretical diagram for mental arousal ch – children (fe = 6 Hz), ad – adults (fe= 10 Hz)

  14. 1.4 From “how” to “why” questions Why is the arousal correlated with EEG frequency? Why in this pattern? Why in this frequency interval? Why alpha band corresponds to “relaxed” state?

  15. 3.1 Definition of arousal General activation of the mind (Kahnemann 1973) General operation of consciousness (Thacher and John 1978) General drive state of the brain and mind (Watkins 1997) Simple increases in activity, lying at the bottom of homeostasis (Damasio 2003) 3. Brain-rate as arousal indicator

  16. 3.2 Integrality of arousal and EEG spectrum By definition arousal is a general, integral characteristic of mental state. Simultaneously, it is correlated with the integral (polyrhythmic, polychromatic) EEG spectrum. The main characteristic of such a spectrum is its mean frequency weighted over the whole spectrum. We named it brain-rate (fb).

  17. 3.3 Brain-rate formula N. Pop-Jordanova and J. Pop-Jordanov (2005) , , or ,

  18. Analogies:

  19. 3.4 Spectral gravity and the state of a system The parameter characterizing the state and the changes (shifts) of any system with spectral properties is the spectrum gravity (Xc, T, σr, …), i.e. the mean value which comprises weighted contributions of all spectral components (bands). So e.g. the state transition (solid ↔ liquid ↔ gas) depends on the integral parameter T, the stability of a boat – on the center of gravity Xc, the criticality of a reactor cell - on the mean reaction rate, etc.

  20. Based on the presented theoretical results and empirical evidence we suppose that the same principle is applicable for the arousal level correlated to EEG spectrum gravity fb. Consequently, the brain-rate fb can indicate the states of underarousal or overarousal, in the same way as the other mentioned indicators (Xc, T, σr, …) differentiate the levels of activation of corresponding systems.

  21. 4. Some clinical results N. Pop-Jordanova (2006) 4.1 NF training of ADHD children

  22. 4.2 Brain-rate as indicator of inner arousal

  23. 4.3 Blood lead level and brain-rateCharacteristic correlations

  24. Scatterplot: 2 / $ vs. fb [Hz] (Casewise MD deletion) fb [Hz] = 7,2468 + ,18796 * 2 / $ Correlation: r = ,29545 9.4 9.2 9.0 8.8 8.6 fb [Hz] 8.4 8.2 8.0 7.8 7.6 7.4 7.2 7.0 6.8 6.6 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 2/β 95% confidence

  25. 4.4 Brain-rate and reference databases Calculated using the spectral data from White 2003 for ten adults with ADHD (NeuroRep: Hudspeth 1999; SKIL: Sterman & Kaiser 2000; EureKa3!: Congedo 2002)

  26. Being in the range of low alpha, the results for fb suggest that in this case ADHD Cluster 3 (Müller 2006) prevails. This is not visible from the /β ratio. Consequently, the “internally directed attention” (Cooper, Croft, Dominey, Burgess, & Gruzelier, 2003) can be considered as a characteristic of psycho-dynamical equilibrium (i.e. max entropy of an isolated system), as well.

  27. 5. Conclusions 1. Brain-rate can be considered as an integral brain state attribute, correlated to its electric, mental and metabolic activity. 2. In preliminary assessment, brain-rate may serve as an indicator of general mental arousal level, similar to heart-rate, blood pressure and temperature as standard indicators of general bodily activation.

  28. 3. By comparing EC and EO brain-rate values the diagnoses of inner arousal can simply be achieved. 4. As a measure of arousal level, brain-rate can be applied to discriminate between subgroups (clusters) of “mixed” disorders (e.g. ADHD, OCD, headache).

  29. 5. Brain-rate can be used as a multiband biofeedback parameter in mediating the underarousal or overarousal states, complementary to few-band parameters and the skin conduction. 6. Brain-rate training is especially suitable to reveal the patterns of sensitivity/rigidity of EEG spectrum and its frequency bands, related to permeability of corresponding neuronal circuits. Based on this information, individually adapted NF protocols can be elaborated.

  30. 7. It is recommended to include the brain-rate values in the standard EEG and NF software's and databases, along with the frequency band values. 8. Further studies of advantages and limitations of brain rate concept for diagnostics and treatment are needed.

  31. References: Bendorfer, K. (2001). Alpha-Theta Neurofeedback: Its Promises & Challenges, BFE 5th Annual Meeting, Prien. Congedo, M. (2002) EureKa! (Version 3.0) [Computer software]. Knoxville, TN: NovaTech EEG, Inc. Cooper, N. R., Croft, R. J., Dominey, S. J., Burgess, A. P., & Gruzelier, J. H. (2003). Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses. International Journal of Psychophysiology, 47 (1), 65-74. Damasio, A. (2003). Looking for Spinoza: Joy, sorrow, and the Feeling Brain, Harcourt, Inc., Orlando … London, p. 30.

  32. Dimitrovski, D. J., Pop-Jordanov, J., Pop-Jordanova, N., Solov’ev, E. (2004). Hidden-crossing transitions between collective states of coupled quantum rotators as a possible mechanism of memory processing, Information sciences: An International Journal, Volume 168, Issues 1-4, pp. 267-276. Eccles, J. C. (1986). Do mental events cause neural events analogously to the probability fields of quantum mechanics?. Proc. R. Soc. Lond.,B,227, pp. 411-28. Hameroff, S. R. (1998). Quantum computation in brain microtubules? The Penrose-Hameroff ‘Orch OR’model of consciousness, Philosophical Transactions: Mathemati-cal, Physical and Engineering Sciences, Volume 356, Number 1743, pp. 1869-1896. Hudspeth, W.J. (1996). NeuroRep: The QEEG analysis and report system-User’s guide (Version 4.0). Reno, Nevada: Gray Matter, Inc. Jibu, M., Yasue, K. (1995). Quantum Brain Dynamics and Consciousness, John Benjamins Publishing Company, Amsterdam/ Philadelphia.

  33. Kahnemann, D. (1973). Attention and effort. Prentice-Hall, New Jersey. Müller, A. (2006). Neurobiological Diagnostics and Therapy in ADHD, in: News in Pediatrics, University of Skopje. Noland White, J. (2003). Comparison of EEG Reference Databases in Basic Signal Analysis and in Evaluation of Adult ADHD, Journal of Neurotherapy, Vol. 7, No. 3/4, p. 161. Pop-Jordanov, J., Pop-Jordanova, N. (2004). Quantum Interpretation of Mental Arousal Spectra, Contributions, Sec. Math. Tech. Sci., XXV, 1-2, pp. 75-94. Pop-Jordanov, J., Solov'ev, E. N., Pop-Jordanova, N., Markovska, N., Dimitrovski, D. (1998). Exploring the quantum concept of memory, International Journal of Psychophysiology, Vol. 30, No. 1-2, p. 147-148. Pop-Jordanova, N., Pop-Jordanov, J. (2005) Spectrum-weighted EEG Frequency (“Brain Rate”) as a Quantitative Indicator of Mental Arousal, Contributions, Sec.Biol.Med.Sci., MASA, XXVI 2, p. 71-80.

  34. Pop-Jordanova, N. (2006) Biofeedback modalities for children and adolescents, Chapter in: New research on Biofeedback (editor Frank Columbus), Nova Biomedical Books. Pritchard, T. C., Alloway, K. D. (1999). Medical Neuroscience, Frence Greek Publishing, LLC, Madison, Connecticut, p. 397. Romijn, H. (2002). Are Virtual Photons the Elementary Carriers of Consciousness?, Journal of Consciousness Studies, 9, No. 1,, pp. 61-81. Sterman, M. B., & Kaiser, D. A. (2000). SKIL QEEG analysis software Version 2.05) [Computer software]. Bel Air, CA: Sterman-Kaiser Imaging Laboratory. Thacher, R. W. and John, E. R. (1977). Functional Neuroscience: Foundations of Cognitive Processing, Hillsdale, NJ: Erlbaum. Watkins (ed.), A. (1997). Mind-body Medicine: A Clinical Guide to Psychoneuroimunology, Churchill Livingstone, New York..Tokyo, p. 43.

More Related