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Is research on brain oscillations in a new “take off-state” in integrative brain function?
International Journal of Psychophysiology 85 (2012) 285–290
Contents lists available at SciVerse ScienceDirect
International Journal of Psychophysiology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i j p s yc h o
Didactic Lectures
Friday, September 14th, 2012 Is research on brain oscillations in a new “take off-state” in integrative brain function? Erol Başar Brain Dynamics, Cognition and Complex Systems Research Center, Istanbul Kultur University, Istanbul, Turkey Three decades ago we launched a working hypothesis stating that “alpha, beta, gamma, theta and delta oscillations and their connectivity govern the whole brain work”. Presently, it is discussed that the autonomic nervous system is also partly processing by EEG-like oscillations and ultra-slow oscillations in the frequency range of 0.01–01 Hz that are observed in smooth muscle contractions in all vegetative organs in the body including the vasculature and visceral organs. The ultra-slow oscillations were initially recorded in electrical activity of the brain by Aladjalova (1957). Currently, these activities are measured in the reticular formation, thalamus and even in the auditory cortex as an extensive survey shows. According to these last results, it is proposed to extend the concept “whole brain work” to “integrative brain–body–mind work”. The event related oscillatory responses (ERO's) and selective connectivity during cognitive tasks (from delta to gamma frequency range) measured by scalp electrodes are highly increased versus evoked oscillations upon a pure sensory signal depending on brain topography. The effects of neurotransmitters such as GABA, Acetylcholine and Dopamine are highly efficient in oscillatory activity. This means that sensory sensitive and/or cognitive sensitive oscillatory neural networks react differentially upon stimulation modality. This electrophysiological differentiation enables to analyze more efficiently the changes in brain's oscillatory responses and selectivity in diseases with cognitive impairment as MCI, Alzheimer's disease, bipolar disorders and schizophrenia. Candidates of biomarkers in these diseases are recently extensively discussed. After four decades of research on ERO's the new constellation of research including extended frequency window with ultraslow oscillations, analyses of multiple frequencies and selective connectivity in diseases opens a new conjecture to understand the integrative brain function. Therefore we pose the question: Is the research on brain oscillations in a “new take off state”? 1. Earlier hypothesis on brain oscillations Neuroscience has provided us some astonishing breakthroughs, from noninvasive imaging of the human brain to uncovering the
0167-8760/$ – see front matter.
molecular mechanisms of some complex processes and disease states. Nevertheless, what makes the brain so special and fundamentally different from all other living tissue is its organized action in time. This temporal domain is where the importance of research on neural oscillators is indispensable (Buszaky, 2006). Three decades ago Basar (1980) launched a working hypothesis stating that “alpha, beta, gamma, theta and delta oscillations and their connectivity govern the whole brain work”. The event related oscillatory responses (ERO's) and selective connectivity during cognitive tasks (in alpha, beta, gamma, theta and delta frequency bands) measured by scalp electrodes are highly increased versus evoked oscillations upon a pure sensory signal depending on brain topography. Each oscillatory activity represents multiple functions; vice versa, each function is represented by multiple oscillations. Functioning in all physiological and biochemical pathways is governed by quasi-invariant natural frequencies and neurotransmitters of brain–body functioning. Several brain functions are manifested by superposition of EEG-Oscillations. The effects of neurotransmitters such as GABA, acetylcholine and dopamine are highly efficient in oscillatory activity. This means that sensory sensitive and/ or cognitive sensitive oscillatory neural networks react differentially upon stimulation modality. This electrophysiological differentiation enables to analyze more efficiently the changes in brain's oscillatory responses and selectivity in diseases with cognitive impairment as MCI, Alzheimer's disease, bipolar disorders and schizophrenia. Candidates of biomarkers in these diseases are recently extensively discussed (Başar et al., in press; Yener et al., 2007; Başar and Güntekin, 2008). 2. A strategy to understand brain–body integration A new hypothesis tries to link brain work with the work of the vegetative system. The proposed steps to understand brain–body– mind incorporation is illustrated in Fig. 1. In pathologic brains, the release of transmitters and, accordingly, oscillatory processes and control of cognitive processes, are highly altered (Başar, 2011). Therefore, the analysis steps in Fig. 1, which also includes a loop indicating the influences of pathology (Alzheimer, Schizophrenia, bipolar disorders) constitute a minimal analysis—prerequisite to approach the integration of brain–body–mind. Fig. 1 indicates further that we also have to observe the machineries of invertebrate ganglia and brains during the evolution of species in order to understand brain–body mind integration (see Fig. 1). Furthermore, physiological processes and anatomical changes need to be analyzed during maturation of the brain, from infancy through adulthood to old age.
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Didactic Lectures / International Journal of Psychophysiology 85 (2012) 285–290
Fig. 1. Schematic explanation of the steps proposed for an approach to brain–body– mind.
3. New emerging hypothesis Presently, it is discussed that the autonomic nervous system is also partly processing by EEG-like oscillations and ultra-slow oscillations in the frequency range of 0.01–01 Hz that are observed in smooth muscle contractions in all vegetative organs in the body including the vasculature and visceral organs. The ultra-slow oscillations were initially recorded in electrical activity of the brain by Aladjalova (1957). Currently, these activities are measured in the reticular formation, thalamus and even in the auditory cortex as an extensive survey shows. These last results led Başar (2011) to propose to extend the concept “whole brain work” to “integrative brain–body– mind work”. A frequency tuning can be found not only in the brain, but also in the brain–body integration. According to the work of Gebber et al. (1995a,b), Aladjalova (1957) and Ruskin et al. (2001a,b), structures in the vegetative system are also tuned to the same frequencies. Transmitters such as acetylcholine or nor-epinephrine are also excellent vehicles for general tuning in the brain–body interaction. The body and the brain use the same transmitters and the same frequencies for general tuning for brain–body interaction. Here, the ensemble of these phenomena is tentatively called “the overall tuning in brain–body interaction”. According to C. Babiloni1 in Başar's new work a new idea is developed: “The new idea is that the brain oscillations reflect, and are the key to understand, the complex homeostatic interactions between brain and body visceral organs in the formation of the mind as the term capturing the representation of the reality and the related cognitive and affective processes. Erol Başar leads the readers to surpass the view of the mind as emerging from brain oscillatory processes, in favor of the view that the mind emerges from the multiple oscillatory processes characterizing the homeostatic interactions between brain and body visceral organs and tissues within a structural and functional continuum termed “Brain–Body–Mind functional syncytium”. Within such “functional syncytium”, quasi-invariant nervous oscillatory processes at different frequencies would ensure a stochastic unstable transfer of (excitatory and inhibitory) signals among different brain nodes by means of neurotransmitter systems and complex biochemical pathways. In parallel, quasi-invariant myogenic slow oscillatory processes would ensure the inter-relatedness and homeostatic adaptation of body visceral organs and tissues by means of vegetative system. Afterwards, Erol Başar outlines the “life span” of such “functional syncytium” along the phylogenesis and human ontogenesis as well as its derangement in the form of Alzheimer's disease, Schizophrenia, and Depression. The concept of the “Nebulous Cartesian System” helps him to emphasize the chaotic nature but tight relationships among the countless dimensions of the mentioned “functional syncytium”.
1
Claudio Babiloni: Private communication.
4. Back to the concept of syncytium: dynamic syncytium The first morphological studies of the nervous system were done by the Spanish anatomist Santiago Ramon y Cajal (1911). He proposed that the functions of the brain could be understood by analyzing the functional architecture of the nervous system. Applying Golgi's silver staining technique to the study of nerve tissue, he observed that only some cells are stained in their entirety. This led to his formulation of the ‘neuron doctrine’ which states that the brain is made up of discrete units rather than a continuous net of nerve tissue or ‘syncytium’ as was originally thought. He proposed that nerve impulses travel from the dendrites of a neuron to its cell body and then along the axon to the dendrites of the neighboring neuron. This flow of information would be a finite process. Why a dynamic syncytium is proposed? Fig. 2 presents a schema for connectivity underlying sensory evoked coherence responses following simple sensory stimuli and eventrelated coherence responses following cognitive task. It is not possible to define clear-cut boundaries for these neural groups that are differentiated upon application of sensory stimulation or upon cognitive stimulation. This schema indicates that there are neural populations, mostly responding to sensory signals, and other populations responding to only cognitive stimulation. Further, there is some overlap or plasticity among these networks. It is also possible that neural groups are not separated into different structures but co-exist also in given structures. These are selectively distributed neuron clusters capable of responding to sensory/cognitive inputs. It is also expected that following sensory stimulation, cognitive neural clusters would remain silent, whereas a cognitive stimulus (i.e. target signal in oddball paradigm) would excite both sensory and cognitive neural clusters. Certainly in the case of cognitive impairment, cognitive neural clusters would be more affected, in turn, giving rise to less unclear responses. Moreover, reduced response amplitude can result from either non-responding neural units or non-phase locked response activity. Fig. 2 illustrates only one local area. However, isolated brain networks can explain only a limited activity. In addition to these local activities, it is important to emphasize the selective connectivity between neural elements of these networks and, more important differential connectivity between distant areas of the brain (for example frontal, limbic and parietal connections). In the case of Alzheimer's disease, the number of neural clusters responding to cognitive stimulus is greatly reduced. Additionally, we observe a selective connectivity deficit between distant neural networks (see articles by Başar et al., 2010 and Güntekin et al., 2008). We use the expression “Dynamic Syncytium” since parts of the brain forms a functional cluster depending on stimulation modality and applied cognitive tasks. There are also periods of silent.
Fig. 2. Neural assemblies involved in sensory and cognitive networks. Cognitive networks (here shown by magenta lines) probably contain sensory neural elements, but also involve additional neural assemblies, as shown by magenta circles. Sensory network elements are illustrated by blue squares and connections by blue lines. It is expected that sensory signals trigger activation of sensory areas, whereas cognitive stimulation would evoke both neural groups reacting to sensory and cognitive inputs (Modified from Yener and Başar, in press).
Didactic Lectures / International Journal of Psychophysiology 85 (2012) 285–290
5. A new tentative model is proposed as a metaphor to string theory in physics In this tentative model the brain (I), the spinal cord (II) and the organs of the vegetative systems (III) are illustrated as three different functional groups that are connected with strings having different innate frequencies (EEG-Oscillations and Ultra-Slow Oscillations). Strings (or oscillators) of different frequencies that are presented with different colors bind these three groups according to coherences described in and organs of the vegetative system. The strings also bind neural groups within the brain, spinal cord.
3)
4)
5) 6)
7)
8)
Fig. 3. A schematically illustration of the Brain–Body–Mind String Theory (Modified from Başar, 2011).
Different neurotransmitters, shown as dashed lines in different colors, influence the frequencies of strings and coherences between neural groups. Several types of strings are represented with different colors. Strings have different amplitudes and are also accompanied (embedded in) with dots, points, short lines with different colors. These lines are either horizontal or oblique and represent different transmitters. The illustration shows three groups of body brain organs A) Brain; B) Spinal Cord; C) Organs of the vegetative system. There are also strings of different sizes and colors between these three groups. These strings present couplings (links) between groups, (i.e. coherences). 6. Reasonings on the web of brain–body–mind 1) “The Functional Syncytium Brain–Body–Mind” replaces the concept of “Mind”, because of the fundamental findings related to EEG oscillations, Ultraslow Oscillations and neurotransmitters that are quasi-invariants, the Overall Myogenic System (OMS) and the vegetative system (including the heart, kidneys, lymphatic system) are functioning in an interwoven way. 2) EEG codes and natural frequencies do process within the limits of the uncertainty principle. Oscillations and neurotransmitters form one combined activity. Therefore, the web of “oscillations and neurotransmitters” can also be jointly considered as joint building blocks for function. The web of oscillations can be demonstrated by modification of brain oscillations and long distance connectivity (coherences) in cognitive deficits as in Alzheimer's disease, bipolar
287
disorders and schizophrenia (Yener et al., 2007; Başar et al., 2010; Ozerdem et al., 2010; Başar et al., 2012). Physiological and biochemical changes observed during the evolution of species will contribute considerably to further advances in the search for brain–body–mind. Spontaneous and event related oscillations in the CNS, OMS, and vegetative organs are all embedded or interacting with the biochemical pathways (neurotransmitters). These oscillatory processes can be considered as manifestations and building units of brain–body functioning. Extended genetic analysis is essential to differentiate clinical disorders in the scope of the work of Begleiter and Porjesz (2006). In pathology, the role of oscillatory dynamics and the coherence between various parts of the brain are determining for breakdown of the mind. As the brain matures, an enormous change of alpha activity is observed. The CNS, the OMS and the organs of the vegetative system show mutual excitability and, accordingly, mutual resonances in communication. This principle supports the fundaments of brain–body–mind and leads to the brain–body string theory. According to the results above the mind cannot be defined with a unique sentence, as stated in Section 1. We can try to address the question “how does the mind work”, but we cannot yet define it: The answer to this question requires multifold functional implications in the brain and body. The brain, body and mind are inseparable entities.
7. Conclusion What will be next steps in the brain–body–mind integration? Wiener (1948) stated: “If a new scientific subject has real vitality, the center of interest in it must, and should, shift in the course of years”. The field of oscillatory brain dynamics is now in the center of interest; I think that now is the time for a new paradigm shift, or at least for a gigantic extension. After four decades of research on ERO's the new constellation of research including extended frequency window with ultraslow oscillations, analyses of multiple frequencies and selective connectivity in diseases, the web of oscillations and new transmitters open a new conjecture to understand the integrative brain body mind functions and also renew the concept of syncytium. Therefore we pose the question: Is the research on brain oscillations in a “new take off state”? References Aladjalova, N.A., 1957. Infra-slow rhythmic oscillations. Nature 179, 957–959. Basar, E., 1980. EEG–brain dynamics. Relation Between EEG and Brain Evoked Potentials. Elsevier, Amsterdam. Başar, E., 2011. Brain–body–mind in the Nebulous Cartesian System. A Holistic Approach by Oscillations. Springer, New York. Başar, E., Güntekin, B., 2008. A review of brain oscillations in cognitive disorders and the role of neurotransmitters. Brain Research 1235, 172–193. Başar, E., Başar-Eroğlu, C., Özerdem, A., Rossini, P.M., Yener, G.G., in press. Applications of Brain Oscillations in Cognitive Impairment Supplement 62 to Clinical Neurophysiology. Elsevier, Amsterdam Başar, E., Güntekin, B., Atagün, I., Turp, B., Tülay, E., Ozerdem, A., 2012. Brain's alpha activity is highly reduced in euthymic bipolar disorder patients. Cognitive Neurodynamics 6 (1), 11–20. Başar, E., Güntekin, B., Tülay, E., Yener, G.G., 2010. Evoked and event related coherence of Alzheimer patients manifest differentiation of sensory–cognitive networks. Brain Research 1357, 79–90. Begleiter, H., Porjesz, B., 2006. Genetics of human brain oscillations. International Journal of Psychophysiology 60, 162–171. Buszaky, G., 2006. Rhythms of the Brain. Oxford University Press, New York.
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Cajal, R., 1911. Histologie du système nerveux de l'homme et des vertebras. Azoulay, L. (trans.), 1972. Consejo superior de investigaciones cientificas. Instituto Ramon y Cajal, Madrid. Gebber, G.L., Zhong, S., Barman, S.M., 1995a. The functional significance of the 10-Hz sympathetic rhythm: a hypothesis. Clinical and Experimental Hypertension 17, 181–195. Gebber, G.L., Zhong, S., Barman, S.M., 1995b. Synchronization of cardiac-related discharges of sympathetic nerves with inputs from widely separated spinal segments. American Journal of Physiology 268 (6 Pt 2), R1472–R1483. Güntekin, B., Saatçi, E., Yener, G., 2008. Decrease of evoked delta, theta and alpha coherence in Alzheimer patients during a visual oddball paradigm. Brain Research 1235, 109–116. Ozerdem, A., Güntekin, B., Saatçi, E., Tunca, Z., Basar, E., 2010. Disturbance in long distance gamma coherence in bipolar disorder. Progress in Neuro-Psychopharmacology & Biological Psychiatry 34 (6), 861–865. Ruskin, D.N., Bergstrom, D.A., Baek, D., Freeman, L.E., Walters, Jr, 2001a. Cocaine or selective block of dopamine transporters influences multisecond oscillations in firing rate in the globus pallidus. Neuropsychopharmacology 25, 28–40. Ruskin, D.N., Bergstrom, D.A., Shenker, A., Freeman, L.E., Baek, D., Walters, Jr, 2001b. Drugs used in the treatment of attention-deficit/ hyperactivity disorder affect postsynaptic firing rate and oscillation without preferential dopamine autoreceptor action. Biological Psychiatry 49, 340–350. Wiener, N., 1948. Cybernetics or Control and Communication in the Animal and the Machine. Massachusetts Institute of Technology, Massachusetts. Yener, G., Başar, E., in press. Biomarkers in Alzheimer's Disease with a Special Emphasis on Event-Related Oscillatory Responses. Clin Neurophysiol. Yener, G., Güntekin, B., Öniz, A., Başar, E., 2007. Increased frontal phase-locking of event related theta oscillations in Alzheimer patients treated with acetylcholine-esterase inhibitors. International Journal of Psychophysiology 64 (1), 46–52 Apr. doi:10.1016/j.ijpsycho.2012.07.180
Sunday, September 16th, 2012 Individualized assessment and treatment using advanced EEG and dynamic localization techniques with live sLORETA☆ Thomas F. Collura BrainMaster Technologies, Inc., Bedford, OH, USA Advanced Psychotherapy Services, Willowick, OH, USA ☆A tribute to Mario Guazzelli
Electroencephalography (EEG) consists of the measurement of brain electrical activity as recorded from the scalp. It has undergone a continual evolution since original reports by Caton, Berger, and others (Collura, 1993; 1995). Initially, it was necessary to expose a photographic paper or film to an oscillating light source, and to develop the resulting image after the fact. Later developments by Grass and others led to the use of inkpens, producing an immediate readout. Signal processing was, however, difficult or impossible. With the advent of special-purpose electronics, it became possible to produce analyses such as Fourier Transforms, signal averages, and other simple secondary measurements. General-purpose computers then opened the door to more elaborate processing including spectral analysis, event-related cognitive potentials, and normative
comparisons using databases and other references. Pascual-Marqui (1999, 2002) reported on an innovative algorithm that produced estimates of cortical activity based upon the surface recordings, effectively reversing the effects of volume conduction, which smears electrical activity from a single source across the entire skull. Historically, most such analyses have been done offline, in the form of postprocessing and reports, and have tended to de-emphasize instantaneous or real-time measurements. Most recently, as reported here, it has become possible to combine these methods into a real-time system, so that it is possible to produce instantaneous real-time representations of brain cortical activity at high resolution, facilitating the visualization of cortical brain electrical processes relevant to thought and behaviour. The implementation reported here takes full use of computer technology developed for the purposes of 3-dimensional imaging and? gaming, using multiple processors optimized for real-time computations and graphics. The improvement in speed compared to existing designs is a factor of approximately 100:1. Rather than having to wait several seconds for a rendered image, it is possible to access any or all of up to 16 images, for different frequency bands, at live speeds and in real time. The agreement between LORETA-based solutions and existing imaging techniques has been established. For reviews, see Pascual-Marqui (1999, 2002) and Fuchs et al. (2002). This therefore provides a valid method of recovering brain electrical activity based upon the surface lead field. In particular, the sLORETA solution features zero localization error and high spatial resolution (5 mm, 6,239 voxels). When compared to methods such as MRI, EEG-based localization provides significantly faster response and comparable resolution, at a significantly lower cost, and minimal facility and personnel support requirements. It provides imaging of brain electrical activity, in contrast to the structural or metabolic representations provided by methods such as CT or MRI. As is typical with any new method, there are applications that may not be initially anticipated. This capability allows clinical practice to go beyond traditional areas of assessment, diagnosis, and treatment planning. For example, it now becomes possible to visualize client brain activity in relation to therapeutic interventions, becoming part of the treatment progress. It is possible, for example, to determine the emotional response to specific questions or cues, and to adapt to them. The extremely fine spatial resolution of this electrical approach makes possible a process of decomposition (deconstruction) of the signal into constituent voxels, and the subsequent recombination (reconstitution) into meaningful activity of regions of interest. A typical region of interest will include dozens, perhaps hundreds, of voxels, each of whose activity is quantified in time, and which can be recombined into an assessment of the activity of that region of interest. Underlying functional dynamics can therefore be determined and quantified, providing insight into the processes that underlie the functional behaviour of the brain. Davidson has emphasized that a dependence on aggregate statistics and group normative data runs the risk of neglecting or missing the importance of individual traits, in particular the presence and importance of individual “outliers.” Certain types of assessments such as narratives or projective tests allow individual qualities and “story” to emerge, while others such as structured tests, including quantitative EEG, tend to assess individuals in light of a normative standard. While structured normative assessment is a powerful approach with significant benefits when confronted with individual suffering from many identified disorders, there is also importance in allowing an individual to emerge with his or her own unique characteristics, and in including these individual qualities in assessment and treatment planning and evaluating outcomes. When sufficient detail is provided in time and space related to individual brain processes, it becomes possible to de-emphasize population averages and z-scored assessment, or at least to supplement these assessments with an individualized picture or story. The ability to separate out brain functional regions directly has relevance to event-related potential work, by reducing or eliminating
Contents lists available at SciVerse ScienceDirect
International Journal of Psychophysiology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i j p s yc h o
Didactic Lectures
Friday, September 14th, 2012 Is research on brain oscillations in a new “take off-state” in integrative brain function? Erol Başar Brain Dynamics, Cognition and Complex Systems Research Center, Istanbul Kultur University, Istanbul, Turkey Three decades ago we launched a working hypothesis stating that “alpha, beta, gamma, theta and delta oscillations and their connectivity govern the whole brain work”. Presently, it is discussed that the autonomic nervous system is also partly processing by EEG-like oscillations and ultra-slow oscillations in the frequency range of 0.01–01 Hz that are observed in smooth muscle contractions in all vegetative organs in the body including the vasculature and visceral organs. The ultra-slow oscillations were initially recorded in electrical activity of the brain by Aladjalova (1957). Currently, these activities are measured in the reticular formation, thalamus and even in the auditory cortex as an extensive survey shows. According to these last results, it is proposed to extend the concept “whole brain work” to “integrative brain–body–mind work”. The event related oscillatory responses (ERO's) and selective connectivity during cognitive tasks (from delta to gamma frequency range) measured by scalp electrodes are highly increased versus evoked oscillations upon a pure sensory signal depending on brain topography. The effects of neurotransmitters such as GABA, Acetylcholine and Dopamine are highly efficient in oscillatory activity. This means that sensory sensitive and/or cognitive sensitive oscillatory neural networks react differentially upon stimulation modality. This electrophysiological differentiation enables to analyze more efficiently the changes in brain's oscillatory responses and selectivity in diseases with cognitive impairment as MCI, Alzheimer's disease, bipolar disorders and schizophrenia. Candidates of biomarkers in these diseases are recently extensively discussed. After four decades of research on ERO's the new constellation of research including extended frequency window with ultraslow oscillations, analyses of multiple frequencies and selective connectivity in diseases opens a new conjecture to understand the integrative brain function. Therefore we pose the question: Is the research on brain oscillations in a “new take off state”? 1. Earlier hypothesis on brain oscillations Neuroscience has provided us some astonishing breakthroughs, from noninvasive imaging of the human brain to uncovering the
0167-8760/$ – see front matter.
molecular mechanisms of some complex processes and disease states. Nevertheless, what makes the brain so special and fundamentally different from all other living tissue is its organized action in time. This temporal domain is where the importance of research on neural oscillators is indispensable (Buszaky, 2006). Three decades ago Basar (1980) launched a working hypothesis stating that “alpha, beta, gamma, theta and delta oscillations and their connectivity govern the whole brain work”. The event related oscillatory responses (ERO's) and selective connectivity during cognitive tasks (in alpha, beta, gamma, theta and delta frequency bands) measured by scalp electrodes are highly increased versus evoked oscillations upon a pure sensory signal depending on brain topography. Each oscillatory activity represents multiple functions; vice versa, each function is represented by multiple oscillations. Functioning in all physiological and biochemical pathways is governed by quasi-invariant natural frequencies and neurotransmitters of brain–body functioning. Several brain functions are manifested by superposition of EEG-Oscillations. The effects of neurotransmitters such as GABA, acetylcholine and dopamine are highly efficient in oscillatory activity. This means that sensory sensitive and/ or cognitive sensitive oscillatory neural networks react differentially upon stimulation modality. This electrophysiological differentiation enables to analyze more efficiently the changes in brain's oscillatory responses and selectivity in diseases with cognitive impairment as MCI, Alzheimer's disease, bipolar disorders and schizophrenia. Candidates of biomarkers in these diseases are recently extensively discussed (Başar et al., in press; Yener et al., 2007; Başar and Güntekin, 2008). 2. A strategy to understand brain–body integration A new hypothesis tries to link brain work with the work of the vegetative system. The proposed steps to understand brain–body– mind incorporation is illustrated in Fig. 1. In pathologic brains, the release of transmitters and, accordingly, oscillatory processes and control of cognitive processes, are highly altered (Başar, 2011). Therefore, the analysis steps in Fig. 1, which also includes a loop indicating the influences of pathology (Alzheimer, Schizophrenia, bipolar disorders) constitute a minimal analysis—prerequisite to approach the integration of brain–body–mind. Fig. 1 indicates further that we also have to observe the machineries of invertebrate ganglia and brains during the evolution of species in order to understand brain–body mind integration (see Fig. 1). Furthermore, physiological processes and anatomical changes need to be analyzed during maturation of the brain, from infancy through adulthood to old age.
286
Didactic Lectures / International Journal of Psychophysiology 85 (2012) 285–290
Fig. 1. Schematic explanation of the steps proposed for an approach to brain–body– mind.
3. New emerging hypothesis Presently, it is discussed that the autonomic nervous system is also partly processing by EEG-like oscillations and ultra-slow oscillations in the frequency range of 0.01–01 Hz that are observed in smooth muscle contractions in all vegetative organs in the body including the vasculature and visceral organs. The ultra-slow oscillations were initially recorded in electrical activity of the brain by Aladjalova (1957). Currently, these activities are measured in the reticular formation, thalamus and even in the auditory cortex as an extensive survey shows. These last results led Başar (2011) to propose to extend the concept “whole brain work” to “integrative brain–body– mind work”. A frequency tuning can be found not only in the brain, but also in the brain–body integration. According to the work of Gebber et al. (1995a,b), Aladjalova (1957) and Ruskin et al. (2001a,b), structures in the vegetative system are also tuned to the same frequencies. Transmitters such as acetylcholine or nor-epinephrine are also excellent vehicles for general tuning in the brain–body interaction. The body and the brain use the same transmitters and the same frequencies for general tuning for brain–body interaction. Here, the ensemble of these phenomena is tentatively called “the overall tuning in brain–body interaction”. According to C. Babiloni1 in Başar's new work a new idea is developed: “The new idea is that the brain oscillations reflect, and are the key to understand, the complex homeostatic interactions between brain and body visceral organs in the formation of the mind as the term capturing the representation of the reality and the related cognitive and affective processes. Erol Başar leads the readers to surpass the view of the mind as emerging from brain oscillatory processes, in favor of the view that the mind emerges from the multiple oscillatory processes characterizing the homeostatic interactions between brain and body visceral organs and tissues within a structural and functional continuum termed “Brain–Body–Mind functional syncytium”. Within such “functional syncytium”, quasi-invariant nervous oscillatory processes at different frequencies would ensure a stochastic unstable transfer of (excitatory and inhibitory) signals among different brain nodes by means of neurotransmitter systems and complex biochemical pathways. In parallel, quasi-invariant myogenic slow oscillatory processes would ensure the inter-relatedness and homeostatic adaptation of body visceral organs and tissues by means of vegetative system. Afterwards, Erol Başar outlines the “life span” of such “functional syncytium” along the phylogenesis and human ontogenesis as well as its derangement in the form of Alzheimer's disease, Schizophrenia, and Depression. The concept of the “Nebulous Cartesian System” helps him to emphasize the chaotic nature but tight relationships among the countless dimensions of the mentioned “functional syncytium”.
1
Claudio Babiloni: Private communication.
4. Back to the concept of syncytium: dynamic syncytium The first morphological studies of the nervous system were done by the Spanish anatomist Santiago Ramon y Cajal (1911). He proposed that the functions of the brain could be understood by analyzing the functional architecture of the nervous system. Applying Golgi's silver staining technique to the study of nerve tissue, he observed that only some cells are stained in their entirety. This led to his formulation of the ‘neuron doctrine’ which states that the brain is made up of discrete units rather than a continuous net of nerve tissue or ‘syncytium’ as was originally thought. He proposed that nerve impulses travel from the dendrites of a neuron to its cell body and then along the axon to the dendrites of the neighboring neuron. This flow of information would be a finite process. Why a dynamic syncytium is proposed? Fig. 2 presents a schema for connectivity underlying sensory evoked coherence responses following simple sensory stimuli and eventrelated coherence responses following cognitive task. It is not possible to define clear-cut boundaries for these neural groups that are differentiated upon application of sensory stimulation or upon cognitive stimulation. This schema indicates that there are neural populations, mostly responding to sensory signals, and other populations responding to only cognitive stimulation. Further, there is some overlap or plasticity among these networks. It is also possible that neural groups are not separated into different structures but co-exist also in given structures. These are selectively distributed neuron clusters capable of responding to sensory/cognitive inputs. It is also expected that following sensory stimulation, cognitive neural clusters would remain silent, whereas a cognitive stimulus (i.e. target signal in oddball paradigm) would excite both sensory and cognitive neural clusters. Certainly in the case of cognitive impairment, cognitive neural clusters would be more affected, in turn, giving rise to less unclear responses. Moreover, reduced response amplitude can result from either non-responding neural units or non-phase locked response activity. Fig. 2 illustrates only one local area. However, isolated brain networks can explain only a limited activity. In addition to these local activities, it is important to emphasize the selective connectivity between neural elements of these networks and, more important differential connectivity between distant areas of the brain (for example frontal, limbic and parietal connections). In the case of Alzheimer's disease, the number of neural clusters responding to cognitive stimulus is greatly reduced. Additionally, we observe a selective connectivity deficit between distant neural networks (see articles by Başar et al., 2010 and Güntekin et al., 2008). We use the expression “Dynamic Syncytium” since parts of the brain forms a functional cluster depending on stimulation modality and applied cognitive tasks. There are also periods of silent.
Fig. 2. Neural assemblies involved in sensory and cognitive networks. Cognitive networks (here shown by magenta lines) probably contain sensory neural elements, but also involve additional neural assemblies, as shown by magenta circles. Sensory network elements are illustrated by blue squares and connections by blue lines. It is expected that sensory signals trigger activation of sensory areas, whereas cognitive stimulation would evoke both neural groups reacting to sensory and cognitive inputs (Modified from Yener and Başar, in press).
Didactic Lectures / International Journal of Psychophysiology 85 (2012) 285–290
5. A new tentative model is proposed as a metaphor to string theory in physics In this tentative model the brain (I), the spinal cord (II) and the organs of the vegetative systems (III) are illustrated as three different functional groups that are connected with strings having different innate frequencies (EEG-Oscillations and Ultra-Slow Oscillations). Strings (or oscillators) of different frequencies that are presented with different colors bind these three groups according to coherences described in and organs of the vegetative system. The strings also bind neural groups within the brain, spinal cord.
3)
4)
5) 6)
7)
8)
Fig. 3. A schematically illustration of the Brain–Body–Mind String Theory (Modified from Başar, 2011).
Different neurotransmitters, shown as dashed lines in different colors, influence the frequencies of strings and coherences between neural groups. Several types of strings are represented with different colors. Strings have different amplitudes and are also accompanied (embedded in) with dots, points, short lines with different colors. These lines are either horizontal or oblique and represent different transmitters. The illustration shows three groups of body brain organs A) Brain; B) Spinal Cord; C) Organs of the vegetative system. There are also strings of different sizes and colors between these three groups. These strings present couplings (links) between groups, (i.e. coherences). 6. Reasonings on the web of brain–body–mind 1) “The Functional Syncytium Brain–Body–Mind” replaces the concept of “Mind”, because of the fundamental findings related to EEG oscillations, Ultraslow Oscillations and neurotransmitters that are quasi-invariants, the Overall Myogenic System (OMS) and the vegetative system (including the heart, kidneys, lymphatic system) are functioning in an interwoven way. 2) EEG codes and natural frequencies do process within the limits of the uncertainty principle. Oscillations and neurotransmitters form one combined activity. Therefore, the web of “oscillations and neurotransmitters” can also be jointly considered as joint building blocks for function. The web of oscillations can be demonstrated by modification of brain oscillations and long distance connectivity (coherences) in cognitive deficits as in Alzheimer's disease, bipolar
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disorders and schizophrenia (Yener et al., 2007; Başar et al., 2010; Ozerdem et al., 2010; Başar et al., 2012). Physiological and biochemical changes observed during the evolution of species will contribute considerably to further advances in the search for brain–body–mind. Spontaneous and event related oscillations in the CNS, OMS, and vegetative organs are all embedded or interacting with the biochemical pathways (neurotransmitters). These oscillatory processes can be considered as manifestations and building units of brain–body functioning. Extended genetic analysis is essential to differentiate clinical disorders in the scope of the work of Begleiter and Porjesz (2006). In pathology, the role of oscillatory dynamics and the coherence between various parts of the brain are determining for breakdown of the mind. As the brain matures, an enormous change of alpha activity is observed. The CNS, the OMS and the organs of the vegetative system show mutual excitability and, accordingly, mutual resonances in communication. This principle supports the fundaments of brain–body–mind and leads to the brain–body string theory. According to the results above the mind cannot be defined with a unique sentence, as stated in Section 1. We can try to address the question “how does the mind work”, but we cannot yet define it: The answer to this question requires multifold functional implications in the brain and body. The brain, body and mind are inseparable entities.
7. Conclusion What will be next steps in the brain–body–mind integration? Wiener (1948) stated: “If a new scientific subject has real vitality, the center of interest in it must, and should, shift in the course of years”. The field of oscillatory brain dynamics is now in the center of interest; I think that now is the time for a new paradigm shift, or at least for a gigantic extension. After four decades of research on ERO's the new constellation of research including extended frequency window with ultraslow oscillations, analyses of multiple frequencies and selective connectivity in diseases, the web of oscillations and new transmitters open a new conjecture to understand the integrative brain body mind functions and also renew the concept of syncytium. Therefore we pose the question: Is the research on brain oscillations in a “new take off state”? References Aladjalova, N.A., 1957. Infra-slow rhythmic oscillations. Nature 179, 957–959. Basar, E., 1980. EEG–brain dynamics. Relation Between EEG and Brain Evoked Potentials. Elsevier, Amsterdam. Başar, E., 2011. Brain–body–mind in the Nebulous Cartesian System. A Holistic Approach by Oscillations. Springer, New York. Başar, E., Güntekin, B., 2008. A review of brain oscillations in cognitive disorders and the role of neurotransmitters. Brain Research 1235, 172–193. Başar, E., Başar-Eroğlu, C., Özerdem, A., Rossini, P.M., Yener, G.G., in press. Applications of Brain Oscillations in Cognitive Impairment Supplement 62 to Clinical Neurophysiology. Elsevier, Amsterdam Başar, E., Güntekin, B., Atagün, I., Turp, B., Tülay, E., Ozerdem, A., 2012. Brain's alpha activity is highly reduced in euthymic bipolar disorder patients. Cognitive Neurodynamics 6 (1), 11–20. Başar, E., Güntekin, B., Tülay, E., Yener, G.G., 2010. Evoked and event related coherence of Alzheimer patients manifest differentiation of sensory–cognitive networks. Brain Research 1357, 79–90. Begleiter, H., Porjesz, B., 2006. Genetics of human brain oscillations. International Journal of Psychophysiology 60, 162–171. Buszaky, G., 2006. Rhythms of the Brain. Oxford University Press, New York.
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Cajal, R., 1911. Histologie du système nerveux de l'homme et des vertebras. Azoulay, L. (trans.), 1972. Consejo superior de investigaciones cientificas. Instituto Ramon y Cajal, Madrid. Gebber, G.L., Zhong, S., Barman, S.M., 1995a. The functional significance of the 10-Hz sympathetic rhythm: a hypothesis. Clinical and Experimental Hypertension 17, 181–195. Gebber, G.L., Zhong, S., Barman, S.M., 1995b. Synchronization of cardiac-related discharges of sympathetic nerves with inputs from widely separated spinal segments. American Journal of Physiology 268 (6 Pt 2), R1472–R1483. Güntekin, B., Saatçi, E., Yener, G., 2008. Decrease of evoked delta, theta and alpha coherence in Alzheimer patients during a visual oddball paradigm. Brain Research 1235, 109–116. Ozerdem, A., Güntekin, B., Saatçi, E., Tunca, Z., Basar, E., 2010. Disturbance in long distance gamma coherence in bipolar disorder. Progress in Neuro-Psychopharmacology & Biological Psychiatry 34 (6), 861–865. Ruskin, D.N., Bergstrom, D.A., Baek, D., Freeman, L.E., Walters, Jr, 2001a. Cocaine or selective block of dopamine transporters influences multisecond oscillations in firing rate in the globus pallidus. Neuropsychopharmacology 25, 28–40. Ruskin, D.N., Bergstrom, D.A., Shenker, A., Freeman, L.E., Baek, D., Walters, Jr, 2001b. Drugs used in the treatment of attention-deficit/ hyperactivity disorder affect postsynaptic firing rate and oscillation without preferential dopamine autoreceptor action. Biological Psychiatry 49, 340–350. Wiener, N., 1948. Cybernetics or Control and Communication in the Animal and the Machine. Massachusetts Institute of Technology, Massachusetts. Yener, G., Başar, E., in press. Biomarkers in Alzheimer's Disease with a Special Emphasis on Event-Related Oscillatory Responses. Clin Neurophysiol. Yener, G., Güntekin, B., Öniz, A., Başar, E., 2007. Increased frontal phase-locking of event related theta oscillations in Alzheimer patients treated with acetylcholine-esterase inhibitors. International Journal of Psychophysiology 64 (1), 46–52 Apr. doi:10.1016/j.ijpsycho.2012.07.180
Sunday, September 16th, 2012 Individualized assessment and treatment using advanced EEG and dynamic localization techniques with live sLORETA☆ Thomas F. Collura BrainMaster Technologies, Inc., Bedford, OH, USA Advanced Psychotherapy Services, Willowick, OH, USA ☆A tribute to Mario Guazzelli
Electroencephalography (EEG) consists of the measurement of brain electrical activity as recorded from the scalp. It has undergone a continual evolution since original reports by Caton, Berger, and others (Collura, 1993; 1995). Initially, it was necessary to expose a photographic paper or film to an oscillating light source, and to develop the resulting image after the fact. Later developments by Grass and others led to the use of inkpens, producing an immediate readout. Signal processing was, however, difficult or impossible. With the advent of special-purpose electronics, it became possible to produce analyses such as Fourier Transforms, signal averages, and other simple secondary measurements. General-purpose computers then opened the door to more elaborate processing including spectral analysis, event-related cognitive potentials, and normative
comparisons using databases and other references. Pascual-Marqui (1999, 2002) reported on an innovative algorithm that produced estimates of cortical activity based upon the surface recordings, effectively reversing the effects of volume conduction, which smears electrical activity from a single source across the entire skull. Historically, most such analyses have been done offline, in the form of postprocessing and reports, and have tended to de-emphasize instantaneous or real-time measurements. Most recently, as reported here, it has become possible to combine these methods into a real-time system, so that it is possible to produce instantaneous real-time representations of brain cortical activity at high resolution, facilitating the visualization of cortical brain electrical processes relevant to thought and behaviour. The implementation reported here takes full use of computer technology developed for the purposes of 3-dimensional imaging and? gaming, using multiple processors optimized for real-time computations and graphics. The improvement in speed compared to existing designs is a factor of approximately 100:1. Rather than having to wait several seconds for a rendered image, it is possible to access any or all of up to 16 images, for different frequency bands, at live speeds and in real time. The agreement between LORETA-based solutions and existing imaging techniques has been established. For reviews, see Pascual-Marqui (1999, 2002) and Fuchs et al. (2002). This therefore provides a valid method of recovering brain electrical activity based upon the surface lead field. In particular, the sLORETA solution features zero localization error and high spatial resolution (5 mm, 6,239 voxels). When compared to methods such as MRI, EEG-based localization provides significantly faster response and comparable resolution, at a significantly lower cost, and minimal facility and personnel support requirements. It provides imaging of brain electrical activity, in contrast to the structural or metabolic representations provided by methods such as CT or MRI. As is typical with any new method, there are applications that may not be initially anticipated. This capability allows clinical practice to go beyond traditional areas of assessment, diagnosis, and treatment planning. For example, it now becomes possible to visualize client brain activity in relation to therapeutic interventions, becoming part of the treatment progress. It is possible, for example, to determine the emotional response to specific questions or cues, and to adapt to them. The extremely fine spatial resolution of this electrical approach makes possible a process of decomposition (deconstruction) of the signal into constituent voxels, and the subsequent recombination (reconstitution) into meaningful activity of regions of interest. A typical region of interest will include dozens, perhaps hundreds, of voxels, each of whose activity is quantified in time, and which can be recombined into an assessment of the activity of that region of interest. Underlying functional dynamics can therefore be determined and quantified, providing insight into the processes that underlie the functional behaviour of the brain. Davidson has emphasized that a dependence on aggregate statistics and group normative data runs the risk of neglecting or missing the importance of individual traits, in particular the presence and importance of individual “outliers.” Certain types of assessments such as narratives or projective tests allow individual qualities and “story” to emerge, while others such as structured tests, including quantitative EEG, tend to assess individuals in light of a normative standard. While structured normative assessment is a powerful approach with significant benefits when confronted with individual suffering from many identified disorders, there is also importance in allowing an individual to emerge with his or her own unique characteristics, and in including these individual qualities in assessment and treatment planning and evaluating outcomes. When sufficient detail is provided in time and space related to individual brain processes, it becomes possible to de-emphasize population averages and z-scored assessment, or at least to supplement these assessments with an individualized picture or story. The ability to separate out brain functional regions directly has relevance to event-related potential work, by reducing or eliminating