Always Under Construction
Last modified:  Feb 18, 2017
Copyright 2010, 2017 by Douglas Dailey - All Rights Reserved.

Sickness Behavior (SBeh), TAG Sync & Neurofeedback:

This is my icon for sickness behavior (SBeh). The reversal of sickness behavior is the central emphasis of my model. We will discuss this icon in detail later. For now please know that the top circle represents adaptive wellness behavior and rigpa (creative, adaptive natural mind). When stress exceeds resources the adaptive behavior deteriorates toward ever more finely dissected ICD and DSM codes represented by the gradations at the base of the sickness behavior pyramid. Attempts to reestablish wellness behavior (return to center at top) may be dissipated by patterns of over-control (arrow to the left) and/or patterns of under-control (arrow to the right). Later we will show how these “dissipations” correspond to the dynamic rigidity and dynamic lability of Ulrich (2013, “The Theoretical Interpretation of Electroencephalography”) as well as the concepts of rigid versus random graphs compared to efficient small world networks. This is a model to use when the adaptive natural mind dissipates into chronic sickness behavior. By “canonical sickness behavior” I mean programmed apoptosis of the self. (See video “Death by Design”.) In general, sickness behavior refers to a common pathway in illness in which there is fatigue, loss of pleasure, loss of interest, disconnection, uncoupling between energy utilization and efficiency, and social, immune and metabolic network failure. Many of the epigenetic factors can be regulated through mindfulness practice [1]. The physical, mental and social dissolution is often accompanied by white matter dissolution in the form in decreased fractional anisotropy with diffusion tensor imaging. Please spend some time with this very important chart I adapted from Schulman et al 2011 [2]. OCD = Obsessive-Compulsive Disorder, SSD = Schizophrenia Spectrum Disorder, DD = Depressive Disorder, and CON = healthy controls. The vertical scale is an arbitrary scale for EEG amplitude. For temporary convenience I am going to simply refer to everything here as “sickness behavior” except for the healthy control shown in green. What differentiates natural adaptive ability (the green curve to the left as well as the circle at the top of the SBeh icon above) from sickness behavior (OCD, SSD, DD) in this chart? The adaptive EEG (Green) has 1) a sharp alpha peak centered around 10 Hz and 2) efficient low amplitude production of both low frequency and high frequency EEG. Sickness behavior exhibits increased inefficient production of low and high frequency EEG signals plus a distorted peak control frequency that is either lowered in frequency (DD), or has two discrete peak frequencies (SSD) or has little or no established control frequency (OCD). If Neurofeedback did not exist it would be necessary for us to invent it. The above graph seems to suggest that in order to turn sickness behavior toward wellness behavior we might 1) reduce the amplitude of 4-8 Hz EEG, 2) reduce the amplitude of 16-30 Hz EEG, and 3) train for a sharp alpha peak around 10 Hz. Please return to the green “healthy control” curve in the power spectral display above. For each condition the chart has measured the power produced in multiple frequency “bins” (e.g., 4-5 Hz, 5-6 Hz, 6-7 Hz, etc) averaged over the time of the recording. For example, because of averaging over time, the large 10 Hz alpha peak in controls would look exactly the same in each of the following very different situations. A) Alpha declines from high to low power during the recording period; B) Alpha increases from low to high power during the recording period; C) Alpha is moderate in power and constant during the recording period; D) Alpha consists of a series of high power alpha bursts interspersed with little or no alpha production during the recording period. Each of these 4 situations would be expected to respond differently when there is a clinical change of state. Unfortunately the colored brain maps commonly used in neurotherapy have thrown away the all the information about dynamics (changes over time) and it can not be reassembled without consulting the raw EEG. This will be discussed in the section on “theoretical interpretation of electroencephalography”. The following illustration shows dynamics of the power spectrum of a client over a period of a few seconds before and after an effective neurofeedback (NFB) session. The previous chart above (group means) has been turned on its side so that the frequencies in the chart are aligned with the frequencies in the NFB cascading spectral display, captured from the popular program BioExplorer. Unlike the group means chart (above) which uses height above the horizontal axis to indicate power, the NFB spectral display uses color. The warmer and whiter colors are high power and correspond to the higher peaks in the group means chart above. The NFB spectral display (also called a cascading chronospectrogram, etc.) shows, using color, how the alpha and other frequencies behave over time, for example, whether the client is becoming more awake or less awake during the recording. This information is not available in the traditional NFB brain maps.
References: [1] Ravnik-Glavac M, et al (2012) - Genome-wide expression changes in a higher state of consciousness.  Consciousness and Cognition 21 (2012) 1322–1344. [Abstract] [2] Schulman JJ, et al (2011) - Imaging of thalamocortical dysrhythmia in neuropsychiatry. Frontiers in Human Neuroscience, 29 July. [Free Full Text] [3] Ulrich G (2013) - The Theoretical Interpretation of Electroencephalography. BMed. [4] Lee GH, et al (2012) - Middle ear myoclonus associated with forced eyelid closure in children: diagnosis and treatment outcome. Laryngoscope. 2012 Sep; 122(9): 2071-5. [Abstract] [5] Panossian A, et al (2005) - Stimulating effect of adaptogens - An overview with particular reference to their efficacy following single dose administration. Phytotherapy Research, 19, 819-838. [Abstract] [6] Vincent K, et al (2013) - Altered network communication following a neuroprotective drug treatment. PLoS One. 2013; 8(1):e54478. [Free Full Text] [7] Dhond RP, et al (2008) - Acupuncture modulates resting state connectivity in default and sensorimotor brain networks. Pain. 2008 Jun, 136(3):407-418. [Free Full Text] [8] Washington SD, et al (2013) - Dysmaturation of the Default Mode Network in Autism. Human Brain Mapping, Apr;35(4):1284-96. [Free Full Text] [9] Russell-Chapin L, et al (2013) - The Effects of Neurofeedback in the Default Mode Network - Pilot Study Results of Medicated Children with ADHD. Routledge Press. [10] Khalid A, et al (2016) - Gamma oscillation in functional brain networks is involved in the spontaneous remission of depressive behavior induced by chronic restraint stress in mice. BMC Neurosci (2016) 17:4. [Free Full Text] [11] Voorhees JL, et al (2013) - Prolonged restraint stress increases IL-6, reduces IL-10, and causes persistent depressive-like behavior that is reversed by recombinant IL-10.  PLoS One. 2013; 8(3). [Free Full Text] [12] Liang S, et al (2015) - Administration of Lactobacillus helveticus NS8 improves behavioral, cognitive, and biochemical aberrations caused by chronic restraint stress.  Neuroscience. 2015 Dec 3; 310:561-77. [Free Full Text] [13] Pahor A, et al (2014) - Theta–gamma cross-frequency coupling relates to the level of human intelligence.  Intelligence 46 (2014) 283-290. [Abstract]
The NFB spectral display uses quantitative EEG methods to provide its live (cascading) output during a session. In our example (below) at each moment in time we can compare the client’s spectral display with that of the previous few moments and observe trends such as intrusion of fragments of sleep states. This type of live QEEG guided NFB can be interpreted in a similar manner as Ulrich’s Ipsative Trend Analysis (ITA) discussed elsewhere. These 2 cascading spectral displays scroll to the left. That means that at the far right of each display (where the vertical axis indicates the frequency components) you see the amplitude of each frequency that was being produced just at the moment the screen capture was completed. The amplitude is represented by color - warmer colors and white have the highest amplitude. The data at the far left of each display shows the amplitudes of each frequency produced 30 seconds previously. The displays scroll to the left and display this quantified EEG (QEEG) data. Attention to detail will show that even though the sensitivity of the instruments was different between the recordings the post- treatment qEEG data still sees in ipsative trend away from sickness behavior. The Pre-NFB may be described as “dynamic lability” in Ulrich’s terms [3] or thalamocortical dysrhythmia similar to what Schulman and Llinas’ describe in MEG. Despite the differences in generation and interpretation between MEG and EEG data, the notion of dissipation of electromagnetic energy along with the dissolution of its control frequencies is paralleled by reduced fractional anisotropy in diffusion tensor imaging (DTI). The loss of efficiency increases the wiring costs such that the functional path length increases. Such loss of small world network efficiency may be accompanied by intrusion of subvigilant states and failure to harness complexity through synchronization. You will often observe clients switching between the two states shown in the spectral displays (below). This may be accompanied by compulsive narrating and rumination. In the “Before training” cascading spectral display below note the repetitive high voltage low frequency eye movement artifacts in the first third of the recording. The client was unaware of and had no control of these forced eye movements that were accompanied by middle ear myoclonus, a thumping type of tinnitus. They were accompanied by sudden intense negative internal dialogue and anxiety. In the spectral display you can see the characteristic cross frequency coupling between delta and beta that characterizes social anxiety disorder. This occurs immediately after the forced eye movements that were accompanied by a large repetitive epileptiform discharge that was found to be palatal myoclonus. [4] I mention this here to emphasis that this sort of ipsative trend analysis during or between neurofeedback sessions requires a thorough familiarity with raw EEG as well as the real time operation and interpretation of the QEEG instrumentation used during training.

Neurofeedback acts like an adaptogen to help self-regulate sickness behavior:

Adaptogens such as herbs and certain pharmaceuticals are substances which are 1) generally nontoxic, 2) have a wide range of application, and 3) epigenetically up-regulate wellness behavior [5]. Adaptogens improve small world efficiency by increasing nodal clustering coefficient (”C”) while reducing average path length between nodes [6]. In 2010 Dhond showed with fMRI that real but not sham acupuncture at point St36 was capable of consolidating the default mode network of the brain [7]. Washington et al (2013) showed that autism involved such dysmaturation of the default mode network (DMN) [8]. In fact St36, the point used by Dhond et al, is a common ancient network point for developmental failure. In 2010 I proposed that NFB acts like an adaptogen to help regulate sickness behavior. In 2013 our team headed by Lori Anne Russell-Chapin at Bradley University showed that standard NFB had a very similar maturational effect on fMRI DMN functioning in adolescents with ADHD [9].

The EEG during spontaneous recovery from sickness behavior:

Very few EEG studies have looked at the brain of a group of animals before overwhelming trauma, then immediately afterwards in the symptomatic state, and then later after spontaneous recovery. Khalid A, et al (2016) showed that mice whose lives had been complicated by four weeks of chronic restraint stress used global broad band EEG synchronization to spontaneously recover from the depressive and inflammatory post-traumatic sequelae. [10,11,12] Elsewhere I explain that neurofeedback, used as a sort of technology-assisted self-regulation / self-realization (TASR) promotes the ability of synchrony to harness complexity. When the brain uses more resources to solve a complex problem, it does so accompanied especially by an increased synchronization of the amplitudes of theta and gamma [13, tagsync.com/cfc].
In Progress: Apoptosis of the self: The importance of network support and neuroplasticity. EEG in healers and the healed; network efficiency in sickness and healing behavior. Complexity, Canonical Sickness Behavior, and EEG Biofeedback.
Always Under Construction
Last modified:  Feb 18, 2017

Sickness Behavior (SBeh), TAG Sync & Neurofeedback:

This is my icon for sickness behavior (SBeh). The reversal of sickness behavior is the central emphasis of my model. We will discuss this icon in detail later. For now please know that the top circle represents adaptive wellness behavior and rigpa (creative, adaptive natural mind). When stress exceeds resources the adaptive behavior deteriorates toward ever more finely dissected ICD and DSM codes represented by the gradations at the base of the sickness behavior pyramid. Attempts to reestablish wellness behavior (return to center at top) may be dissipated by patterns of over-control (arrow to the left) and/or patterns of under-control (arrow to the right). Later we will show how these “dissipations” correspond to the dynamic rigidity and dynamic lability of Ulrich (2013, “The Theoretical Interpretation of Electroencephalography”) as well as the concepts of rigid versus random graphs compared to efficient small world networks. This is a model to use when the adaptive natural mind dissipates into chronic sickness behavior. By “canonical sickness behavior” I mean programmed apoptosis of the self. (See video “Death by Design”.) In general, sickness behavior refers to a common pathway in illness in which there is fatigue, loss of pleasure, loss of interest, disconnection, uncoupling between energy utilization and efficiency, and social, immune and metabolic network failure. Many of the epigenetic factors can be regulated through mindfulness practice [1]. The physical, mental and social dissolution is often accompanied by white matter dissolution in the form in decreased fractional anisotropy with diffusion tensor imaging. Please spend some time with this very important chart I adapted from Schulman et al 2011 [2]. OCD = Obsessive-Compulsive Disorder, SSD = Schizophrenia Spectrum Disorder, DD = Depressive Disorder, and CON = healthy controls. The vertical scale is an arbitrary scale for EEG amplitude. For temporary convenience I am going to simply refer to everything here as “sickness behavior” except for the healthy control shown in green. What differentiates natural adaptive ability (the green curve to the left as well as the circle at the top of the SBeh icon above) from sickness behavior (OCD, SSD, DD) in this chart? The adaptive EEG (Green) has 1) a sharp alpha peak centered around 10 Hz and 2) efficient low amplitude production of both low frequency and high frequency EEG. Sickness behavior exhibits increased inefficient production of low and high frequency EEG signals plus a distorted peak control frequency that is either lowered in frequency (DD), or has two discrete peak frequencies (SSD) or has little or no established control frequency (OCD). If Neurofeedback did not exist it would be necessary for us to invent it. The above graph seems to suggest that in order to turn sickness behavior toward wellness behavior we might 1) reduce the amplitude of 4-8 Hz EEG, 2) reduce the amplitude of 16-30 Hz EEG, and 3) train for a sharp alpha peak around 10 Hz. Please return to the green “healthy control” curve in the power spectral display above. For each condition the chart has measured the power produced in multiple frequency “bins” (e.g., 4-5 Hz, 5-6 Hz, 6-7 Hz, etc) averaged over the time of the recording. For example, because of averaging over time, the large 10 Hz alpha peak in controls would look exactly the same in each of the following very different situations. A) Alpha declines from high to low power during the recording period; B) Alpha increases from low to high power during the recording period; C) Alpha is moderate in power and constant during the recording period; D) Alpha consists of a series of high power alpha bursts interspersed with little or no alpha production during the recording period. Each of these 4 situations would be expected to respond differently when there is a clinical change of state. Unfortunately the colored brain maps commonly used in neurotherapy have thrown away the all the information about dynamics (changes over time) and it can not be reassembled without consulting the raw EEG. This will be discussed in the section on “theoretical interpretation of electroencephalography”. The following illustration shows dynamics of the power spectrum of a client over a period of a few seconds before and after an effective neurofeedback (NFB) session. The previous chart above (group means) has been turned on its side so that the frequencies in the chart are aligned with the frequencies in the NFB cascading spectral display, captured from the popular program BioExplorer. Unlike the group means chart (above) which uses height above the horizontal axis to indicate power, the NFB spectral display uses color. The warmer and whiter colors are high power and correspond to the higher peaks in the group means chart above. The NFB spectral display (also called a cascading chronospectrogram, etc.) shows, using color, how the alpha and other frequencies behave over time, for example, whether the client is becoming more awake or less awake during the recording. This information is not available in the traditional NFB brain maps.
The NFB spectral display uses quantitative EEG methods to provide its live (cascading) output during a session. In our example (below) at each moment in time we can compare the client’s spectral display with that of the previous few moments and observe trends such as intrusion of fragments of sleep states. This type of live QEEG guided NFB can be interpreted in a similar manner as Ulrich’s Ipsative Trend Analysis (ITA) discussed elsewhere. These 2 cascading spectral displays scroll to the left. That means that at the far right of each display (where the vertical axis indicates the frequency components) you see the amplitude of each frequency that was being produced just at the moment the screen capture was completed. The amplitude is represented by color - warmer colors and white have the highest amplitude. The data at the far left of each display shows the amplitudes of each frequency produced 30 seconds previously. The displays scroll to the left and display this quantified EEG (QEEG) data. Attention to detail will show that even though the sensitivity of the instruments was different between the recordings the post-treatment qEEG data still sees in ipsative trend away from sickness behavior. The Pre-NFB may be described as “dynamic lability” in Ulrich’s terms [3] or thalamocortical dysrhythmia similar to what Schulman and Llinas’ describe in MEG. Despite the differences in generation and interpretation between MEG and EEG data, the notion of dissipation of electromagnetic energy along with the dissolution of its control frequencies is paralleled by reduced fractional anisotropy in diffusion tensor imaging (DTI). The loss of efficiency increases the wiring costs such that the functional path length increases. Such loss of small world network efficiency may be accompanied by intrusion of subvigilant states and failure to harness complexity through synchronization. You will often observe clients switching between the two states shown in the spectral displays (below). This may be accompanied by compulsive narrating and rumination. In the “Before training” cascading spectral display below note the repetitive high voltage low frequency eye movement artifacts in the first third of the recording. The client was unaware of and had no control of these forced eye movements that were accompanied by middle ear myoclonus, a thumping type of tinnitus. They were accompanied by sudden intense negative internal dialogue and anxiety. In the spectral display you can see the characteristic cross frequency coupling between delta and beta that characterizes social anxiety disorder. This occurs immediately after the forced eye movements that were accompanied by a large repetitive epileptiform discharge that was found to be palatal myoclonus. [4] I mention this here to emphasis that this sort of ipsative trend analysis during or between neurofeedback sessions requires a thorough familiarity with raw EEG as well as the real time operation and interpretation of the QEEG instrumentation used during training.
Copyright 2010, 2017 by Douglas Dailey - All Rights Reserved.

Neurofeedback acts like an adaptogen to help self-regulate

sickness behavior:

Adaptogens such as herbs and certain pharmaceuticals are substances which are 1) generally nontoxic, 2) have a wide range of application, and 3) epigenetically up-regulate wellness behavior [5]. Adaptogens improve small world efficiency by increasing nodal clustering coefficient (”C”) while reducing average path length between nodes [6]. In 2010 Dhond showed with fMRI that real but not sham acupuncture at point St36 was capable of consolidating the default mode network of the brain [7]. Washington et al (2013) showed that autism involved such dysmaturation of the default mode network (DMN) [8]. In fact St36, the point used by Dhond et al, is a common ancient network point for developmental failure. In 2010 I proposed that NFB acts like an adaptogen to help regulate sickness behavior. In 2013 our team headed by Lori Anne Russell-Chapin at Bradley University showed that standard NFB had a very similar maturational effect on fMRI DMN functioning in adolescents with ADHD [9].

The EEG during spontaneous recovery from sickness

behavior:

Very few EEG studies have looked at the brain of a group of animals before overwhelming trauma, then immediately afterwards in the symptomatic state, and then later after spontaneous recovery. Khalid A, et al (2016) showed that mice whose lives had been complicated by four weeks of chronic restraint stress used global broad band EEG synchronization to spontaneously recover from the depressive and inflammatory post-traumatic sequelae. [10,11,12] Elsewhere I explain that neurofeedback, used as a sort of technology- assisted self-regulation / self-realization (TASR) promotes the ability of synchrony to harness complexity. When the brain uses more resources to solve a complex problem, it does so accompanied especially by an increased synchronization of the amplitudes of theta and gamma [13, tagsync.com/cfc].
In Progress: Apoptosis of the self: The importance of network support and neuroplasticity. EEG in healers and the healed; network efficiency in sickness and healing behavior. Complexity, Canonical Sickness Behavior, and EEG Biofeedback.
References: [1] Ravnik-Glavac M, et al (2012) - Genome-wide expression changes in a higher state of consciousness. Consciousness and Cognition 21 (2012) 1322–1344. [Abstract] [2] Schulman JJ, et al (2011) - Imaging of thalamocortical dysrhythmia in neuropsychiatry. Frontiers in Human Neuroscience, 29 July. [Free Full Text] [3] Ulrich G (2013) - The Theoretical Interpretation of Electroencephalography. BMed. [4] Lee GH, et al (2012) - Middle ear myoclonus associated with forced eyelid closure in children: diagnosis and treatment outcome. Laryngoscope. 2012 Sep; 122(9): 2071-5. [Abstract] [5] Panossian A, et al (2005) - Stimulating effect of adaptogens - An overview with particular reference to their efficacy following single dose administration. Phytotherapy Research, 19, 819-838. [Abstract] [6] Vincent K, et al (2013) - Altered network communication following a neuroprotective drug treatment. PLoS One. 2013; 8(1):e54478. [Free Full Text] [7] Dhond RP, et al (2008) - Acupuncture modulates resting state connectivity in default and sensorimotor brain networks. Pain. 2008 Jun, 136(3):407-418. [Free Full Text] [8] Washington SD, et al (2013) - Dysmaturation of the Default Mode Network in Autism. Human Brain Mapping, Apr;35(4):1284-96. [Free Full Text] [9] Russell-Chapin L, et al (2013) - The Effects of Neurofeedback in the Default Mode Network - Pilot Study Results of Medicated Children with ADHD. Routledge Press. [10] Khalid A, et al (2016) - Gamma oscillation in functional brain networks is involved in the spontaneous remission of depressive behavior induced by chronic restraint stress in mice. BMC Neurosci (2016) 17:4. [Free Full Text] [11] Voorhees JL, et al (2013) - Prolonged restraint stress increases IL-6, reduces IL-10, and causes persistent depressive-like behavior that is reversed by recombinant IL-10.  PLoS One. 2013; 8(3). [Free Full Text] [12] Liang S, et al (2015) - Administration of Lactobacillus helveticus NS8 improves behavioral, cognitive, and biochemical aberrations caused by chronic restraint stress.  Neuroscience. 2015 Dec 3; 310:561-77. [Free Full Text] [13] Pahor A, et al (2014) - Theta–gamma cross-frequency coupling relates to the level of human intelligence. Intelligence 46 (2014) 283- 290. [Abstract]