Brain Oscillating Neuronal Information Transfer and Alarms

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HUMAN BRAIN CRITICAL STATE ALARMS

• Homes, businesses and similarly the Human Brain have security alarm systems. 
• Human Brain systems are composed of all possible forces, particles, energies and neuroanatomy. 
• Human Brain security alarm systems are equipped with sentinel cells, neurochemicals, NeuroNetworks 
• and specialized Synchronized Broadband Vibration Neuronal Networks for Instant Responses to Internal and External Human Emergency Conditions
• from oscillations incited by Critical Dynamics: 
• aka Dynamic NeuroBioPhysicological Systems in Critical States 
• which usually have scale-invariant organization (dimensionless, Infinitesimally small quantities) 
• meaning there will be similar energy wave frequency oscillations occurring at all time and length scales in the synchronized system 
• with frequencies inaudible to the Humans ear, but discernible within synchronized Brain regions 
• and, postulated, harmonically synchronized externally and interconnected universally.
• Human Brain Critical State Broadband Broadcast Alarms are inaudible oscillatory crescendos alerting the Brain and Interconnections to critical events,
• and ‘tuned in’ for reactions, executions, adjustments, adaptations, cognition, memory and every Mindful response. 1.
• The primary function of the alarm is to call attention to a critical situation and/or emergency and mobilize the Human body and mind for action.
• The Brain Alarm identifies problems that need correction and reaction for Humans and others well-being and possibly survival. 2.

References:

1. [Kitzbichler MG, Smith ML, Christensen SR, Bullmore E (2009) Broadband Criticality of Human Brain Network Synchronization. PLoS Comput Biol 5(3): e1000314. https://doi.org/10.1371/journal.pcbi.1000314]

2. [Did You Know Your Brain Has an Alarm? By Julian Ford Ph.D., Jan 31, 2013, Psychology Today]

The main systems of the human body are:

• Circulatory system: Circulates blood around the body via the heart, arteries and veins, delivering oxygen and nutrients to organs and cells and carrying their waste products away. Keeps the body's temperature in a safe range
• Digestive system and Excretory system: System to absorb nutrients and remove waste via the gastrointestinal tract, including the mouth, esophagus, stomach and intestines. Eliminates waste from the body.
• Endocrine system: Influences the function of the body using hormones.
• Integumentary system / Exocrine system: Skin, hair, nails, sweat and other exocrine glands
• Immune system and lymphatic system: Defends the body against pathogens that may endanger the body. The system comprising a network of lymphatic vessels that carry a clear fluid called lymph.
• Muscular system: Enables the body to move using muscles.
• Nervous system: Collects and processes information from the senses via nerves and the brain and tells the muscles to contract to cause physical actions.
• Renal system and Urinary system; The system where the kidneys filter blood to produce urine, and get rid of waste.
• Reproductive system: The reproductive organs required for the production of offspring.
• Respiratory system: Brings air into and out of the lungs to absorb oxygen and remove carbon dioxide.
• Skeletal system: Bones maintain the structure of the body and its organs
• References 
• [Gray's Anatomy is an English written textbook of human anatomy originally written by Henry Gray and illustrated by Henry Vandyke Carter.1858, English first edition was published in the United States in 1859, with slight alterations.[2][3] Gray prepared a second, revised edition, which was published in the United Kingdom in 1860, also by J.W. Parker. [4][5]  The full American rights were purchased by Blanchard and Lea, who published the first of twenty-five[a] distinct American editions of Gray's Anatomy in 1862, and whose company became Lea & Febiger in 1908. Lea & Febiger continued publishing the American editions until the company was sold in 1990. [9]  W ith the sale of Lea & Febiger in 1990, the 30th edition was the last American Edition.]  
• Gray, Henry; Carter, Henry Vandyke (1858), Anatomy Descriptive and Surgical, London: John W. Parker and Son, retrieved 16 October 2011
• Richardson, Ruth (2005), "A Historical Introduction to Gray's Anatomy" (PDF), in Susan Standring (ed.), Gray's Anatomy: The Anatomical Basis of Clinical Practice (39th (electronic version) ed.), Edinburgh: Elsevier Churchill Livingston, p. 4, retrieved 16 October 2011
• Gray, Henry; Carter, H.V (1859), Anatomy, descriptive and surgical, Philadelphia: Blanchard and Lea, retrieved 16 October 2011(Per National Library of Medicine holdings). Note: This is not the 'American' edition. American rights had yet to be purchased. It is an American publication of the English edition.
•  Moore, Wendy (30 March 2008), "Gray's Anatomy celebrates 150th anniversary", The Telegraph, Telegraph Media Group, retrieved 16 October 2011
• A brief history of Gray's Anatomy (PDF), ElsevierHealth, retrieved 16 October 2011
• Poynter, F. N. L. (6 September 1958). "Gray's Anatomy: The First Hundred Years" (PDF). British Medical Journal: 610–11.
• "Longman's 1863 publication of Gray's Anatomy", Pearson Education: History, Pearson Education, archived from the original on 9 March 2012, retrieved 16 October2011
• Warwick, Roger; Williams, Peter L., eds. (1973), Gray's Anatomy (35th ed.), London: Longman p. iv (Previous Editions and Editors – listings)
• Lea & Febiger in Tredyffrin East Town Historical Society History Quarterly Digital Archives, pp. 68–70 (Source: April 1999, Vol. 37, No. 2, pp. 63–70)
• "Gray's Anatomy: The Jefferson Years" in Jeffline Forum, September 200
• Gray, Henry Gray's Anatomy Descriptive and Surgical, 1896 13th edition
• Carmine D. Clemente, ed. (1985), Gray's Anatomy (30th ed.), Philadelphia: Lea & Febiger, ISBN 0-8121-0644-X pp. vi–ix

Reference: Please review the complete publlication: [Kitzbichler MG, Smith ML, Christensen SR, Bullmore E (2009) Broadband Criticality of Human Brain Network Synchronization. PLoS Comput Biol 5(3): e1000314. https://doi.org/10.1371/journal.pcbi.1000314]

Introduction:  Critical dynamics are recognized as typical of many different physical systems including piles of rice or sand, earthquakes and mountain avalanches.

Critical dynamics: Dynamic systems in a critical state will generally demonstrate scale-invariant organization in space and/or time, meaning that there will be similar fluctuations occurring at all time and length scales in the system.

In other words, there is no characteristic scale to critical dynamics, which will be optimally described byscale-invariant or fractal metrics.

Thus, power law or fractal scaling has been widely accepted as a typical empirical signature of non-equilibrium systems in a self-organized critical state [1], although the existence of power law scaling does not by itself prove that the system is self-organized critical (SOC). 

For example, turbulence is a conceptually distinct class of dynamics, which is also characterized by self-similar or scale-invariant energy cascades, that can be empirically disambiguated from criticality (Defined as not critical) [2],[3]. 

The existence of power laws for the spatial and temporal statistics of critical systems is compatible with the related observations that the dynamics of individual units or components of such systems will show long-range correlations in space and time, and change in state of a single unit can rapidly trigger macroscopic reconfiguration of the system. 

Many of these phenomena can be studied using computational models of dynamic systems such as the Ising model of magnetization (see Figure 1) and the Kuramoto model of phase coupled oscillators (see Figure 2). In both these models, the dynamics can be controlled by continuous manipulation of a single parameter. For the Ising model, this control parameter is the temperature; whereas for the Kuramoto model it is the strength of coupling between oscillators.

In both cases, as the control parameter is gradually increased (or decreased), the dynamics of the systems will pass through a phase transition, from an ordered to a random state (or vice versa), at which point the emergence of power law scaling and other fractal phenomena will be observed at the so-called critical value of the control parameter. 

Self-organized critical systems differ from these computational models in the sense that they are not driven to the cusp of a phase transition by external manipulation of an control parameter but instead spontaneously evolve to exist dynamically at that point.

Self-organized criticalityis an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. 

• Here, we consider 2 measures of phase synchronization: 
• 1 the phase-lock interval, or duration of phase synchronization coupling between a pair of (neurophysiological) processes 
• 2 and the lability of global synchronization of a (brain functional) network. 

Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, 

• we show that both 1 and 2 synchronization metrics have power law probability distributions specifically when these systems are in a critical state. 
• We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic datarecorded from normal volunteers under resting conditions. 
• These results strongly suggest that human brain functional systems exist in an endogenous state of critical dynamics, 
• characterized by a greater than random probability of both 
• 1 prolonged periods of phase-locking 
• 2 and occurrence of large rapid changes in the state of global synchronization, 
• analogous to the neuronal “avalanches” previously described in cellular systems. 
• Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, 
• confirming that critical dynamicsis a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth. {{{ Like a dog-whistle that sends-out an alarm to the rest of the Brain, and maybe body, alerting the brain, and maybe body, to potential global environmental or other changes. i.e. Dangers or other criticalities mbm}}} 

Author Summary

Systems in a critical state are poised on the cusp of a transition between ordered and random behavior. At this point, they demonstrate complex patterning of fluctuations at all scales of space and time. 

• Criticality is an attractive model for brain dynamics 
• because it optimizes information transfer 
• storage capacity
• and sensitivity to external stimuli in computational models. 

However, to date there has been little direct experimental evidence for critical dynamics of human brain networks. 

Here, we considered two measures of functional coupling or phase synchronization between components of a dynamic system: the phase lock interval or duration of synchronization between a specific pair of time series or processes in the system and the lability of global synchronization among all pairs of processes.

We confirmed that both synchronization metrics demonstrated scale invariant behaviors (objects or laws that do not change if scales of length, energy, or other variables, are multiplied by a common factor, and thus represent a universality) in two computational models of critical dynamics as well as in human brain functional systems oscillating at low frequencies (<0.5 Hz, measured using functional MRI) 

and at higher frequencies (1–125 Hz, measured using magnetoencephalography). 

We conclude that human brain functional NeuroNetworks demonstrate dynamic critical scale invariant oscillating synchronization states in all frequency intervals, a phenomenon we have named as ‘Broadband Criticality’.

[Kitzbichler MG, Smith ML, Christensen SR, Bullmore E (2009) Broadband Criticality of Human Brain Network Synchronization. PLoS Comput Biol 5(3): e1000314. https://doi.org/10.1371/journal.pcbi.1000314

Abstract: “The brain’s mature functional network architecture has been extensively studied but the early emergence of the brain’s network organization remains largely unknown. In this study, leveraging a large sample (143 subjects) with longitudinal rsfMRI scans (333 datasets), we aimed to characterize the important developmental process of the brain’s functional network architecture during the first 2 years of life. 

“Based on spatial independent component analysis and longitudinal linear mixed effect modeling, our results unveiled the detailed topology and growth trajectories of nine cortical functional networks.

“Within networks, our findings clearly separated the brains networks into 2 categories: 

1. primary networks were topologically adult-like in neonates
2. while higher-order networks were topologically incomplete and isolated in neonates but demonstrated consistent synchronization during the first 2 years of life (connectivity increases 0.13–0.35).

“Significant sex differences were observed with boys demonstrating faster network-level connectivity increases among the 2 frontoparietal networks (growth rate was 1.63 e-4 per day for girls and 2.69 e-4 per day for boys, p < 1e-4). Overall, our study delineated the development of the whole brain functional architecture during the first 2 years of life featuring significant changes of both within- and between-network interactions.

Please review the complete publications;   [Gao, W., Alcauter, S., Smith, J.K. et al. Development of human brain cortical network architecture during infancy. Brain Struct Funct 220, 1173–1186 (2015) doi:10.1007/s00429-014-0710-3]

 " Specifically, synchronous  transcranial alternating current stimulation (tACS)  during the verbal Working Memory (WM) task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization.

Please review the complete publication. Reference: [Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance. By Violante IR1,2, Li LM1, Carmichael DW3, Lorenz R1, Leech R1, Hampshire A1, Rothwell JC2, Sharp DJ1. Elife. 2017 Mar 14;6. pii: e22001. doi: 10.7554/eLife.22001.]

Abstract: "Neuronal systems have a high propensity to engage in oscillatory activity because both the properties of individual neurons and established circuit motifs favor rhythmic activity. 

In addition, coupled oscillators can engage in a large variety of dynamical regimes, ranging from 

1. synchronization with different phase offsets 
2. to chaotic behavior.
3. which regime prevails depends on differences between preferred oscillation frequencies, 
4. coupling strength 
5. and coupling delays. 

• The ability of delay coupled oscillator networks to generate a rich repertoire of temporally structured activation sequences is exploited by central pattern generator networks for the control of movements.
• However, it is less clear whether temporal patterning of neuronal discharges also plays a role in cognitive processes. 
• Here, it will be argued that the temporal patterning of neuronal discharges emerging from delay coupled oscillator networks plays a pivotal role in all instances in which selective relations have to be established between the responses of distributed assemblies of neurons. 
• Examples are 
• dynamic formation of functional networks,
• selective routing of activity in densely interconnected networks, 
• attention-dependent selection of sensory signals, 
• fast and context-dependent binding of responses for further joint processing in pattern recognition 
• and formation of associations by learning. 
• Special consideration is given to arguments that challenge a functional role of oscillations and synchrony in cognition because of the volatile nature of these phenomena 
• and recent evidence will be reviewed suggesting that this volatility is functionally advantageous.

 Please review the complete publication. Reference:  [Neuronal oscillations: unavoidable and useful? Singer W1,2,3. Eur J Neurosci. 2018 Oct;48(7):2389-2398. doi: 10.1111/ejn.13796. Epub 2018 Jan 22]  

• Author information

1. Max Planck Institute for Brain Research (MPI), Frankfurt am Main, Germany.
2. Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany.
3. Ernst Struengmann Institute for Neuroscience, Deutschorenstrasse 48, 60528, Frankfurt am Main, Germany.

   Abstract:  "Researchers applied transcranial alternating current stimulation (tACS) to the primary motor cortex (M1) at different frequencies during an index–thumb pinch-grip observation task. To estimate changes in the corticospinal output, we used the size of motor evoked potentials (MEPs) obtained by transcranial magnetic stimulation (TMS) of M1 using an online MRI-guided simultaneous TMS-tACS approach. 

The results of the beta-tACS confirm a non-selective increase in corticospinal excitability in subjects at rest; an increase was observed for both of the tested hand muscles, the first dorsal interosseous (FDI) and the abductor digiti minimi (ADM). However, during action observation of the pinch-grip movement, the increase of corticospinal excitability was only observed for the prime mover FDI muscle and took place during alpha-tACS, while gamma-tACS affected both the FDI and control muscle (ADM) responses. These phenomena likely reflect the hypothesis that the mu and gamma rhythms specifically index the downstream modulation of primary sensorimotor areas by engaging mirror neuron activity. 

The current neuromodulation approach confirms that tACS can be used to induce neurophysiologically detectable state-dependent enhancement effects, even in complex motor-cognitive tasks.

This evidence suggests to us that β–tACS, at least with for the current montage and setup, likely has more transcranial (transcranial alternating current stimulation (tACS) effects than transcutaneous effects and produces a robust replicable effect in subjects at rest.

The current neuromodulatory approach helps to further unveil the functional relevance of brain rhythms in precentral brain regions during AO. The statistical analysis reveals weak and mixed effects of transcranial alternating current (tACS) stimulation, which certainly require replication in follow-up studies. However, we believe that it is important to provide a speculative interpretation of the data. Given the novelty of our results in relation to the existent literature on transcranial oscillatory stimulation in the motor system, we believe it is necessary to offer a detailed discussion of the mixed effects related to the interactions between the muscles involved, tACS frequencies and subjects’ cognitive engagements.

the current online TMS-tACS approach suggests frequency and state-dependent effects which appear to be task specific, as shown by α modulation of the FDI and γ modulation of both FDI and ADM during the AO task only (Fig. 3). In a previous study, we have shown how α- and θ–tACS modulate MI processing 2. 

Whereas α-tACS seems to have an impact on both MI and AO, the results of γ and θ stimulation in the previous and current studies2 suggest that these two mechanisms are distinctive. Finally, β–tACS confirmed the corticospinal increase at rest. This study sheds light on the neural underpinning of the sensorimotor system from rest to action. Its findings may be of relevance for neurorehabilitation purposes, where a combined approach of AO and MI has been hypothesisedzed to induce beneficial effects 90.

 Please review the complete publication. Reference:  [Feurra M, Blagovechtchenski E, Nikulin VV, et al. State-Dependent Effects of Transcranial Oscillatory Currents on the Motor System during Action Observation [published correction appears in Sci Rep. 2019 Nov 27;9(1):18046]. Sci Rep. 2019;9(1):12858. Published 2019 Sep 6. doi:10.1038/s41598-019-49166-1]

   Abstract:  "The theory of communication through coherence predicts that effective connectivity between nodes in a distributed oscillating neuronal network depends on their instantaneous excitability state and phase synchronicity (Fries, 2005). 

Research tested this prediction by using state-dependent millisecond-resolved real-time electroencephalography-triggered dual-coil transcranial magnetic stimulation (EEG-TMS) (Zrenner et al., 2018) to target the EEG-negative (high-excitability state) versus EEG-positive peak (low-excitability state) of the sensorimotor μ-rhythm in the left (conditioning) and right (test) motor cortex (M1) of 16 healthy human subjects (9 female, 7 male). 

Effective connectivity was tested by short-interval interhemispheric inhibition (SIHI); that is, the inhibitory effect of the conditioning TMS pulse given 10–12 ms before the test pulse on the test motor-evoked potential. We compared the four possible combinations of excitability states (negative peak, positive peak) and phase relations (in-phase, out-of-phase) of the μ-rhythm in the conditioning and test M1 and a random phase condition. 

Strongest SIHI was found when the two M1 were in phase for the high-excitability state (negative peak of the μ-rhythm), whereas the weakest SIHI occurred when they were out of phase and the conditioning M1 was in the low-excitability state (positive peak). Phase synchronicity contributed significantly to SIHI variation, with stronger SIHI in the in-phase than out-of-phase conditions. 

These findings are in exact accord with the predictions of the theory of communication through coherence. They open a translational route for highly effective modification of brain connections by repetitive stimulation at instants in time when nodes in the network are phase synchronized and excitable.

SIGNIFICANCE STATEMENT:  The theory of communication through coherence predicts that effective connectivity between nodes in distributed oscillating brain networks depends on their instantaneous excitability and phase relation. We tested this hypothesis in healthy human subjects by real-time analysis of brain states by electroencephalography in combination with transcranial magnetic stimulation of left and right motor cortex. We found that short-interval interhemispheric inhibition, a marker of interhemispheric effective connectivity, was maximally expressed when the two motor cortices were in phase for a high-excitability state (the trough of the sensorimotor μ-rhythm). 

We conclude that findings are consistent with the theory of communication through coherence. They open a translational route to highly effectively modify brain connections by repetitive stimulation at instants in time of phase-synchronized high-excitability states.

 Please review the complete publication. Reference:  [Stefanou MI, Desideri D, Belardinelli P, Zrenner C, Ziemann U. Phase Synchronicity of μ-Rhythm Determines Efficacy of Interhemispheric Communication Between Human Motor Cortices. J Neurosci. 2018;38(49):10525–10534. doi:10.1523/JNEUROSCI.1470-18.2018]

  Abstract:  At any one moment, many neuronal groups in our brain are active. Microelectrode recordings have characterized the activation of single neurons and fMRI has unveiled brain-wide activation patterns. 

Now it is time to understand how the many active neuronal groups interact with each other and how their communication is flexibly modulated to bring about our cognitive dynamics. I hypothesize that neuronal communication is mechanistically subserved by neuronal coherence. 

Activated neuronal groups oscillate and thereby undergo rhythmic excitability fluctuations that produce temporal windows for communication. Only coherently oscillating neuronal groups can interact effectively, because their communication windows for input and for output are open at the same times. Thus, a flexible pattern of coherence defines a flexible communication structure, which subserves our cognitive flexibility.

 Please review the complete publication. Reference:  [A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Fries P1. Author information: F.C. Donders Centre for Cognitive Neuroimaging, Radboud University Nijmegen, 6525 EN Nijmegen, The Netherlands. p.fries@fcdonders.ru.nl]

Lowet et al research results showed "phase-locking value (PLV) estimates based on the instantaneous phase reconstructed by SSD-HT (singular spectrum decomposition (SSD)- Hilbert Transform (HT) [105] gave more robust estimates of the true underlying phase-locking values and reflected better variations in synchronization–dependent information flow between neural networks compared to spectral coherence. 

The estimates for phase-relation dependent frequency modulations (PrFM) and phase-relation amplitude modulations (PrAM) were evaluated.  The estimates  were robust against amplitude fluctuation in the form of PrAM, although small effects of PrAM could be observed in the low signal-to-noise ratio (SNR) regimes. Although PLV could severely underestimate phase locking for lower SNR, increasing SNR allowed the PLV to approach the expected phase locking value. 

In terms of explained variance of information flow changes between networks, the PLV (phase-locking value) estimates were superior to coherence for any SNR (signal-to-noise ratio) investigated. We expect similarly superior results for all approaches that are based on reconstruction of the instantaneous phase (based on the use of wavelets or the Hilbert transform preceded by some decomposition technique). 

Finally, although there are conditions in which spectral coherence might be appropriate, methods that combine appropriate signal decomposition with instantaneous phase reconstruction permit a more detailed and accurate look on time-dependent changes in synchronization properties of neural signals. 

This provides definite advantages when trying to determine neural network mechanisms underlying perception, cognition, and behavior.

 Please review the publication. Reference:  [Lowet E, Roberts MJ, Bonizzi P, Karel J, De Weerd P. Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches. PLoS One. 2016;11(1):e0146443. Published 2016 Jan 8. doi:10.1371/journal.pone.0146443]