The Network Physiology field frames the multi-scale multi-dimensional nature of the body system emerging in the interaction among organs, which interplay via hemodynamic and metabolic functions under hormonal and neuronal controlling communication (Bashan et al., 2012; Ivanov and Bartsch, 2014; Bartsch et al., 2015; Ivanov et al., 2016; Lin et al., 2016). Thus, while the Network Physiology models networks consisting of organs (nodes) that are heterogeneous and connected by systems (connectors) of a still different nature, the brain is made up of elements that are at the same time nodes (soma of the neuron) and connectors (axons), so that the communicative—necessary and sufficient—nature confers to the sets of neurons the status of Network. Here we refer to Neuronal Networks [NN], which structurally include at least one node receiving inputs from the environment, and one node producing outputs to the environment; the NN connections are necessarily both negative and positive; every NN's node “necessarily” produces a pattern-OUT when the pattern-IN arrives, overall resulting in a specific local time course of the electrical neuronal activity, the local neurodynamics.
Here, grounding on existing knowledge, we propose a unique functional organizing principle—the feedback-synchrony-plasticity triad—which, governing the neuronal networks at multiple scales, emerges as a potential explanatory framework for the fractal properties exhibited by neurodynamics. In a translational perspective, via the strategy of “listening” to the body-brain organization by non-invasive electrophysiological techniques (electro- and magneto-encephalography and electromyography) integrated with “intervening” by non-invasive brain stimulation techniques, we exploited the communication means used by neuronal networks to enhance the capability of fighting symptoms secondary to neurodynamics dysfunctions. In other words, we introduce precision approaches to electroceuticals, i.e., the cure of ailments by means of electrical signals (Reardon, 2014).
The Feedback-Synchrony-Plasticity Triadic Principle (FeeSyCy) Governs the Body-Brain System
We consider the whole brain as a neurons' ensemble which coordinates the interaction of the body brain network with the environment, where input depends on the output and the other way round, the output depends on the input, working in a feedback loop. Via somatic, proprioceptive (Rossi et al., 1998; Fink et al., 2014), visual and auditory sensory receptors, our motor actions produce from the environment feedback, that our brain shapes dependently on the desired goal (Friston, 2018). This feedback loop stimulates our brain neurons inducing locally specific dynamic synchronizations among the nodes of dedicated functional networks (Tecchio et al., 2008; Gandolla et al., 2014). Such synchronizations within the network's subsystems imply a desynchronization of those very subsystems with the wider regions they are part of, resulting in a reduction of the resting-state high power of the cortical activity paced within the thalamocortical loops (Gent et al., 2018), e.g., alpha reactivity (Klimesch, 1999). In turn, these modulations of synchrony engage the system in adaptations either sustaining the execution as planned or enabling proper corrections (Fink et al., 2014). In this process, our neurons implement output changes following a key rule (Kandel and Schwartz, 1985): if two input signals reach the neuron together, the neuron increases its probability to fire (Hebb, 1949), that is to produce an action potential transmitting a message. Some authors indicate that the Hebbian rule subtends main trial-and-error (Hoerzer et al., 2014) and imitation (Keysers and Gazzola, 2014) learning mechanisms. This continuous adaptation capability shapes the ability of our neurons to change their output according to what is required, quantified depending on the distance between the expected outcome and the current one. When the distance is small, behavioral adaptations emerge through the current network setup [working adaptation (Wolpert et al., 2011)]. When the distance is big, new skill acquisitions emerge through even huge structural changes (plastic adaptation, i.e., learning). A richness and complexity of molecular and cellular phenomena and of signaling, in continuous discovery, underlie the cellular and network modifications that implement the plastic adaptations. Plasticity mechanisms occurring at the synapses' level with non-unitary interplaying potentiation and depression phenomena (Malenka and Bear, 2004) are integrated by intrinsic plasticity mechanisms (Zhang and Linden, 2003) and changes in myelin multi-laminar sheaths that modulate the timing of information transmission between relay points through neural circuits, inducing changes in spike arrival-time, with which a high degree of precision controls the probability of activation (Gibson et al., 2014; Fields, 2015). It is supposed that Hebbain rules acting in day time, are supported during sleep spontaneous activity, by renormalizations of net synaptic strengths (Tononi and Cirelli, 2014) implementing homeostatic plasticity (Turrigiano and Nelson, 2004).
Notably, the feedback-synchrony-plasticity (FeeSyCy) triadic principle that governs motor control, controls the whole body-brain system. We can recognize some paradigmatic examples of the breakup of one of the three links in the FeeSyCy chain, which generates the breakup of the whole process.
Feedback Link Breakup
The lack of auditory training and feedback condemned for centuries deaf individuals, despite owning intact motor executive functionality, to the inability to develop linguistic production, that is it condemned them to live as a deaf-mute (Sacks, 1989). The role of feedback is strongly proven by deaf people who grow nowadays. Starting from the last century, the teaching models and techniques -guided by neuroscientific comprehension–have definitely revolutionized the condition of deaf people, who now can, in parallel to the sign language, achieve an excellent production of language vocal expression by exploiting during their development the feedback about their produced words properly translated in signals from the spared sensory channels, mainly the visual one.
Synchrony Link Breakup
In dystonic individuals, despite proper sensory stimuli being transmitted via intact sensory systems, the impaired intracerebral synchronizations subtending the sensorimotor integration (Melgari et al., 2013), impairs the motor control (Abbruzzese and Berardelli, 2003).
Plasticity Link Breakup
Schizophrenic individuals are able to move and receive proper sensory feedback from the environment but cannot engage in proper adaptation due to neuronal inability to involve the metabolic chains and adapt the cells via plasticity (Ramocki and Zoghbi, 2008).
The FeeSyCy Triadic Principle Manifests Itself Recursively at Multiple Scales
Single Neurons' Network
In in-vitro primary cell culture of single cortical pyramidal neurons of postnatal rats, the synaptic changes implementing long-term potentiation and depression emerged as a function of incoming activity (Turrigiano et al., 1998; Sjöström and Nelson, 2002). Synaptic potentiation increases the postsynaptic firing rates in correlation with presynaptic activity, producing a positive feedback loop. Multiplicative scaling of synaptic strengths preserves relative differences between inputs, allowing a non-saturated implementation of Hebbian modifications (Hebb, 1949).
Neuronal Pools' Network
In functioning of multiple brain areas networks, a parallel capturing of bottom-up patterns of activation in sensory-motor areas occurs together with a top-down processing that selects sensory-motor activations to implement long-lasting storage. As memories organize themselves in central structures, they implement an active selection of sensory experience, proprioception and emotional knowledge for further learning (Barsalou, 1999).
Body-Brain Network
Deepening the paradigmatic example of motor execution, skilled actions require the actual gathering of sensory information, which is processed extracting what is relevant to the planned action. Such feedback comes from different types of information that the motor system uses as a learning signal, including error-based, reinforcement, observational and use-dependent information. In all cases, motor learning occurs implementing adaptations dependent on the distance between the expected and occurring inputs (Wolpert et al., 2011).
We can recognize an expression at the whole system level of the multi-scale recursive FeeSyCy principle in the human gait showing fractal dynamics (Hausdorff et al., 1996; Phinyomark et al., 2020) and also across species, in experimental data about food-searching strategies in insect, mammal and bird species (Edwards et al., 2007).
Working at Multiple Scales, the FeeSyCy Principle Subtends a Fractal Neurodynamics
When a system presents the whole structure that is made up of single blocks, which are similar to the whole, and are in turn made of smaller blocks, similar to it and to the whole structure, it is a fractal. Its name comes from a non-integer number that quantifies its dimension. In our case, FD estimates on a time window the distance between the amplitudes of successive neuronal electrical activity points, in relationship with the time sampling.
Brain neurodynamics displays the so-called “power law” (He, 2011), i.e., the power of the signal generated by a neuronal population follows an exponential behavior. Among the multiple signals with a spectrum that distributes as power law, we propose the hypothesis that brain signals are fractal (Buzsaki and Mizuseki, 2014).
The findings from our laboratory support this hypothesis. We observed that the fractal dimension (FD) of EEG signals successfully senses the modulation of the brain activity in physiological conditions, related to aging (Zappasodi et al., 2015; Smits et al., 2016), circadian rhythm (Croce et al., 2018), behavioral states (Cottone et al., 2017) and neuronal networks' functional role (Marino et al., 2019), and the alterations of the brain activity in clinical conditions (Zappasodi et al., 2014; Smits et al., 2016; Porcaro et al., 2019). Notably, beyond being sensitive to the networks' state, FD offers a tool to parcel the cortex on the base of the local neurodynamics, complementing the Brodmann's cytoarchitectonics criterion (Cottone et al., 2017) (Figure 1).
Figure 1
Neuronal Network Spoken Language and Electroceuticals
Nowadays the ability to develop therapeutic procedures by intervening on the body physiology by electric signals gives rise to the innovative branch in the medical field: the Electroceuticals (Reardon, 2014). Parallel to the need for technological advancements, they require further knowledge about the correct signals to be provided to the appropriate targets. We propose here a hypothesis on this matter, in the case of neuromodulation, the change of neuronal excitability.
By linking theoretical and experimental studies, the neuroscientific community is revealing network dynamics properties attuned with FeeSyCy mechanisms (Destexhe and Marder, 2004; Deco et al., 2011) that inspired our model of communication within neuronal networks. The model states that every NN—were nodes can be made of neurons, groups of neurons or wider brain regions—develops a “language” shared by its nodes made of exchanged electric pattern, which dynamics' shape brings information (word, Neuronal Network Spoken Language). Notably, when assessing the fractal dimension of the bipolar EEG whole-brain signals we sensed phenomena sensed even by other measures. Noteworthy, when we assessed local neuronal ensemble neurodynamics, the fractal dimension, and not other measures, sensed in resting-state tiny changes with clinical relevance (Porcaro et al., 2019).
The neuroscientific community states that the efficacy of neuromodulation, the change of neuronal electric excitability, depends on the frequency of the stimulation in a region-dependent manner (Brinkman et al., 2016; Fusco et al., 2018), revealing that the intrinsic dynamics of the stimulation target enhances neuromodulation capability. In a seminal non-invasive transcranial electric stimulation (tES) study (Cottone et al., 2018), we proved that a current which mimics the endogenous dynamics of the target neuronal pools, neuromodulates more efficiently than the sinusoid at a locally-tuned frequency, suggesting that structured patterns transmit entrainment more than a non-structured stationary signal.
Near and more long-term future will see further electroceutical personalizations, by developing tools to “speak” the neuronal network language, thus better tuning the neuromodulation to the desired neuronal pool target and obtaining higher efficacy in compensating symptoms secondary to alterations of the neurodynamics, like depression, addiction, pain, fatigue.
This nature of the body-brain in continuous adaptive communication with the environment makes a continuously changing structure that is “to be is to become”.
Statements
Author contributions
FT conceived the paper and supervised the writing. FT and FZ contributed to the writing of the original draft. MB contributed to figures creation. MB, TL, EG, and LP contributed to the writing and the editing of the manuscript. All authors reviewed and approved the final manuscript.
Acknowledgments
The authors would like to thank Carlo Salustri very much for his careful sharing of our reasoning and tuning in communicating the contents of the Opinion.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1
AbbruzzeseG.BerardelliA. (2003). Sensorimotor integration in movement disorders. Mov. Disord.51, 427–436. 10.1002/mds.10327
2
BarsalouL. W. (1999). Perceptual symbol systems. Behav. Brain Sci.22, 577–609. 10.1017/S0140525X99002149
3
BartschR. P.LiuK. K. L.BashanA.IvanovP. C. (2015). Network physiology: how organ systems dynamically interact. PLoS ONE10:e0142143. 10.1371/journal.pone.0142143
4
BashanA.BartschR. P.KantelhardtJ. W.HavlinS.IvanovP. C. (2012). Network physiology reveals relations between network topology and physiological function. Nat. Commun.3:702. 10.1038/ncomms1705
5
BrinkmanL.StolkA.MarshallT. R.EstererS.SharpP.DijkermanH. C.et al. (2016). Independent causal contributions of Alpha- and Beta-band oscillations during movement selection. J. Neurosci.36, 8726–8733. 10.1523/JNEUROSCI.0868-16.2016
6
BuzsakiG.MizusekiK. (2014). The log-dynamic brain: how skewed distributions affect network operations. Nat. Rev. Neurosci.15, 264–278. 10.1038/nrn3687
7
CottoneC.CancelliA.PasqualettiP.PorcaroC.SalustriC.TecchioF. (2018). A new, high-efficacy, noninvasive transcranial electric stimulation tuned to local neurodynamics. J. Neurosci.38, 586–594. 10.1523/JNEUROSCI.2521-16.2017
8
CottoneC.PorcaroC.CancelliA.OlejarczykE.SalustriC.TecchioF. (2017). Neuronal electrical ongoing activity as a signature of cortical areas. Brain Struct. Funct.222, 2115–2126. 10.1007/s00429-016-1328-4
9
CroceP.QuerciaA.CostaS.ZappasodiF. (2018). Circadian rhythms in fractal features of EEG signals. Front. Physiol.9:1567. 10.3389/fphys.2018.01567
10
DecoG.JirsaV. K.McIntoshA. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat. Rev. Neurosci.12, 43–56. 10.1038/nrn2961
11
DestexheA.MarderE. (2004). Plasticity in single neuron and circuit computations. Nature431, 789–795. 10.1038/nature03011
12
EdwardsA. M.PhillipsR. A.WatkinsN. W.FreemanM. P.MurphyE. J.AfanasyevV.et al. (2007). Revisiting Lévy flight search patterns of wandering albatrosses, bumblebees and deer. Nature449, 1044–1048. 10.1038/nature06199
13
FieldsR. D. (2015). A new mechanism of nervous system plasticity: activity-dependent myelination. Nat. Rev. Neurosci.16, 756–767. 10.1038/nrn4023
14
FinkA. J. P.CroceK. R.HuangZ. J.AbbottL. F.JessellT. M.AzimE. (2014). Presynaptic inhibition of spinal sensory feedback ensures smooth movement. Nature509, 43–48. 10.1038/nature13276
15
FristonK. (2018). Does predictive coding have a future?Nat. Neurosci.21, 1019–1021. 10.1038/s41593-018-0200-7
16
FuscoG.ScandolaM.FeurraM.PavoneE. F.RossiS.AgliotiS. M. (2018). Midfrontal theta transcranial alternating current stimulation modulates behavioural adjustment after error execution. Eur. J. Neurosci.48, 3159–3170. 10.1111/ejn.14174
17
GandollaM.FerranteS.MolteniF.GuanziroliE.FrattiniT.MarteganiA.et al. (2014). Re-thinking the role of motor cortex: context-sensitive motor outputs?Neuroimage91, 366–374. 10.1016/j.neuroimage.2014.01.011
18
GentT. C.BandarabadiM.HerreraC. G.AdamantidisA. R. (2018). Thalamic dual control of sleep and wakefulness. Nat. Neurosci.21, 974–984. 10.1038/s41593-018-0164-7
19
GibsonE. M.PurgerD.MountC. W.GoldsteinA. K.LinG. L.WoodL. S.et al. (2014). Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain. Science344:1252304. 10.1126/science.1252304
20
HausdorffJ. M.PurdonP. L.PengC. K.LadinZ.WeiJ. Y.GoldbergerA. L. (1996). Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations. J. Appl. Physiol.80, 1448–1457. 10.1152/jappl.1996.80.5.1448
21
HeB. J. (2011). Scale-free properties of the functional magnetic resonance imaging signal during rest and task. J. Neurosci.31, 13786–13795. 10.1523/JNEUROSCI.2111-11.2011
22
HebbD. O. (1949). Organization of Behavior.1949th ed. New York, NY: John Wiley & Sons, Ltd
23
HoerzerG. M.LegensteinR.MaassW. (2014). Emergence of complex computational structures from chaotic neural networks through reward-modulated hebbian learning. Cereb. Cortex24, 677–690. 10.1093/cercor/bhs348
24
IvanovP. C.BartschR. P. (2014). Network physiology: mapping interactions between networks of physiologic networks, in Networks of Networks: The Last Frontier of Complexity. Understanding Complex Systems, eds D'AgostinoG.ScalaA. (Cham: Springer). 10.1007/978-3-319-03518-5_10
25
IvanovP. C. H.LiuK. K. L.BartschR. P. (2016). Focus on the emerging new fields of network physiology and network medicine. New J. Phys.18:100201. 10.1088/1367-2630/18/10/100201
26
KandelE.SchwartzJ. (1985). Principles of Neural Sciences. 2nd Edn.New York, NY, Oxford, Amsterdam: Elsevier.
27
KeysersC.GazzolaV. (2014). Hebbian learning and predictive mirror neurons for actions, sensations and emotions. Philos. Trans. R. Soc. B Biol. Sci.369:20130175. 10.1098/rstb.2013.0175
28
KlimeschW. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev.29, 169–195. 10.1016/S0165-0173(98)00056-3
29
LinA.LiuK. K. L.BartschR. P.IvanovP. C. (2016). Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci.374:20150182. 10.1098/rsta.2015.0182
30
MalenkaR. C.BearM. F. (2004). LTP and LTD: an embarrassment of riches. Neuron44, 5–21. 10.1016/j.neuron.2004.09.012
31
MarinoM.LiuQ.SamoginJ.TecchioF.CottoneC.MantiniD.et al. (2019). Neuronal dynamics enable the functional differentiation of resting state networks in the human brain. Hum. Brain Mapp.40, 1445–1457. 10.1002/hbm.24458
32
MelgariJ. M.ZappasodiF.PorcaroC.TomasevicL.CassettaE.RossiniP. M.et al. (2013). Movement-induced uncoupling of primary sensory and motor areas in focal task-specific hand dystonia. Neuroscience250, 434–445. 10.1016/j.neuroscience.2013.07.027
33
PhinyomarkA.LarracyR.SchemeE. (2020). Fractal Analysis of Human Gait Variability via Stride Interval Time Series. Front. Physiol.11:333. 10.3389/fphys.2020.00333
34
PorcaroC.CottoneC.CancelliA.RossiniP. M.ZitoG.TecchioF. (2019). Cortical neurodynamics changes mediate the efficacy of a personalized neuromodulation against multiple sclerosis fatigue. Sci. Rep.9:18213. 10.1038/s41598-019-54595-z
35
RamockiM. B.ZoghbiH. Y. (2008). Failure of neuronal homeostasis results in common neuropsychiatric phenotypes. Nature455, 912–918. 10.1038/nature07457
36
ReardonS. (2014). Electroceuticals spark interest. Nature511:18. 10.1038/511018a
37
RossiS.PasqualettiP.TecchioF.SabatoA.RossiniP. M. (1998). Modulation of corticospinal output to human hand muscles following deprivation of sensory feedback. Neuroimage8, 163–175. 10.1006/nimg.1998.0352
38
SacksO. (1989). Seeing Voices: A Journey into the World of the Deaf. 1989th Edn., ed. California, CA: University of California Press Berkeley.
39
SjöströmP. J.NelsonS. B. (2002). Spike timing, calcium signals and synaptic plasticity. Curr. Opin. Neurobiol.12, 305–314. 10.1016/S0959-4388(02)00325-2
40
SmitsF. M.PorcaroC.CottoneC.CancelliA.RossiniP. M.TecchioF. (2016). Electroencephalographic fractal dimension in healthy ageing and alzheimer's disease. PLoS ONE11:e0149587. 10.1371/journal.pone.0149587
41
TecchioF.ZappasodiF.PorcaroC.BarbatiG.AssenzaG.SalustriC.et al. (2008). High-gamma band activity of primary hand cortical areas: a sensorimotor feedback efficiency index. Neuroimage40, 256–264. 10.1016/j.neuroimage.2007.11.038
42
TononiG.CirelliC. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron81, 12–34. 10.1016/j.neuron.2013.12.025
43
TurrigianoG. G.LeslieK. R.DesaiN. S.RutherfordL. C.NelsonS. B. (1998). Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature391, 892–896. 10.1038/36103
44
TurrigianoG. G.NelsonS. B. (2004). Homeostatic plasticity in the developing nervous system. Nat. Rev. Neurosci.5, 97–107. 10.1038/nrn1327
45
WolpertD. M.DiedrichsenJ.FlanaganJ. R. (2011). Principles of sensorimotor learning. Nat. Rev. Neurosci.12, 739–751. 10.1038/nrn3112
46
ZappasodiF.MarzettiL.OlejarczykE.TecchioF.PizzellaV. (2015). Age-related changes in electroencephalographic signal complexity. PLoS ONE10:e0141995. 10.1371/journal.pone.0141995
47
ZappasodiF.OlejarczykE.MarzettiL.AssenzaG.PizzellaV.TecchioF. (2014). Fractal dimension of EEG activity senses neuronal impairment in acute stroke. PLoS ONE9:e100199. 10.1371/journal.pone.0100199
48
ZhangW.LindenD. J. (2003). The other side of the engram: experience-driven changes in neuronal intrinsic excitability. Nat. Rev. Neurosci.4, 885–900. 10.1038/nrn1248
Summary
Keywords
plasticity, synchrony, feedback, neurodynamics, recursive multiscale triadic principle
Citation
Tecchio F, Bertoli M, Gianni E, L'Abbate T, Paulon L and Zappasodi F (2020) To Be Is To Become. Fractal Neurodynamics of the Body-Brain Control System. Front. Physiol. 11:609768. doi: 10.3389/fphys.2020.609768
Received
24 September 2020
Accepted
25 November 2020
Published
15 December 2020
Volume
11 - 2020
Edited by
Plamen Ch. Ivanov, Boston University, United States
Reviewed by
Daniela Dentico, University of Bari Aldo Moro, Italy; Yuan Yang, University of Oklahoma, United States
Updates
Copyright
© 2020 Tecchio, Bertoli, Gianni, L'Abbate, Paulon and Zappasodi.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Franca Tecchio franca.tecchio@cnr.it; orcid.org/0000-0002-1325-5059
This article was submitted to Fractal and Network Physiology, a section of the journal Frontiers in Physiology
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.