However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on the manner in which NFB is able to achieve these outcomes. The present work attempts to bring together various concepts from neurobiology, engineering, and dynamical systems so as to propose a contemporary theoretical framework for the mechanistic effects of NFB. In sum, it is argued that pathological oscillations emerge from an abnormal formation of brain-state attractor landscape s. The central thesis put forward is that NFB tunes brain oscillations toward a homeostatic set-point which affords an optimal balance between network flexibility and stability i. When the subject shut his eyes and was given a simple problem in mental arithmetic, as long as he was working it out the waves were absent and the line was irregular, as when his eyes were open. When he had solved the problem, the waves reappeared.
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However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on the manner in which NFB is able to achieve these outcomes. The present work attempts to bring together various concepts from neurobiology, engineering, and dynamical systems so as to propose a contemporary theoretical framework for the mechanistic effects of NFB.
In sum, it is argued that pathological oscillations emerge from an abnormal formation of brain-state attractor landscape s. The central thesis put forward is that NFB tunes brain oscillations toward a homeostatic set-point which affords an optimal balance between network flexibility and stability i.
When the subject shut his eyes and was given a simple problem in mental arithmetic, as long as he was working it out the waves were absent and the line was irregular, as when his eyes were open. When he had solved the problem, the waves reappeared. Here, as the E-neurons fire they activate the I-neurons, which after some delay retroactively silence the E-neurons, and so ad perpetuum.
In essence, this E-I connectivity serves to keep neuronal activity within a restricted range, as purely E-E or I-I coupling would risk producing run-away excitation or inhibition although such connections naturally also exist. As seen in Figure 1 , when neuronal activities occur in a spatially circumscribed region and become temporally synchronized, their local field potentials LFPs are then strongly summated giving rise to large amplitude electroencephalogram EEG or magnetoencephalogram MEG rhythms.
The generation of electroencephalogram EEG network oscillations. EEG signals are generated by the integration of neural activity at multiple spatial A and temporal B scales.
After Le Van Quyen Conversely, reductions in amplitude result from a breakdown of such synchronization, in accordance with the historical expression: desynchronization. Likewise, the speed frequency of the wave will be determined by how quickly the individual elements rise and decay Nunez, , and this will depend on the intrinsic nature resonance of the person neuron.
Here, a greater lower number of oscillations occurring in the same period of time will equate to faster slower frequencies. Historically, EEG synchronization patterns were discovered to differentiate levels of psychological arousal in the progression from deep sleep to wakefulness, to high alertness Jasper and Droogleever-Fortuyn, Low-frequency delta 1—4 Hz waves were found to dominate deeper sleep states, while during lighter or more activated REM sleep the frequencies are more accelerated, but slower than in waking states.
Interestingly, progressively greater degrees of EEG activation could be provoked by simple electrical stimulation of the brainstem Moruzzi and Magoun, , enhancing the precision and speed of visual discrimination in monkeys Fuster, Consequently, EEG activation is widely regarded to be necessary for the emergence as well as the characteristic nature of consciousness Villablanca, , which once established, invites a fascinating question: how is intrinsic brain activity regulated further to give rise to volitional control of cognition?
On the other hand, a large body of evidence in humans points to the key role of cortical oscillations in top-down processing during attention and cognition Palva and Palva, Thus, during waking consciousness, there is a critical involvement of higher-order cortical regions in orchestrating the phasic i.
A good example of the former is the way motor cortex is able to concurrently trigger desynchronization of somatosensory cortex Zagha et al. Similarly, there is evidence of a direct cortico-subcortical dialog during maintenance of wakefulness in a novel environment , since destruction of either anterior cingulate cortex or locus coeruleus is sufficient to block exploratory activity and associated EEG activation Gompf et al. Moreover, when major anatomical routes are severed, as with targeted lesions to the lateral prefrontal cortex plus corpus callosum, it leads to increased distractibility coupled with abnormally high neural synchronization in visual areas during attention Gregoriou et al.
It has become evident that both the tonic sleep-wake cycle and phasic top-down shifts in brain-state are regulated by an intricate interplay of neuromodulators for a detailed review see Lee and Dan, Accordingly, attentional behavior and distinct EEG rhythms have been reported to be affected by the lesion and pharmacological blockade of noradrenergic pathways Delagrange et al.
Moreover, local application of acetylcholine in the monkey primary visual cortex is able to enhance the behavioral modulation of neuronal firing rates Herrero et al.
Such effects have been verified directly in vitro, as for example, dopaminergic antagonists are found to increase EEG spectral power 0—20 Hz while agonists decrease it Sebban et al. Similarly, optogenetic studies report EEG desynchronization following selective activation of cholinergic Kalmbach and Waters, or noradrenergic Carter et al. In sum, the studies above reveal that in addition to the tonic sleep-wake cycle, cortical-subcortical neuromodulatory circuits are able to control brain oscillations phasically i.
Control of EEG de synchronization via shifts in intrinsic brain state. Adapted with permission from Harris and Thiele However, the observations above invite the inevitable question: what is the functional significance of such synchronized and desynchronized states? Why does the cortex, for example, display highly-synchronous low-frequency states during unconsciousness, and what necessitates the desynchronized, higher-frequency oscillations of wakefulness Gervasoni et al.
Neuroscience is of course still answering these questions, and there is no encompassing theory as yet. However, several emerging perspectives are beginning to shed light on these phenomena. The first perspective involves the observation that upon intracellular recording of corticothalamic Contreras and Steriade, as well as corticospinal Ezure and Oshima, neurons, cell-membrane depolarization excitation is found to be greater during desynchronized EEG states.
Conversely, during sleep, membrane potentials are more hyperpolarized inhibited leading to slower oscillations which are characterized by large alternating cortical up higher excitability and down lower excitability states Castro-Alamancos, Thus, in the simplest scenario, desynchronization stems from a rise in neuromodulators which elevate depolarize membrane potentials and their voltage-gated-ion channels closer to their firing threshold, enhancing their sensitivity to incoming sensory inputs Castro-Alamancos, ; Wang et al.
The second perspective involves the fact that desynchronized states have been attributed to larger background synaptic activity, which leads to higher resting membrane conductance Wang et al. From yet another perspective, desynchronized patterns may be seen to minimize functional correlations of synaptic activities, thus maximizing their informational complexity called entropy. Several studies report reduced inter-neuronal correlations during attention Cohen and Maunsell, and memory formation Bermudez Contreras et al.
This notion has received direct experimental support during perceptual-decision making Werkle-Bergner et al. As a corollary, extremes of too much or too little synchronization would both have negative consequences for population coding, as this would lead to abnormal redundancy of information, reflective of a highly ordered or chaotic system Hanslmayr et al. In general, the covered evidence suggests that low-frequency oscillations appear to limit the complexity of available computational states, so why should they feature so prominently in the brain?
A potential biological compromise may be that oscillations enable segregated communication channels to be established in the brain, which would prevent a disorganized mixing of processing streams.
Thus far, we have mainly considered the features of locally synchronized activities i. Although this complex topic is beyond the scope of this paper, we touch upon it briefly in light of its relevance to pathological states. In essence, distributed brain regions have been observed to functionally co-activate on a variety of measures, including synchronization of phase, frequency, or amplitude Engel et al.
Recent studies indicate that these mechanisms enable the collective binding of neural assemblies to form functional networks independent of inter-neuron distance Canolty et al. Hence, it is not difficult to envisage the emergence of a dynamic interplay between local- and network-oscillation states, as the former would influence the latter via long-range connections Zemankovics et al. As we will see in the next section, adaptive behavior and consciousness can be altered when this delicate oscillatory balance is disturbed.
Thus, a science of ab normal oscillations should also be supported by observations that quantitative measures e. This is qualified by a proviso that EEG parameters are not static from birth, but follow an established developmental trajectory consisting of a frequency acceleration of the dominant resting rhythm, and a decrease of the overall spectral power until adulthood Dustman et al. Such age-matched measures from healthy reference populations are implicitly used by neuroscience studies that seek to uncover meaningful differences with pathophysiological conditions.
The literature on this topic is vast, but we provide a few representative examples of low-frequency EEG abnormalities prevalent in brain disorders.
For instance, slower-waves e. The list is virtually endless given the plethora as well as complexity of disorders, and the interested reader is referred to comprehensive reviews on the subject Coburn et al. Importantly, EEG can also be employed to assess recovery or response to treatment. For example, reduced delta 2—4 Hz rhythm amplitude can be used as a biomarker of long-term recovery from ischemic cerebral stroke Cuspineda et al.
Interestingly, administration of psychostimulants improves behavior in attention-deficit hyperactivity disorder ADHD and is found to normalize slow-wave patterns of EEG activity Clarke et al. However a non-trivial caveat is that the notion of EEG abnormality and its normalization following treatment appears to be state-dependent Arns et al. For example, oscillatory and topographical differences between ADHD and healthy subjects manifest distinctly or not at all depending on the attentional task used Sohn et al.
The actual neuromolecular processes underpinning aberrant oscillations are likely to be both complex and diverse across pathologies. In addition, several reviews have provided in-depth treatments of the diverse cellular mechanisms that appear to subserve ab normal brain oscillations Steriade et al. This can be explained by the presence of multiple comorbidities and the possibility for similar behavioral patterns to be generated by dissimilar neural substrates Tognoli and Kelso, Thus, a mixture of heterogeneity and selective sampling could be a feasible explanation for both the similar and contradicting EEG signatures reported between and within disorders, respectively.
Over time, and upon establishment of several databases Thatcher and Lubar, , the general approach of examining or classifying patients based on multivariate EEG patterns was re-christened as quantitative EEG qEEG , to differentiate it from qualitative EEG interpretation.
A key objective of qEEG has been to improve sensitivity i. Recent efforts have concentrated on identifying EEG biomarkers that are recurrently expressed by particular sub types of brain disorders Coburn et al. It is important to note that biomarker differences can also appear between different age-groups of the same disorder, e. Hence the key message is that brain disorders seem to fall on a multi-dimensional continuum, with scarce evidence to support a one-to-one mapping between specific EEG abnormalities and cognitive-behavioral traits i.
This does not negate the existence of a relationship per se, but rather that it is complex and has the interesting property of degeneracy Edelman and Gally, EEG spectral signatures of healthy and psychiatric populations. From Schulman et al. The Brain as a Dynamical System In light of the complex linkage between brain activity and behavior, scientists have tried to expand the scope of their analyses by introducing more dynamical measures of neuronal oscillations, such as burst Montez et al.
The dynamical designation relates to considering the temporal evolution of a brain signal, as this can be overlooked upon computing the traditional Fourier transform e. In other words, introducing time into analyses takes into account the fact that brain oscillations are non-stationary, i. Interestingly, such time-varying behavior can be accommodated within the framework of dynamical systems theory, opening the door to a whole new world of exotic phenomena: bifurcations, attractors, dynamic repertoires, and phase transitions.
Although we cannot give these full treatment for an excellent review see Stam, , a few visual analogies may serve as an introduction. As depicted in Figure 4A , this can be simplified to 2-dimensions and envisaged as a ball with random energy i. Here, the ball dynamic state will experience greater stability i.
In Figure 4B , a deeper attractor right offers more stability than a shallower one left , as it will keep the ball within its basin at relatively greater energy perturbations. However, is there explicit evidence of attractor-like signatures in the brain? Put differently, alternating de synchronization patterns can be understood to display non-random statistical properties, exemplified by different temporal distributions i. Such state transitions, known as bifurcations, may be driven by both internal Freyer et al.
Secondly, phasic or tonic alternations between EEG frequencies may also be seen as reflecting dynamic transitions between attractors. One of the clearest examples can be found in the sleep-wake cycle which reveals distinct yet recurring states as well as trajectories corresponding to each neurobehavioral transition as shown in Figure 4D Gervasoni et al.
A visual portrayal of state-space landscapes. A A hill and valley representation of a repellor left and an attractor right ; B the shallow attractor left has a shorter dwell-time than the deeper attractor right ; C a multi-attractor landscape exhibiting multistability; D EEG state transitions during sleep-wake activity in the rat, comprising of whisker twitching WT , active exploration AE , quiet wake QW , rapid-eye movement REM , slow-wave sleep SWS , intermediate stage IS.
From Gervasoni et al. Phase-space dynamical plots of EEG rhythms during sleep. Attractor-like limit cycle shapes are more pronounced for alpha A and delta rhythms C , compared to the beta rhythm B. From Pradhan et al. This conveniently brings us the concept of multistability, illustrated in Figure 4C. Here, a ball with a continuous source of energy may revisit multiple states without settling into any of them permanently e.
Evidence for recurring, spatiotemporally discrete brain patterns has emerged from both EEG Van de Ville et al. The tentative implication is that such patterns reflect dynamic circuit motifs which coordinate specific computational operations, including gating and integration of inputs Womelsdorf et al. The direct impact of neural multistability on cognition is beautifully exemplified by the phenomenon of bistable perception Braun and Mattia, , where perceptual alternations occur in spite of constant sensory stimulation e.
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