Self-organized criticality and stochastic resonance in the human brain

The human brain spontaneously generates neuronal network oscillations at around 10 and 20 Hz with a large variability in amplitude, duration, and recurrence. Despite more than 70 years of research, the complex dynamics and functional significance of these oscillations have remained poorly understood. This Thesis concerns the dynamic character and functional significance of noninvasively recorded 10- and 20-Hz oscillations in the human brain. The hypotheses, experimental paradigms, data analyses, and interpretations of the results are inspired by recent insights from physics - most notable the theory of self-organized criticality and the phenomenon of stochastic resonance whose applicability to large-scale neuronal networks is explained. We show that amplitude fluctuations of 10- and 20-Hz oscillations during wakeful rest are correlated over thousands of oscillation cycles and that the decay of temporal correlations exhibits power-law scaling behavior. However, when these ongoing oscillations are perturbed with sensory stimuli, the amplitude attenuates quickly, reliably, and transiently, and the long-range temporal dynamics is affected as evidenced by changes in scaling exponents compared to rest. In addition to the rich temporal dynamics in local areas of the cortex, ongoing oscillations tend to synchronize their phases and exhibit correlated amplitude fluctuations across the two hemispheres, as shown for oscillations in homologous areas of the sensorimotor cortices. Finally, it is revealed that intermediate amplitude levels of ongoing oscillations provide the optimal oscillatory state of the sensorimotor cortex for reliable and quick conscious detection of weak somatosensory stimuli. We propose that the long-range temporal correlations, the power-law scaling behavior, the high susceptibility to stimulus perturbations, and the large amplitude variability of ongoing oscillations may find a unifying explanation within the theory of self-organized criticality. This theory offers a general mechanism for the ubiquitous emergence of complex dynamics with power-law decay of spatiotemporal correlations in non-linear self-organizing stochastic systems consisting of many units. The optimal ability to detect consciously and respond behaviorally to weak somatosensory stimuli at intermediate levels of ongoing sensorimotor oscillations is attributed to stochastic resonance - the intuitively paradoxical phenomenon that the signal-to-noise ratio of detecting or transmitting a signal in a non-linear system can be enhanced by noise. Based on the above results, we conjecture that a mechanism of intrinsic stochastic resonance between self-organized critical and stimulus-induced activities may be a general organizing principle of great importance for central nervous system function and account for some of the variability in the way we perceive and react to the outside world.reviewe

Long-Range Amplitude Coupling Is Optimized for Brain Networks That Function at Criticality

Brain function depends on segregation and integration of information processing in brain networks often separated by long-range anatomic connections. Neuronal oscillations orchestrate such distributed processing through transient amplitude and phase coupling, yet surprisingly, little is known about local network properties facilitating these functional connections. Here, we test whether criticality, a dynamical state characterized by scale-free oscillations, optimizes the capacity of neuronal networks to couple through amplitude or phase, and transfer information. We coupled in silico networks which exhibit oscillations in the α band (8–16 Hz), and varied excitatory and inhibitory connectivity. We found that phase coupling of oscillations emerges at criticality, and that amplitude coupling, as well as information transfer, are maximal when networks are critical. Importantly, regulating criticality through modulation of synaptic gain showed that critical dynamics, as opposed to a static ratio of excitatory and inhibitory connections, optimize network coupling and information transfer. Our data support the idea that criticality is important for local and global information processing and may help explain why brain disorders characterized by local alterations in criticality also exhibit impaired long-range synchrony, even before degeneration of axonal connections. SIGNIFICANCE STATEMENT To perform adaptively in a changing environment, our brains dynamically coordinate activity across distant areas. Empirical evidence suggests that long-range amplitude and phase coupling of oscillations are systems-level mechanisms enabling transient formation of spatially distributed functional networks on the backbone of a relatively static structural connectome. However, surprisingly little is known about the local network properties that optimize coupling and information transfer. Here, we show that criticality, a dynamical state characterized by scale-free oscillations and a hallmark of neuronal network activity, optimizes the capacity of neuronal networks to couple through amplitude or phase and transfer information

Scale-free amplitude modulation of neuronal oscillations tracks comprehension of accelerated speech

Speech comprehension is preserved up to a threefold acceleration, but deteriorates rapidly at higher speeds. Current models posit that perceptual resilience to accelerated speech is limited by the brain's ability to parse speech into syllabic units using δ/θ oscillations. Here, we investigated whether the involvement of neuronal oscillations in processing accelerated speech also relates to their scale-free amplitude modulation as indexed by the strength of long-range temporal correlations (LRTC). We recorded MEG while 24 human subjects (12 females) listened to radio news uttered at different comprehensible rates, at a mostly unintelligible rate and at this same speed interleaved with silence gaps. δ, θ, and low-γ oscillations followed the nonlinear variation of comprehension, with LRTC rising only at the highest speed. In contrast, increasing the rate was associated with a monotonic increase in LRTC in high-γ activity. When intelligibility was restored with the insertion of silence gaps, LRTC in the δ, θ, and low-γ oscillations resumed the low levels observed for intelligible speech. Remarkably, the lower the individual subject scaling exponents of δ/θ oscillations, the greater the comprehension of the fastest speech rate. Moreover, the strength of LRTC of the speech envelope decreased at the maximal rate, suggesting an inverse relationship with the LRTC of brain dynamics when comprehension halts. Our findings show that scale-free amplitude modulation of cortical oscillations and speech signals are tightly coupled to speech uptake capacity.SIGNIFICANCE STATEMENT One may read this statement in 20-30 s, but reading it in less than five leaves us clueless. Our minds limit how much information we grasp in an instant. Understanding the neural constraints on our capacity for sensory uptake is a fundamental question in neuroscience. Here, MEG was used to investigate neuronal activity while subjects listened to radio news played faster and faster until becoming unintelligible. We found that speech comprehension is related to the scale-free dynamics of δ and θ bands, whereas this property in high-γ fluctuations mirrors speech rate. We propose that successful speech processing imposes constraints on the self-organization of synchronous cell assemblies and their scale-free dynamics adjusts to the temporal properties of spoken language

Association Between Resting-State Microstates and Ratings on the Amsterdam Resting-State Questionnaire

There is a gap in understanding on how physiologically observed activity is related to the subjective, internally oriented experience during resting state. Microstate analysis is a frequent approach to evaluate resting-state EEG. But the relationship of commonly observed resting-state microstates to psychological domains of resting is not clear. The Amsterdam Resting-State Questionnaire (ARSQ) was recently introduced, offering an effective way to quantify subjective states after a period of resting and associate these quantifiers to psychological and physiological variables. In a sample of 94 healthy volunteers who participated in closed-eyes 5 min resting session with concurrent EEG recording and subsequent filling of the ARSQ we evaluated parameters of microstate Classes A, B, C, D. We showed a moderate negative association between contribution (r = -0.40) of Class C and experienced somatic awareness (SA). The negative correlation between Class C and SA seems reasonable as Class C becomes more dominant when connections to contextual information (and bodily sensations as assessed with SA) are loosened (in reduced attention states, during sleep, hypnosis, or psychosis). We suggest that the use of questionnaires such as the ARSQ is helpful in exploring the variation of resting-state EEG parameters and its relationship to variation in sensory and non-sensory experiences

Novel Candidate Genes Associated with Hippocampal Oscillations

The hippocampus is critical for a wide range of emotional and cognitive behaviors. Here, we performed the first genome-wide search for genes influencing hippocampal oscillations. We measured local field potentials (LFPs) using 64-channel multi-electrode arrays in acute hippocampal slices of 29 BXD recombinant inbred mouse strains. Spontaneous activity and carbachol-induced fast network oscillations were analyzed with spectral and cross-correlation methods and the resulting traits were used for mapping quantitative trait loci (QTLs), i.e., regions on the genome that may influence hippocampal function. Using genome-wide hippocampal gene expression data, we narrowed the QTLs to eight candidate genes, including Plcb1, a phospholipase that is known to influence hippocampal oscillations. We also identified two genes coding for calcium channels, Cacna1b and Cacna1e, which mediate presynaptic transmitter release and have not been shown to regulate hippocampal network activity previously. Furthermore, we showed that the amplitude of the hippocampal oscillations is genetically correlated with hippocampal volume and several measures of novel environment exploration

Aberrant Long-Range Temporal Correlations in Depression Are Attenuated after Psychological Treatment

The spontaneous oscillatory activity in the human brain shows long-range temporal correlations (LRTC) that extend over time scales of seconds to minutes. Previous research has demonstrated aberrant LRTC in depressed patients; however, it is unknown whether the neuronal dynamics normalize after psychological treatment. In this study, we recorded EEG during eyes-closed rest in depressed patients (N = 71) and healthy controls (N = 25), and investigated the temporal dynamics in depressed patients at baseline, and after attending either a brief mindfulness training or a stress reduction training. Compared to the healthy controls, depressed patients showed stronger LRTC in theta oscillations (4–7 Hz) at baseline. Following the psychological interventions both groups of patients demonstrated reduced LRTC in the theta band. The reduction of theta LRTC differed marginally between the groups, and explorative analyses of separate groups revealed noteworthy topographic differences. A positive relationship between the changes in LRTC, and changes in depressive symptoms was observed in the mindfulness group. In summary, our data show that aberrant temporal dynamics of ongoing oscillations in depressive patients are attenuated after treatment, and thus may help uncover the mechanisms with which psychotherapeutic interventions affect the brain

Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics

cited By 0Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network's activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.Peer reviewe