What constitutes normal cortical dynamics in healthy human subjects is a major question in systems neuroscience. Numerousin vitroandin vivoanimal studies have shown that ongoing or resting cortical dynamics are characterized by cascades of activity across many spatial scales, termed neuronal avalanches. In experiment and theory, avalanche dynamics are identified by two measures: (1) a power law in the size distribution of activity cascades with an exponent of −3/2 and (2) a branching parameter of the critical value of 1, reflecting balanced propagation of activity at the border of premature termination and potential blowup. Here we analyzed resting-state brain activity recorded using noninvasive magnetoencephalography (MEG) from 124 healthy human subjects and two different MEG facilities using different sensor technologies. We identified large deflections at single MEG sensors and combined them into spatiotemporal cascades on the sensor array using multiple timescales. Cascade size distributions obeyed power laws. For the timescale at which the branching parameter was close to 1, the power law exponent was −3/2. This relationship was robust to scaling and coarse graining of the sensor array. It was absent in phase-shuffled controls with the same power spectrum or empty scanner data. Our results demonstrate that normal cortical activity in healthy human subjects at rest organizes as neuronal avalanches and is well described by a critical branching process. Theory and experiment have shown that such critical, scale-free dynamics optimize information processing. Therefore, our findings imply that the human brain attains an optimal dynamical regime for information processing.

Population rate models provide powerful tools for investigating the principles that underlie the cooperative function of large neuronal systems. However, biophysical interpretations of these models have been ambiguous. Hence, their applicability to real neuronal systems and their experimental validation have been severely limited. In this work, we show that conductance-based models of large cortical neuronal networks can be described by simplified rate models, provided that the network state does not possess a high degree of synchrony. We first derive a precise mapping between the parameters of the rate equations and those of the conductance-based network models for time-independent inputs. This mapping is based on the assumption that the effect of increasing the cell's input conductance on its f-I curve is mainly subtractive. This assumption is confirmed by a single compartment Hodgkin-Huxley type model with a transient potassium A-current. This approach is applied to the study of a network model of a hypercolumn in primary visual cortex. We also explore extensions of the rate model to the dynamic domain by studying the firing-rate response of our conductance-based neuron to time-dependent noisy inputs. We show that the dynamics of this response can be approximated by a time-dependent second-order differential equation. This phenomenological single-cell rate model is used to calculate the response of a conductance-based network to time-dependent inputs.

Sleep encompasses approximately a third of our lifetime, yet its purpose and biological function are not well understood. Without sleep optimal brain functioning such as responsiveness to stimuli, information processing, or learning may be impaired. Such observations suggest that sleep plays a crucial role in organizing or reorganizing neuronal networks of the brain toward states where information processing is optimized.Increasing evidence suggests that cortical neuronal networks operate near a critical state characterized by balanced activity patterns, which supports optimal information processing. However, it remains unknown whether critical dynamics is affected in the course of wake and sleep, which would also impact information processing. Here, we show that signatures of criticality are progressively disturbed during wake and restored by sleep. We demonstrate that the precise power-laws governing the cascading activity of neuronal avalanches and the distribution of phase-lock intervals in human electroencephalographic recordings are increasingly disarranged during sustained wakefulness. These changes are accompanied by a decrease in variability of synchronization. Interpreted in the context of a critical branching process, these seemingly different findings indicate a decline of balanced activity and progressive distance from criticality toward states characterized by an imbalance toward excitation where larger events prevail dynamics. Conversely, sleep restores the critical state resulting in recovered power-law characteristics in activity and variability of synchronization. These findings support the intriguing hypothesis that sleep may be important to reorganize cortical network dynamics to a critical state thereby assuring optimal computational capabilities for the following time awake.

Measuring and assessing the cognitive load associated with different tasks is crucial for many applications, from the design of instructional materials to monitoring the mental well-being of aircraft pilots. The goal of this paper is to utilize EEG to infer the cognitive workload of subjects during intelligence tests. We chose the well established advanced progressive matrices test, an ideal framework because it presents problems at increasing levels of difficulty and has been rigorously validated in past experiments. We train classic machine learning models using basic EEG measures as well as measures of network connectivity and signal complexity. Our findings demonstrate that cognitive load can be well predicted using these features, even for a low number of channels. We show that by creating an individually tuned neural network for each subject, we can improve prediction compared to a generic model and that such models are robust to decreasing the number of available channels as well.

In recent years, numerous studies have found that the brain at resting state displays many features characteristic of a critical state. Here we examine whether stimulus-evoked activity can also be regarded as critical. Additionally, we investigate the relation between resting-state activity and stimulus-evoked activity from the perspective of criticality. We found that cortical activity measured by magnetoencephalography (MEG) is near critical and organizes as neuronal avalanches at both resting-state and stimulus-evoked activities. Moreover, a significantly high intrasubject similarity between avalanche size and duration distributions at both cognitive states was found, suggesting that the distributions capture specific features of the individual brain dynamics. When comparing different subjects, a higher intersubject consistency was found for stimulus-evoked activity than for resting state. This was expressed by the distance between avalanche size and duration distributions of different participants and was supported by the spatial spreading of the avalanches involved. During the course of stimulus-evoked activity, time locked to the stimulus onset, we demonstrate fluctuations in the gain of the neuronal system and thus short timescale deviations from the critical state. Nonetheless, the overall near-critical state in stimulus-evoked activity is retained over longer timescales, in close proximity and with a high correlation to spontaneous (not time-locked) resting-state activity. Spatially, the observed fluctuations in gain manifest through anticorrelative activations of brain sites involved, suggesting a switch between task-negative (default mode) and task-positive networks and assigning the changes in excitation–inhibition balance to nodes within these networks. Overall, this study offers a novel outlook on evoked activity through the framework of criticality.SIGNIFICANCE STATEMENTThe organization of stimulus-evoked activity and ongoing cortical activity is a topic of high importance. The article addresses several general questions. What is the spatiotemporal organization of stimulus-evoked cortical activity in healthy human subjects? Are there deviations from excitation–inhibition balance during stimulus-evoked activity? What is the relationship between stimulus-evoked activity and ongoing resting-state activity? Using magnetoencephalography (MEG), we demonstrate that stimulus-evoked activity in humans follows a critical branching process that produces neuronal avalanches. Additionally, we investigate the spatiotemporal relationship between resting-state activity and stimulus-evoked activity from the perspective of critical dynamics. These analyses reveal new aspects of this complex relationship and offer novel insights into the interplay between excitation and inhibition that were not observed previously using conventional approaches.