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Evidence of criticality at various levels of neuronal organization has accumulated over the past 2 decades. However, little is known of how criticality affects brain function, whether the critical state is always optimal for information processing, and whether the brain can change its operating point with regards to criticality so as to accommodate varying information processing requirements. Therefore, the aim of this thesis is to elucidate mechanisms that regulate brain criticality and to understand their impact on local and global information processing. We empirically validated that the brain can control the level of criticality via neuromodulation, with implications on perception. Then, we relied on computational modeling to gain full control on the parameters that determine criticality, and tested the implications of the level of criticality on two theories of brain function. Last, since excitation/inhibition balance is known to be a crucial determinant of criticality, which can vary across individuals, but also within subjects across time with neuromodulation, we developed a method to non-invasively estimate the functional E/I ratio.

Balance 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.

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 coupledin siliconetworks 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 STATEMENTTo 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.

Brain function depends on segregation and integration of information processing in brain networks often separated by long-range anatomical 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 with varying excitatory and inhibitory connectivity, and found that phase coupling emerges at criticality, and that amplitude coupling, as well as information transfer, are maximal when networks are critical. Importantly, regulating criticality through neuromodulation of synaptic strength 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 prior to degeneration of axonal connections.

Item does not contain fulltext BACKGROUND: Mechanism-based treatments such as bumetanide are being repurposed for autism spectrum disorder. We recently reported beneficial effects on repetitive behavioral symptoms that might be related to regulating excitation-inhibition (E/I) balance in the brain. Here, we tested the neurophysiological effects of bumetanide and the relationship to clinical outcome variability and investigated the potential for machine learning-based predictions of meaningful clinical improvement. METHODS: Using modified linear mixed models applied to intention-to-treat population, we analyzed E/I-sensitive electroencephalography (EEG) measures before and after 91 days of treatment in the double-blind, randomized, placebo-controlled Bumetanide in Autism Medication and Biomarker study. Resting-state EEG of 82 subjects out of 92 participants (7-15 years) were available. Alpha frequency band absolute and relative power, central frequency, long-range temporal correlations, and functional E/I ratio treatment effects were related to the Repetitive Behavior Scale-Revised (RBS-R) and the Social Responsiveness Scale 2 as clinical outcomes. RESULTS: We observed superior bumetanide effects on EEG, reflected in increased absolute and relative alpha power and functional E/I ratio and in decreased central frequency. Associations between EEG and clinical outcome change were restricted to subgroups with medium to high RBS-R improvement. Using machine learning, medium and high RBS-R improvement could be predicted by baseline RBS-R score and EEG measures with 80% and 92% accuracy, respectively. CONCLUSIONS: Bumetanide exerts neurophysiological effects related to clinical changes in more responsive subsets, in whom prediction of improvement was feasible through EEG and clinical measures.

International audience; When reading, orthographic information is extracted not only from the word the reader is looking at, but also from adjacent words in the parafovea. Here we examined, using the recently introduced OB1-reader computational model, how orthographic information can be processed in parallel across multiple words and how orthographic information can be integrated across time and space. Although OB1-reader is a model of text reading, here we used it to simulate single-word recognition experiments in which parallel processing has been shown to play a role by manipulating the surrounding context in flanker and priming paradigms. In flanker paradigms, observers recognize a central word flanked by other letter strings located left and right of the target and separated from the target by a space. The model successfully accounts for the finding that such flankers can aid word recognition when they contain bigrams of the target word, independent of where those flankers are in the visual field. In priming experiments, in which the target word is preceded by a masked prime, the model accounts for the finding that priming occurs independent of whether the prime and target word are in the same location or not. Crucial to these successes is the key role that spatial attention plays within OB1-reader, as it allows the model to receive visual input from multiple locations in parallel, while limiting the kinds of errors that can potentially occur under such spatial pooling of orthographic information.

Understanding why identical stimuli give differing neuronal responses and percepts is a central challenge in research on attention and consciousness. Ongoing oscillations reflect functional states that bias processing of incoming signals through amplitude and phase. It is not known, however, whether the effect of phase or amplitude on stimulus processing depends on the long-term global dynamics of the networks generating the oscillations. Here, we show, using a computational model, that the ability of networks to regulate stimulus response based on pre-stimulus activity requires near-critical dynamics—a dynamical state that emerges from networks with balanced excitation and inhibition, and that is characterized by scale-free fluctuations. We also find that networks exhibiting critical oscillations produce differing responses to the largest range of stimulus intensities. Thus, the brain may bring its dynamics close to the critical state whenever such network versatility is required.

Publication bias promotes papers providing “significant” findings, thus incentivizing researchers to produce such findings. Prior studies suggested that researchers’ focus on “p .05 (88.3%) or vice versa (11.7%). Analyzing p-value frequencies just below.05 using a novel method did not unequivocally demonstrate “p-hacking”—excess p-values could be alternatively explained by (severe) publication bias. Results for 19,830 p-values from social psychology were strikingly similar. We conclude that publication bias, publication pressure, and verification bias distort the communication science knowledge base, and suggest solutions to this problem.

The ascending modulatory systems of the brain stem are powerful regulators of global brain state. Disturbances of these systems are implicated in several major neuropsychiatric disorders. Yet, how these systems interact with specific neural computations in the cerebral cortex to shape perception, cognition, and behavior remains poorly understood. Here, we probed into the effect of two such systems, the catecholaminergic (dopaminergic and noradrenergic) and cholinergic systems, on an important aspect of cortical computation: its intrinsic variability. To this end, we combined placebo-controlled pharmacological intervention in humans, recordings of cortical population activity using magnetoencephalography (MEG), and psychophysical measurements of the perception of ambiguous visual input. A low-dose catecholaminergic, but not cholinergic, manipulation altered the rate of spontaneous perceptual fluctuations as well as the temporal structure of “scale-free” population activity of large swaths of the visual and parietal cortices. Computational analyses indicate that both effects were consistent with an increase in excitatory relative to inhibitory activity in the cortical areas underlying visual perceptual inference. We propose that catecholamines regulate the variability of perception and cognition through dynamically changing the cortical excitation–inhibition ratio. The combined readout of fluctuations in perception and cortical activity we established here may prove useful as an efficient and easily accessible marker of altered cortical computation in neuropsychiatric disorders., Author summary The human brain is equipped with a number of modulatory neurotransmitter systems, which have widespread projections and regulate global brain states. Disturbances of these systems are implicated in several neuropsychiatric disorders, but how they modulate specific neural computations to shape perception, cognition, and behavior remains unknown. Here, we combined pharmacological interventions with electrophysiological and behavioral measurements to investigate the impact of two classes of such neuromodulatory systems—catecholamines and acetylcholine—on the variability of the activity of neuronal populations in the cerebral cortex and on cognition in humans. We addressed a prominent hypothesis, which holds that noradrenaline—a catecholamine—boosts the variability of inference and decision-making to promote exploration of alternative options—for example, exploring distinct perceptual interpretations of an ambiguous sensory input. Pharmacologically elevating catecholamine levels, but not acetylcholine levels, altered the temporal structure of intrinsic variability of population activity in two cortical regions and increased the rate of spontaneous perceptual alternations induced by ambiguous visual stimulation, in line with an increase in exploration. Computational modeling revealed that the observed changes can be explained by an increase in the ratio between excitation and inhibition in the circuits processing the stimulus.

Fate Agent EAs form a novel flavour or subclass in EC. The idea is to decompose the main loop of traditional evolutionary algorithms into three independently acting forces, implemented by the so-called Fate Agents, and create an evolutionary process by injecting these agents into a population of candidate solutions. This paper introduces an extension to the original concept, adding a mechanism to self-adapt the mutation of the Breeder Agents. The method improves the behaviour of the original Fate Agent EA on dynamically changing fitness landscapes.