Biology subjects

Linkenkaer-Hansen, K.

Publications and source records attributed to Linkenkaer-Hansen, K..

8 recordsLinked to original sources

bioRxiv2026

From criticality to cognitive effort: scale-invariant EEG dynamics supporting cognitive flexibility are suppressed by effort

The critical brain hypothesis contends that brains operate near a phase transition where excitation and inhibition are balanced, enabling neural dynamics to rapidly adapt and reorganize for cognitive demands. Allocating control resources to maintain stable task representations likely shifts brains away from criticality. Here, we test whether proximity to criticality indexes the balance between flexible adaptation and effortful task engagement. To do so, we adapt a time-resolved measure of scale invariance in EEG amplitude fluctuations (d2), capable of quantifying distance-to-criticality under non-stationary conditions - as during cognitive tasks. We benchmark our measure using ground-truth simulations of a neural mass model and show that d2 is lowest when excitation and inhibition are balanced. Next, we apply d2 to data collected during a task-switching paradigm and find that more demanding trials increased deviation from criticality, whereas greater flexibility, faster responses, and higher accuracy occurred closer to criticality. These effects were region-specific: deviation at posterior electrodes predicted worse performance, while deviations at frontal midline electrodes predicted better performance. Together, these results suggest that deviations from criticality reflect both cognitive load and effort exertion, highlighting EEG amplitude scale-invariance as a sensitive marker of adaptive neural dynamics under cognitive demand.

neuroscience↗

bioRxiv2026

Brain criticality emerges with developmental shifts in frequency-specific excitation-inhibition balance

Adolescent brain maturation involves structural changes effecting a shift in excitation/inhibition (E/I) balance, yet the functional implications of these changes remain unclear. One implication is a shift with respect to criticality. Adult brains, at rest, operate near a critical phase transition - at the boundary between an active, excitation-dominant phase, and an absorbing, inhibition-dominant phase. Special properties emerge when neural systems are balanced at criticality, including maximal susceptibility to perturbation, dynamic range, and information transmission. Thus, a clear picture of how adolescent brain maturation affects E/I balance and criticality is needed to understand how maturational processes shape cognition. Here, we leverage the dynamical properties of longitudinally-collected resting-state EEG recordings during N = 310 sessions from 169 healthy human participants ranging in age from 10 to 33 years old to quantify E/I and proximity to criticality. We find that adult brains operate closer to criticality, including spectrally-widespread increases in long-range temporal correlations and amplitude bistability. We also find band-specific changes in excitation versus inhibition whereby the mechanisms driving low-frequency ({theta} to ) oscillations shift towards lower E/I-possibly because of increasing inhibition-while the mechanisms driving high-frequency ({gamma}) oscillations shift towards higher E/I, possibly because of decreasing inhibition. Opening eyes shifts brains towards lower E/I, and these state-dependent shifts are larger in adults. We simulate developmental effects with a neural mass model of coupled excitatory and inhibitory neurons providing a parsimonious account of how changes in brain dynamics could arise as a function of changes in local connectivity of excitatory and inhibitory neurons. Results indicate developmental movement towards criticality, and greater adaptability to state-specific demands, through adolescence to adulthood, reflecting changes in E/I balance with implications for cognitive development.

neuroscience↗

bioRxiv2025

E/I ratio and net E+I strength are differentially affected across brain disorders

Neuronal oscillations wax and wane near criticality, switching between high- and low-power states. However, the link between this bistability and excitation-inhibition balance is not well understood. Using a computational model, we demonstrate that bistability is influenced by two key factors: the excitatory-inhibitory (E/I) ratio and the combined strength of excitatory and inhibitory activity (E+I). We developed algorithms to infer both metrics from electrophysiological data and validated them in vivo. In intracranial recordings from epilepsy patients, both E/I and E+I increased during seizures, while interictally, E+I remained elevated in seizure zones as E/I decreased--consistent with compensatory inhibition. Extending the framework to scalp EEG from eight disorders, we found that Alzheimers disease is better classified by E/I changes, whereas autism spectrum disorder and ADHD are distinguished by E+I alterations. Our findings highlight the importance of bistability in understanding EI regulation and offer handles for diagnostic and treatment decision support.

neuroscience↗

bioRxiv2025

DMT-induced shifts in criticality correlate with ego-dissolution

Psychedelics profoundly alter subjective experience and brain dynamics. Brain oscillations express signatures of near-critical dynamics, relevant for healthy function. Alterations in the proximity to criticality have been suggested to underlie the experiential and neurological effects of psychedelics. Here, we investigate the effects of a psychedelic substance (DMT) on the criticality of brain oscillations, and in relation to subjective experience. We find that DMT shifts the dynamics of brain oscillations away from criticality in alpha and adjacent frequency bands. In this context, entropy is increased while complexity is reduced. We find that the criticality shifts observed in alpha and theta bands correlate with the intensity ratings of ego-dissolution, a hallmark of psychedelic experience. Finally, using a recently developed metric, the functional excitatory-inhibitory ratio, we find that the DMT-induced criticality shift in brain oscillations is towards subcritical regimes. These findings have major implications for the understanding of psychedelic mechanisms of action in the human brain and for the neurological basis of altered states of consciousness.

neuroscience↗

bioRxiv2024

Qualitative EEG abnormalities signal a shift towards inhibition-dominated brain networks. Results from the EU-AIMS LEAP studies

Qualitative EEG abnormalities are common in Autism Spectrum Disorder (ASD) and hypothesized to reflect disrupted excitation/inhibition balance. To test this, we recently introduced a functional measure of network-level E/I ratio (fE/I). Here, we applied fE/I and other EEG measures to alpha oscillations from source-reconstructed data in the EU-AIMS dataset (267 ASD, 209 controls). We analyzed these measures alongside qualitative EEG abnormalities ranging from slowing of activity to epileptiform patterns, aiming to replicate the findings from the SPACE-BAMBI study. EEG abnormalities were rare in adults and could not be statistically assessed. ASD children-adolescents with EEG abnormalities exhibited lower relative alpha power and fE/I compared to those without. However, EEG-abnormality scoring did not stratify the behavioral heterogeneity of ASD using clinical measures. Surprisingly, several controls also exhibited qualitative EEG abnormalities with a strikingly similar anatomical distribution of reduced fE/I, suggesting a shift towards inhibition-dominated network dynamics in sensory processing regions. The robustness of this association between EEG abnormalities and reduced fE/I was further supported by re-analysis of the SPACE-BAMBI study in source space. Stratification by the presence of EEG abnormalities and their effects on network activity may help understand neurodevelopmental physiological heterogeneity and the difficulties in implementing E/I targeting treatments in unselected cohorts.

neuroscience↗

bioRxiv2022

Robin's Viewer: Using Deep-Learning Predictions to Assist EEG Annotation

Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely automated processes do not offer the users the opportunity to inspect the models output and re-evaluate potential false predictions. As a first step towards addressing these challenges, we developed Robins Viewer (RV), a Python-based EEG viewer for annotating time-series EEG data. The key feature distinguishing RV from existing EEG viewers is the visualization of output predictions of deep-learning models trained to recognize patterns in EEG data. RV was developed on top of the plotting library Plotly, the app-building framework Dash, and the popular M/EEG analysis toolbox MNE. It is an open-source, platform-independent, interactive web application, which supports common EEG-file formats to facilitate easy integration with other EEG toolboxes. RV includes common features of other EEG viewers, e.g., a view-slider, tools for marking bad channels and transient artifacts, and customizable preprocessing. Altogether, RV is an EEG viewer that combines the predictive power of deep-learning models and the knowledge of scientists and clinicians to optimize EEG annotation. With the training of new deep-learning models, RV could be developed to detect clinical patterns other than artifacts, for example sleep stages and EEG abnormalities.

neuroscience↗

bioRxiv2021

Amplitude and phase coupling optimize information transfer between brain networks that function at criticality

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.

neuroscience↗

bioRxiv2017

Catecholamines, not acetylcholine, alter cortical and perceptual dynamics in line with increased excitation inhibition ratio

The ratio between excitatory and inhibitory neurons (E/I ratio) is vital for cortical circuit dynamics, computation, and behavior. This ratio may be under the dynamic control of neuromodulatory systems, which are in turn implicated in several neuropsychiatric disorders. In particular, the catecholaminergic (dopaminergic and noradrenergic) and cholinergic systems have highly specific effects on excitatory and inhibitory cortical neurons, which might translate into changes in the local net E/I ratio. Here, we assessed and compared their net effects on net E/I ratio in human cortex, through an integrated application of computational modeling, placebo-controlled pharmacological intervention, magnetoencephalographic recordings of cortical activity dynamics, and perceptual psychophysics. We found that catecholamines, but not acetylcholine, altered both the temporal structure of intrinsic activity fluctuations in visual and parietal cortex, and the volatility of perceptual inference based on ambiguous visual input. Both effects indicate that catecholamines increase the net E/I ratio in visual and parietal cortex.

neuroscience↗