Biology subjects

Jajcay, N.

Publications and source records attributed to Jajcay, N..

6 recordsLinked to original sources

bioRxiv2026

Uncovering the spatiotemporal structure of neural avalanches through optimal transport and dynamic time warping

Neural avalanches--or threshold-defined bursts of coordinated activity--are traditionally characterised by scale-free statistics. However the study of their detailed spatiotemporal structure and their recurrence within spontaneous activity has been hindered by the variability of avalanches in duration and spatial extent. To tackle this challenge, we propose the use of flexible alignment: we employ a distance metric combining unbalanced optimal transport with subsequence dynamic time warping, enabling comparison across events of different lengths and spatial configurations. Applied to 64-channel EEG from 63 participants in the PsiConnect psilocybin study, hierarchical clustering revealed 12 recurring propagation patterns. These cluster templates were then traced in the original continuous recordings, identifying sequences where the same pattern recurred consecutively and immediately at least twice. Sequences with alternating polarity were classified as oscillating; those with consistent polarity as stable. Oscillating sequences predominantly corresponded to clusters exhibiting visually confirmed spatial propagation, while stable sequences corresponded to spatially fixed patterns. Under psilocybin, oscillating sequences were reduced relative to stable sequences, shifting the polarity balance toward stable; this overall shift was confirmed by a subject-level permutation test, while the apparent task- and training-specific effects did not survive it. This effect was also mostly driven by a specific cluster, suggesting that there is concrete neural dynamics that are temporally affected by the consumption of psilocybin. The developed methodology has been implemented in a publicly available stppy Python package. Author SummaryWhen large groups of neurons activate together in brief bursts--called neural avalanches--they create patterns that sweep across the brains surface. These patterns are usually studied only as statistics (how big, how frequent), giving little attention to what they actually look like. We developed a method to compare the shapes of individual avalanches, group similar ones together, and then use the resulting templates as detectors to track when each pattern reappears in continuous brain recordings. Applied to high-density EEG data from 63 human subjects, we discovered 12 propagation patterns, and analysed whether they oscillate (i.e., repeat with alternating polarity) or remain stable, maintaining a fixed spatial arrangement. We found that during psilocybin intoxication, oscillating patterns were suppressed relative to stable patterns, making stable patterns relatively more prevalent in the detected sequences. This overall shift was statistically reliable across participants, although the apparent differences between individual tasks were not. Our approach bridges three previously separate research areas--avalanche dynamics, EEG microstates, and travelling waves--suggesting they describe the same underlying brain activity from different angles: some of our clusters resemble canonical microstate topographies, while others reveal the travelling wave dynamics that microstate analysis, by design, cannot sufficiently capture.

neuroscience↗

bioRxiv2026

EEG microstate dynamics during psilocybin intoxication relate to acute experience and persisting psychological changes

Psilocybin and other serotonergic psychedelics show therapeutic promise for psychiatric disorders, yet objective neural correlates linking the acute psychedelic state to persisting psychological outcomes remain limited. Electroencephalography (EEG) microstate analysis characterizes the rapid spatiotemporal organization of large-scale brain activity, offering a millisecond-resolution window into neural dynamics. Here, we examined resting-state EEG microstates in 15 healthy volunteers who participated in a double-blind, randomized, placebo-controlled crossover study of psilocybin, using both data-driven (three-microstate) and canonical (four-microstate) analysis solutions. EEG was recorded at five time points spanning pre-drug baseline, peak intoxication, and recovery. Psilocybin significantly increased the number of global field power (GFP) peaks and reduced microstate lifespan while increasing frequency of occurrence during peak intoxication (50-100 min post-administration), consistent with accelerated transitions between brain states. Notably, microstate coverage was largely preserved, with only a transient difference at peak intoxication in the 2-20 Hz band-width, suggesting that access to the repertoire of canonical brain states is broadly maintained despite altered temporal dynamics. Critically, individual differences in microstate dynamics during peak intoxication correlated with both acute subjective experience intensity and self-reported psychological changes measured 28 days post-administration, providing exploratory evidence for a link between acute neural dynamics and longer-term experiential outcomes in healthy volunteers. These findings suggest that psilocybin is associated with altered temporal organization of large-scale brain dynamics with largely preserved microstate coverage, and identify EEG microstates as candidate neural markers for psychedelic-induced alterations in consciousness with potential relevance to therapeutic research.

neuroscience↗

bioRxiv2026

Replication Challenges in Linking Personality to Resting-State Functional Connectomics

An increasing number of studies are currently focusing on personality neuroscience, a term denoting the research aimed at neuroimaging correlates of inter-individual temperament and character variability. Among other methods, a graph theoretical analysis of the functional connectivity in resting-state functional magnetic resonance imaging data was applied in a study by Gao et al. (2013), reporting novel functional connectivity correlates of personality traits. The current paper presents a conceptual replication of the results of this study and discusses the related challenges, including an extension of the original statistical methods in order to illustrate the effect of the multiple comparison problem. Five personality dimensions were obtained using the revised Big Five Personality Inventory, including scores of Extraversion and Neuroticism covered in the original paper. Using a larger sample (84 subjects) with adequate statistical power (ranging from 0.75 to 0.95 across analyses), we failed to replicate any of the nine specific neuroimaging correlates of personality presented by Gao et al. While acknowledging differences in the experimental procedures, we discuss that the lack of replication might be caused by the relatively liberal control of false positives in the original study. Indeed, the original testing scheme leads to an expected count of about 10 false positive observations among all tests; applying this scheme to our data we observed a similar number of positive tests, albeit for different relations. No significant correlations were found in our data when standard family-wise error control was applied. These results illustrate the importance of combining exploration with independent validation, use of large datasets, as well as appropriate control of multiple comparison problem in order to prevent false alarms in research into neural substrates of personality differences. Importantly, our findings do not disprove the existence of a link between personality and the brains intrinsic functional architecture; but rather suggest that such a link might be even more subtle and elusive than previously reported.

neuroscience↗

bioRxiv2023

Towards a dynamical understanding of microstate analysis of M/EEG data

One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three "classically" used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.

neuroscience↗

bioRxiv2021

Cross-frequency slow oscillation-spindle coupling in a biophysically realistic thalamocortical neural mass model

Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often timelock and are implicated in the memory formation. Here, we analyze a neural mass model of the thalamocortical loop of which the cortical node can generate slow oscillations (approx. 1 Hz) while its thalamic component can generate fast sleep spindles of{sigma} -band activity (12-15 Hz). We study the dynamics for different coupling strengths between the thalamic and cortical nodes, for different conductance values of the thalamic nodes potassium leak and anomalous rectifying currents, and for different parameter regimes of the cortical node. The latter are: (1) a low activity (DOWN) state with noise-induced, transient excursions into a high activity (UP) state, (2) an adaptation induced slow oscillation limit cycle with alternating UP and DOWN states, and (3) a high activity (UP) state with noise-induced, transient excursions into the low activity (DOWN) state. During UP states, thalamic spindling is abolished or reduced. During DOWN states, the thalamic node generates sleep spindles, which in turn can cause DOWN to UP transitions in the cortical node. Consequently, this leads to spindle-induced UP state transitions in parameter regime (1), thalamic spindles induced in some but not all DOWN states in regime (2), and thalamic spindles following UP to DOWN transitions in regime (3). The spindle-induced{sigma} -band activity in the cortical node, however, is typically strongest during the UP state, which follows a DOWN state "window of opportunity" for spindling. When the cortical node is parametrized in regime (3), the model well explains the interactions between slow oscillations and sleep spindles observed experimentally during Non-Rapid Eye Movement sleep. The model is computationally efficient and can be integrated into large-scale modeling frameworks to study spatial aspects like sleep wave propagation.

neuroscience↗

bioRxiv2021

neurolib: a simulation framework for whole-brain neural mass modeling

neurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e. the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is possible using a parameter exploration module, which allows one to characterize the models behavior given a set of changing parameters. An optimization module can fit a model to multimodal empirical data using an evolutionary algorithm. neurolib is designed to be extendable such that custom neural mass models can be implemented easily, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure-function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

neuroscience↗