Biology subjects

Varley, T. F.

Publications and source records attributed to Varley, T. F..

14 recordsLinked to original sources

bioRxiv2026

Living multicellular systems induce decodable spatial patterns in bacterial collectives

Living systems continuously modify their environments through chemical, mechanical, metabolic and bioelectrical activity. Whether a presence of a multicellular system can be encoded into the emergent spatial organization of another living collective in a distributed and decodable way is unknown. Here we show that motile Bacillus subtilis populations reorganize their spatial and ionic collective states in response to nearby Xenopus embryos and Xenobots. The bacteria in a liquid culture formed autonomous motility-dependent patterns that were redirected by living targets into attraction halos, which tracked target position at a distance. Extracellular levels of potassium amplified attraction, altered local potassium dynamics, and coupled target presence to global pattern complexity. Self-supervised machine learning further identified distributed bacterial spatial signatures predictive of Xenopus embryo vs. Xenobot presence at a distance from the target. Together, these findings suggest that bacterial collectives can encode information about the state of other biota in their environment, revealing a previously unrecognized form of inter-kingdom interaction between living morphogenetic systems.

systems biology↗

bioRxiv2026

Purinergic signaling disrupts emergent patterns of multicellular coordination in basal Xenobots.

The ability of self-organizing systems to display emergent, adaptive capabilities is a fundamental feature of biological life. Understanding the mechanisms by which cells co-ordinate at the micro-scale to produce macro-scale structures and behaviors is a fundamental problem in developmental biology. Moreover, it is an important goal of biomedicine to identify triggers that re-wire the physiological patterns of information flow and control signals. Here, we use a recently developed, synthetic biology platform known as basal Xenobots to explore how patterns of information flow and multi-cellular integration are regulated by chemical signaling pathways. Basal Xenobots are modified, organoid-like systems constructed from embryos, and have previously been shown to display complex patterns of information flows. In this study, we recorded calcium signals before and after exposing basal Xenobots to extra-cellular adenosine triphosphate (eATP), and used statistics from multivariate information theory to explore the differences in global patterns of information flow across the cells in each state. We found that eATP resulted in a dramatic reconfiguration of global information processing dynamics, characterized by a global decrease in multi-cellular co-ordination, reduced information transfer and integration, and a decrease in the global entropy rate of the basal Xenobots. These results provide evidence that purinergic signaling may play a key role in the regulation of multi-cellular self-organization, with implications for a variety of clinical disorders thought to involve aberrant purinergic signaling. These results also suggest the possibility that bioengineers may be able to "tune" the degree of self-organizing capacity in living systems via pharmacological intervention.

systems biology↗

bioRxiv2025

Evaluating oscillatory mechanisms underlying flexible neural communication in the human brain

How the brain orchestrates the flow of information between its multiple functional units flexibly, quickly, and accurately, remains a fundamental question in neuroscience. Multiple theories identify neural oscillations as a likely basis for this process. However, a lack of empirical validation of proposed theories, particularly at the whole-brain scale, has hampered consensus on oscillatory principles governing neural communication, limiting our understanding of a process central to perception and cognition and its integration into experiments and clinical applications. Here, we empirically validate previously proposed neural-oscillatory communication mechanisms in the human brain - specifically those involving power and interareal phase coherence - at the whole-brain scale. We do this by estimating the dependence of inferred communication on oscillatory measures that have been theorised to facilitate communication, in source-localised resting-state magnetoencephalography (MEG) recordings. We find that power and phase coherence in the alpha, beta, and high-gamma bands track communication better than others. Crucially, the relation between communication and oscillatory measures varied across regions, indicating spatial heterogeneity in routing mechanisms. Notably, power and coherence-based principles tracked communication patterns of unimodal regions better than those of transmodal regions. In sum, these findings suggest that the human brain implements regionally specific communication mechanisms with complex neural-oscillatory dependence. Significance statementFlexible and dynamic routing of information between neural elements is vital for human brain function. Despite its importance, the mechanisms underlying flexible communication in the brain are not fully understood. Several lines of evidence suggest that neural oscillations might mediate flexible communication, but whether such mechanisms operate at whole-brain scales remains uncertain. Here, we investigate the oscillatory underpinnings of communication by assessing how well observed communication patterns from magnetoencephalography data align with the predictions of established theories. Our findings suggest that the brain follows complex region-specific routing principles to realise flexible communication. Knowledge of these principles can develop our fundamental understanding of functions like perception and cognition, refine models of interareal interactions, and help develop more effective stimulation-based clinical interventions.

neuroscience↗

bioRxiv2025

Effect of acute elevated magnesium on bursting activity and information-processing dynamics in cortical cultures

Magnesium (Mg2+) plays a significant role in hippocampal memory and learning and is implicated in a variety of neurological disorders, such as migraine. Despite this crucial role Mg2+ has on brain health, its effect on the dynamics of networks of neurons is still not fully understood. This study investigates the impact of several doses of elevated extracellular Mg2+ on cortical organotypic cultures. Cultures were recorded on a 512-microelectrode array and analyzed using the burstiness index (BI), the rate of network-wide bursts relative to other neuronal activity. We also use a suite of information theoretic measures to further establish the role Mg2+ plays in the brain. Elevated Mg2+ is found to have a dose-dependent increase on BI caused by a loss of network activity not contained within a burst or high firing rate activity. Information dynamics further show that the network experiences a loss of entropy and an increase in time-reversibility, connectivity, and transfer of redundant information. These factors indicate that Mg2+ causes a loss of complex activity and an increase in highly integrated, constant dynamics. This lays the groundwork for a deeper examination of the role Mg2+ plays in learning, memory and treating neurological disorders. AUTHOR SUMMARYMagnesium is crucial for neurological health. In pyramidal neurons, magnesium blocks NMDA receptors, thereby reducing synchronous neural activity in the brain. This has led to the canonical view of magnesium as a "burst quieter". Here, we take a closer look at magnesiums role in cortical slice cultures by examining network burstiness with normal and chronically elevated levels of magnesium. We show that magnesium can effectively increase burstiness, not reduce it. Magnesium is further shown to cause significant changes in how neurons process information. These findings may have impact how magnesium is used as a treatment for neurological disorders.

neuroscience↗

bioRxiv2024

Event-marked Windowed Communication: Inferring activity propagation from neural time series

Tracking signal propagation in nervous systems is crucial to our understanding of brain function and information processing. Current methods for inferring neural communication track patterns of sustained co-activation over time, making them unsuitable to detect discrete instances of signal transmission. Here, we propose Event-marked Windowed Communication (EWC), a new analytical framework to infer functional interactions arising from discrete signalling events between neural elements, in otherwise continuous time series data. In contrast to conventional measures of functional connectivity, our method utilises an event-based subsampling of neural time series, which allows it to capture the statistical analogue of activity propagation. We test EWC on simulations of neural dynamics and show that it is capable of retrieving ground truth motifs of directional signalling, over a range of model configurations. Critically, we demonstrate that EWCs subsampling approach affords profound reductions in computation times, compared to established network inference methods such as transfer entropy. Lastly, we showcase the utility of EWC to infer whole-brain functional networks from MEG recordings. Networks computed using EWC and transfer entropy were highly correlated (median r=0.821 across subjects), but EWC inference was approximately 6.5 times faster per epoch. In summary, our work presents a new method to infer signalling from time series of neural activity at low computational costs. Our framework is flexible and can be applied to activity time series captured by diverse functional neuroimaging modalities, opening up new avenues for the study of neural communication.

neuroscience↗

bioRxiv2024

Leveraging multivariate information for community detection in functional brain networks

Embedded in neuroscience is the concept that brain functioning is underpinned by specialized systems whose integration enables cognition and behavior. Modeling the brain as a network of interconnected brain regions, allowed us to capitalize on network science tools and identify these segregated systems (modules, or communities) by optimizing the weights of pairwise connections within them. However, just knowing how strongly two brain areas are connected does not paint the whole picture. Brain dynamics is also engendered by interactions involving more areas at the same time, namely, higher-order interactions. In this paper, we propose a community detection algorithm that accounts for higher-order interactions and finds modules of brain regions whose brain activity is maximally redundant. Compared to modules identified with methods based on bivariate interactions, our redundancy-dominated modules are more symmetrical between the hemispheres, they overlap with canonical systems at the level of the sensory cortex, but describe a new organization of the transmodal cortex. By detecting redundant modules across spatial scales, we identified a sweet spot of maximum balance between segregation and integration of information as that scale where redundancy within modules and synergy between modules peaked. Moreover, we defined a local index that distinguishes brain regions in segregators and integrators based on how much they participate in the redundancy of their modules versus the redundancy of the whole system. Finally, we applied the algorithm to a lifespan dataset and tracked how redundant subsystems change across time. The results of this paper serve as educated guesses on how the brain organizes itself into modules accounting for higher-order interactions of its fundamental units, and pave the way for further investigation that could link them to cognition, behavior, and disease.

neuroscience↗

bioRxiv2024

Time-varying synergy/redundancy dominance in the human cerebral cortex

Recent work has emphasized the ubiquity of higher-order interactions in brain function. These interactions can be characterized as being either redundancy or synergy-dominated by the heuristic O-information [1]. Though the O-information can be decomposed into local values to measure the synergy-redundancy dominance at each point in a time series [2] no such analysis of fMRI dynamics has been carried out. Here we analyze the moment-to-moment synergy and redundancy dominance of the fMRI BOLD signal during rest for 95 unrelated subjects. We present results from several interaction sizes. The whole brain is rarely synergy-dominated, with some subjects never experiencing a whole-brain synergistic moment. Randomly sampled subsets of many sizes reveal that subsets that are the most redundancy dominated on average exhibit both the most synergistic and most redundant time points. Exhaustive calculation of the optimally synergistic and optimally redundant triads further emphasizes this finding, with recurrent nodes frequently belonging to a single coherent functional system. We find that when a triad is momentarily synergistic, it is often split between two instantaneously co-fluctuating communities, but is collectively co-fluctuating when it is momentarily redundant. After optimizing for synergy and redundancy in subsets of size five to seventy-five, we show that this effect is consistent across interaction sizes. Additionally, we find notable temporal structure in all optimized redundant and synergistic subsets: higher order redundant and synergistic interactions change smoothly in time and recur more than expected by chance.

neuroscience↗

bioRxiv2024

Revealing non-trivial information structures in aneural biological tissues via functional connectivity

A central challenge in the progression of a variety of open questions in biology, such as morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Information-theoretic approaches provide a quantitative framework for extracting patterns from data, but so far have been predominantly applied to neuronal systems at the tissue-level. Here, we demonstrate how time series of Ca2+ dynamics can be used to identify the structure and information dynamics of other biological tissues. To this end, we expressed the calcium reporter GCaMP6s in an organoid system of explanted amphibian epidermis derived from the African clawed frog Xenopus laevis, and imaged calcium activity pre- and post- a puncture injury, for six replicate organoids. We constructed functional connectivity networks by computing mutual information between cells from time series derived using medical imaging techniques to track intracellular Ca2+. We analyzed network properties including degree distribution, spatial embedding, and modular structure. We find organoid networks exhibit more connectivity than null models, with high degree hubs and mesoscale community structure with spatial clustering. Utilizing functional connectivity networks, we show the tissue retains non-random features after injury, displays long range correlations and structure, and non-trivial clustering that is not necessarily spatially dependent. Our results suggest increased integration after injury, possible cellular coordination in response to injury, and some type of generative structure of the anatomy. While we study Ca2+ in Xenopus epidermal cells, our computational approach and analyses highlight how methods developed to analyze functional connectivity in neuronal tissues can be generalized to any tissue and fluorescent signal type. Our framework therefore provides a bridge between neuroscience and more basal modes of information processing. Author summaryA central challenge in understanding several diverse processes in biology, including morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Significant progress in understanding information integration has occurred in neuroscience via the use of observable live calcium reporters throughout neural tissues. However, these same techniques have seen limited use in the non-neural tissues of multicellular organisms despite similarities in tissue communication. Here we utilize methods designed for neural tissues and modify them to work on any tissue type, demonstrating how non-neural tissues also contain non-random and potentially meaningful structures to be gleaned from information theoretic approaches. In the case of epidermal tissue derived from developing amphibians, we find non-trivial informational structure over greater spatial and temporal scales than those found in neural tissue. This hints at how more exploration into information structures within these tissue types could provide a deeper understanding into information processing within living systems beyond the nervous system.

systems biology↗

bioRxiv2024

Emergence of a synergistic scaffold in the brains of human infants

The human brain is a complex organ comprising billions of interconnected neurons which enables interaction with both physical and social environments. Neural dynamics of the whole brain go far beyond just the sum of its individual elements; a property known as "synergy". Previously it has been shown that synergy is crucial for many complex brain functions and cognition, however, it remains unknown how and when the large number of discrete neurons evolve into the unified system able to support synergistic interactions. Here we analysed high-density electroencephalography data from late fetal to early postnatal period. We found that the human brain transitions from redundancy-dominated to synergy-dominated system around birth. Frontal regions lead the emergence of a synergistic scaffold comprised of overlapping subsystems, while the integration of sensory areas developed gradually, from occipital to central regions. Strikingly, early developmental trajectories of brain synergy were modulated by environmental enrichment associated with enhanced mother-infant interactions, and the level of synergy near term equivalent age was associated with later neurocognitive development.

neuroscience↗

bioRxiv2023

Co-Evolving Dynamics and Topology in a Coupled Oscillator Model of Resting Brain Function

Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel structural edge time series it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function.

neuroscience↗

bioRxiv2021

Self-organization of in vitro neuronal assemblies drives to complex network topology

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of electrophysiological signals recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the neuronal network topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular community topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of communities. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

neuroscience↗

bioRxiv2021

Fluctuations in Neural Complexity During Wakefulness Relate To Conscious Level and Cognition

There has been considerable recent progress in measuring conscious level using neural complexity measures. For instance, such measures can reliably distinguish healthy awake from asleep subjects and vegetative state patients. However, this line of research has never explored the dynamics of conscious level during normal wakefulness. Being able to capture meaningful differences in conscious level during wakefulness may provide a vital new insight into the nature of consciousness, by demonstrating what biological, behavioural and cognitive factors relate to such differences. Here we take advantage of a large MEG and fMRI dataset of healthy adults, to examine within-subject conscious level fluctuations during resting state and tasks, by using a range of complexity measures. We first establish the validity of this approach in both neuroimaging domains by relating neural complexity measures to pre-existing techniques for capturing transitions of consciousness from full wakefulness into drowsiness and the earliest stages of sleep, finding decreased complexity as participants become increasingly drowsy. We further demonstrate that neural complexity measures in both MEG and fMRI change both within and between tasks, and relate to performance on an executive task, with higher complexity associated with better performance and faster reaction times. This approach provides a powerful new route to further explore the cognitive and neural underpinnings of consciousness.

neuroscience↗

bioRxiv2020

Topological Analysis of Differential Effects of Ketamine and Propofol Anesthesia on Brain Dynamics

Research has found that the vividness of conscious experience is related to brain dynamics. Despite both being anesthetics, propofol and ketamine produce different subjective states: we explore the different effects of these two anaesthetics on the structure of dynamic attractors reconstructed from electrophysiological activity recorded from cerebral cortex of two macaques. We used two methods: the first embeds the recordings in a continuous high-dimensional manifold on which we use topological data analysis to infer the presence of higher-order dynamics. The second reconstruction, an ordinal partition network embedding, allows us to create a discrete state-transition network, which is amenable to information-theoretic analysis and contains rich information about state-transition dynamics. We find that the awake condition generally had the "richest" structure, visiting the most states, the presence of pronounced higher-order structures, and the least deterministic dynamics. In contrast, the propofol condition had the most dissimilar dynamics, transitioning to a more impoverished, constrained, low-structure regime. The ketamine condition, interestingly, seemed to combine aspects of both: while it was generally less complex than the awake condition, it remained well above propofol in almost all measures. These results provide provides deeper and more comprehensive insights than what is typically gained by using point-measures of complexity.

neuroscience↗

bioRxiv2020

Differential Effects of Propofol and Ketamine on Critical Brain Dynamics

Whether the brain operates at a critical tipping" point is a long standing scientific question, with evidence from both cellular and systems-scale studies suggesting that the brain does sit in, or near, a critical regime. Neuroimaging studies of humans in altered states of consciousness have prompted the suggestion that maintenance of critical dynamics is necessary for the emergence of consciousness and complex cognition, and that reduced or disorganized consciousness may be associated with deviations from criticality. Unfortunately, many of the cellular-level studies reporting signs of criticality were performed in non-conscious systems (in vitro neuronal cultures) or unconscious animals (e.g. anaesthetized rats). Here we attempted to address this knowledge gap by exploring critical brain dynamics in invasive ECoG recordings from multiple sessions with a single macaque as the animal transitioned from consciousness to unconsciousness under different anaesthetics (ketamine and propofol). We use a previously-validated test of criticality: avalanche dynamics to assess the differences in brain dynamics between normal consciousness and both drug-states. Propofol and ketamine were selected due to their differential effects on consciousness (ketamine, but not propofol, is known to induce an unusual state known as dissociative anaesthesia"). Our analyses indicate that propofol dramatically restricted the size and duration of avalanches, while ketamine allowed for more awake-like dynamics to persist. In addition, propofol, but not ketamine, triggered a large reduction in the complexity of brain dynamics. All states, however, showed some signs of persistent criticality when testing for exponent relations and universal shape-collapse. Further, maintenance of critical brain dynamics may be important for regulation and control of conscious awareness. Author summaryHere we explore how different anaesthetic drugs change the nature of brain dynamics, using neural activity recorded from sub-dural electrophysiological arrays implanted in a macaque brain. Previous research has suggested that loss of consciousness under anaesthesia is associated with a movement away from critical brain dynamics, towards a less flexible regime. When comparing ketamine and propofol, two anaesthetics with largely different effects on consciousness, we find that propofol, but not ketamine, produces a dramatic reduction in the complexity of brain activity and restricts the range of scales where critical dynamics are plausible. These results suggest that maintenance of critical dynamics may be important for regulation and control of conscious awareness.

neuroscience↗