Biology subjects

Davidsen, J.

Publications and source records attributed to Davidsen, J..

9 recordsLinked to original sources

bioRxiv2026

Synchronization properties in C. elegans: Relating behavioral circuits to structural and functional neuronal connectivity

A central question in neuroscience is how neural processing generates or encodes behavior. Caenorhabditis elegans is well suited to addressing this question, given its compact nervous system and near-complete structural connectome. Despite this, findings from previous studies remain inconclusive. While some have shown that the connectome can robustly encode specific behaviors such as locomotion, others report that functional connectivity can be reconfigured across behaviors. We aim to understand the relationship between structural connectivity, functional connectivity and biological behavior in silico by using an experimentally motivated computational model leveraging the structural connectome. Stimulation of specific neurons in the model induces oscillatory neural responses, enabling us to infer neuronal functional connectivity. Functional connectivity is found to be stronger among some neurons, allowing us to identify functional communities. We find that electrical synapses play a critical role in determining functional communities, and the resulting mesoscale functional architecture is predominantly gap junctionally assortative. Furthermore, comparison with behavioral circuits shows that locomotion circuits are largely segregated into distinct functional communities while other circuits are more distributed across multiple functional communities. We also observe that stimulation of neurons belonging to these distributed circuits elicits a more synchronized neuronal response compared to stimulation of neurons within the more segregated circuits. This is consistent with the presence of behavioral patterns that originate in one circuit and terminate in another (e.g., chemosensation leading to locomotion), such that stimulation of one circuit can activate the other and eventually result in a synchronized response. We also find a large repertoire of chimera-like synchronization patterns upon stimulation of certain behavioral circuits (chemosensation, mechanosensation) indicating high dynamical flexibility. Overall, our results demonstrate that while certain behaviors are governed by functionally segregated circuits, others emerge from the synchronization of multiple functional communities, which are, to begin with, influenced by the underlying structural connectivity. Author summaryAnimals constantly transform sensory inputs into actions, but it is still unclear how this mapping from neural activity to behavior is implemented in a real nervous system. Caenorhabditis elegans offers a unique testbed for this question because its entire wiring diagram is nearly completely mapped. Yet, previous works have reached mixed conclusions about how well this anatomical circuit diagram predicts actual patterns of activity and behavior. Here, we use a biologically inspired computational model of the C. elegans nervous system to bridge this gap between structure, function, and behavior. By virtually stimulating individual neurons and observing the resulting network-wide oscillations, we infer how strongly different pairs and groups of neurons interact in functional terms. We then use network analysis tools to identify groups of neurons that tend to co-activate, and relate these functional communities to known behavioral circuits for locomotion and sensory processing. We find that gap junctions play a key role in shaping functional communities, and that locomotion-related neurons are more functionally segregated than neurons involved in other behaviors, which are more functionally distributed. Our results suggest that some behaviors rely on specialized, functionally isolated circuits, whereas others emerge from the coordinated activity of multiple functional communities.

neuroscience↗

bioRxiv2025

Changing cognitive chimera states in human brain networks with age: Variations in cognitive integration and segregation

The aging process profoundly impacts the human brain, leading to notable changes in cognitive abilities. Although the brains structural and functional alterations with age are individually well documented, how differences in cognitive abilities emerge from variations in the underlying spatio-temporal patterns of regional brain activity is largely unknown. Patterns of increased synchronization between brain regions are taken as enhanced cognitive integration, while decreased synchronization is indicative of cognitive segregation. The ability to dynamically switch between different levels of integration and segregation across different cognitive systems is believed to be crucial for overall cognitive performance. Building on a recently proposed cognitively informed, synchronization-based framework, we study here age-related variations in dynamical flexibility between segregation and integration, as captured by changes in the variable patterns of partial synchronization or chimera states. Leveraging personalized brain network models based on large-scale, multisite datasets of cross-sectional healthy cohorts, we systematically show how regional brain stimulation produces distinct patterns of synchronization. We find that chimera states play a crucial role in regulating the balance between cognitive integration and segregation as the brain ages, providing new insights into the mechanisms underlying cognitive decline and preservation in aging. Whereas the emergent synchronization behavior of brain regions belonging to the same cognitive system often show the same aging trends, different cognitive systems can demonstrate distinct trends. This supports the idea that aging affects cognitive systems differently and that understanding this variability is essential for a more comprehensive view of neuro-cognitive aging. At the same time, dynamical flexibility increases in the oldest age groups across most cognitive systems. This may reflect compensatory mechanisms to counteract age-related cognitive declines and points towards a phenomenon of dedifferentiation. Yet, the multiplicity of behaviors highlights that whereas dedifferentiation emerges in certain cognitive systems, differentiation can also occur in others. This illustrates that these processes, though seemingly oppositional, can coexist and unfold in parallel across different cognitive systems. Author summaryThe healthy brain relies on a dynamic balance between two complementary processes--integration and segregation. Integration enables coordination between distributed brain regions, while segregation reflects specialization. Optimal cognitive function emerges from a highly flexible balance between these processes as implicated by greater cognitive performance. As we age, changes in cognitive abilities occur, especially at older age, while at the same time the brain reorganizes. The exact relationship between the two is largely unknown and our work aims to tackle this challenge. Using a data-informed computational model we examine age-related differences in dynamical flexibility between segregation and integration, as captured by changes in the variable patterns of partial synchronization called chimera states--patterns where some brain regions cooperate while others remain largely independent. Our research shows that chimera states are key to maintaining the brains balance between integration and segregation as we age. This balance shifts differently in brain systems responsible for distinct cognitive skills, which may provide new insights into the mechanisms underlying cognitive decline and preservation in aging. In particular, it supports the idea that aging affects brain systems differently and that understanding this variability is essential for a more comprehensive view of neuro-cognitive aging.

neuroscience↗

bioRxiv2025

Efficacy of functional connectome fingerprinting using tangent-space brain networks

Functional connectomes (FCs) are estimations of brain region interaction derived from brain activity, often obtained from functional Magnetic Resonance Imaging recordings. Quantifying the distance between FCs is important for understanding the relation between behaviour, disorders, disease, and changes in connectivity. Recently, tangent space projections, which account for the curvature of the mathematical space of FCs, have been proposed for calculating FC distances. We compare the efficacy of this approach relative to the traditional method in the context of subject identification using the Midnight Scan Club dataset, in order to study resting-state and task-based subject discriminability. The tangent space method is found to universally out-perform the traditional method. We also focus on the subject identification efficacy of subnetworks. Certain subnetworks are found to outperform others, a dichotomy which largely follows the control and processing categorization of resting state networks, and relates subnetwork flexibility with subject discriminability. Identification efficacy is also modulated by tasks, though certain subnetworks appear task independent. The uniquely long recordings of the dataset also allow for explorations of resource requirements for effective subject identification. The tangent space method is found to universally require less data, making it well suited when only short recordings are available. AUTHOR SUMMARYFunctional connectomes, which describe the similarity between the recorded activity of different brain regions, are ubiquitous for researcher trying to understand brain dynamics on short and long time scales in the presence and absence of neurophysiological diseases. This work applies a Tangent Space approach, a novel method to calculate the distance between functional connectomes, to a unique high-quality dataset (the Midnight Scan Club) in order to better understand the variability and uniqueness of connectomes across subjects, and how subject identification (also called fingerprinting) compares across tasks. We also show that not only does the tangent space method offer greater sensitivity to changes, it does so with significantly fewer resources, and in some scenarios reveals qualitatively different interpretations than the traditional method.

neuroscience↗

bioRxiv2024

Criticality and universality in neuronal cultures during 'up' and 'down' states

The brain can be seen as a self-organized dynamical system that optimizes information processing and storage capabilities. This is supported by studies across scales, from small neuronal assemblies to the whole brain, where neuronal activity exhibits features typically associated with phase transitions in statistical physics. Such a critical state is characterized by the emergence of scale-free statistics as captured, for example, by the sizes and durations of activity avalanches corresponding to a cascading process of information flow. Another phenomenon observed during sleep, under anesthesia, and in in vitro cultures, is that cortical and hippocampal neuronal networks alternate between "up" and "down" states characterized by very distinct firing rates. Previous theoretical work has been able to relate these two concepts and proposed that only up states are critical whereas down states are subcritical, also indicating that the brain spontaneously transitions between the two. Using high-speed high-resolution calcium imaging recordings of neuronal cultures, we test this hypothesis here by analyzing the neuronal avalanche statistics in populations of thousands of neurons during "up" and "down" states separately. We find that both "up" and "down" states can exhibit scale-free behavior when taking into account their intrinsic time scales. In particular, the statistical signature of "down" states is indistinguishable from those observed previously in cultures without "up" states. We show that such behavior can not be explained by network models of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression, even when realistic noise levels, spatial network embeddings, and heterogeneous populations are taken into account, which instead exhibits behavior consistent with previous theoretical models. Similar differences were also observed when taking into consideration finite-size scaling effects, suggesting that the intrinsic dynamics and self-organization mechanisms of these cultures might be more complex than previously thought. In particular, our findings point to the existence of different mechanisms of neuronal communication, with different time scales, acting during either highactivity or low-activity states, potentially requiring different plasticity mechanisms. Author summaryUp and down states, where populations of neurons transition between periods of high and low-frequency activity, are ubiquitous in the brain. They are present during development, sleep, and anesthesia, and have been associated with memory consolidation and the regulation of homeostatic processes. Using large-scale high-speed calcium imaging recordings of neuronal cultures, we show that self-similar behavior can appear during both up and down states, but with different characteristic timescales. Detailed simulations of neuronal cultures are only able to capture the statistics during up states, suggesting that a different mechanism might be governing the dynamics of the down states. The presence of scale-free statistics with switching time scales points to novel self-organization mechanisms in neuronal systems.

neuroscience↗

bioRxiv2023

Changes in functional connectivity preserve scale-freeneuronal and behavioral dynamics

Does the brain optimize itself for storage and transmission of information and if so, how? The critical brain hypothesis is based in statistical physics and posits that the brain self-tunes its dynamics to a critical point or regime to maximize the repertoire of neuronal responses. Yet, the robustness of this regime, especially with respect to changes in the functional connectivity, remains an unsolved fundamental challenge. Here, we show that both scale-free neuronal dynamics and self-similar features of behavioral dynamics persist following significant changes in functional connectivity. Specifically, we find that the psychedelic compound ibogaine that is associated with an altered state of consciousness fundamentally alters the functional connectivity in the retrosplenial cortex of mice. Yet, the scale-free statistics of movement and of neuronal avalanches among behaviorally-related neurons remain largely unaltered. This indicates that the propagation of information within biological neural networks is robust to changes in functional organization of sub-populations of neurons, opening up a new perspective on how the adaptive nature of functional networks may lead to optimality of information transmission in the brain.

neuroscience↗

bioRxiv2023

The hippocampal theta oscillation may be generated by chimera dynamics

The 8-12 Hz theta rhythm observed in hippocampal local field potentials of animals can be regarded as a "clock" that regulates the timing of spikes. While different interneuron sub-types synchronously phase lock to different phases for every theta cycle, the phase of pyramidal neurons spikes asynchronously vary in each theta cycle, depending on the animals position. On the other hand, pyramidal neurons tend to fire slightly faster than the theta oscillation in what is termed hippocampal phase precession. Chimera states are specific solutions to dynamical systems where synchrony and asynchrony coexist, similar to the hippocampal theta oscillation. Here, we test the hypothesis that the hippocampal theta oscillation emerges from chimera dynamics with computational modelling. We utilized multiple network topologies and sizes of Kuramoto oscillator networks that are known to collectively display chimera dynamics. We found that by changing the oscillators intrinsic frequency, the frequency ratio between the synchronized and unsynchronized oscillators can match the frequency ratio between the hippocampal theta oscillation ({approx}8 Hz) and phase precessing pyramidal neurons ({approx}9 Hz). The faster firing population of oscillators also displays theta-sequence-like behaviour and phase precession. Finally, we trained networks of spiking integrate-and-fire neurons to output a chimera state by using the Kuramoto-chimera system as a dynamical supervisor. We found that the firing times of subsets of individual neurons display phase precession. These results imply that the hippocampal theta oscillation may be a chimera state, further suggesting the importance of chimera states in neuroscience.

neuroscience↗

bioRxiv2023

Metabolic Interactive Nodular Network for Omics (MINNO): Refining and investigating metabolic networks based on empirical metabolomics data

Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms, and metabolic network modeling is commonly used to frame results in the context of a broader homeostatic system. However, network modeling of poorly characterized, non-model organisms remains challenging due to gene homology mismatches. To address this challenge, we developed Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that takes in empirical metabolomics data to refine metabolic networks for both model and unusual organisms. MINNO allows users to create and modify interactive metabolic pathway visualizations for thousands of organisms, in both individual and multi-species contexts. Herein, we demonstrate an important application of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme disease and relapsing fever. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for three Borrelia species. Using these empirically refined networks, we were able to metabolically differentiate these genetically similar species via their nucleotide and nicotinate metabolic pathways that cannot be predicted from genomic networks. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study non-model organisms. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/548964v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8cdee6org.highwire.dtl.DTLVardef@7de3ddorg.highwire.dtl.DTLVardef@fac57forg.highwire.dtl.DTLVardef@1bc2efe_HPS_FORMAT_FIGEXP M_FIG MINNO tool facilitates refining of metabolic networks, multi omics integration and investigation of cross-species interactions. C_FIG

bioinformatics↗

bioRxiv2020

Prenatal Stress Dysregulates Resting-State Functional Connectivity and Sensory Motifs

Prenatal stress (PS) can impact fetal brain structure and function and contribute to higher vulnerability to neurodevelopmental and neuropsychiatric disorders. To understand how PS alters evoked and spontaneous neocortical activity and intrinsic brain functional connectivity, mesoscale voltage imaging was performed in adult C57BL/6NJ mice that had been exposed to auditory stress on gestational days 12-16, the age at which neocortex is developing. PS mice had a four-fold higher basal corticosterone level and reduced amplitude of cortical sensory-evoked responses to visual, auditory, whisker, forelimb, and hindlimb stimuli. Relative to control animals, PS also led to a general reduction of resting-state functional connectivity, as well as reduced inter-modular connectivity, enhanced intra-modular connectivity, and altered frequency of auditory and forelimb spontaneous sensory motifs. These resting-state changes resulted in a cortical connectivity pattern featuring disjoint but tight modules and a decline in network efficiency. The findings demonstrate that cortical connectivity is sensitive to PS and exposed offspring may be at risk for adult stress-related neuropsychiatric disorders.

neuroscience↗

bioRxiv2019

A biochemical mechanism for time-encoding memory formation within individual synapses of Purkinje cells

Within the classical eye-blink conditioning, Purkinje cells within the cerebellum are known to suppress their tonic firing rates for a well defined time period in response to the conditional stimulus after training. The temporal profile of the drop in tonic firing rate, i.e., the onset and the duration, depend upon the time interval between the onsets of the training conditional and unconditional stimulus. Direct stimulation of parallel fibers and climbing fiber by electrodes was found to be sufficient to reproduce the same characteristic drop in the firing rate of the Purkinje cell. In addition, the specific metabotropic glutamate-based receptor type 7 (mGluR7), which resides on the Purkinje cell synapses, was found responsible for the initiation of the response, suggesting an intrinsic mechanism within the Purkinje cell for the temporal learning. In an attempt to look for a mechanism for time-encoding memory formation within individual Purkinje cells, we propose a biochemical mechanism based on recent experimental findings. The proposed model tries to answer key aspects of the \"Coding problem\" of Neuroscience by focussing on the Purkinje cells ability to encode time intervals through training. According to the proposed mechanism, the time memory is encoded within the dynamics of a set of proteins -- mGluR7, G-protein, G-protein coupled Inward Rectifier Potassium ion channel, Protein Kinase A and Protein Phosphatase 1 -- which self-organize themselves into a protein complex. The intrinsic dynamics of these protein complexes can differ and thus can encode different time durations. Based on their amount and their collective dynamics within individual synapses, the Purkinje cell is able to suppress its own tonic firing rate for a specific time interval. The time memory is encoded within the effective rate constants of the biochemical reactions and altering these rates constants means storing a different time memory. The proposed mechanism is verified by a simplified mathematical model and corresponding dynamical simulations of the involved biomolecules, yielding testable experimental predictions.\n\nAuthor summaryHebbian plasticity is a widely accepted form of learning that can encode memories in our brain. Spike-timing dependent plasticity resulting in Long-term Potentiation or Depression of synapses has become the default mechanistic explanation behind memory formation within a neuronal population. However, recent experiments of conditional eyeblink response in Purkinje cells have challenged this point of view by showing that these mechanisms alone cannot account for time memory formation in the Purkinje cell. To explain the underlying mechanism behind this novel synaptic plasticity, we introduce a biochemical mechanism based on protein interactions occurring within a single synapse. These protein interactions and the associated effective rate constants are sufficient to encode time delays by auto-induced inhibition on a single excitatory synapse, suggesting that synapses are capable of storing more information than previously thought.

neuroscience↗