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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Inferring macroscopic intrinsic neural timescales using optimal control theory

The temporal evolution of brain activity relies on complex interactions within and between brain regions that are mediated by neurobiology and connectivity. To understand these interactions, many large-scale efforts have measured structural connectivity, neural activity, gene expression, and cognition across multiple modalities and species. However, data-driven discovery of large-scale activity models remains difficult owing to the lack of flexible quantitative frameworks for estimating the interplay between brain structure and function while preserving biophysical realism. Here, we provide such a framework by integrating network control theory (NCT) with automatic differentiation to estimate model parameters with greater biophysical realism from data. Specifically, we estimate the structural form of regional self-inhibition--a quantity that is experimentally difficult to measure--from MRI data. Next, we demonstrate that the resulting model-based self-inhibition parameters correlate significantly with regions intrinsic neural timescales (INTs), neurobiological measures of gene expression and cell-type densities, as well as behavioral measures of cognition. We demonstrate consistent results across multiple datasets and species. Finally, we demonstrate that our self-inhibition parameters enable the efficient control of brain dynamics from fewer brain regions. Taken together, our results provide a simple and flexible quantitative framework that more accurately captures the interplay between brain structure, function, and dynamics with greater biophysical realism.

neuroscience↗

bioRxiv2025

Systems biology analysis of vasodynamics in mouse cerebral arterioles during resting state and functional hyperemia

Cerebral hemodynamics is tightly regulated by arteriolar vasodynamics. In this study, a systems biology approach was employed to investigate how the interplay between passive, myogenic, neurogenic, and astrocytic responses shapes arteriolar vasodynamics in small rodents. A model of neurovascular coupling is proposed in which neurons inhibit and dampen the myogenic response to promote vasodilation during activation, and facilitate the myogenic response to promote rapid vasoconstriction immediately post-activation. In this model, inhibition of the myogenic response is mediated by the hyperpolarization of smooth muscle and endothelial cells. Dampening and facilitation of the response are mediated by neuronal production of nitric oxide and release of neuropeptide Y, respectively. We also introduce a model for gliovascular coupling, in which astrocytes periodically inhibit the myogenic response upon detecting an increase in myogenic activity through interactions between their endfeet and arterioles. Our study revealed that in the resting state, the interplay between the delayed myogenic response and passive distension, acting as negative and positive feedbacks respectively, generates undamped oscillations in vessel diameter, known as vasomotion. In the active state, these oscillations are disrupted by the neurogenic and astrocytic responses. The biophysical model of arteriolar vasodynamics presented in this study lays the foundation for quantitative analysis of cerebral hemodynamics for cerebrovascular health diagnostics and hemodynamic neuroimaging. Author summaryCerebral hemodynamic imaging is widely used to investigate brain function in-vivo. These signals are primarily shaped by arteriolar vasodynamics, which result from a combination of physiological processes mediated by multiple interacting cell types. A biophysical model of this dynamics offers a valuable computational framework for achieving more accurate and quantitative interpretation of hemodynamic signals. In this study, I applied a computational biology approach to incorporate several well-established cellular signaling pathways into a unified model, which was used to investigate system-level arteriolar behavior and identify missing or less understood mechanisms involved in cerebral blood flow regulation. Our results show that arteriolar vasodynamics is not solely driven by neurogenic responses; astrocytic response and hemo-vascular interactions also play important roles in shaping the observed dynamics. The model also provided a means to explore how in-silico analysis of hemodynamic signals can reveal potential cellular-level impairments that manifest as system-level changes in cerebral hemodynamics. Incorporating our proposed biophysical model into cerebral hemodynamic analysis can improve the fidelity of hemodynamic imaging--enabling more accurate inference of regional neuronal and astrocytic activity from hemodynamic signals, and enhancing our ability to diagnose cerebrovascular pathologies.

neuroscience↗

bioRxiv2025

From Structure to Immunogenicity: Decoding Correlated Dynamics atthe Peptide MHC interface to Understand TCR Recognition

The interaction between a class I peptide-major histocompatibility complex (pMHC) and a T cell receptor (TCR) plays a central role in the elicitation of CD8+ T cell immune responses. As a result, considerable effort has been invested in understanding the structural, dynamic, and biophysical parameters that govern this recognition event, including designing altered peptide ligands (APLs) which seek to modulate the downstream signaling outcomes. However, dynamic links between modified peptide positions and distant residues have until yet been ill resolved. Using an integrative approach combining crystallographic ensemble and single models with atomistic molecular dynamics simulations and correlational analysis, we have established an approach that allows us to identify coupled dynamics between spatially distant residues at the pMHC interface. Furthermore, we constructed a network encoding the inter-residue couplings observed throughout the simulations. This computational workflow corroborates well with experimental data and leads to novel insights regarding the differential immunogenicity of the closely related peptides analyzed in this study. Ultimately, we present an intuitive and comprehensive strategy for decoding the linked dynamics at the pMHC interface allowing for mechanistic insights into the biophysical bases governing immunogenicity. One Sentence SummaryThe dynamics at the pMHC interface can be encoded as a biophysically relevant network to yield molecular insights into immunogenicity

immunology↗

bioRxiv2025

Intrinsic plasticity underlies malleability of network heterogeneity

Diversity exists throughout biology, playing an important role in maintaining robustness and stability. The same is true of the brain, as has become increasingly apparent in recent years with the accumulation of datasets of unparalleled resolution. These datasets show widespread neural heterogeneity, spanning cells, circuits and system dynamics, marking it as an unavoidable component of the brains composition. Recent experiments found declines in heterogeneity amongst neurons may accompany pathological states. While heterogeneity has been linked to stability, robustness and increased computational potential, the loss of biophysical diversity was found conducive to the onset of seizure-like activity, suggesting an important functional role. Despite this, how changes in heterogeneity arise remains unknown. Oftentimes considered a static metaparameter resulting from solely genetic disposition, heterogeneity is, in fact, a highly dynamic property of biological networks arising from various sources. Here, we consider this through the lens of intrinsic plasticity, the activity-dependent modulation of neuron biophysical properties, which we propose allows the degree of biophysical diversity to fluctuate in time. Using a network of Poisson neurons endowed with intrinsic plasticity, we combine analytical and numerical approaches to measure the effect of input statistics on the excitability of individual cells, and how this translates into changes in network heterogeneity at the population scale. Our results indicate that, through intrinsic plasticity, diversity in synaptic inputs promotes heterogeneity in cell-to-cell excitability due to changes in the statistics of presynaptic firing rates, and network topology. In contrast, whenever the statistics of synaptic input between cells were too similar, intrinsic plasticity promoted the decline in heterogeneity. Further, we show that changes in heterogeneity can coexist with degeneracy in the firing rate between neurons. Taken together, understanding how input statistics affect neuronal network heterogeneity may provide key insights into brain function, resilience and the manipulability of neural diversity through intrinsic plasticity.

neuroscience↗

bioRxiv2025

Regulatory architectures optimized for rapid evolution of gene expression

Cis-regulatory elements (CREs), such as enhancers and promoters, control gene expression by binding regulatory proteins. In contrast to their bacterial counterparts, eukaryotic CREs typically bind transcription factors with short recognition motifs across multiple functional yet often weak binding sites. The evolutionary origin of this genetic architecture remains unclear. Here we study adaptive evolution of entire CREs under selection for controllable gene expression. In a biophysical toy model that recapitulates the essential non-linearities of eukaryotic regulation, a regulatory phenotype requires a gene to be active when its CRE binds cognate TFs, yet inactive in the presence of noncognate TFs that can cause deleterious crosstalk. We explore CRE evolutionary outcomes utilizing "optimize-to-adapt" approach suggested by the theory presented in a companion paper. In this approach, CRE evolution is simulated explicitly at the sequence level, while the parameters of the biophysical model itself - i.e., the properties of the replicated genotype-phenotype map - are numerically optimized for evolvability of CREs. In the optimal regime, selection navigates the tradeoff between slowly evolving strong and long binding sites (which guarantee low crosstalk), and rapidly evolving multiple short and weak binding sites (which necessitate diffuse selection against too much noncognate binding across the entire CRE). When we further explore various scenarios for cooperative regulation, we find that the optimal regime predicts a diversity of strong and weak, yet always short, binding sites and favors "synergistic activation" of transcription, as reported empirically for eukaryotes. These results showcase how information theory can link evolutionary dynamics with biophysical constraints to rationalize - and possibly even predict - optimal regulatory architectures.

evolutionary biology↗

bioRxiv2025

Single-Cell Microwave Cytometry for Drug Resistance Detection in Cancer

Monitoring biophysical changes in single cells induced by drugs is crucial for advancing cancer therapies, especially for highly heterogeneous tumours. Emerging methods for single-cell drug sensitivity testing have largely relied on phenomenological analyses. Here, we present a novel electronic cytometry platform integrating microwave resonators with impedance cytometry, enabling simultaneous, label-free measurements of cell volume and dielectric permittivity at microwave frequencies. This approach uniquely captures intracellular differential responses indicative of drug-induced biophysical states, thus providing deeper insights beyond traditional phenomenological analyses. We first validated the platform using varying salt concentrations. We then demonstrated that the sensor platform could differentiate between drug resistant versus sensitive phenotypes in multiple isogenic cancer cell lines treated with cytostatic, cytotoxic and mixed-effect drugs. Notably, we showed that the technique performs successfully in patient-derived tumor organoids, a model system highlighting its immediate clinical relevance. Our findings underscore the potential of electronic measurements at the single cell level to provide informative and actionable biophysical signals relating to cellular drug response. The implementation of this platform could significantly advance cancer treatment by identifying the resistant cell populations in heterogenous tumours and optimizing selection of drugs for each patient, paving the way towards precision medicine.

bioengineering↗

bioRxiv2025

Frequency-dependent communication of information innetworks of non-oscillatory neurons in response to oscillatory inputs

Understanding how neuronal networks process oscillatory inputs is key for deciphering the brains information processing dynamics. Neuronal filters describe the frequency-dependent relationship of neuronal outputs (e.g., membrane potential amplitude, firing rate) and their inputs for the level of neuronal organization (e.g., cellular, network) considered. Band-pass filters are associated to the notion of resonance and reflect the systems ability to respond maximally to inputs at a nonzero (resonant) frequency or a limited (resonant) frequency band. The complementary notion of phasonance refers to the ability of a system to exhibit a zero-phase response for a nonzero (phasonant) input frequency. The biophysical and dynamic mechanisms that shape neuronal filters and give raise to preferred frequency responses to oscillatory inputs are poorly understood beyond single cells. Moreover, the mechanisms that control the frequency-dependent communication of information across cells in a network remain unclear. Here, we use mathematical modeling, analytical calculations, computational simulations and dynamical systems tools to investigate how the complex and nonlinear interaction of the systemss biophysical properties and interacting time scales shape neuronal filters in minimal network models receiving oscillatory inputs with frequencies (f) within some range. The minimal networks consist of one directly stimulated cell (cell 1) connected to another (not directly stimulated) cell (cell 2) via graded chemical synapses. Individual cells are either passive or resonators and chemical synapses are either excitatory or inhibitory. The network outputs consist of the voltage peak envelopes and the impedance amplitude and phase profiles (as a function of f) for the two cells. We introduce the frequency-dependent amplitude K(f) and phase {Delta}{Phi}(f) communication coefficients, defined as the ratio of the amplitude responses of the indirectly and directly stimulated cells and the phase difference between these two cells, respectively. Extending previous work, we also introduce the K-curve, parametrized by f, in the phase-space diagram for the voltage variables of the two participating cells. This curve joins the peak voltage values of the two cells in response to the oscillatory inputs and is a geometric representation of the communication coefficient. It allows to interpret the results and explain the dependence of the properties of the communication coefficient in terms of the biophysical and dynamic properties of the participating cells and synaptic connectivity when analytical calculations are not possible. We describe the conditions under which one or the two cells in the network exhibit resonance and phasonance and the conditions under which the network exhibits K-resonance and {Delta}{Phi}-phasonance and more complex network responses depending as the complexity of the participating cells increases. For linear networks (linear nodes and linear connectivity), K is proportional to the impedance of the indirectly activated cell 2 and {Delta}{Phi} is equal to the phase of the indirectly stimulated cell 2, independent of the directly stimulated cell 1 in both cases. We show that the presence of nonlinear connectivity in the network creates (nonlinear) interactions between the two cells that give rise to K-resonance, {Delta}{Phi}-phasonance and more complex responses that are absent in the corresponding linear networks. The results and methods developed in this paper have implications for the processing of information in more complex networks.

neuroscience↗

bioRxiv2025

Next-Generation Neural Mass Models Reproduce Features of Speech Processing

Segregation of speech into syllables is a key step in neural speech processing. It relies on the alignment of neural activity with the rhythmic structure of speech. Two competing hypotheses explain this neural speech tracking, phase-resetting and evoked responses. While phenomenological modelling of these hypotheses has been successful, we still lack understanding of the underlying cortical circuits. To investigate these mechanisms, we evaluate whether a biophysical next-generation neural mass model can reproduce several features of neural speech tracking, using phenomenological models of the competing hypotheses as algorithmic baselines. We investigate the models dynamics with four tests: recreating in-silico an EEG experiment that identified a correlation between tracking strength and phoneme sharpness, computing the Phase Concentration Metric, testing the effect of varying syllabic rates, and evaluating the Inter Event Phase Coherence across phoneme onsets. While all of the models that we study reproduce the sharpness-tuned rhythmic speech tracking, the evoked model requires a pre-processed acoustic edge impulse stimulus. We demonstrate that the neural mass model is performing thresholded phase-resetting triggered by sharp onsets in the continuous speech envelope. This produces cross-frequency nested oscillations that qualitatively match an experimentally-observed dual-peak signature in the Inter Event Phase Coherence. Our results indicate that the biophysical neural mass model provides a mechanistic bridge between generic oscillatory dynamics in cortical populations and the cognitive computations of speech tracking. Indeed, the non-linear dynamics of the neural mass model offer an explanation for how peak-rate event representations in auditory cortex activity arise in response to continuous acoustic input. Significance StatementSyllable segregation is crucial but challenging as natural speech lacks clear boundaries, yet humans perform this computation effortlessly. Speech aligns neural activity to syllabic rhythms, predicting syllable timing, but the underlying cortical mechanisms remain unknown. Relating this macroscopic behaviour to neurobiology is challenging; however, next-generation neural mass models promise to resolve this. We demonstrate that these models reproduce sharpness-tuned tracking and acoustic edge extraction. Dynamical analyses indicate this occurs through thresholded phase-resetting to phoneme onsets, triggering cross-frequency nested oscillations. Our results both advance biophysical understanding of syllable segregation and validate the models capacity for simulating macroscopic neural activity. These models offer a bridge between the neurobiology of the auditory cortex and speech processing dynamics that phenomenological models cannot provide.

neuroscience↗

bioRxiv2017

Engineering Human CNS Morphogenesis: Controlled Induction of Singular Neural Rosette Emergence

Human pluripotent stem cell (hPSC)-derived neural organoids have revolutionized in vitro modelling of human neurological disorders. Cell-intrinsic morphogenesis processes displayed within these tissues could serve as the basis for ex vivo manufacture of brain and spinal cord tissues with biomimetic anatomy and physiology. However, we must first understand how to control their emergent properties starting at the genesis of neural organoid formation, i.e. emergence of polarized neuroepithelium. In vivo, all CNS tissues develop from a singular neuroepithelial tube. Yet, current protocols yield organoids with multiple neuroepithelial rings, a.k.a. neural rosettes, each acting as independent centers of morphogenesis and thereby impeding coordinate tissue development. We discovered that the morphology of hPSC-derived neural tissues is a critical biophysical parameter for inducing singular neural rosette emergence. Tissue morphology screens conducted using micropatterned array substrates and an automated image analysis determined that circular morphologies of 200-250 and 150m diameter are optimal for inducing singular neural rosette emergence within 80-85% forebrain and 73.5% spinal tissues, respectively. The discrepancy in optimal circular morphology for Pax6+/N-cadherin+ neuroepithelial forebrain versus spinal tissues was due to previously unknown differences in ROCK-mediated cell contractility. The singular neuroepithelium induced within geometrically confined tissues persisted as the tissues morphed from a 2-D monolayer to multilayered 3-D hemispherical aggregate. Upon confinement release using clickable micropatterned substrates, the tissue displayed radial outgrowth with maintenance of a singular neuroepithelium and peripheral neuronal differentiation. Thus, we have quantitatively defined a pertinent biophysical parameter for effectively inducing a singular neuroepithelium emergence within morphing hPSC-derived neural tissues.\n\nSignificance StatementHuman pluripotent stem cell (hPSC)-derived neural organoids display emergent properties that, if harnessed, could serve as the basis for ex vivo manufacture of brain and spinal cord tissues with biomimetic macroscale anatomy and physiology. Their chaotic terminal structure arises from uncontrolled morphogenesis at their genesis, resulting in spontaneous induction of multiple neuroepithelial morphogenesis centers,a.k.a. neural rosettes. Here, we determined that neural tissue morphology is a pertinent biophysical parameter for controlling subsequent morphogenesis, and identified discrete circular tissue morphologies as optimal and effective at inducing singular neural rosette emergence within forebrain and spinal neural tissues. Thus, we developed an approach to reproducibly control the initial stage of hPSC-derived neural tissue morphogenesis enabling their manufacture with a biomimetic nascent CNS anatomy.

bioengineering↗

bioRxiv2018

Fast calcium transients in neuronal spines is driven by extreme statistics

Extreme statistics describe the distribution of rare events that can define the timescales of transduction within cellular microdomains. We combine biophysical modeling and analysis of live-cell calcium imaging to explain the fast calcium transient in spines. We show that in the presence of a spine apparatus (SA), which is an extension of the smooth endoplasmic reticulum (ER), calcium transients during synaptic inputs rely on rare and extreme calcium ion trajectories. Using numerical simulations, we predicted the asymmetrical distributions of Ryanodine receptors and SERCA pumps that we confirmed experimentally. When calcium ions are released in the spine head, the fastest ions arriving at the base determine the transient timescale through a calcium-induced calcium release mechanism. In general, the fastest particles arriving at a small target are likely to be a generic mechanism that determines the timescale of molecular transduction in cellular neuroscience.\n\nSignificance statementIntrigued by fast calcium transients of few milliseconds in dendritic spines, we investigated its underlying biophysical mechanism. We show here that it is generated by the diffusion of the fastest calcium ions when the spine contains a Spine Apparatus, an extension of the endoplasmic reticulum. This timescale is modulated by the initial number of released calcium ions and the asymmetric distribution of its associated calcium release associated Ryanodyne receptors, present only at the base of a spine. This novel mechanism of calcium signaling that we have unraveled here is driven by the fastest particles. To conclude, the rate of arrival of the fastest particles (ions) to a small target receptor defines the timescale of activation instead of the classical forward rate of chemical reactions introduced by von Smoluchowski in 1916. Applying this new rate theory to transduction should refine our understanding of the biophysical mechanisms underlying molecular signaling.

cell biology↗

bioRxiv2018

Linking single-cell measurements of mass, growth rate, and gene expression

We introduce a microfluidic platform that enables single-cell mass and growth rate measurements upstream of single-cell RNA-sequencing (scRNA-seq) to generate paired single-cell biophysical and transcriptional data sets. Biophysical measurements are collected with a serial suspended microchannel resonator platform (sSMR) that utilizes automated fluidic state switching to load individual cells at fixed intervals, achieving a throughput of 120 cells per hour. Each single-cell is subsequently captured downstream for linked molecular analysis using an automated collection system. From linked measurements of a murine leukemia (L1210) and pro-B cell line (FL5.12), we identify gene expression signatures that correlate significantly with cell mass and growth rate. In particular, we find that both cell lines display a cell-cycle signature that correlates with cell mass, with early and late cell-cycle signatures significantly enriched amongst genes with negative and positive correlations with mass, respectively. FL5.12 cells also show a significant correlation between single-cell growth efficiency and a G1-S transition signature, providing additional transcriptional evidence for a phenomenon previously observed through biophysical measurements alone. Importantly, the throughput and speed of our platform allows for the characterization of phenotypes in dynamic cellular systems. As a proof-of-principle, we apply our system to characterize activated murine CD8+ T cells and uncover two unique features of CD8+ T cells as they become proliferative in response to activation: i) the level of coordination between cell cycle gene expression and cell mass increases, and ii) translation-related gene expression increases and shows a correlation with single-cell growth efficiency. Overall, our approach provides a new means of characterizing the transcriptional mechanisms of normal and dysfunctional cellular mass and growth rate regulation across a range of biological contexts.

bioengineering↗

bioRxiv2018

Deletion of the alternative splice variant KChIP4a in midbrain dopamine neurons selectively enhances extinction learning

Midbrain dopamine neurons are essential for flexible control of adaptive behaviors. DA neurons that project to different target regions have unique biophysical properties, and it is thought that this diversity reflects functional specialization. This assumption implies the presence of specific genetic determinants with precise impacts on behavior. We tested this general hypothesis by homing in on one particular biophysical mechanism, Kv4 channel inactivation, using a combination of molecular, proteomic, electrophysiological, computational, and behavioral approaches. We demonstrate that KChIP4a, a singular Kv4 {beta}-subunit splice variant, prolongs hyperpolarization-rebound delays selectively in dopamine neurons projecting to the nucleus accumbens core, shifts the integration of inhibitory inputs and, in turn, selectively regulates learning from negative prediction-errors. Our results reveal a highly specialized, gene-to-behavior mechanistic chain that is only operative in a particular dopaminergic subsystem, illuminating how molecularly defined biophysical switches are employed for neuron subtype-specific information processing in the brain. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/344499v3_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@167984borg.highwire.dtl.DTLVardef@3f8fbborg.highwire.dtl.DTLVardef@f5402aorg.highwire.dtl.DTLVardef@147bb3b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2018

Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, it can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, GPU-based designs can offer accelerations of more than two orders of magnitude in both cases.

neuroscience↗

bioRxiv2018

Weak electric fields promote resonance in neuronal spiking activity: analytical results from two-compartment cell and network models

Transcranial brain stimulation and evidence of ephaptic coupling have sparked strong interests in understanding the effects of weak electric fields on the dynamics of neuronal populations. While their influence on the subthreshold membrane voltage can be biophysically well explained using spatially extended neuron models, mechanistic analyses of neuronal spiking and network activity have remained a methodological challenge. More generally, this challenge applies to phenomena for which single-compartment (point) neuron models are oversimplified. Here we employ a pyramidal neuron model that comprises two compartments, allowing to distinguish basal-somatic from apical dendritic inputs and accounting for an extracellular field in a biophysically minimalistic way. Using an analytical approach we fit its parameters to reproduce the response properties of a canonical, spatial model neuron and dissect the stochastic spiking dynamics of single cells and large networks. We show that oscillatory weak fields effectively mimic anti-correlated inputs at the soma and dendrite and strongly modulate neuronal spiking activity in a rather narrow frequency band. This effect carries over to coupled populations of pyramidal cells and inhibitory interneurons, boosting network-induced resonance in the beta and gamma frequency bands. Our work contributes a useful theoretical framework for mechanistic analyses of population dynamics going beyond point neuron models, and provides insights on modulation effects of extracellular fields due to the morphology of pyramidal cells.\n\nAuthor SummaryThe elongated spatial structure of pyramidal neurons, which possess large apical dendrites, plays an important role for the integration of synaptic inputs and mediates sensitivity to weak extracellular electric fields. Modeling studies at the population level greatly contribute to our mechanistic understanding but face a methodological challenge because morphologically detailed neuron models are too complex for use in noisy, in-vivo like conditions and large networks in particular. Here we present an analytical approach based on a two-compartment spiking neuron model that can distinguish synaptic inputs at the apical dendrite from those at the somatic region and accounts for an extracellular field in a biophysically minimalistic way. We devised efficient methods to approximate the responses of a spatially more detailed pyramidal neuron model, and to study the spiking dynamics of single neurons and sparsely coupled large networks in the presence of fluctuating inputs. Using these methods we focused on how responses are affected by oscillatory weak fields. Our results suggest that ephaptic coupling may play a mechanistic role for oscillations of population activity and indicate the potential to entrain networks by weak electric stimulation.

neuroscience↗

bioRxiv2018

Perceptron learning and classification in a modeled cortical pyramidal cell

The perceptron learning algorithm and its multiple-layer extension, the backpropagation algorithm, are the foundations of the present-day machine learning revolution. However, these algorithms utilize a highly simplified mathematical abstraction of a neuron; it is not clear to what extent real biophysical neurons with morphologically-extended nonlinear dendritic trees and conductance-based synapses could realize perceptron-like learning. Here we implemented the perceptron learning algorithm in a realistic biophysical model of a layer 5 cortical pyramidal cell. We tested this biophysical perceptron (BP) on a memorization task, where it needs to correctly binarily classify 100, 1000, or 2000 patterns, and a generalization task, where it should discriminate between two \"noisy\" patterns. We show that the BP performs these tasks with an accuracy comparable to that of the original perceptron, though the memorization capacity of the apical tuft is somewhat limited. We concluded that cortical pyramidal neurons can act as powerful classification devices.

neuroscience↗

bioRxiv2019

Why Pacing Frequency Affects the Production of Early Afterdepolarizations in Cardiomyocytes: An Explanation Revealed by Slow/Fast Analysis of a Minimal Model

Early afterdepolarizations (EADs) are pathological voltage oscillations in cardiomyocytes that have been observed in response to a number of pharmacological agents and disease conditions. Phase-2 EADs consist of small voltage fluctuations that occur during the plateau of an action potential, typically under conditions in which the action potential is elongated. Although a single-cell behavior, EADs can lead to tissue-level arrhythmias, including ventricular tachycardia. Much is currently known about the biophysical mechanisms (i.e., the roles of ion channels and intracellular Ca2+ stores) for the various forms of EADs, due partially to the development and analysis of mathematical models. This includes the application of slow/fast analysis, which takes advantage of timescale separation inherent in the system to simplify its analysis. We take this further, using a minimal 3D model to demonstrate that the phase-2 EADs are canards that are formed in the neighborhood of a folded node singularity. This knowledge allows us to determine the number of EADs that can be produced for a given parameter set without performing computer simulations, and provides guidance on parameter changes that can facilitate or inhibit EAD production. With this approach, we demonstrate why periodic stimulation, as would occur in an intact heart, preferentially facilitates EAD production when applied at low frequencies. We also explain the origin of complex alternan dynamics that can occur with intermediate-frequency stimulation, in which varying numbers of EADs are produced with each stimulation. These revelations fall out naturally from an understanding of folded node singularities, but are hard or impossible to glean from a knowledge of the biophysical mechanism for EADs alone. Therefore, an understanding of the canard mechanism is a useful complement to an understanding of the biophysical mechanism that has been developed over years of experimental and computational investigations.

systems biology↗

bioRxiv2019

Modeling mechanisms of tremor reduction for essential tremor using symmetric biphasic DBS

Essential tremor (ET) is the most common movement disorder, in which the primary symptom is a prominent, involuntary 4-10 Hz movement. For severe, medication refractory cases, deep brain stimulation (DBS) targeting the ventral intermediate nucleus of the thalamus (VIM) can be an effective treatment for cessation of tremor and is thought to work in part by disrupting tremor frequency oscillations (TFOs) in VIM. However, DBS is not universally effective and may be further disrupting cerebellar-mediated activity in the VIM. Here, we applied biophysically detailed computational modeling to investigate whether the efficacy of DBS is affected by the mechanism of generation of TFOs or by the pattern of stimulation. We simulated the effects of DBS using standard, asymmetric pulses as well as biphasic, symmetric pulses to understand biophysical mechanisms of how DBS disrupts TFOs generated either extrinsically or intrinsically. The model results suggested that the efficacy of DBS in the VIM is affected by the mechanism of generation of TFOs. Symmetric biphasic DBS reduced TFOs more than standard DBS in both networks, and these effects were stronger in the intrinsic network. For intrinsic tremor frequency activity, symmetric biphasic DBS was more effective at reducing TFOs. Simulated non-tremor signals were also transmitted during symmetric biphasic DBS, suggesting that this type of DBS may help to reduce side effects caused by disruption of the cerebellothalamocortical pathway. Biophysical details in the model provided a mechanistic interpretation of the cellular and network dynamics contributing to these effects that can be empirically tested in future studies.\n\nSignificance StatementEssential tremor (ET) is a common movement disorder, whose primary symptom is an involuntary rhythmic movement of the limbs or head. An area of the human tha-lamus demonstrates electrical activity that oscillates at the frequencies of tremor, and deep brain stimulation (DBS) in this area can reduce tremor. It is not fully understood how DBS affects tremor frequency activity in the thalamus, and studying different patterns of DBS stimulation may help to clarify these mechanisms. We created a computational model of different shapes of DBS and studied how they reduce different hypothesized generators of tremor frequency activity. A greater understanding of how DBS affects the thalamus may lead to improved treatments to reduce tremor and alleviate side effects in patients with ET.

neuroscience↗

bioRxiv2019

The effects of dyslipidaemia and cholesterol modulation on erythrocyte susceptibility to malaria parasite infection

Malaria disease commences when blood-stage parasites, called merozoites, invade human red blood cells (RBCs). Whilst the process of invasion is traditionally seen as being entirely merozoite-driven, emerging data suggests RBC biophysical properties markedly influence invasion. Cholesterol is a major determinant of cell membrane biophysical properties. We set out to assess whether cholesterol content in the RBC membrane affects susceptibility to merozoite invasion. Here we demonstrate that RBC bending modulus (a measure of deformability) is markedly affected by artificial modulation of cholesterol content and negatively correlated with merozoite invasion efficiency. Contextualising this observation, we tested a mouse model of hypercholesterolemia and human clinical samples from patients with a range of serum cholesterol concentrations for parasite susceptibility. Hypercholesterolaemia in both human and murine subjects had little effect merozoite invasion efficiency. Furthermore, on testing, RBC cholesterol content in both murine and human hypercholesterolaemia settings was found to be unchanged from normal controls. Serum cholesterol is, therefore, unlikely to impact on RBC susceptibility to merozoite entry. Our work, however, suggests that native polymorphisms that affect RBC membrane lipid composition would be expected to affect parasite entry. This supports investigation of RBC biophysical properties in endemic settings, which may yet identify naturally protective lipid-related polymorphisms.

microbiology↗