Biology subjects

Trotter, D.

Publications and source records attributed to Trotter, D..

7 recordsLinked to original sources

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

Hypoxia responses in arginase 2 deficient mice enhance cardiovascular health

RATIONALEPhysiological responses to hypoxia involve adaptations in the hematopoietic and cardiovascular systems, which work together to ensure adequate oxygen delivery to tissues for energy production. The arginine/nitric oxide (NO) pathway regulates both systems through its effects on erythropoiesis and vasodilation. In Tibetan populations native to high-altitude hypoxia, increased NO production from arginine and decreased arginine metabolism by arginase contribute to these adaptive mechanisms. These metabolic changes enhance tissue oxygen delivery and reduce the risk of hypoxic pulmonary hypertension. Here, we hypothesize that genetic deletion of mitochondrial arginase 2 (Arg2) in mice will enhance cardiovascular effects and mitigate hypoxia-induced pulmonary hypertension. METHODSComplete blood counts, bone marrow erythroid differentiation, plasma arginine and NO (measured as nitrite), right ventricular systolic pressure (RVSP), heart rate, heart weight, and blood pressure were measured in wild-type (WT) and Arg2 knockout (Arg2KO) mice exposed to short-term (6, 12, 48, or 72 hours) or long-term (3 weeks) hypoxia. RESULTSUnder normoxic conditions, Arg2KO and WT mice exhibit similar RBC counts, hemoglobin levels, hematocrit, heart rate, systolic and diastolic blood pressures, and heart weight (all P > 0.05). WT mice increase erythropoiesis at 12 hours of hypoxia, including proerythroblasts (stage I, P = 0.004), polychromatic erythroblasts (stage III, P = 0.0004), and orthochromatic erythroblasts (stage IV, P = 0.03), but Arg2KO mice do not increase erythropoiesis. After 48 hours of hypoxia, Arg2KO mice increase proerythroblasts (stage I, P = 0.0008), but levels remain significantly lower than in WT mice. Plasma arginine and NO levels increase under hypoxia. NO levels peak at 12 hours of hypoxia in WT mice, then decline rapidly. In contrast, NO levels in Arg2KO mice are higher than in WT mice, with sustained elevations at 48 hours of hypoxia (P = 0.03). Arg2KO mice have significantly higher plasma arginine levels than WT at 6, 12, and 72 hours of hypoxia (all P < 0.05). Under chronic hypoxia, Arg2KO and WT mice show similar RBC counts, hemoglobin levels, hematocrit, and NO levels. Unlike WT, Arg2KO mice do not increase RVSP (P = 0.4) and have lower mean arterial (P = 0.03) and diastolic blood pressures (P = 0.01), as well as much lower heart rates (P < 0.0001). Additionally, small blood vessels increase in lungs of Arg2KO mice (CD31, P = 0.02; vWF, P = 0.6). CONCLUSIONSArginine metabolism in the mitochondria plays a key role in modulating adaptive responses to hypoxia. Deletion of Arg2 results in delayed erythropoiesis under acute hypoxia, but better cardiovascular health, as indicated by higher levels of nitrite and arginine, and lower RVSP, blood pressure, and heart rate with chronic hypoxia.

physiology↗

bioRxiv2023

Morphological variability may limit single-cell specificity to electric field stimulation

Non-invasive brain stimulation techniques are widely used for manipulating the behaviour of neuronal circuits and the excitability of the neurons therein. While the usage of these techniques is widely studied at the meso- and macroscopic scales, less is known about the specificity of such approaches at the level of individual cells. Here we use models based on the morphologies of real pyramidal and parvalbumin neurons from mouse primary visual cortex created by the Allen Institute for Brain Science to explore the variability and evoked response susceptibility of different morphologies to uniform electric fields. We devised a range of metrics quantifying various aspects of cellular morphology, ranging from whole cell attributes to net compartment length, branching, diameter to orientation. In supporting layer- and cell-type specific responses, none of these physical traits passed statistical significance tests. While electric fields can modulate somatic, dendritic and axonal compartments reliably and subtype-specific responses could be observed, the specificity of such stimuli was blurred by the variability in cellular morphology. These null results suggest that morphology alone may not account for the reported subtype specificity of brain stimulation paradigms, and question the extent to which such techniques may be used to probe and control neural circuitry. Author summaryOver the last several decades there has been increased interest in the efficacy of non-invasive brain stimulation, particularly in determining the limits of specificity of such techniques. Despite this growing area of research, much remains unknown about the interactions of non-invasive techniques with neurons at the single-cell level, notably the importance of morphology to these interactions. We make use of detailed single-neuron models and simulate them in a uniform electric field and demonstrate that the high variability in neuron morphologies may limit how specifically single neurons can be targeted non-invasively. We confirmed this for neuron morphology characteristics at macro- and meso- scales and at varied orientations. Our work suggests that previously reported subtype specificities in non-invasive frameworks are not accounted for by considering only morphological factors.

neuroscience↗

bioRxiv2023

Elevated sleep need in a stress-resilient Drosophila species

Sleep is broadly conserved across the animal kingdom, but can vary widely between species. It is currently unclear which types of selective pressures and sleep regulatory mechanisms influence differences in sleep between species. The fruit fly Drosophila melanogaster has become a successful model system for examining sleep regulation and function, but little is known about the sleep patterns and need for sleep in many related fly species. Here, we find that Drosophila mojavensis, a fly species that has adapted to extreme desert environments, exhibits strong increases in sleep compared to D. melanogaster. Long-sleeping D. mojavensis show intact sleep homeostasis, indicating that these flies carry an elevated need for sleep. In addition, D. mojavensis exhibit altered abundance or distribution of several sleep/wake related neuromodulators and neuropeptides that are consistent with their reduced locomotor activity, and increased sleep. Finally, we find that in a nutrient-deprived environment, the sleep responses of individual D. mojavensis are correlated with their survival time. Our results demonstrate that D. mojavensis is a novel model for studying organisms with high sleep need, and for exploring sleep strategies that provide resilience in extreme environments.

neuroscience↗

bioRxiv2022

Selective control of synaptic plasticity in heterogeneous networks through transcranial alternating current stimulation (tACS)

Transcranial alternating current stimulation (tACS) represents a promising non-invasive treatment for an increasingly wide range of neurological and neuropsychiatric disorders. The ability to use periodically oscillating electric fields to non-invasively engage neural dynamics opens up the possibility of recruiting synaptic plasticity and to modulate brain function. However, despite consistent reports about tACS clinical effectiveness, strong state-dependence combined with the ubiquitous heterogeneity of cortical networks collectively results in high outcome variability. Introducing variations in intrinsic neuronal timescales, we explored how such heterogeneity influences stimulation-induced change in synaptic connectivity. We examined how spike timing dependent plasticity, at the level of cells, intra- and inter-laminar cortical networks, can be selectively and preferentially engaged by periodic stimulation. Using computational simulations informed by human experimental data, we analyzed cortical circuits comprised of multiple cell-types, alongside superficial multi-layered networks expressing distinct layer-specific timescales. Our results show that mismatch in neuronal timescales within and/or between cells - and the resulting variability in excitability, temporal integration properties and frequency tuning - enables selective and directional control on synaptic connectivity by tACS. Our work provides new vistas on how to recruit neural heterogeneity to guide brain plasticity using non-invasive stimulation paradigms. Author summaryBrain stimulation techniques, such as transcranial alternating current stimulation (tACS), are increasingly used to treat mental health disorders and to probe brain function. Despite promising results, it remains unclear how these non-invasive interventions impact both the dynamics and connectivity of neural circuits. We developed an interdisciplinary framework showing that heterogeneity in neuronal timescales, and its consequences on cellular excitability and temporal integration properties of cortical neurons, may lead to selective and directional control on synaptic modifications by tACS. Differences in neuron responses resulting from timescale mismatch establishes phase- and frequency-specific tuning relationships which may be recruited by periodic stimuli to guide synaptic plasticity. We confirmed this using both intra - and inter-laminar cortical circuit models comprised of multiple cell-types and informed by experimental data. Our work showcases how heterogeneity might be used to guide synaptic plasticity using non-invasive stimulation paradigms.

neuroscience↗

bioRxiv2020

Synaptic dynamics as convolutional units

Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks. Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.

neuroscience↗

bioRxiv2019

Parsing out the variability of transmission at central synapses using optical quantal analysis

Properties of synaptic release dictates the core of information transfer in neural circuits. Despite decades of technical and theoretical advances, distinguishing bona fide information content from the multiple sources of synaptic variability remains a challenging problem. Here, we employed a combination of computational approaches with cellular electrophysiology, two-photon uncaging of MNI-Glutamate and imaging at single synapses. We describe and calibrate the use of the fluorescent glutamate sensor iGluSnFR and found that its kinetic profile is close to that of AMPA receptors, therefore providing several distinct advantages over slower methods relying on NMDA receptor activation (i.e., chemical or genetically encoded Calcium indicators). Using an array of statistical methods, we further developed, and validated on surrogate data, an expectation-maximization algorithm that, by biophysically constraining release variability, extracts the quantal parameters n (maximum number of released vesicles) and p (unitary probability of release) from single-synapse iGluSnFR-mediated transients. Together, we present a generalizable mathematical formalism which, when applied to optical recordings, paves the way to an increasingly precise investigation of information transfer at central synapses.

neuroscience↗