Biology subjects

Stern, M.

Publications and source records attributed to Stern, M..

12 recordsLinked to original sources

bioRxiv2026

Regional gene-environment association models improve insights into agents and targets of selection by climate in Mimulus guttatus

Genotype-environment association (GEA) approaches identify genetic variants that likely contribute to adaptation along environmental gradients. These associations can inform predictive models of maladaptation risk and evolutionary rescue in future climates. However, because population structure and climate parameters often co-vary, tradeoffs arise between accounting for population structure to reduce false positive associations and removing true positives. Here, we examine whether conducting GEA analyses at multiple spatial scales provides more informative associations by mitigating confounds between population structure and environment, and we evaluate what factors influence how congruent GEA results are across regions. We performed GEA analyses for climate parameters on genome resequencing data obtained for 110 annual populations of the common monkeyflower, Mimulus guttatus. We find that more genetic variation was specifically attributable to environmental variation in regional GEA models compared to a full-range model, and that agents of selection differ considerably in importance among regions and range-wide. Few genetic targets of selection shared across regions were detected, yet all regions possess ample standing genetic variation for most environment-associated variants, suggesting that regional differences in climate breadth may be a primary cause of this incongruence. In combination with a genome-wide association study for several climate-associated phenotypes, we also find that regulators of flowering repressors in the FLC/MAF gene family often harbor adaptive variation in this species. Together, our results suggest that GEA analyses of widespread species considerably benefit from examining both range-wide and regional trends to more fully capture and understand the agents and targets of climate adaptation.

evolutionary biology↗

bioRxiv2025

Temporal Deconvolution of Mesoscale Recordings

1Mesoscale calcium recordings, such as wide-field imaging, enable high-temporal-resolution recordings of neuronal activity across extensive brain regions. However, because these recordings capture light emitted by calcium indicator fluorescence, the underlying neural activity is obscured by the temporal dynamics of the indicators. Here, we develop and evaluate four deconvolution methods to recover neuronal spiking rates from fluorescence traces recorded in wide-field imaging. The methods--Dynamically-Binning (using adaptive discrete-time bins), Continuously-Varying (estimating smooth spiking rates), First-Differences (providing efficient estimation), and a modified Wiener Filter (robust to large fluorescence magnitude variations)-consistently outperform the previously used adapted Lucy-Richardson algorithm on both synthetic data and simultaneous fluorescence-spike recordings. Critically, we demonstrate that using raw fluorescence signals in advanced analysis methods can yield distorted results, including spuriously inflated correlations between brain regions activity. This highlights the necessity of accurate temporal deconvolution for reliable interpretation of brain activity.

neuroscience↗

bioRxiv2024

Exact Continuous Spiking Rate Inference

Capturing and inferring brain-wide neural activity remains a significant challenge. Wide-field imaging techniques offer a solution in the shape of simultaneous recording of neural activity across large cortical surfaces at high temporal resolution. Nevertheless, the broad field of view introduced by wide-field imaging limits its spatial resolution, as each camera pixel in a wide-field setup integrates calcium-dependent fluorescence signals from many neurons. Furthermore, calcium indicators that convert neural activity into light emissions distort the neural activity by their dynamics. The inherent noise in recordings, combined with the low spatial resolution and the distorted dynamics introduced by the calcium indicators, makes it particularly challenging to infer underlying neural activity from wide-field fluorescence data. Despite its importance, a rigorously studied analytic solution for this inference problem in the wide-field context has not yet been established. In this study, we present an analytic solution to the inference problem posed by wide-field imaging. We formulate an optimization problem that establishes a relationship between the biological quantities and the properties of the inferred solution, thereby elucidating biologically interpretable results. The analytic solution we find provides significant advantages, including rapid and accurate inference. Furthermore, we introduce a novel approach to parameter tuning within the optimization framework, which leverages the extensive datasets typical of wide-field imaging. The results demonstrate that our solution surpasses a previously used method for this inference in both accuracy and efficiency. We rigorously validate our solution through comprehensive simulations, large-scale biophysical modeling, and parallel recordings of fluorescence and spiking activity. Collectively, these analyses provide a robust foundation for future applications of our proposed analytical inference.

neuroscience↗

bioRxiv2023

Motor neuron activity induces proteomic stress in the target muscle

Several lines of evidence demonstrate that increased neuronal excitability can enhance proteomic stress. For example, epilepsy can enhance the proteomic stress caused by the expression of certain aggregation-prone proteins implicated in neurodegeneration. However, unanswered questions remain concerning the mechanisms by which increased neuronal excitability accomplishes this enhancement. Here we test whether increasing neuronal excitability at a particular identified glutamatergic synapse, the Drosophila larval neuromuscular junction, can enhance the proteomic stress caused by mutations in the ER fusion gene atlastin (atl). It was previously shown that larval muscle from the atl2 null mutant is defective in autophagy and accumulates protein aggregates containing ubiquitin (poly-UB aggregates). To determine if increased neuronal excitability might enhance the increased proteomic stress caused by atl2, we activated the TrpA1-encoded excitability channel within neurons. We found that TrpA1 activation had no effect on poly-UB aggregate accumulation in wildtype muscle, but significantly increased poly-UB aggregate number in atl2muscle. Previous work has shown that atl loss from either neuron or muscle increases muscle poly-UB aggregate number. We found that neuronal TrpA1 activation enhanced poly-UB aggregate number when atl was removed from muscle, but not from neuron. Neuronal TrpA1 activation enhanced other phenotypes conferred by muscle atl loss, such as decreased pupal size and decreased viability. Taken together, these results indicate that the proteomic stress caused by muscle atl loss is enhanced by increasing neuronal excitability.

neuroscience↗

bioRxiv2023

The Physical Effects of Learning

Interacting many-body physical systems ranging from neural networks in the brain to folding proteins to self-modifying electrical circuits can learn to perform specific tasks. This learning, both in nature and in engineered systems, can occur through evolutionary selection or through dynamical rules that drive active learning from experience. Here, we show that learning leaves architectural imprints on the Hessian of a physical system. Compared to a generic organization of the system components, (a) the effective physical dimension of the response to inputs (the participation ratio of low-eigenvalue modes) decreases, (b) the response of physical degrees of freedom to random perturbations (or system "susceptibility") increases, and (c) the low-eigenvalue eigenvectors of the Hessian align with the task. Overall, these effects suggest a method for discovering the task that a physical network may have been trained for.

biophysics↗

bioRxiv2022

Quantitative assessment of angioplasty induced vascular inflammation with 19F cardiovascular magnetic resonance imaging

Early macrophage rich vascular inflammation is a key feature in the pathophysiology of restenosis after angioplasty. 19F MRI with intravenously applied perfluorooctyl bromide-nanoemulsion (PFOB-NE) could offer ideal features for serial imaging of the inflammatory response after angioplasty. We aimed to non-invasively image monocyte/macrophage infiltration in response to angioplasty in pig carotid arteries using Fluorine-19 magnetic resonance imaging (19F MRI) to assess early inflammatory response to mechanical injury. Early macrophage rich vascular inflammation is a key feature in the pathophysiology of restenosis after angioplasty. 19F MRI with intravenously applied perfluorooctyl bromide-nanoemulsion (PFOB-NE) could offer ideal features for serial imaging of the inflammatory response after angioplasty. In eight minipigs, injury of the right carotid artery was induced by either balloon oversize angioplasty only (BA, n=4) or in combination with endothelial denudation (BA + ECDN, n=4). PFOB-NE was administered intravenously three days after injury followed by 1H and 19F MRI to assess vascular inflammatory burden at day six. Vascular response to mechanical injury was validated using immunohistology. Angioplasty was successfully induced in all eight pigs. Response to injury was characterized by positive remodeling with predominantly adventitial wall thickening and adventitial infiltration of monocytes/macrophages. 19F signal could be detected in vivo in four pigs following BA + ECDN with a robust signal-to-noise ratio (SNR) of 14.7 {+/-} 4.8. Ex vivo analysis revealed a linear correlation of 19F SNR to local monocyte/macrophage cell density. Minimum detection limit of infiltrated monocytes/macrophages was as about 400 cells/mm2. Therefore, 19F MRI enables quantification of monocyte/macrophage infiltration after vascular injury with sufficient sensitivity. This might open an avenue to non-invasively monitor inflammatory response to mechanical injury after angioplasty and thus to identify individuals with distinct patterns of vascular inflammation promoting restenosis. One Sentence Summary19F MRI enables radiation-free quantification of monocyte/macrophage infiltration after vascular injury with sufficient sensitivity.

immunology↗

bioRxiv2022

The Drosophila attP40 docking site and derivatives are insertion mutations of MSP300

The {phi}C31 integrase system is widely used in Drosophila to allow transgene targeting to specific loci. Over the years, flies bearing any of more than 100 attP docking sites have been constructed. One popular docking site, termed attP40, is located close to the Nesprin-1 orthologue MSP300 and lies upstream of certain MSP300 isoforms and within the first intron of others. Here we show that attP40 causes larval muscle nuclear clustering, which is a phenotype also conferred by MSP300 mutations. We also show that flies bearing insertions within attP40 can exhibit decreased MSP300 transcript levels in third instar larvae. Finally, chromosomes carrying certain "transgenic RNAi project" (TRiP) insertions into attP40 can confer pupal or adult inviability, or infertility. These phenotypes do not require transcription from the insertions within attP40. These results demonstrate that attP40 and insertion derivatives act as MSP300 insertional mutations. These findings should be considered when interpreting data from attP40-bearing flies.

genetics↗

bioRxiv2021

A reservoir of timescales in random neural networks

The temporal activity of many biological systems, including neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. The mechanisms leading to this temporal heterogeneity are yet unknown. Here we show that random neural networks endowed with a distribution of self-couplings, representing functional neural clusters of different sizes, generate multiple timescales of activity spanning several orders of magnitude. When driven by a time-dependent broadband input, slow and fast neural clusters preferentially entrain slow and fast spectral components of the input, respectively, suggesting a potential mechanism for spectral demixing in cortical circuits.

neuroscience↗

bioRxiv2020

Plume dynamics structure the spatiotemporal activity of glomerular networks in the mouse olfactory bulb

Although mice locate resources using turbulent airborne odor plumes, the stochasticity and intermittency of fluctuating plumes create challenges for interpreting odor cues in natural environments. Population activity within the olfactory bulb (OB), is thought to process this complex spatial and temporal information, but how plume dynamics impact odor representation in this early stage of the mouse olfactory system is not known. Limitations in odor detection technology have made it impossible to measure plume fluctuations while simultaneously recording from the mouses brain. Thus, previous studies have measured OB activity following controlled odor pulses of varying profiles or frequencies, but this approach only captures a subset of features found within olfactory plumes. Adequately sampling this feature space is difficult given a lack of knowledge regarding which features the brain extracts during exposure to natural olfactory scenes. Here we measured OB responses to naturally fluctuating odor plumes using a miniature, adapted odor sensor combined with wide-field GCaMP6f signaling from the dendrites of mitral and tufted (MT) cells imaged in olfactory glomeruli of head-fixed mice. We precisely tracked plume dynamics and imaged glomerular responses to this fluctuating input, while varying flow conditions across a range of ethologically-relevant values. We found that a consistent portion of MT activity in glomeruli follows odor concentration dynamics, and the strongest responding glomeruli are the best at following fluctuations within odor plumes. Further, the reliability and average response magnitude of glomerular populations of MT cells are affected by the flow condition in which the animal samples the plume, with the fidelity of plume following by MT cells increasing in conditions of higher flow velocity where odor dynamics result in intermittent whiffs of stronger concentration. Thus, the flow environment in which an animal encounters an odor has a large-scale impact on the temporal representation of an odor plume in the OB. Additionally, across flow conditions odor dynamics are a major driver of activity in many glomerular networks. Taken together, these data demonstrate that plume dynamics structure olfactory representations in the first stage of odor processing in the mouse olfactory system.

neuroscience↗

bioRxiv2020

Inferring Neural Population Spiking Rate from Wide-Field Calcium Imaging

Wide-field calcium imaging techniques allow recordings of high-resolution neuronal activity across one or more brain regions. However, since the recordings capture light emission generated by the fluorescence of the calcium indicator, the neural activity that drives the calcium changes is masked by the calcium indicator dynamics. Here we develop and evaluate new methods to deconvolve the calcium traces and estimate the underlying neural spiking rate. Our methods take into account both the noise in the recordings and the temporal dynamics of the calcium indicator response. Our first proposal estimates firing rates that are constant over discrete time bins. The size of each time bin depends on the data and is determined dynamically. Our second proposal estimates the rate as a continuous function and is meant for studies that look for slow rate fluctuations rather than abrupt changes. We compare our results with those of two alternative approaches: direct deconvolution using a first differences approach, and the Lucy-Richardson image recovery method, adapted to recover temporal dynamics. We show that our methods outperform competitors on synthetic data as well as on wide-field calcium recordings in which the spikes were recorded in parallel using multi-channel silicon probe.

neuroscience↗

bioRxiv2019

Estimating numerosity of visual concepts with a single spiking neuron.

Insects are able to solve basic numerical cognition tasks. We show that estimation of numerosity can be realized and learned by a single spiking neuron with an appropriate synaptic plasticity rule. This model can be efficiently trained to detect arbitrary spatio-temporal spike patterns on a noisy and dynamic background with high precision and low variance. When put to test in a task that requires counting of visual concepts in a static image it required considerably less training epochs than a convolutional neural network to achieve equal performance. When mimicking a behavioral task in free flying bees that requires numerical cognition the model reaches a similar success rate in making correct decisions. We propose that using action potentials to represent basic numerical concepts with a single spiking neuron is beneficial for organisms with small brains and limited neuronal resources.

neuroscience↗

bioRxiv2017

A Transformation From Latency To Ensemble Coding In A Model Of Piriform Cortex

Different coding strategies are used to represent odor information at various stages of the mammalian olfactory system. A temporal latency code represents odor identity in olfactory bulb (OB), but this temporal information is discarded in piriform cortex (PCx) where odor identity is instead encoded through ensemble membership. We developed a spiking PCx network model to understand how this transformation is implemented. In the model, the impact of OB inputs activated earliest after inhalation is amplified within PCx by diffuse recurrent collateral excitation, which then recruits strong, sustained feedback inhibition that suppresses the impact of later-responding glomeruli. Simultaneous OB-PCx recordings indicate that indeed, over a single sniff, the earliest-active OB inputs are most effective at driving PCx activity. We model increasing odor concentrations by decreasing glomerulus onset latencies while preserving their activation sequences. This produces a multiplexed cortical odor code in which activated ensembles are robust to concentration changes while concentration information is encoded through population synchrony. Our model demonstrates how PCx circuitry can implement multiplexed ensemble-identity/temporal-concentration odor coding.

neuroscience↗