Biology subjects

Kirst, C.

Publications and source records attributed to Kirst, C..

7 recordsLinked to original sources

bioRxiv2025

Identity-stable multi-animal tracking using bidirectional segmentation with object-level memory

Tracking animal behavior in naturalistic settings is essential for understanding social dynamics and their neural underpinnings. Pose estimation methods can produce accurate keypoints using framewise inference. However, post hoc tracking steps often struggle to maintain consistent identity over time, particularly during close and rapid social interactions between visually similar animals. We present a pipeline for bidirectional video object segmentation (VOS) to correct identity swaps with much less manual annotation effort, addressing the prohibitive cost of identity correction of pose estimation data in long recordings and large cohorts. Our approach makes use of a state-of-the-art VOS algorithm, Cutie, which leverages both pixel- and object-level representations across multiple memory timescales. By comparing segmentation masks from independent forward and reverse inference runs, we identify localized zones of disagreement and flag them for manual review. When applied to more than 160 hours of dyadic vole interaction videos, our method reduces identity swaps by two orders of magnitude compared to typical pose estimation workflows and requires review of less than 0.3% of frames per video to achieve identity error-free segmentation masks and aligned keypoints. Our approach generalizes to social interactions involving three or more animals, with scalability constrained primarily by behavioral complexity (e.g., complete occlusion of multiple individuals). Our method enables scalable, long-term tracking of unmarked animals in group settings and provides a practical foundation for more naturalistic studies of social behavior. To lower the barrier for researchers facing similar tracking challenges, we provide an accessible graphical user interface for general use.

animal behavior and cognition↗

bioRxiv2025

Extracellular matrix proteolysis maintains synapse plasticity during brain development

Maintaining a dynamic neuronal synapse pool is critical to brain development. The extracellular matrix (ECM) regulates synaptic plasticity via mechanisms that are still being defined and are studied predominantly in adulthood. Using live imaging of excitatory synapses in zebrafish hindbrain we observed a bimodal distribution of short-lived (dynamic) and longer-lived (stable) synapses. Disruption of ECM via digestion or brevican deletion destabilized dynamic but not stable synapses and led to decreased synapse density. Conversely, loss of matrix metalloproteinase 14 (MMP14) led to accumulation of brevican and increased the stable synapse pool, resulting in increased synapse density. Microglial MMP14 was essential to these effects in both fish and human iPSC-derived cultures. Both MMP14 and brevican were required for experience-dependent synapse plasticity in a motor learning assay. These data, complemented by mathematical modeling, define an essential role of ECM remodeling in maintaining a dynamic subset of synapses during brain development.

neuroscience↗

bioRxiv2024

Regional specialization manifests in the reliability of neural population codes

The brain has the remarkable ability to learn and guide the performance of complex tasks. Decades of lesion studies suggest that different brain regions perform specialized functions in support of complex behaviors1-3. Yet recent large-scale studies of neural activity reveal similar patterns of activity and encoding distributed widely throughout the brain4-6. How these distributed patterns of activity and encoding are compatible with regional specialization of brain function remains unclear. Two frontal brain regions, the dorsal medial prefrontal cortex (dmPFC) and orbitofrontal cortex (OFC), are a paradigm of this conundrum. In the setting complex behaviors, the dmPFC is necessary for choosing optimal actions2,7,8, whereas the OFC is necessary for waiting for3,9 and learning from2,7,9-12 the outcomes of those actions. Yet both dmPFC and OFC encode both choice- and outcome-related quantities13-20. Here we show that while ensembles of neurons in the dmPFC and OFC of rats encode similar elements of a cognitive task with similar patterns of activity, the two regions differ in when that coding is consistent across trials ("reliable"). In line with the known critical functions of each region, dmPFC activity is more reliable when animals are making choices and less reliable preceding outcomes, whereas OFC activity shows the opposite pattern. Our findings identify the dynamic reliability of neural population codes as a mechanism whereby different brain regions may support distinct cognitive functions despite exhibiting similar patterns of activity and encoding similar quantities.

neuroscience↗

bioRxiv2023

Network-level encoding of local neurotransmitters in cortical astrocytes

Summary ParagraphAstrocytes--the most abundant non-neuronal cell type in the mammalian brain--are crucial circuit components that respond to and modulate neuronal activity via calcium (Ca2+) signaling1-8. Astrocyte Ca2+ activity is highly heterogeneous and occurs across multiple spatiotemporal scales: from fast, subcellular activity3,4 to slow, synchronized activity that travels across connected astrocyte networks9-11. Furthermore, astrocyte network activity has been shown to influence a wide range of processes5,8,12. While astrocyte network activity has important implications for neuronal circuit function, the inputs that drive astrocyte network dynamics remain unclear. Here we used ex vivo and in vivo two-photon Ca2+ imaging of astrocytes while mimicking neuronal neurotransmitter inputs at multiple spatiotemporal scales. We find that brief, subcellular inputs of GABA and glutamate lead to widespread, long-lasting astrocyte Ca2+ responses beyond an individual stimulated cell. Further, we find that a key subset of Ca2+ activity--propagative events--differentiates astrocyte network responses to these two major neurotransmitters, and gates responses to future inputs. Together, our results demonstrate that local, transient neurotransmitter inputs are encoded by broad cortical astrocyte networks over the course of minutes, contributing to accumulating evidence across multiple model organisms that significant astrocyte-neuron communication occurs across slow, network-level spatiotemporal scales13-15. We anticipate that this study will be a starting point for future studies investigating the link between specific astrocyte Ca2+ activity and specific astrocyte functional outputs, which could build a consistent framework for astrocytic modulation of neuronal activity.

neuroscience↗

bioRxiv2023

Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling

In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets. 1 Author SummaryExperimental data of interacting cells under the influence of external as well as unobserved factors are ubiquitous. Parametric models are often used to gain understanding of the processes that generated such data. As such, biological understanding hinges upon accurate inference of model parameters. Whether and how systemic parameter bias manifests in such models is poorly understood. We study this issue in the specific context of estimating the static and dynamic interactions of simultaneously recorded neurons influenced by stimuli and unobserved neurons. Through extensive numerical study and analytic calculations, we identify and mitigate bias in such models. When applied to diverse neural data sets, we found that common models and inference procedures often overestimate the importance of coupling and underestimate tuning. In contrast to common intuition, we find that model non-identifiability contributes to estimation bias, not variance, making it a particularly insidious form of statistical error. As the experimental and statistical issues examined here are common, the insights and solutions we developed will likely impact many fields of biology.

neuroscience↗

bioRxiv2018

WNT signaling memory is required for ACTIVIN to function as a morphogen in human gastruloids

Self-organization of discrete fates in human gastruloids is mediated by a hierarchy of signaling pathways. How these pathways are integrated in time, and whether cells maintain a memory of their signaling history remains obscure. Here, we dissect the temporal integration of two key pathways, WNT and ACTIVIN, which along with BMP control gastrulation. CRISPR/Cas9 live reporters of SMAD1, 2 and 4 demonstrate that in contrast to the stable signaling by SMAD1, signaling and transcriptional response by SMAD2 is transient, and while necessary for pluripotency, it is insufficient for differentiation. Pre-exposure to WNT, however, endows cells with the competence to respond to graded levels of ACTIVIN, which induces differentiation without changing SMAD2 dynamics. This cellular memory of WNT signaling is necessary for ACTIVIN morphogen activity. A re-evaluation of the evidence gathered over decades in model systems, re-enforces our conclusions and points to an evolutionarily conserved mechanism.

developmental biology↗

bioRxiv2017

GABA regulates resonance and spike rate encoding via a universal mechanism that underlies the modulation of action potential generation

Different mechanisms for action potential (AP) generation exist that shape neuronal coding and network dynamics. The neuro-transmitter GABA regulates neuronal activity but its role in modulating AP dynamics itself is unclear. Here we show that GABA indeed changes the AP mechanism: it causes regularly firing hippocampal CA3 neurons to bistably switch between spiking and quiescence, converts graded discharge-to-current relationships to have abrupt onsets, and induces resonance. Modeling reveals that A-currents enable these GABA-induced transitions. Mathematically, we prove that this transition sequence originates from a single universal principle that generically underlies the modulation of AP dynamics in any conductance-based neuron model. Conductance clamp experiments in hippocampal and brainstem neurons show the same transitions, confirming the universal theory. In simulated networks, synaptically controlled AP dynamics, permits dynamic gating of signals and targeted synchronization of neuronal sub-ensembles. These results advance the systematic understanding of AP modulation and its role in neuronal and network function.

neuroscience↗