Biology subjects

Charles, A. S.

Publications and source records attributed to Charles, A. S..

10 recordsLinked to original sources

bioRxiv2026

GIAnT: a Glutamate Imaging Analysis Toolbox

Recent advances in fluorescent indicators and optical microscopy now enable in vivo synaptic imaging of glutamate, which transmits the majority of signals between neurons in the brain. Extracting fluorescence signals from these recordings is complicated by the minuscule scale and dense clustering of synapses on dendrites, as well as brain motion in behaving animals. Here we present the Glutamate Imaging Analysis Toolbox (GIAnT), a set of automated tools for glutamate imaging data that corrects sample motion, identifies active synapses with super-resolution precision, and extracts synaptic fluorescence signals. Compared to methods designed for cellular imaging, GIAnT reduces motion artifacts, more accurately identifies active synapses, and improves extracted signal quality by reducing contamination from overlapping synapses. By pairing in vivo glutamate imaging with post hoc expansion microscopy, we find that >70% of the putative synapses extracted using GIAnT matched one-to-one with glutamatergic synapses onto the postsynaptic cell. Our results establish GIAnT as an automated and validated pipeline for processing synaptic glutamate imaging data at scale.

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bioRxiv2026

High Axial Resolution Is Necessary for Quantitative Two-Photon Calcium Imaging of Neuronal Populations

Two-photon calcium imaging is a standard tool for measuring neuronal population activity in vivo, yet how axial resolution, sensor expression strategy, and analysis pipeline jointly affect data accuracy remains poorly characterized. Here, we imaged the same L2/3 neurons in mouse primary visual cortex at five axial resolutions (3.6-21.0 m), spanning current two-photon systems from benchtop microscopes to large-field-of-view and miniaturized designs. We expressed cytosolic, transgenic, and soma-targeted GCaMP variants and applied five analysis pipelines. Reducing axial resolution systematically attenuated {Delta}F/F0, corrupted visual responsiveness and orientation tuning classifications, and biased pairwise correlations. No pipeline corrected these resolution-dependent artifacts, and pipeline choice alone produced quantitatively divergent results even at the highest resolution. Soma-targeted sensors mitigated but did not eliminate these artifacts. Our findings demonstrate that high axial resolution is necessary for accurate quantitative population imaging, and that robust separation of somatic from neuropil signals remains an unresolved challenge. In BriefImaging the same cortical neurons across five axial resolutions and five analysis pipelines, Ji and colleagues show that lower resolution corrupts neuronal tuning and population correlations. No pipeline corrects these artifacts, and pipeline choice yields divergent results even at high resolution. Soma-targeted sensors mitigate but do not eliminate these failures.

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bioRxiv2025

Uncertainty-Gated Min-Cost Flows for In Vivo NanoScale Synaptic Plasticity Tracking

Synapses are the fundamental unit of neural connectivity, exhibiting dynamic functional and structural changes that enable the brain to learn, adapt, and form memories. Recent advances in fluorescent labeling of endogenous proteins offer an opportunity to image synaptic strength in vivo and study mechanisms underlying adaptive neural computation. Studying synaptic dynamics requires tracking signals of small, densely packed synapses over days as they change in size, position, and intensity between imaging sessions, and may even appear or disappear. Associating >50,000 dynamic, submicrometer particles across time is difficult, even for state-of-the-art algorithms. Moreover, most algorithms assign equal weight to the lateral (XY) and noisier axial (Z) dimensions, reducing performance. To address these challenges and accurately track synapses in vivo, we developed SynTrack. We formulate tracking as a Maximum A Posteriori estimation problem that identifies the K most likely disjoint paths in a Hidden Markov Model, solved using min-cost circulation optimization. An anisotropic uncertainty model accounts for poorer axial resolution, and a fully temporally connected spatio-temporal graph overcomes long-term occlusions. SynTrack achieves a mean displacement of 0.50 {micro}m with a Multiple Object Tracking Accuracy (MOTA) score of 89.8%, on par with expert annotators but with substantially increased speed and scalability. In a large-scale volume imaged over two weeks, SynTrack reconstructed 74,000 synapse trajectories detected in 4.9 out of 8 imaging sessions on average, with 18,000 synapses tracked in at least seven sessions. We present a state-of-the-art algorithm capable of high-fidelity longitudinal tracking of individual synapses in behaving mice at an unprecedented scale.

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bioRxiv2025

Continuous partitioning of neuronal variability

Neurons exhibit substantial trial-to-trial variability in response to repeated stimuli, posing a major challenge for understanding the information content of neural spike trains. In the visual cortex, responses show greater-than-Poisson variability, whose origins and structure remain unclear. To address this puzzle, we introduce a continuous, doubly stochastic model of spike train variability that partitions neural responses into a smooth stimulus-driven component and a time-varying stochastic gain process. We applied this model to spike trains from four visual areas (LGN, V1, V2, and MT) and found that the gain process is well described by an exponentiated power law, with increasing amplitude and slower decay at higher levels of the visual hierarchy. The model also provides analytical expressions for the Fano factor of binned spike counts as a function of timescale, linking observed variability to underlying modulatory dynamics. Together, these results establish a principled framework for characterizing neural variability across cortical processing stages.

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bioRxiv2025

SLAy-ing oversplitting errors in high-density electrophysiology spike sorting

The growing channel count of silicon probes has substantially increased the number of neurons recorded in electrophysiology (ephys) experiments, rendering traditional manual spike sorting impractical. Instead, modern ephys recordings are processed with automated methods that use waveform template matching to isolate putative single neurons. While scalable, automated methods rely on assumptions that often fail to account for biophysical changes in action potential waveforms, leading to systematic oversplitting of individual neurons into multiple putative units. Consequently, manual curation of these errors, which is both time-consuming and lacking in reproducibility, remains necessary. To improve efficiency and reproducibility in the spike-sorting pipeline, we introduce the Spike-sorting Lapse Amelioration System (SLAy), an algorithm that automatically merges oversplit spike units. SLAy employs two novel metrics: (1) a waveform similarity metric that uses a neural network to obtain spatially informed, nonlinear waveform representations, and (2) a cross-correlogram significance metric based on the earth movers distance between the observed and null cross-correlograms. To improve reproducibility and remove the need for manual tuning, we also develop an automatic parameter setting procedure for SLAy that accounts for dataset-specific characteristics. On simulated oversplitting across a diverse set of animal models, brain regions, and probe geometries, SLAy substantially outperforms an existing merging algorithm, achieving high recall without merging extraneous units. On the original datasets without simulated oversplitting, SLAy recovers [~] 95% of merges found by human curators and human curators agree with [~] 90% of merges suggested by SLAy. SLAy leverages multithreading for computational efficiency, running in less than 10 minutes for all recordings we tested. SLAy is also compatible with SpikeInterface, making it a practical and flexible solution for large-scale ephys data analysis across acquisition systems and spike sorters.

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bioRxiv2022

Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice

Synaptic plasticity encodes learning as changes in the strength of synapses, sub-micron structures that mediate communication between brain cells. Due to their small size and high density, synapses are extremely difficult to image in vivo, limiting our ability to directly relate synaptic plasticity with behavior. Here, we developed a combination of computational and biological methods to overcome these challenges. First, we trained a deep learning image restoration algorithm that combines the advantages of ex vivo super-resolution and in vivo imaging modalities to overcome limitations specific to each optical system. Applied to in vivo images from transgenic mice expressing fluorescently labeled synaptic proteins, this restoration algorithm super-resolved diffraction-limited synapses, enabling identification and logitudinal tracking of synaptic plasticity underlying behavior with unprecedented spatial resolution. More generally, our method demonstrates the capabilities of image enhancement to learn from ex vivo data and imaging techniques to improve in vivo imaging resolution.

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bioRxiv2021

GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging

Optical imaging of calcium signals in the brain has enabled researchers to observe the activity of hundreds-to-thousands of individual neurons simultaneously. Current methods predominantly focus on matrix factorization and aim at detecting neurons in the imaged field-of-view, and then inferring the corresponding time-traces. The explicit locality constraints on the cell shapes additionally limits the applicability to optical imaging at different scales (i.e., dendritic or widefield data). Here we present a new method that frames the problem of isolating independent fluorescing components as a dictionary learning problem. Specifically, we focus on the time-traces, which are the main quantity used in scientific discovery, and learn the dictionary of time traces with the spatial maps acting as the presence coefficients encoding which pixels the time traces are active in. Furthermore, we present a novel graph filtering model which redefines connectivity between pixels in terms of their shared temporal activity, rather than spatial proximity. This model greatly eases the ability of our method to handle data with complex non-local spatial structure, such as dendritic imaging. We demonstrate important properties of our method, such as robustness to initialization, implicitly inferring number of neurons and simultaneously detecting different neuronal types, on both synthetic data and real data examples. Specifically, we demonstrate applications of our method to calcium imaging both at the dendritic, somatic, and widefield scales.

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bioRxiv2019

Neural Anatomy and Optical Microscopy (NAOMi) Simulation for evaluating calcium imaging methods

The past decade has seen a multitude of new in vivo functional imaging methodologies. However, the lack of ground-truth comparisons or evaluation metrics makes large-scale, systematic validation impossible. Here we provide a new framework for evaluating TPM methods via in silico Neural Anatomy and Optical Microscopy (NAOMi) simulation. Our computationally efficient model generates large anatomical volumes of mouse cortex, simulates neural activity, and incorporates optical propagation and scanning to create realistic calcium imaging datasets. We verify NAOMi simulations against in vivo two-photon recordings from mouse cortex. We leverage this access to in silico ground truth to perform direct comparisons between different segmentation algorithms and optical designs. We find modern segmentation algorithms extract strong neural time-courses comparable to estimation using oracle spatial information, but with an increase in the false positive rate. Comparison between optical setups demonstrate improved resilience to motion artifacts in sparsely labeled samples using Bessel beams, increased signal-to-noise ratio and cell-count using low numerical aperture Gaussian beams and nuclear GCaMP, and more uniform spatial sampling with temporal focusing versus multi-plane imaging. Overall, by leveraging the rich accumulated knowledge of neural anatomy and optical physics, we provide a powerful new tool to assess and develop important methods in neural imaging.

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bioRxiv2018

Detecting and Correcting False Transients in Calcium Imaging

Population recordings of calcium activity are a major source of insight into neural function. Large dataset sizes often require automated methods, but automation can introduce errors that are difficult to detect. Here we show that automatic time course estimation can sometimes lead to significant misattribution errors, in which fluorescence is ascribed to the wrong cell. Misattribution arises when the shapes of overlapping cells are imperfectly defined, or when entire cells or processes are not identified, and misattribution can even be produced by methods specifically designed to handle overlap. To diagnose this problem, we develop a transient-by-transient metric and a visualization tool that allow users to quickly assess the degree of misattribution in large populations. To filter out misattribution, we also design a robust estimator that explicitly accounts for contaminating signals in a generative model. Our methods can be combined with essentially any cell finding technique, empowering users to diagnose and correct at large scale a problem that has the potential to significantly alter scientific conclusions.

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bioRxiv2017

Dethroning the Fano Factor: a flexible, model-based approach to partitioning neural variability

Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are over-dispersed relative to a Poisson distribution. Recent work (Goris et al., 2014) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships between spike count mean and variance. Here we examine this quadratic assumption and propose a more flexible family of models that can account for a more diverse set of mean-variance relationships. Our model contains additive Gaussian noise that is transformed nonlinearly to produce a Poisson spike rate. Different choices of the nonlinear function can give rise to qualitatively different mean-variance relationships, ranging from sub-linear to linear to multiplicative. Intriguingly, a rectified squaring nonlinearity produces a linear mean-variance function, corresponding to responses with constant Fano factor. We describe a computationally efficient method for fitting this model to data, and demonstrate that a majority of neurons in a V1 population are better described by a model with non-quadratic relationship between mean and variance. Lastly, we develop an application to Bayesian adaptive stimulus selection in closed-loop neurophysiology experiments, which shows that accounting for overdispersion can lead to dramatic improvements in adaptive tuning curve estimation.

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