Biology subjects

Park, C. F.

Publications and source records attributed to Park, C. F..

7 recordsLinked to original sources

bioRxiv2026

Brain-wide representations of olfactory behavior in C. elegans

Olfactory navigation emerges from dynamic interactions between sensory input, brain activity state and the animal's own movements. In C. elegans, a compact nervous system enables brain-wide imaging in behaving animals exposed to controlled olfactory environments, with an undulatory locomotion that reduces navigation to transitions among forward runs, reversals, and turns. Here, we used tracking microscopy to record and compare brain-wide neural activity in crawling worms navigating spatial odor gradients or isotropic environments, and in immobilized worms responding to temporal odor pulses. Across conditions, we identified strongly correlated brain-wide activity patterns spanning olfactory sensory neurons, interneurons, and premotor interneurons. Comparisons between navigation in odor gradients and exploration in isotropic environments revealed a compact functional circuit linking olfactory sensory neurons, interneurons, and premotor neurons. Comparisons between different odors distinguished sensory activity relationships associated with odor identity from a shared sensorimotor circuit for navigational movement. Comparisons between crawling and immobilized animals revealed profound differences in motor-state transitions and brain-wide correlation structure, further demonstrating the dependence of sensory representations and motor decision-making on behavioral context. Our findings reveal a functional organization linking olfactory sensory activity to navigational movement and show how this organization depends on behavioral state and environmental context.

neuroscience↗

bioRxiv2026

Whole-organism spatial transcriptomics at single-cell resolution in C. elegans

Spatial transcriptomics has advanced our understanding of tissue organization by mapping gene expression in its native context yet applying these techniques to whole organisms remains a significant challenge. Caenorhabditis elegans is well-suited to whole organism-level analysis because its compact size, transparency, reproducible anatomy, and genetic tractability make it possible to link molecular and cellular changes to circuit function and behavior within the same animal. However, current transcriptomic approaches in C. elegans are often limited by spatial resolution or multiplexing capacity, making it challenging to profile multiple gene expression patterns across intact worms while preserving spatial context. Here, we present a single-molecule fluorescence in situ hybridization workflow that enables multiplex imaging with single-cell resolution across the entire worm. This approach allows sequential imaging of one gene per fluorescent channel using two channels across 20 hybridization rounds, enabling the profiling of up to 40 genes while preserving spatial context. We further provide a curated marker-gene panel for reproducible neuron identification, which, together with probabilistic assignment of transcripts to segmented nuclei, enables quantitative measurements of gene expression levels. We used this method to identify up to 86 neuronal classes and reveal sex- and neuron-specific expression patterns at single-cell resolution. Together, these results establish a scalable framework for the spatial analysis of gene expression and cell identity in intact C. elegans.

neuroscience↗

bioRxiv2024

Efficient pheromone navigation via antagonistic detectors

Chemotaxis to a moving potential mate that emits a volatile sex pheromone poses a navigation challenge requiring rapid, precise responses to maximize reproductive success. Volatile chemicals form gradients that differ from soluble compounds, potentially making navigation based on comparisons between spatially separated sensors unreliable for small-bodied animals. Here we show that, rather than a simple spatial comparison, Caenorhabditis elegans males employ an antagonistic strategy, comparing inputs from sex-shared head (AWA) and male-specific tail (PHD) sensory neurons with distinct response properties. Despite sharing a receptor, SRD-1, these detectors play different roles: AWAs promote forward movement and acceleration, while PHDs induce reversals and deceleration. In rising pheromone gradients, AWA activity dominates; in falling gradients, AWA inactivates, allowing PHD to correct trajectories. AWAs are essential for mate-searching, while PHDs are crucial for complex tasks. A minimal computational model reproduces these behaviors and infers how head-tail signals are combined. Thus, a sexually dimorphic, antagonistic sensory system enables adaptive navigation in dynamic environments

neuroscience↗

bioRxiv2024

EM-Compressor: Electron Microscopy Image Compression in Connectomics with Variational Autoencoders

The ongoing pursuit to map detailed brain structures at high resolution using electron microscopy (EM) has led to advancements in imaging that enable the generation of connectomic volumes that have reached the petabyte scale and are soon expected to reach the exascale for whole mouse brain collections. To tackle the high costs of managing these large-scale datasets, we have developed a data compression approach employing Variational Autoencoders (VAEs) to significantly reduce data storage requirements. Due to their ability to capture the complex patterns of EM images, our VAE models notably decrease data size while carefully preserving important image features pertinent to connectomics-based image analysis. Through a comprehensive study using human EM volumes (H01 dataset), we demonstrate how our approach can reduce data to as little as 1/128th of the original size without significantly compromising the ability to subsequently segment the data, outperforming standard data size reduction methods. This performance suggests that this method can greatly alleviate requirements for data management for connectomics applications, and enable more efficient data access and sharing. Additionally, we developed a cloud-based application named EM-Compressor on top of this work to enable on-thefly interactive visualization: https://em-compressor-demonstration.s3.amazonaws.com/EM-Compressor+App.mp4.

neuroscience↗

bioRxiv2023

SmartEM: machine-learning guided electron microscopy

Connectomics provides nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole brain or even whole circuit reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step in automated connectomics. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a single-beam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM saves time by allocating the proper imaging time for each region of interest - first scanning all pixels rapidly, then rescanning more slowly only the small subareas where a higher quality signal is required. We demonstrate that SmartEM achieves up to a ~7-fold acceleration of image acquisition time for connectomic samples using a commercial single-beam SEM in samples from nematodes, mice and human brain. We apply this fast imaging method to reconstruct a portion of mouse cerebral cortex with an accuracy comparable to traditional electron microscopy.

neuroscience↗

bioRxiv2023

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Connectomics is fundamental in propelling our understanding of the nervous systems organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

neuroscience↗

bioRxiv2022

Automated neuron tracking inside moving and deforming animals using deep learning and targeted augmentation

Advances in functional brain imaging now allow sustained rapid 3D visualization of large numbers of neurons inside behaving animals. To decode circuit activity, imaged neurons must be individually segmented and tracked. This is particularly challenging when the brain itself moves and deforms inside a flexible body. The field has lacked general methods for solving this problem effectively. To address this need, we developed a method based on a convolutional neural network (CNN) Awith specific enhancements which we apply to freely moving Caenorhabditis elegans. For a traditional CNN to track neurons across images of a brain with different postures, the CNN must be trained with ground truth (GT) annotations of similar postures. When these postures are diverse, an adequate number of GT annotations can be prohibitively large to generate manually. We introduce targeted augmentation, a method to automatically synthesize reliable annotations from a few manual annotations. Our method effectively learns the internal deformations of the brain. The learned deformations are used to synthesize annotations for new postures by deforming the manual annotations of similar postures in GT images. The technique is germane to 3D images, which are generally more difficult to analyze than 2D images. The synthetic annotations, which are added to diversify training datasets, drastically reduce manual annotation and proofreading. Our method is effective both when neurons are represented as individual points or as 3D volumes. We provide a GUI that incorporates targeted augmentation in an end-to-end pipeline, from manual GT annotation of a few images to final proofreading of all images. We apply the method to simultaneously measure activity in the second-layer interneurons in C. elegans: RIA, RIB, and RIM, including the RIA neurite. We find that these neurons show rich behaviors, including switching entrainment on and off dynamically when the animal is exposed to periodic odor pulses.

neuroscience↗