Biology subjects

Gauthier, J. L.

Publications and source records attributed to Gauthier, J. L..

4 recordsLinked to original sources

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2018

CaImAn: An open source tool for scalable Calcium Imaging data Analysis

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

neuroscience↗

bioRxiv2017

Context-invariant encoding of reward location in a distinct hippocampal population

The hippocampus plays a critical role in goal-directed navigation. Across different environments, however, the hippocampal map seems to be randomized, making it unclear how goal locations might be encoded. To address this question, we trained mice to seek reward at several locations on virtual linear tracks, and optical recordings of calcium activity were made at cellular resolution from two major hippocampal output structures, CA1 and the subiculum. These experiments revealed a population of neurons that were consistently active near reward locations. Their pattern of activity sharply contrasted with simultaneously-recorded place cells and even persisted across environments, when other cells remapped randomly. Their timing was closely correlated with reward anticipation behaviors, yet could not be explained by the behaviors themselves, raising the possibility that reward-associated cell signals were used by mice to identify the reward location. These results demonstrate that the hippocampus employs a context-invariant marker of goal locations, and reveal a novel target for studying how the hippocampus contributes to navigation.

neuroscience↗