Shetler, O. | Search bioRxiv

bioRxiv2024

Open-Source Tools to Analyze Temporal and Spatial Properties of Local Field Potentials

Analysis of local field potentials (LFPs) is important for understanding how ensemble neurons function as a network in a specific region of the brain. Despite the availability of tools for analyzing LFP data, there are some missing features such as analysis of high frequency oscillations (HFOs) and spatial properties. In addition, accessibility of most tools is restricted due to closed source code and/or high costs. To overcome these issues, we have developed two freely available tools that make temporal and spatial analysis of LFP data easily accessible. The first tool, hfoGUI (High Frequency Oscillation Graphical User Interface), allows temporal analysis of LFP data and scoring of HFOs such as ripples and fast ripples which are important in understanding memory function and neurological disorders. To complement the temporal analysis tool, a second tool, SSM (Spatial Spectral Mapper), focuses on the spatial analysis of LFP data. The SSM tool maps the spectral power of LFPs as a function of subjects position in a given environment allowing investigation of spatial properties of LFP signal. Both hfoGUI and SSM are open-source tools that have unique features not offered by any currently available tools, and allow visualization and spatio-temporal analysis of LFP data.

neuroscience↗

bioRxiv2023

Beyond Correlation: Optimal Transport Metrics For Characterizing Representational Stability and Remapping in Neurons Encoding Spatial Memory

Spatial representations in the entorhinal cortex (EC) and hippocampus (HPC) are fundamental to cognitive functions like navigation and memory. These representations, embodied in spatial field maps, dynamically remap in response to environmental changes. However, current methods, such as Pearsons correlation coefficient, struggle to capture the complexity of these remapping events, especially when fields do not overlap, or transformations are non-linear. This limitation hinders our understanding and quantification of remapping, a key aspect of spatial memory function. To address this, we propose a family of metrics based on the Earth Movers Distance (EMD) as a versatile framework for characterizing remapping. Applied to both normalized and unnormalized distributions, the EMD provides a granular, noise-resistant, and rate-robust description of remapping. This approach enables the identification of specific cell types and the characterization of remapping in various scenarios, including disease models. Furthermore, the EMDs properties can be manipulated to identify spatially tuned cell types and to explore remapping as it relates to alternate information forms such as spatiotemporal coding. By employing approximations of the EMD, we present a feasible, lightweight approach that complements traditional methods. Our findings underscore the potential of the EMD as a powerful tool for enhancing our understanding of remapping in the brain and its implications for spatial navigation, memory studies and beyond.

neuroscience↗