Monitoring brain activity with protein voltage and calcium sensors

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 5 (2015): 10212, doi:10.1038/srep10212.Understanding the roles of different cell types in the behaviors generated by neural circuits requires protein indicators that report neural activity with high spatio-temporal resolution. Genetically encoded fluorescent protein (FP) voltage sensors, which optically report the electrical activity in distinct cell populations, are, in principle, ideal candidates. Here we demonstrate that the FP voltage sensor ArcLight reports odor-evoked electrical activity in the in vivo mammalian olfactory bulb in single trials using both wide-field and 2-photon imaging. ArcLight resolved fast odorant-responses in individual glomeruli, and distributed odorant responses across a population of glomeruli. Comparisons between ArcLight and the protein calcium sensors GCaMP3 and GCaMP6f revealed that ArcLight had faster temporal kinetics that more clearly distinguished activity elicited by individual odorant inspirations. In contrast, the signals from both GCaMPs were a saturating integral of activity that returned relatively slowly to the baseline. ArcLight enables optical electrophysiology of mammalian neuronal population activity in vivo.Supported by US NIH DC005259, WCI 2009-003 from the National Research Foundation of Korea, a James Hudson Brown – Alexander Brown Coxe fellowship from Yale University, and a Ruth L. Kirschstein National Research Service Award DC012981

NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology

Understanding how neural networks generate activity patterns and communicate with each other requires monitoring the electrical activity from many neurons simultaneously. Perfectly suited tools for addressing this challenge are genetically encoded voltage indicators (GEVIs) because they can be targeted to specific cell types and optically report the electrical activity of individual, or populations of neurons. However, analyzing and interpreting the data from voltage imaging experiments is challenging because high recording speeds and properties of current GEVIs yield only low signal-to-noise ratios, making it necessary to apply specific analytical tools. Here, we present NOSA (Neuro-Optical Signal Analysis), a novel open source software designed for analyzing voltage imaging data and identifying temporal interactions between electrical activity patterns of different origin. In this work, we explain the challenges that arise during voltage imaging experiments and provide hands-on analytical solutions. We demonstrate how NOSA’s baseline fitting, filtering algorithms and movement correction can compensate for shifts in baseline fluorescence and extract electrical patterns from low signal-to-noise recordings. NOSA allows to efficiently identify oscillatory frequencies in electrical patterns, quantify neuronal response parameters and moreover provides an option for analyzing simultaneously recorded optical and electrical data derived from patch-clamp or other electrode-based recordings. To identify temporal relations between electrical activity patterns we implemented different options to perform cross correlation analysis, demonstrating their utility during voltage imaging in Drosophila and mice. All features combined, NOSA will facilitate the first steps into using GEVIs and help to realize their full potential for revealing cell-type specific connectivity and functional interactions

A high-density, high-channel count, multiplexed mu ECoG array for auditory-cortex recordings

Our understanding of the large-scale population dynamics of neural activity is limited, in part, by our inability to record simultaneously from large regions of the cortex. Here, we validated the use of a large-scale active microelectrode array that simultaneously records 196 multiplexed micro-electrocortigraphical (mu ECoG) signals from the cortical surface at a very high density (1,600 electrodes/cm(2)). We compared mu ECoG measurements in auditory cortex using a custom "active" electrode array to those recorded using a conventional "passive" mu ECoG array. Both of these array responses were also compared with data recorded via intrinsic optical imaging, which is a standard methodology for recording sound-evoked cortical activity. Custom active mu ECoG arrays generated more veridical representations of the tonotopic organization of the auditory cortex than current commercially available passive mu ECoG arrays. Furthermore, the cortical representation could be measured efficiently with the active arrays, requiring as little as 13.5 s of neural data acquisition. Next, we generated spectrotemporal receptive fields from the recorded neural activity on the active mu ECoG array and identified functional organizational principles comparable to those observed using intrinsic metabolic imaging and single-neuron recordings. This new electrode array technology has the potential for large-scale, temporally precise monitoring and mapping of the cortex, without the use of invasive penetrating electrodes.