Dynamic Tracking Algorithm for Time-Varying Neuronal Network Connectivity using Wide-Field Optical Image Video Sequences

Abstract

propagation of signals between neurons and brain regions provides information about the functional properties of neural networks, and thus information transfer. Advances in optical imaging and statistical analyses of acquired optical signals have yielded various metrics for inferring neural connectivity, and hence for mapping signal intercorrelation. However, a single coefficient is traditionally derived to classify the connection strength between two cells, ignoring the fact that neural systems are inherently time-variant systems. To overcome these limitations, we utilized a time-varying Pearson's correlation coefficient, spike-sorting, wavelet transform, and wavelet coherence of calcium transients from DIV 12-15 hippocampal neurons from GCaMP6s mice after applying various concentrations of glutamate. Results provide a comprehensive overview of resulting firing patterns, network connectivity, signal directionality, and network properties. Together, these metrics provide a more comprehensive and robust method of analyzing transient neural signals, and enable future investigations for tracking the effects of different stimuli on network properties.

Ultra-parallel label-free optophysiology of neural activity

“…For instance, the time-varying correlation matrices were used to describe statistical similarities between different ROIs in the current set of experiments; these similarities were used to infer connectivity patterns. Such analogies have been proven valid for both electrode-based ( Berdondini et al., 2009 ; Pastore et al., 2018 ) and optical neurophysiology setups ( Renteria et al., 2020 ; Voleti et al., 2019 ). However, the origin of these connectivity patterns, be it mechanical, electrical, synaptical, or chemical, could be discerned using physical or pharmacological modulation of the cultured neural network, which could provide further context to the interpretability of these results for specific biological systems.…”

“…Quantitatively, similar to the methods implemented previously ( Renteria et al., 2020 ; Voleti et al., 2019 ), the correlation coefficient and the lag/lead times between two responses can be calculated from Equation (3) , where is the signal from region of interest n , C mn is the cross-correlation between S m and S n , r mn is the normalized correlation coefficient, and is the lag or lead of region n compared to m . …”

“…The result of this analysis for the responses shown in Figure 2 D is shown in Figures 3 A–3C, in matrix format, and as an accompanying pictorial representation showing the response correlation matrix, where and correspond to . The response correlation matrix has been previously used to describe connectivity patterns in neural networks ( Renteria et al., 2020 ; Voleti et al., 2019 ). Tracking the correlation matrices highlights that the electrical stimulation of neurons not just activates a localized region, but the overall network, as observed in the increased correlation ( Figure 3 A) and sudden onset of connections ( Figure 3 C) between 800 and 1100 ms and again between 1500 and 1700 ms.…”

“…These demonstrations highlighted the optical characteristics of millisecond-scale neural activity which could be used to generate instantaneous correlation matrices and compare with electrophysiology responses qualitatively. The experimental schemes of these studies, the analysis of the correlation matrices, and the visualizations were chosen to emulate previous demonstrations of neurophysiology setups and studies ( Berdondini et al., 2009 ; Pastore et al., 2018 ; Renteria et al., 2020 ; Voleti et al., 2019 ). These studies have analyzed their data either for specific ROIs ( Renteria et al., 2020 ; Voleti et al., 2019 ), similar to the results shown in Figures 2 , 3 , and 4 , or for the whole FOV at once ( Pastore et al., 2018 ), similar to the results in Figures 5 and 6 .…”

“…The experimental schemes of these studies, the analysis of the correlation matrices, and the visualizations were chosen to emulate previous demonstrations of neurophysiology setups and studies ( Berdondini et al., 2009 ; Pastore et al., 2018 ; Renteria et al., 2020 ; Voleti et al., 2019 ). These studies have analyzed their data either for specific ROIs ( Renteria et al., 2020 ; Voleti et al., 2019 ), similar to the results shown in Figures 2 , 3 , and 4 , or for the whole FOV at once ( Pastore et al., 2018 ), similar to the results in Figures 5 and 6 . Each of these experiments merits focused studies of its own to effectively utilize the spatiotemporal throughput of optophysiology with SPoOF OCM.…”