TY - JOUR
T1 - Dynamic Tracking Algorithm for Time-Varying Neuronal Network Connectivity using Wide-Field Optical Image Video Sequences
AU - Renteria, Carlos
AU - Liu, Yuan Zhi
AU - Chaney, Eric J.
AU - Barkalifa, Ronit
AU - Sengupta, Parijat
AU - Boppart, Stephen A.
N1 - Funding Information:
The authors would like to thank Marina Marjanovic for her careful review and editing of this manuscript, and Darold Spillman for his administrative support. This research was supported in part by grants from the Air Force Office of Scientific Research, FA9550-17-1-0387, the National Science Foundation, CBET 18-41539, and by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Award Number T32EB019944. The content is solely the responsibility of the authors and does not necessarily represent the official views of these federal agencies. We would also like to thank the Sloan University Center of Exemplary Mentoring at Illinois for their support. Additional information can be found at http://biophotonics.illinois.edu.
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41598-020-59227-5
DO - 10.1038/s41598-020-59227-5
M3 - Article
C2 - 32054882
AN - SCOPUS:85079338743
VL - 10
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 2540
ER -