All-optical systems for stimulating and imaging neuronal activity have served as powerful tools for understanding the underlying circuitry of the brain. Experiments using these setups, however, tend to choose stimulation locations based solely on what brain regions are of interest, and take for granted that stimulation effects may vary even within localized brain regions. We thus have developed an algorithm for acquiring neuronal activity via calcium imaging data to assess network connectivity. These parameters include the signal rise time, decay time, inter-event intervals, and the timing and amplitude of signal peaks. These parameters are then compared between cell clusters for similarities, and used as a basis for establishing interconnectivity. Additionally, we have incorporated both temporal and spatial correlation functions to assess inter-neuronal connectivity based on these parameters. This data is then run through a genetic algorithm, applying weights to cells with similar parameters to learn which are interconnected in a given field-of-view. For this study, hippocampal neurons extracted from 2 day old transgenic mice (GCaMP6s, Jackson Labs), - cultured for 2 weeks and imaged under single and two-photon conditions. Single-photon imaging was performed under a commercial Zeiss microscope, whereas two-photon imaging was performed with an in-house imaging system. Results demonstrate a strong correlation between these parameters and cellular connectivity, making them noteworthy markers for targeted stimulation. This study demonstrates an efficient method of assessing network connectivity for various imaging techniques, and hence directed targeting for optogenetic stimulation.
Carlos Renteria, Shreya M. Santhanam, Yuan-Zhi Liu, Denise Medina, Parijat Sengupta, and Stephen A. Boppart, "Computational algorithm for assessing inter-neuronal connectivity to optimize optogenetic stimulation and neural circuit activity (Conference Presentation)," Proc. SPIE 10482, Optogenetics and Optical Manipulation 2018, 1048210 (Presented at SPIE BiOS: January 28, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2291233.5751486317001.
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