Associations of neuro-glial network calcium activity with mice movements in vivo
- Authors: Krivonosov M.I.1,2, Varekhina A.V.2, Anokhin K.V.3, Ivanchenko M.V.1,2
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Affiliations:
- Ivannikov Institute for System Programming of the Russian Academy of Sciences
- National Research Lobachevsky State University of Nizhny Novgorod
- Lomonosov Moscow State University
- Issue: Vol 18, No 4 (2023)
- Pages: 850-853
- Section: Conference proceedings
- URL: https://genescells.ru/2313-1829/article/view/623434
- DOI: https://doi.org/10.17816/gc623434
- ID: 623434
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Abstract
Calcium imaging of nerve activity in the mouse hippocampus provides insights into cell-to-cell interactions [1]. Over time, fluctuations in cellular calcium levels encode distinct brain states during mouse brain imaging. Although synapses connect cells and transmit information, it is challenging to detect the spatiotemporal transmission pathway via calcium imaging of multiple cells due to the complexity of the spatial structure and imaging in a separate focal plane [2]. Reconstruction of the dynamic graph of connections between cells was proposed to overcome this problem.
A dynamic graph comprises of individual graphs for each time point. A graph linked to a particular time t is composed of vertices representing cells and edges that depict the transmission of signals between cells at that moment. This paper puts forth 3 approaches for creating networks. The first method considers the overlapping time intervals of calcium events in individual cells [3]. The link is established between the cell with the earlier event and the cell with the later start of the event during the moments when the events occurred simultaneously in separate cells. Alternatively, time intervals between the start of events were taken into account. Potentially connected events that began no later than 2 s were linked, with the edge drawn from the cell depicting the earlier starting event to the second cell. The third technique involved linking sequentially occurring events in distinct cells. The edge is drawn from all active cells at the previous time step to newly activated cells at the subsequent time step.
Data analysis is based on an experiment in which a mouse moved along a circular track while the fluorescence of neuronal calcium activity and the mouse’s position were recorded at a frequency of 20 frames per second [4]. A red dot was marked on the mouse’s head to track its position. The reconstructed dynamic graph was compared to the angular coordinate of the mouse on the ring to look for repeating patterns of activity. The racetrack was divided into 20 overlapping sectors, each spanning 36 degrees. The reconstructed networks were then assigned to sectors that aligned with the mouse’s angular position on the track.
Next, we estimated the frequency of individual edge repetitions within each network group. Only edges that occurred at least three times were chosen for further analysis. We found repeated activations of various cell pairs that corresponded to clockwise and counterclockwise movement within the same sector. Furthermore, we identified the presence of alternating activation, where activity occurred in the first cell, then the second cell, and then again in the first cell. In addition, we identified complex sequences of 5–6 non-sequential activations, represented as a digraph without cycles, which is typical for single sectors.
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Full Text
Calcium imaging of nerve activity in the mouse hippocampus provides insights into cell-to-cell interactions [1]. Over time, fluctuations in cellular calcium levels encode distinct brain states during mouse brain imaging. Although synapses connect cells and transmit information, it is challenging to detect the spatiotemporal transmission pathway via calcium imaging of multiple cells due to the complexity of the spatial structure and imaging in a separate focal plane [2]. Reconstruction of the dynamic graph of connections between cells was proposed to overcome this problem.
A dynamic graph comprises of individual graphs for each time point. A graph linked to a particular time t is composed of vertices representing cells and edges that depict the transmission of signals between cells at that moment. This paper puts forth 3 approaches for creating networks. The first method considers the overlapping time intervals of calcium events in individual cells [3]. The link is established between the cell with the earlier event and the cell with the later start of the event during the moments when the events occurred simultaneously in separate cells. Alternatively, time intervals between the start of events were taken into account. Potentially connected events that began no later than 2 s were linked, with the edge drawn from the cell depicting the earlier starting event to the second cell. The third technique involved linking sequentially occurring events in distinct cells. The edge is drawn from all active cells at the previous time step to newly activated cells at the subsequent time step.
Data analysis is based on an experiment in which a mouse moved along a circular track while the fluorescence of neuronal calcium activity and the mouse’s position were recorded at a frequency of 20 frames per second [4]. A red dot was marked on the mouse’s head to track its position. The reconstructed dynamic graph was compared to the angular coordinate of the mouse on the ring to look for repeating patterns of activity. The racetrack was divided into 20 overlapping sectors, each spanning 36 degrees. The reconstructed networks were then assigned to sectors that aligned with the mouse’s angular position on the track.
Next, we estimated the frequency of individual edge repetitions within each network group. Only edges that occurred at least three times were chosen for further analysis. We found repeated activations of various cell pairs that corresponded to clockwise and counterclockwise movement within the same sector. Furthermore, we identified the presence of alternating activation, where activity occurred in the first cell, then the second cell, and then again in the first cell. In addition, we identified complex sequences of 5–6 non-sequential activations, represented as a digraph without cycles, which is typical for single sectors.
ADDITIONAL INFORMATION
Authors’ contribution. All authors made a substantial contribution to the conception of the work, acquisition, analysis, interpretation of data for the work, drafting and revising the work, final approval of the version to be published and agree to be accountable for all aspects of the work.
Funding sources. This study was conducted within the framework of the scientific program of the National Center for Physics and Mathematics, section No. 9 “Artificial Intelligence and Big Data in Technical, Industrial, Natural and Social Systems”.
Competing interests. The authors declare that they have no competing interests.
About the authors
M. I. Krivonosov
Ivannikov Institute for System Programming of the Russian Academy of Sciences; National Research Lobachevsky State University of Nizhny Novgorod
Author for correspondence.
Email: krivonosov@itmm.unn.ru
Russian Federation, Moscow; Nizhny Novgorod
A. V. Varekhina
National Research Lobachevsky State University of Nizhny Novgorod
Email: krivonosov@itmm.unn.ru
Russian Federation, Nizhny Novgorod
K. V. Anokhin
Lomonosov Moscow State University
Email: krivonosov@itmm.unn.ru
Russian Federation, Moscow
M. V. Ivanchenko
Ivannikov Institute for System Programming of the Russian Academy of Sciences; National Research Lobachevsky State University of Nizhny Novgorod
Email: krivonosov@itmm.unn.ru
Russian Federation, Moscow; Nizhny Novgorod
References
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- Mitroshina EV, Krivonosov MI, Burmistrov DE, et al. Signatures of the consolidated response of astrocytes to ischemic factors in vitro. Int J Mol Sci. 2020;21(21):7952. doi: 10.3390/ijms21217952
- Mitroshina EV, Pakhomov AM, Krivonosov MI, et al. Novel algorithm of network calcium dynamics analysis for studying the role of astrocytes in neuronal activity in alzheimer’s disease models. Int J Mol Sci. 2022;23(24):15928. doi: 10.3390/ijms232415928
- Sotskov VP, Pospelov NA, Plusnin VV, Anokhin KV. Calcium imaging reveals fast tuning dynamics of hippocampal place cells and CA1 population activity during free exploration task in mice. Int J Mol Sci. 2022;23(2):638. doi: 10.3390/ijms23020638
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