Time Series Analysis of VGLUT1-pH Fluorescence in Rat Hippocampal Neurons
Main Article Content
Abstract
This project uses the vesicular glutamate transporter 1-pHluorin fluorescent reporter to examine synaptic vesicle release in rat hippocampus neurons. The primary objective is to evaluate time series data of mean fluorescence intensity measurements taken before and after stimulation in a neuroscience experiment across different regions of interest, identifying underlying patterns in the data by building autoregressive integrated moving average models. The methods aim to reveal patterns within the data by using decomposition analysis to understand hidden trends, “seasonal” fluctuations, and relationships between fluorescence intensity metrics. Results demonstrate notable seasonal patterns and temporal dependencies, indicating that Seasonal Autoregressive Integrated Moving Average models are necessary to analyze electrically stimulated neuronal activity.
Article Details
How to Cite
RAHMANI, Bahareh et al.
Time Series Analysis of VGLUT1-pH Fluorescence in Rat Hippocampal Neurons.
Medical Research Archives, [S.l.], v. 13, n. 7, aug. 2025.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/6764>. Date accessed: 08 dec. 2025.
doi: https://doi.org/10.18103/mra.v13i7.6764.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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https://www.ncbi.nlm.nih.gov/books/NBK542179/
2. Ellenbroek B, Youn J. Rodent models in neuroscience research: is it a rat race? Dis Model Mech. 2016 Oct 1;9(10):1079-1087. doi: 10.1242/dm m.026120. PMID: 27736744; PMCID: PMC5087838.
3. Ma S, Skarica M, Li Q, et al. Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science. 2022. doi: 10.1126/science.abo7257.
4. Onciul R, Tataru CI, Dumitru AV, et al. Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications. J Clin Med. 2025;14(2):550. Published 2025 Jan 16. doi:10.3390/jcm14020550
5. Frizzell TO, Glashutter M, Liu CC, et al. Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review. Ageing Res Rev. 77:101614, 2022. doi: 10.1016/j.a rr.2022.101614
6. Nakakubo Y, Abe S, Yoshida T, et al. Vesicular Glutamate Transporter Expression Ensures High-Fidelity Synaptic Transmission at the Calyx of Held Synapses. Cell Rep. 2020;32(7):108040. doi:10.101 6/j.celrep.2020.108040
7. Sankaranarayanan S, Ryan TA. Real-time measurements of vesicle-SNARE recycling in synapses of the central nervous system. Nature Cell Biology, 2(4), 197-204. 2000. doi: 10.1038/35008615
8. Burrone J, Li Z, & Murthy, V. N. Synaptic vesicle recycling in hippocampal neurons. Proceedings of the National Academy of Sciences, 99(23), 14488-14493, 2002. doi: 10.1073/pnas.0501145102
9. Gandhi SP, Stevens, CF. Three modes of synaptic vesicular recycling revealed by single-vesicle imaging. Nature, 423(6940), 607-613, 2003. Doi:10.1038/nature01677
10. Miller, K., Yi, G., Snir, E. et al. Precipitation analysis and forecasting weather of Texas, United States. Int. J. Inf. Tecnol. 15, 549–556. 2023. Doi: 10.1007/s41870-022-01063-z
11. Breakspear M. Dynamic models of large-scale brain activity. Nature Neuroscience, 20(3), 340-352. 2017. doi: 10.1038/nn.4497
12. Honey CJ, Kotter R, Breakspear M, Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences, 104(24), 10240-10245. 2007. doi: 10.1073/pnas.0701519104
13. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. Time Series Analysis: Forecasting and Control. John Wiley & Sons, 2015.
14. Shumway RH, Stoffer DS. Time Series Analysis and Its Applications: With R Examples. Springer, 2017
15. Cunningham JP, Yu BM, Shenoy KV. Inferring neural firing rates from spike trains using Gaussian processes. Advances in Neural Information Processing Systems, 21, 329-336, 2009.
16. Gao P, Trautmann E, Yu BM, Santhanam G, Ryu S, Shenoy KV, Ganguli S, A theory of multineuronal dimensionality, dynamics, and measurement. Neuron, 91(4), 857-873, 2015. doi: 10.1101/214262
17. Quian Quiroga R, Panzeri S. Extracting information from neuronal populations: Information theory and decoding approaches. Nature Reviews Neuroscience, 10(3), 173-185, 2009.
18. Rahmani B, Yiyuan H,, Aqeel M. , Norouzzadeh P., Maazallahi A., Snir E., Ashrafi G., Brain information processing analysis using artificial intelligence methods, Signal & Image Processing: An International Journal , 16(6), Page 23-34, 2024.
19. Balaji, J., Ryan, T. A. Single-vesicle imaging reveals that synaptic vesicle exocytosis and endocytosis are coupled by a single stochastic mode. Neuron, 56(6), 929–939. 2007. doi: 10.1073/p nas.0707574105
20. Banker, G., Cowan, W. M. Rat hippocampal neurons in dispersed cell culture. Brain Research, 126(3), 397–425. 1977. doi: 10.1016/0006-8993(77) 90594-7
21. Sudhof, T. C. The synaptic vesicle cycle. Annual Review of Neuroscience, 27, 509–547. 2004. doi:10.1146/annurev.neuro.26.041002.131412
22. Vieira, S., Pinaya, W. H. L., Mechelli, A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, 58–75. 2017. doi: 10.10 16/j.neubiorev.2017.01.002
23. Brown, E. N., Kass, R. E., Mitra, P. P. Multiple neural spike train data analysis: State-of-the-art and future challenges. Nature Neuroscience, 7(5), 456–461.2004. doi: 10.1038/nn1228
24. Shlens, J. (2014). A tutorial on principal component analysis. arXiv. 2014. Available at: https://arxiv.org/abs/1404.1100
25. Luan, Y., & Li, H. Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics, 19(4), 474–482. 2003. doi: 10.1093/bioinformatics/btg014
26. Leise, T. L. Wavelet analysis of circadian and ultradian behavioral rhythms. Journal of Circadian Rhythms, 11(1), 5. 2013. doi: 10.1186/1740-3391-11-5
27. Matsumura, G., Honda, S., Kikuchi, T., Mizuno, Y., Hara, H., Kondo, Y., Nakamura, H., Watanabe, S., Hayakawa, K., Nakajima, K., Takei, K., Real-time personal healthcare data analysis using edge computing for multimodal wearable sensors, Device, 3(2). 2025. 100597, ISSN 2666-9986, 2025. doi: 10.1016/j.device.2024.100597.
28. Chén OY, Roberts B. Personalized Health Care and Public Health in the Digital Age. Front Digit Health. 2021 Mar 30;3:595704. doi: 10.3389/fdgt h.2021.595704. PMID: 34713084; PMCID: PMC85 21939.