normalize1_withoutremove.zip is the time course of whole-brain activities of C. elegans for 28 animals used in Tsuyuzaki et al, "WormTensor: a clustering method for time-series whole-brain activity data from C. elegans” (BMC Bioinformatics, 2023, https://doi.org/10.1186/s12859-023-05230-2)
This dataset is superset of the data used in Toyoshima et al, "Ensemble dynamics and information flow deduction from whole-brain imaging data" (now in press at PLoS Comp Biol, https://doi.org/10.1371/journal.pcbi.1011848) or its preprint on bioRxiv 2022.11.18.517011 (https://doi.org/10.1101/2022.11.18.517011)
20221118freezed.7z is the dataset used in with TDE-RICA (https://github.com/YuToyoshima/TDE-RICA) and gKDR-GMM (https://github.com/yuichiiino1/gKDR-GMM).
See details in Toyoshima et. al. (2024) PLoS Comput Biol.
Yu Toyoshima, Hirofumi Sato, Daiki Nagata, Manami Kanamori, Moon Sun Jang, Koyo Kuze, Suzu Oe, Takayuki Teramoto, Yuishi Iwasaki, Ryo Yoshida, Takeshi Ishihara, Yuichi Iino (2024) Ensemble dynamics and information flow deduction from whole-brain imaging data., PLoS computational biology, Volume 20, Number 3, pp. e1011848
Published in 2024 Mar (Electronic publication in March 15, 2024, midnight )
(Abstract) The recent advancements in large-scale activity imaging of neuronal ensembles offer valuable opportunities to comprehend the process involved in generating brain activity patterns and understanding how information is transmitted between neurons or neuronal ensembles. However, existing methodologies for extracting the underlying properties that generate overall dynamics are still limited. In this study, we applied previously unexplored methodologies to analyze time-lapse 3D imaging (4D imaging) data of head neurons of the nematode Caenorhabditis elegans. By combining time-delay embedding with the independent component analysis, we successfully decomposed whole-brain activities into a small number of component dynamics. Through the integration of results from multiple samples, we extracted common dynamics from neuronal activities that exhibit apparent divergence across different animals. Notably, while several components show common cooperativity across samples, some component pairs exhibited distinct relationships between individual samples. We further developed time series prediction models of synaptic communications. By combining dimension reduction using the general framework, gradient kernel dimension reduction, and probabilistic modeling, the overall relationships of neural activities were incorporated. By this approach, the stochastic but coordinated dynamics were reproduced in the simulated whole-brain neural network. We found that noise in the nervous system is crucial for generating realistic whole-brain dynamics. Furthermore, by evaluating synaptic interaction properties in the models, strong interactions within the core neural circuit, variable sensory transmission and importance of gap junctions were inferred. Virtual optogenetics can be also performed using the model. These analyses provide a solid foundation for understanding information flow in real neural networks.(MeSH Terms)