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- Title
Ensemble dynamics and information flow deduction from whole-brain imaging data.
- Authors
Toyoshima, Yu; Sato, Hirofumi; Nagata, Daiki; Kanamori, Manami; Jang, Moon Sun; Kuze, Koyo; Oe, Suzu; Teramoto, Takayuki; Iwasaki, Yuishi; Yoshida, Ryo; Ishihara, Takeshi; Iino, Yuichi
- 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. Author summary: Brain is a complex network of interconnected neurons that process sensory and other information through synaptic connections. In this study we measured the activity of all neurons in the head of a nematode worm, C. elegans, by using a high-speed fluorescent microscope. Through the application of cutting-edge mathematical methods to the acquired data, we successfully extracted recurring dynamics of subsets of neurons and predicted activities of each neuron. Remarkably, the model autonomously generated network dynamics of the whole brain. By carefully analyzing these reconstructions, neuronal interactions and information flow in the brain could be deduced. Our results present a methodology for understanding the basic construction of brain dynamics through observation of brain activity, which are likely applicable to the brain of other animals.
- Subjects
NEURAL transmission; INDEPENDENT component analysis; NEURAL circuitry; NERVOUS system; THREE-dimensional imaging; CAENORHABDITIS elegans
- Publication
PLoS Computational Biology, 2024, Vol 20, Issue 3, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1011848