Modelling the propagation of optogenetic stimulation in the C. elegans connectome
Poster No:
1269
Submission Type:
Late-Breaking Abstract Submission
Authors:
Caio Seguin1, Sophie Dvali2, Rick Betzel3, Andrew Leifer2, Andrew Zalesky4
Institutions:
1University of Melbourne, Melbourne, Victoria, 2Princeton University, NJ, NJ, 3University of Minnesota, Minneapolis, MN, 4Systems Lab, Department of Psychiatry, The University of Melbourne, Melbourne, Australia
First Author:
Co-Author(s):
Andrew Zalesky
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Australia
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Australia
Late Breaking Reviewer(s):
Eduardo Garza-Villarreal, M.D., Ph.D.
Universidad Nacional Autónoma de México campus Juriquilla
Juriquilla, Querétaro
Universidad Nacional Autónoma de México campus Juriquilla
Juriquilla, Querétaro
Ruby Kong
Computational Brain Imaging Group, Yong Loo Lin School of Medicine, National University of Singapor
Singapore, Singapore
Computational Brain Imaging Group, Yong Loo Lin School of Medicine, National University of Singapor
Singapore, Singapore
Introduction:
Understanding how information is communicated in complex brain networks-connectomes-remains a major challenge in modern neuroscience [1,2]. Here, we investigate this question using optogenetic stimulation of single neurons in the C. elegans model organism. We show that network communication models computed on the C. elegans structural connectome can parsimoniously predict causal, polysynaptic signalling elicited by targeted neuronal perturbations.
Methods:
Randi and colleagues [3] performed systematic optogenetic of single neurons in the C. elegans head while recording organism-wide neural activity using calcium imaging. The authors combined stimulation experiments from a total of 113 were compiled into a neuron-by-neuron matrix of signal propagation, where matrix entry ij denotes the strength of the causal response observed in neuron j following controlled stimulation of neuron i.
We used an established map of the entire synaptic wiring of the C. elegans nervous systems-the worm's structural connectome-as the anatomical substrate for models of neural communication [3]. Specifically, we employed graph-theoretical models aimed at describing polysynaptic communication in complex brain networks. We considered a range of models covering the full spectrum of signalling conceptualisations, from diffusion processes (diffusion efficiency [DE], communicability [CMY], search information [SI]) to routing protocols (shortest path [SPE] efficiencies) [4,5,6]. Computed for every pair of neurons, each of these models yielded a communication matrix, where entry ij denotes the structural capacity for connectome communication from neuron i to neuron j, under a putative conceptualisation of signalling.
We tested for an association between signal propagation and connectome communication matrices using the Spearman rank correlation. We focused on pairs of neurons ij for which (A) a significant response was observed in j following stimulation to i and (B) the strength of stimulation response in j was computed based on at least 20 stimulations to i.
We used an established map of the entire synaptic wiring of the C. elegans nervous systems-the worm's structural connectome-as the anatomical substrate for models of neural communication [3]. Specifically, we employed graph-theoretical models aimed at describing polysynaptic communication in complex brain networks. We considered a range of models covering the full spectrum of signalling conceptualisations, from diffusion processes (diffusion efficiency [DE], communicability [CMY], search information [SI]) to routing protocols (shortest path [SPE] efficiencies) [4,5,6]. Computed for every pair of neurons, each of these models yielded a communication matrix, where entry ij denotes the structural capacity for connectome communication from neuron i to neuron j, under a putative conceptualisation of signalling.
We tested for an association between signal propagation and connectome communication matrices using the Spearman rank correlation. We focused on pairs of neurons ij for which (A) a significant response was observed in j following stimulation to i and (B) the strength of stimulation response in j was computed based on at least 20 stimulations to i.
Results:
Network communication models computed on the C. elegans structural connectome explained significant variance in the strength of causal responses to single-neuron optogenetic perturbations. In particular, the communicability model-predicated on propagation via diffusive broadcasting-provided the most accurate explanations of signalling strength (r = 0.53, p < 10-12; 160 neurons pairs; Fig 1A,C), outperforming alternative models as well as structural connectivity weights alone. Importantly, communicability was predictive of polysynaptic signalling between neuron pairs that did not share a synaptic contact (r = 0.35, p < 10-12; 112 neurons pairs; Fig 1B,D), indicating that this model can explain indirect transmission via multi-hop signalling pathways.
Conclusions:
Our findings indicate that simple network communication models-in particular communicability-can predict the propagation of causal neuronal signalling through complex synaptic connectivity. These results add to recent work in the human connectome, which reported that communicability could predict the cortex-wide propagation of direct electrical stimulation [7]. Together, these studies suggest simple principles of signal propagation in complex brain networks conversed across drastically different species and spatial scales.
Brain Stimulation:
Direct Electrical/Optogenetic Stimulation 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Keywords:
Computational Neuroscience
Modeling
Other - Connectome
1|2Indicates the priority used for review
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[2] Seguin, C., Sporns, O., & Zalesky, A. (2023). Brain network communication: concepts, models and applications. Nature reviews neuroscience, 24(9), 557-574.
[3] Randi, F., Sharma, A. K., Dvali, S., & Leifer, A. M. (2023). Neural signal propagation atlas of Caenorhabditis elegans. Nature, 623(7986), 406-414.
[4] Goñi, J., Avena-Koenigsberger, A., Velez de Mendizabal, N., van den Heuvel, M. P., Betzel, R. F., & Sporns, O. (2013). Exploring the morphospace of communication efficiency in complex networks. PLoS One, 8(3), e58070.
[5] Estrada, E., & Hatano, N. (2008). Communicability in complex networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 77(3), 036111.
[6] Goñi, J., Van Den Heuvel, M. P., Avena-Koenigsberger, A., Velez de Mendizabal, N., Betzel, R. F., Griffa, A., ... & Sporns, O. (2014). Resting-brain functional connectivity predicted by analytic measures of network communication. Proceedings of the National Academy of Sciences, 111(2), 833-838.
[7] Seguin, C., Jedynak, M., David, O., Mansour, S., Sporns, O., & Zalesky, A. (2023). Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation. Neuron, 111(9), 1391-1401.