P019 Distinguishing spatiotemporal scales within a connectome reveals integration and segregation efficiency in global patterns of neuronal activity
Diego Becerra*1,2, Ignacio Ampuero1,2, Pedro Mediano3, Christopher Connor4, Andrea Calixto2, & Patricio Orio1,2
1 Valparaíso Neural Dynamics Laboratory, Faculty of Sciences, Universidad de Valparaíso, Chile
2 Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Chile
3 Department of Computing, Imperial College London, United Kingdom
4 Brigham & Women’s Hospital, Harvard Medical School, Boston, USA.
* Email: becerra.q.diego@gmail.com
Introduction
Neurons in the brain communicate in different ways, and thus connectomes can be conceived as overlapping dissimilar networks depending on the type of signal being transmitted. One crucial difference between modes of signalling is given by the connectivity timescales, yielding four layers of paths (ordered from fastest to slowest): gap junctions, amino acid, monoaminergic, and peptidergic transmitters.Caenorhabditis elegans, a 302-neuron nematode, is an excellent model for exploring both topological and functional properties of the interaction between layers of networks, because its full connectome is known, alongside a lot of its electrophysiology.
Methods
A full structural connectome ofC. eleganswas built from the latest empirical works available. Functional connectomes were built from twoC. elegans‘whole-brain’ calcium imaging datasets of global states: npr-1mutants undergoing quiescence and wakefulness [2]; and QW1217 mutants undergoing 4% and 8% of isoflurane anesthesia [1].
We analyzed integration and segregation applying network topology measures to the datasets and to the connectome layers. Also, we developed a partial network decomposition [PND] algorithm which analyzes the shortest path between nodes of a pair of overlapping networks. We then compared the network properties of theC. elegansconnectomes with lattiziced and randomized surrogates.
Results
While the peptidergic connectome is dense, the others are sparse. Applying PND, we determined if a path between nodes is redundant, uniquely contributed, or synergistic for a pair of spatiotemporal scales. Unique paths are predominant in all pairs of scales, the highest redundancy is found between electrical and amino acid transmission, and the highest synergy is between electrical and monoaminergic.
Empirical pairs of connectomes are more synergistic than their latticized or randomized surrogates, suggesting that the empirical network yields an improvement in efficiency. Comparing segregation and integration between structural (SC) and functional (FC) connectivity, FC of asleep and anesthetized worms is closer to SC than the FC of awake worms.
Discussion
We were able to characterize complementary (synergistic), redundant, and unique paths between nodes of the connectomes. Yet, the recent integration of gene-expression datasets and ligand-receptor interaction shows a pervasive extra-synaptic transmission network. To discover the effect of differences in connectivity density between peptidergic and the other scales of neurotransmission require thus including the temporal dimension: both by using empirical ‘whole-brain’ datasets portraying different global states (wakefulness, sleep, anesthesia) and a mathematical model using the full topology of the network, which is in process of being implemented.
Figure 1. Fig. 1. (A) Center: Shortest paths of the empirical connectome layers favor synergy as distance between nodes increase, when compared to latticized (left) and randomized (right) versions. Binarizing top Pearson correlations of awake vs. asleep (B); and anesthetized vs. awake (C) timeseries show that structural segregation (left cols.) and integration (right cols.) values are closer to asleep ones.
Acknowledgements
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYTPatricio Orio grant number 1241469; ANID-Basal: Patricio Orio grant number AFB240002; ANID-Doctoral Fellowship: Diego Becerra 21210914.
References
1. Chang, A. S., Wirak, G. S., Li, D., Gabel, C. V., & Connor, C. W. (2023).Measures of Information Content during Anesthesia and Emergence in the Caenorhabditis elegans Nervous System.Anesthesiology, 139(1), 49–62.https://doi.org/10.1097/ALN.0000000000004579
2. Nichols, A. L. A., Eichler, T., Latham, R., & Zimmer, M. (2017).A global brain state underlies C. Elegans sleep behavior.Science, 356(6344), 1247–1256.https://doi.org/10.1126/science.aam6851
