Seeking for principles of brain structure organization and its control mechanisms has been one of the central pursuits in brain science [1]. Brain is a complex system comprising of large number neurons that interact with each other giving rise to its functions. Hence, going beyond reductionist approaches, holistic study of structure and function of brain as a networked system is expected to yield insights into its architecture, evolution and control [2,3]. We argue that investigating brain as a complex network of neuronal connections provides a systems perspective of emergent properties and integrative functions such as behavior and memory.
We investigated the neuronal wiring diagram of C. elegans, the only complete connectome available till date [4,5], to study basic governing principles that drive structure and function of its neuronal architecture. This modest worm with its elementary nervous system possesses complex repertoire of functions linked to sensation, movement, conditioning and memory [6,7,8].
In our studies, we identified the 'driver neurons [9]' in the neuronal network of this worm with a combination of network theory and control systems analysis, and related them to feed forward neuronal motifs. We propose a 'distance constrained synaptic plasticity model' that captures control mechanisms of C. elegans brain network. Essentially, our results suggest that the extent of synaptic plasticity in this neuronal network is optimized so as to acquire key structural and dynamical network features.
With increasing availability and refinement of neuronal connectivity data from higher organisms (drosophila [10], mouse [11]), including humans [12], we hope our study could provide valuable insights into brain organization and its control.
References:
[1] E. Kandel, J. Schwartz, T. Jessel, Principles of Neural Science, 4th Edition, McGraw-Hill, 2000.
[2] O. Sporns, Networks of the Brain, MIT Press, 2011.
[3] Y.-Y. Liu, J.-J. Slotine, A.-L. Barabasi, Controllability of complex networks, Nature 473 (7346) (2011) 167–73.
[4] J. G. White et al., The structure of the nervous system of the nematode Caenorhabditis elegans, Philosophical Transactions of the Royal Society B: Biological Sciences 314 (1165) (1986) 1–340.
[5] B. L. Chen, D. H. Hall, D. B. Chklovskii, Wiring optimization can relate neuronal structure and function, PNAS 103 (12) (2006) 4723–8.
[6] WormAtlas, Z.F. Altun, L.A. Herndon, C.A. Wolkow, C. Crocker, R. Lints, D.H. Hall, (ed.s) 2002-2016. http://www.wormatlas.org
[7] K. L. Howe et al., WormBase 2016: Expanding to enable helminth genomic research, Nucleic Acids Research 44 (D1) (2016) D774–80.
[8] N. Chatterjee, S. Sinha, Understanding the mind of a worm: Hierarchical network structure underlying nervous system function in C. elegans, Progress in Brain Research 168 (07) (2008) 145–53.
[9] R. Badhwar, G. Bagler, Control of neuronal network in Caenorhabditis elegans, PLoS ONE 10 (9) (2015) e0139204.
[10] A.-S. Chiang et al., Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution, Current Biology 21 (1) (2011) 1–11.
[11] B. Zingg et al., Neural networks of the mouse neocortex, Cell 156 (5) (2014) 1096–111.
[12] O. Sporns, The human connectome: Origins and challenges, NeuroImage 80 (2013) 53–61.
We investigated the neuronal wiring diagram of C. elegans, the only complete connectome available till date [4,5], to study basic governing principles that drive structure and function of its neuronal architecture. This modest worm with its elementary nervous system possesses complex repertoire of functions linked to sensation, movement, conditioning and memory [6,7,8].
In our studies, we identified the 'driver neurons [9]' in the neuronal network of this worm with a combination of network theory and control systems analysis, and related them to feed forward neuronal motifs. We propose a 'distance constrained synaptic plasticity model' that captures control mechanisms of C. elegans brain network. Essentially, our results suggest that the extent of synaptic plasticity in this neuronal network is optimized so as to acquire key structural and dynamical network features.
With increasing availability and refinement of neuronal connectivity data from higher organisms (drosophila [10], mouse [11]), including humans [12], we hope our study could provide valuable insights into brain organization and its control.
Rahul Badhwar and Ganesh Bagler*, "A distance constrained synaptic plasticity model of C. elegans neuronal network", Physica A (In Press) (2016). arXiv:1603.03867[q-bio.NC]
References:
[1] E. Kandel, J. Schwartz, T. Jessel, Principles of Neural Science, 4th Edition, McGraw-Hill, 2000.
[2] O. Sporns, Networks of the Brain, MIT Press, 2011.
[4] J. G. White et al., The structure of the nervous system of the nematode Caenorhabditis elegans, Philosophical Transactions of the Royal Society B: Biological Sciences 314 (1165) (1986) 1–340.
[5] B. L. Chen, D. H. Hall, D. B. Chklovskii, Wiring optimization can relate neuronal structure and function, PNAS 103 (12) (2006) 4723–8.
[6] WormAtlas, Z.F. Altun, L.A. Herndon, C.A. Wolkow, C. Crocker, R. Lints, D.H. Hall, (ed.s) 2002-2016. http://www.wormatlas.org
[7] K. L. Howe et al., WormBase 2016: Expanding to enable helminth genomic research, Nucleic Acids Research 44 (D1) (2016) D774–80.
[8] N. Chatterjee, S. Sinha, Understanding the mind of a worm: Hierarchical network structure underlying nervous system function in C. elegans, Progress in Brain Research 168 (07) (2008) 145–53.
[9] R. Badhwar, G. Bagler, Control of neuronal network in Caenorhabditis elegans, PLoS ONE 10 (9) (2015) e0139204.
[10] A.-S. Chiang et al., Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution, Current Biology 21 (1) (2011) 1–11.
[11] B. Zingg et al., Neural networks of the mouse neocortex, Cell 156 (5) (2014) 1096–111.
[12] O. Sporns, The human connectome: Origins and challenges, NeuroImage 80 (2013) 53–61.
