Impact of Physical Obstacles on the Structural and Effective Connectivity of in silico Neuronal Circuits

Abstract

Scaffolds and patterned substrates are among the most successful strategies to dictate the connectivity between neurons in culture. Here, we used numerical simulations to investigate the capacity of physical obstacles placed on a flat substrate to shape structural connectivity, and in turn collective dynamics and effective connectivity, in biologically-realistic neuronal networks. We considered μ-sized obstacles placed in mm-sized networks. Three main obstacle shapes were explored, namely crosses, circles and triangles of isosceles profile. They occupied either a small area fraction of the substrate or populated it entirely in a periodic manner. From the point of view of structure, all obstacles promoted short length-scale connections, shifted the in- and out-degree distributions toward lower values, and increased the modularity of the networks. The capacity of obstacles to shape distinct structural traits depended on their density and the ratio between axonal length and substrate diameter. For high densities, different features were triggered depending on obstacle shape, with crosses trapping axons in their vicinity and triangles funneling axons along the reverse direction of their tip. From the point of view of dynamics, obstacles reduced the capacity of networks to spontaneously activate, with triangles in turn strongly dictating the direction of activity propagation. Effective connectivity networks, inferred using transfer entropy, exhibited distinct modular traits, indicating that the presence of obstacles facilitated the formation of local effective microcircuits. Our study illustrates the potential of physical constraints to shape structural blueprints and remodel collective activity, and may guide investigations aimed at mimicking organizational traits of biological neuronal circuits.

In Vitro Development of Human iPSC-Derived Functional Neuronal Networks on Laser-Fabricated 3D Scaffolds

“…In line with these experimental observations, recent numerical simulations on neuronal networks grown in patterned substrates indicate that physical obstacles or scaffolds favor the emergence of microcircuits that translate into an abundance of functional communities . The study also showed that it was not possible to directly link the arrangement of the physical obstacles with the functional traits of the network except for the extreme case in which cell positioning and connectivity were strongly dictated.…”

“…In line with these experimental observations, recent numerical simulations on neuronal networks grown in patterned substrates indicate that physical obstacles or scaffolds favor the emergence of microcircuits that translate into an abundance of functional communities. 40 The study also Each configuration is an average over 4 network realizations, recorded at day 50 and containing on the order of 500 neurons. Data are shown as the mean ± standard deviation of the mean.…”

“…In line with these experimental observations, recent numerical simulations on neuronal networks grown in patterned substrates indicate that physical obstacles or scaffolds favor the emergence of microcircuits that translate into an abundance of functional communities. 40 The study also showed that it was not possible to directly link the arrangement of the physical obstacles with the functional traits of the network except for the extreme case in which cell positioning and connectivity were strongly dictated. Thus, although the biological mechanisms that promote neuronal connectivity are complex, we would expect that the hollow cylinders in our 3D scaffold could promote strongly coupled microcircuits at the bottom layer, which would be weakly linked to other microcircuits through the top layer.…”

“…The community statistic reflects the existence of functional microcircuits and varies between 0 (the whole network is the only functional circuit) and 1 (each single neuron is a functional microcircuit). The detailed description of these quantities and their use in the context of neuronal networks can be found in refs and . Network plots were produced using the Python package NetworkX.…”

Rich dynamics and functional organization on topographically designed neuronal networksin vitro

“…Causal relationships among pairs of ROIs’ activity were computed by using a modified version of Generalized Transfer Entropy (GTE) (Ludl and Soriano, 2020; Stetter et al, 2012; Tibau et al, 2020) run in Matlab. Binarized vectors for the 30 min activity trains (‘1’ for the presence of a spike, ‘0’ for absence) were constructed using a time bin of 20 ms, and an effective connection from ROI to ROI J (TE I→ J ) was established whenever the information contained in I significantly increased the capacity to predict future states of J .…”