1) The study investigated how varying the convergence (number of connections between nodes) of patterned neuronal networks affected their functional connectivity and information transmission fidelity. 2) Networks with higher convergence showed slightly better information transmission fidelity, especially over shorter paths, but convergence had little effect on functional connection strengths. 3) Functional connectivity decreased significantly with distance between nodes, though less so in networks with higher convergence. Overall, convergence modestly influenced information propagation depending on path length, while distance more strongly shaped functional connectivity.

1. SPATIAL DISTRIBUTION OF FUNCTIONAL
CONNECTION STRENGTHS IN PATTERNED NETWORKS
OF VARYING CONVERGENCE
Sankar Alagapan1, Eric W. Franca1, Liangbin Pan1, Thomas B. DeMarse1, Gregory J. Brewer2 and Bruce C. Wheeler1
1 J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL
2 Department of Biomedical Engineering, University of California, Irvine, CA
Introduction
Results
•
We used Conditional Granger Causality (CGC) [4],[5] as a measure of
functional connectivity and Victor – Purpura’s (VP) spike train similarity metric
[6] as a measure of fidelity of information propagation.
Methods
•
Poly-d-lysine (PDL) was patterned onto each
microelectrode array’s (MEA) surface (60
electrodes in 6 x 10 arrangement, 30µm
diameter and 500 µm inter electrode distance)
and treated with 3-glycidoxypropyltrimethoxysilane (3-GPS) by microcontact
printing.
4 Connect
• E18 rat cortical neurons were dissociated
and plated at a density of 700 cells/mm2
Axonal
Bundles
• Patterns consisted of circular nodes (50µm
diameter) connected to neighboring nodes by
straight lines (20µm width). Convergence was
varied by varying number of nearest
neighboring nodes each node was connected to
and the patterns are referred according to this
number as shown in Fig 1 (2 Connect – 2
nearest neighbors along horizontal axis, 4
Connect – 4 nearest neighbors in both
horizontal and vertical axis and 8 Connect – 8
nearest neighbors as in 4 connect but including
diagonal).
2 Connect
Neurons
(Cell Body
Cluster)
Electrode
8 Connect
Fig 1.Three different
convergence patterns based
on a serial chain (2
connection), city-block (4
connection), and 8
connection network
topologies.
• CGC was calculated from smoothed spike
trains of the spontaneous activity recorded from
these networks and VP dissimilarity metric (Dv)
was calculated from the spike trains of
spontaneous activity and the cost parameter
varied to account for multiple lengths of time
segments.
Results
Effect of Convergence on Functional Connection Strengths
Fig 2. Overall Distribution of CGC
Strengths.
•We hypothesized that higher convergence
may lead to higher functional connection
strengths between nodes in the network.
•However, the distribution of CGC
strengths among patterned networks were
not significantly different. (Random
networks were significantly different than
patterned)
Effect of Convergence on Fidelity of Information Propagation
Dissimilarity
• Patterned networks interfaced with planar multi electrode arrays (MEAs) [1]
provide a living model system to study the effect a network’s structure on its
function [2],[3].
• We varied the convergence of structural connections (i.e. number of nearest
connecting neighbor nodes) to study the influence of convergence on functional
connectivity and fidelity of information transmission.
A
B
Fig 4 A. Fidelity of Information Propagation vs. Convergence
•Higher convergence should lead to better fidelity of information propagation
between nodes in the network. Spike trains increased in similarity (decreased
dissimilarity) increasing convergence but were most similar in random cultures.
•Dissimilarity decreased with longer time windows supporting a strong role for
a rate modulation as neural code during transmission.
•Random networks had the least dissimilarity (highest fidelity) in transmission
and 2 Connect networks had high dissimilarity (low fidelity)
Fig 4 B. Fidelity of Information propagation vs. Distance
•No significant trend was observed in the fidelity of information propagation
with respect to distance between nodes
Fig 5. Fidelity of
information propagation
vs. Path Length
•Fidelity of propagating
spike trains was affected by
the number of mediating
nodes (path length)
•The effect of convergence
is pronounced at shorter
path lengths (path lengths
of 1 and 2).
•At longer path lengths
effect was not significant
Conclusion
• Fidelity of information was high at longer time windows suggesting rate
based code during propagation.
• Convergence did affect the fidelity of information propagation but depended
more upon path length (number of intermediate connections) than physical
distance.
• Convergence does not affect the functional connection strengths between
nodes in a network.
• Functional connectivity is affected by physical distance. This effect depends
on the levels of convergence. Higher the convergence, lesser the effect
distance had on connection strength.
Acknowledgement
This work was partly supported by NIH grant NS052233
2 Connect
Fig 3. CGC strengths vs.
distance.
•2 Connect Networks
showed a steeper decline
in mean CGC strengths
compared to 4 and 8
Connect Networks which
in turn were steeper than
Random Networks.
Slope = -0.0449
Mean Normalized CGC Values
•The strength of any
functional connectivity
decreased with distance
from each node.
4 Connect
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References
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networks on reliable patterned substrates. Journal of Neuroscience Methods. Elsevier.
8 Connect
Random
Slope = -0.0205
Slope = -0.0064
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(2012). Emergent Functional Properties of Neuronal Networks with Controlled
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Distance in µm
TEMPLATE DESIGN © 2008
Slope = -0.0207
6. Victor, J. D. & Purpura, K. P. (1996). Nature and precision of temporal coding in visual
cortex: a metric-space analysis. Journal of Neurophysiology. Am Physiological Soc.