FUNCTIONAL CONNECTIVITY OF HIGH- AND LOW-DENSITY HIPPOCAMPAL NEURAL NETWORKS COUPLED TO HIGH-DENSITY MEAs
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1
University of Genova, Department of Informatics, Bioengineering, Robotics, System Engineering , Italy
Motivation
One of the major challenge of contemporary neuroscience is approaching the complex interplay between single cells, small cell networks (i.e. microcircuits) and large population cell assemblies. Since the system under study (i.e. brain areas, cell assemblies) is highly complex, it is useful to adopt a reductionist approach. A possible strategy makes use of in vitro experimental models with different architectures (e.g. degree of complexity) coupled to Micro-Electrode Arrays (MEAs). A detailed analysis of functional connectivity of such in vitro models, as well as the possibility to understand the interplay between topology, structure, function and dynamics, is very important for better understanding how the central nervous system represents and stores the information (i.e. the neural code). However, achieving this goal requires the analysis of high resolution system with thousands of electrodes, allowing to obtain ideally that one neuron is coupled to one electrode. Nowadays, these types of analysis are feasible thanks to high-density MEAs (HD-MEAs) like the Active Pixel Sensor (APS) array with 4096 microelectrodes [1]. In this work, we performed a functional connectivity analysis based on our customized version of the 1-bin delayed Transfer Entropy (TE) algorithm on hippocampal networks coupled to the APS with different seeding conditions (high and low cell density).
Material and Methods
TE is an information-theoretic measure that describes the part of activity of a neuron that is not dependent on its own past but dependent on the past activity of another neuron [2]. Let x and y be two spike trains, TE is defined as [3]:
Where and are present and past activity of x, respectively and yt-1 is the past of y. We implemented a customized version of the 1-bin TE extending its temporal range to overcome the weakness of the single delay TE. In particular, shifting the past of train y by different time delays (from 1 to 30 ms) we evaluated TE as a function of time delay taking the peak value for further analysis. We applied our version of TE algorithm to hippocampal networks coupled to the APS with two different seeding densities. In particular, we considered three low-density cultures (80-200 cell/mm2), and three high-density cultures (350-1200 cell/ mm2). Analysis have been performed on a recording chunk of 10 minutes acquired at the sampling frequency of 7022 Hz. Once that the TE connectivity matrix is obtained, we used a hard threshold whose value has been set as µ + ?, where µ and ? are the mean value and the standard deviation of the CM values, respectively [4].
Results
Figure 1 shows the thresholded connectivity graphs relative to one high-density (1A) and one low-density (1B) hippocampal culture. We can see that the high-density graph shows an higher number of links (12’434) involving an higher number of nodes (271) than the low-density one (756 links and 120 nodes). Figure 2 shows the degree (i.e., the total number of incoming and outgoing links for each node) distribution for the same networks of figure 1. We can notice higher degree values for the high-density network (2A) than the low-density one (2B). We also applied two well-known graph theory metrics to our networks: Cluster Coefficient (CC) and Path Length (PL). The high-density networks had a CC equal to 0.032 ± 0.013 with PL of 1.94 ± 0.35. The low-density cultures show a CC of 0.007 ± 0.001 with a PL of 2.63 ± 0.32. Finally, we inveTopic: Signal analysis and statistics (information coding in neural networks).
Discussion
Our customized version of 1 bin delayed TE is able to distinguish between high and low-density hippocampal networks coupled to the high-density MEAs. In particular, more links and more nodes survive to the thresholding procedure for the high-density networks than the low-density ones. High-density networks are more clustered and show a higher PL than the low-density ones. Both low- and high-density hippocampal networks showed a small world topology, with a small world index higher for the low-density ones.
Conclusions
Although the analyzed dataset is still limited, the plating density of hippocampal neural networks coupled to high-density MEAs influences the properties of segregation and integration of the network. Even if both low and high seeding densities produce small world topologies, our analysis showed that lower densities correspond to higher small world indexes.
References
1. Berdondini L, Imfeld K, Maccione A, Tedesco M, Neukom S, Koudelka-Hep M, Martinoia S. Active pixel sensor array for high spatio-temporal resolution electrophysiological recordings from single cell to large scale neuronal networks. Lab Chip. 2009 Sep 21;
2. Lungarella M, Pitti A, Kuniyoshi Y (2007) Information transfer at multiple scales Physical Review E 76;
3. Garofalo M, Nieus T, Massobrio P, Martinoia S. Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks. PLoS One. 2009 Aug 4;
4. Poli D, Pastore VP, Massobrio P. Functional connectivity in in vitro neuronal assemblies. Front Neural Circuits. 2015 Oct 7;
5. Downes,J.H.,Hammond,M.W.,Xydas,D.,Spencer,M.C.,Becerra,V. M.,Warwick,K., et al.(2012).Emergence of a small-world functional network in cultured neurons. PLoS Comput. Biol.
Figure Legend
Figure 1. Connectivity graphs correspondent to hippocampal networks coupled to APS. (A) High-density network (B) Low- density network.
Figure 2. Degree distribution relative to a high-density network (A) and a low-density one (B).
Keywords:
functional connectivity,
neural networks,
transfer entropy,
high-density array
Conference:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016.
Presentation Type:
Poster Presentation
Topic:
MEA Meeting 2016
Citation:
Pastore
VP,
Godjoski
A,
Martinoia
S and
Massobrio
P
(2016). FUNCTIONAL CONNECTIVITY OF HIGH- AND LOW-DENSITY HIPPOCAMPAL NEURAL NETWORKS COUPLED TO HIGH-DENSITY MEAs.
Front. Neurosci.
Conference Abstract:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays.
doi: 10.3389/conf.fnins.2016.93.00075
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Received:
22 Jun 2016;
Published Online:
24 Jun 2016.
*
Correspondence:
Dr. Paolo Massobrio, University of Genova, Department of Informatics, Bioengineering, Robotics, System Engineering, Genova, Italy, paolo.massobrio@unige.it