Mark Shein | PubMed Health

PubMed2006

Study of hypothermia on cultured neuronal networks using multi-electrode arrays.

Efficient and safe use of hypothermia during various neuro-medical procedures requires sound understanding of low temperature effects on the neuronal network's activity. In this report, we introduce the use of cultivated dissociated neuronal networks on temperature controlled multi-electrode arrays (MEAs) as a simple methodology for studying the long-term effects of hypothermia. The networks exhibit spontaneous activity in the form of synchronized bursting events (SBEs), followed by long intervals of sporadic firing. Through the use of our correlation method, these SBEs can be clustered into sub-groups of similar spatio-temporal patterns. Application of hypothermia to the network resulted in a reduction in the SBE rate, the spike intensity and an increase in inter-neuronal correlations. Within 2h following the cessation of hypothermia, the cultured network returned to its initial spatio-temporal SBE structure. These results suggest that the network survived cold exposure and demonstrate the feasibility of long-term continuous neural network recording during hypothermic conditions.

Action Potentials↗

PubMed2006

Functional holography analysis: simplifying the complexity of dynamical networks.

We present a novel functional holography (FH) analysis devised to study the dynamics of task-performing dynamical networks. The latter term refers to networks composed of dynamical systems or elements, like gene networks or neural networks. The new approach is based on the realization that task-performing networks follow some underlying principles that are reflected in their activity. Therefore, the analysis is designed to decipher the existence of simple causal motives that are expected to be embedded in the observed complex activity of the networks under study. First we evaluate the matrix of similarities (correlations) between the activities of the network's components. We then perform collective normalization of the similarities (or affinity transformation) to construct a matrix of functional correlations. Using dimension reduction algorithms on the affinity matrix, the matrix is projected onto a principal three-dimensional space of the leading eigenvectors computed by the algorithm. To retrieve back information that is lost in the dimension reduction, we connect the nodes by colored lines that represent the level of the similarities to construct a holographic network in the principal space. Next we calculate the activity propagation in the network (temporal ordering) using different methods like temporal center of mass and cross correlations. The causal information is superimposed on the holographic network by coloring the nodes locations according to the temporal ordering of their activities. First, we illustrate the analysis for simple, artificially constructed examples. Then we demonstrate that by applying the FH analysis to modeled and real neural networks as well as recorded brain activity, hidden causal manifolds with simple yet characteristic geometrical and topological features are deciphered in the complex activity. The term "functional holography" is used to indicate that the goal of the analysis is to extract the maximum amount of functional information about the dynamical network as a whole unit.

Algorithms↗