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. 2024 Mar 6;3(3):pgae092.
doi: 10.1093/pnasnexus/pgae092. eCollection 2024 Mar.

Cocaine diminishes functional network robustness and destabilizes the energy landscape of neuronal activity in the medial prefrontal cortex

Ahmad Borzou  1   2 , Sierra N Miller  3 , Jonathan D Hommel  3 , J M Schwarz  1   4
Affiliations

Cocaine diminishes functional network robustness and destabilizes the energy landscape of neuronal activity in the medial prefrontal cortex

Ahmad Borzou et al. PNAS Nexus. .

Abstract

We present analysis of neuronal activity recordings from a subset of neurons in the medial prefrontal cortex of rats before and after the administration of cocaine. Using an underlying modern Hopfield model as a description for the neuronal network, combined with a machine learning approach, we compute the underlying functional connectivity of the neuronal network. We find that the functional connectivity changes after the administration of cocaine with both functional-excitatory and functional-inhibitory neurons being affected. Using conventional network analysis, we find that the diameter of the graph, or the shortest length between the two most distant nodes, increases with cocaine, suggesting that the neuronal network is less robust. We also find that the betweenness centrality scores for several of the functional-excitatory and functional-inhibitory neurons decrease significantly, while other scores remain essentially unchanged, to also suggest that the neuronal network is less robust. Finally, we study the distribution of neuronal activity and relate it to energy to find that cocaine drives the neuronal network towards destabilization in the energy landscape of neuronal activation. While this destabilization is presumably temporary given one administration of cocaine, perhaps this initial destabilization indicates a transition towards a new stable state with repeated cocaine administration. However, such analyses are useful more generally to understand how neuronal networks respond to perturbations.

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Figures

Fig. 1.
Fig. 1.
Schematic and data from the experiments. Top: Implanted microscope with image of neurons fluorescensing. Bottom: Representative relative calcium intensity traces time prior and post cocaine administration for several neurons; partially created from Biorender.com.
Fig. 2.
Fig. 2.
RC model of a single neuron. a) The battery represents the voltage from all other neurons as well as any external signal. The switch S represents charging and discharging cycles of the neuron. When a neuron consumes ATP to open the ion channels, S is connected to the battery. During the discharge phase S moves from the battery to the other side to form a closed self-circuit. In this phase regardless of the incoming signals from the other neurons, the neuron does not fire. b) The current and voltage cycle of a neuron after firing.
Fig. 3.
Fig. 3.
The functional connectivity matrix functional-inhibitory and functional-excitatory neurons prior a) and post b) cocaine administration. The Tij matrix is derived using a time series machine learning analysis of the calcium signaling in the neuronal network with negative weights representing functional-inhibitory connections and positive weights representing functional-excitatory connections.
Fig. 4.
Fig. 4.
Changes in Tij to highlight the differences in the functional connectivity prior and post cocaine administration. a) The histogram demonstrates that there both increases and decreases in the weight connections. b) The changes in Tij prior and post cocaine.
Fig. 5.
Fig. 5.
Perturbing the neuronal network. a) A time-varying external current is applied to all neurons in the neuronal network prior to cocaine administration. b) The time-varying current of all the neurons of the network induced by the same external signal. Tsame external signal induce different currents in the neurons given the functional connectivity weights. Moreover, when the external current is disconnected, all induced current begin to decay, each with their own decay rate.
Fig. 6.
Fig. 6.
Energy functional of neuronal activity. a) Distributions of the neuronal activity m in before and after cocaine administration. The plot indicates the differentiation in the collective behavior of the two systems due to the variation in the experiments. In this plot, the data prior to cocaine administration is weighed such that the two histograms have the same under-the-curve area. b) Minus the logarithm of the probability function, as defined in Eq. 15, learned from data in the left panel. This plot indicates that the energy of the neuronal network prior to cocaine administration is stable where the energy has a well-defined minimum. However, following cocaine administration, the energy of the neural network is pushed to an unstable state where the probability function does not have a minimum.
Fig. 7.
Fig. 7.
The inferred functional-excitatory and functional-inhibitory neuronal networks prior and post cocaine administration. a) and b) Networks represent prior to cocaine administration. c) and d) networks represent post cocaine administration. a) and c) Networks represent the functional-excitatory connections, while b) and d) networks represent the functional-inhibitory connections. The colors define the modularity classes, or clusters of closely interconnected nodes, using a community-finding algorithm outlined in Ref. (59) and implemented in Gephi (60). Moreover, the node sizes show the betweenness centralities. The interaction strengths are represented by the width of the edges.
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