DOI: 10.5061/dryad.86337
title: Data from: Chemogenetic interrogation of a brain-wide fear memory network in mice

description: Behavior depends on coordinated activity across multiple brain regions.
 Within such networks, highly connected hub regions are assumed to
 disproportionately influence behavioral output, although this hypothesis
 has not been systematically evaluated. Previously, by mapping brain-wide
 expression of the activity-regulated gene c-fos, we identified a network
 of brain regions co-activated by fear memory. To test the hypothesis that
 hub regions are more important for network function, here, we simulated
 node deletion in silico in this behaviorally defined functional network.
 Removal of high degree nodes produced the greatest network disruption
 (e.g., reduction in global efficiency). To test these predictions in vivo,
 we examined the impact of post-training chemogenetic silencing of
 different network nodes on fear memory consolidation. In a series of
 independent experiments encompassing 25% of network nodes (i.e., 21/84
 brain regions), we found that node degree accurately predicted observed
 deficits in memory consolidation, with silencing of highly connected hubs
 producing the largest impairments.
description: Figure_1AAll pair-wise correlations of Fos counts for the fear memory.Figure_1BWeighted adjacency matrix for the fear memory network.Figure_1CAll pair-wise correlations of Fos counts from control animals.Figure_1DWeighted adjacency matrix for the control network.Figure_2AWeighted adjacency matrix following application of the disruption propagation model starting from node Re.Figure_2BGlobal efficiency before and after the application of the disruption propagation model to the fear memory network targeting node Re.Figure_2CThe change in global efficiency following application of the disruption propagation model to each individual node, and the degree of each node, in the fear memory network.Figure_3BThe effect of CNO administration on Fos expression in across different zeitgeber times.Figure_3CThe effect of CNO administration on Fos expression in different brain regions.Figure_3DThe effect of prolonged CNO administration on Fos expression.Figure_5BPercent freezing levels for mice expressing hM4Di in 21 different brain regions and administered CNO or VEH.Figure_6ABDDifference in freezing between CNO and VEH treated mice, changes in global efficiency for the fear memory and control networks following application of the disruption propagation model, and relative Fos levels.Figure_6CDifference in freezing between CNO and VEH treated mice, and the change in global efficiency following application of the disruption propagation model to the anatomical network.Figure_S1ABCNode degrees and clustering coefficients for the fear memory network.Figure_S1DNode degrees for the control network.Figure_S1EWeighted adjacency matrix for the anatomical network built using the same p-value as the fear memory network (p &lt; 0.01).Figure_S1FWeighted adjacency matrix for the anatomical network built to have the same density as the fear memory network.Figure_S1GNode degrees for the anatomical network thresholded using the same p-value as the fear memory network (p &lt; 0.01).Figure_S1HNode degrees for the anatomical network thresholded to have the same density as the fear memory network.Figure_S1IJNode degrees in the fear memory and control networks.Figure_S1KLNode degrees in the fear memory and anatomical networks.Figure_S3ACShannon's entropy (H) for changes in the global efficiency and giant component size following application of the disruption propagation model to nodes of the fear memory network. The number of edge changes allowed during modeling was varied from 0 to 60.Figure_S3BDShannon's entropy (H) for changes in the global efficiency and giant component size following application of the disruption propagation model to nodes of the fear memory network. The threshold applied following propagation was varied from 0 to 1.Figure_S3EGiant component size before and after the application of the disruption propagation model to the fear memory network targeting node Re.Figure_S3FThe change in giant component size following application of the disruption propagation model to each individual node, and the degree of each node, in the fear memory network.Figure_S4ABThe change in global efficiency and giant component size following either simple node deletion or the application of the disruption propagation model for each node in the fear memory network.Figure_S4CDChange in global efficiency and giant component size following simple node deletion, and node degrees, in the fear memory network.Figure_S5APercent freezing in mice infused with a control virus and administered VEH or CNO.Figure_S5BCDEFDifference in freezing between CNO and VEH treated animals. Changes in the giant component size for the fear memory network following application of the disruption propagation model. Node degrees in the fear memory network. Changes in the global efficiency of the fear memory network following simple node deletion. Alpha critical values derived from the cascading failure model applied to the fear memory network. Volume of each region targeted in behavioral experiments.Figure_S6ADifference in freezing between CNO and VEH treated animals. Change in the global efficiency following application of the disruption propagation model to the control network with the same density as the fear memory network.Figure_S6BDifference in freezing between CNO and VEH treated animals. Change in the global efficiency following application of the disruption propagation model to the anatomical network with the same density as the fear memory network.Cascading_failure_modelR implementation of the cascading failure modelDisruption_propagation_modelR implementation of the disruption propagation modelDPM.R