Contextual experience modifies functional connectome indices of topological strength and efficiency

Abstract

Stimuli presented at short temporal delays before functional magnetic resonance imaging (fMRI) can have a robust impact on the organization of synchronous activity in resting state networks. This presents an opportunity to investigate how sensory, affective and cognitive stimuli alter functional connectivity in rodent models. In the present study we assessed the effect on functional connectivity of a familiar contextual stimulus presented 10 min prior to sedation for imaging. A subset of animals were co-presented with an unfamiliar social stimulus in the same environment to further investigate the effect of familiarity on network topology. Rats were imaged at 11.1 T and graph theory analysis was applied to matrices generated from seed-based functional connectivity data sets with 144 brain regions (nodes) and 10,152 pairwise correlations (after excluding 144 diagonal edges). Our results show substantial changes in network topology in response to the familiar (context). Presentation of the familiar context, both in the absence and presence of the social stimulus, strongly reduced network strength, global efficiency, and altered the location of the highest eigenvector centrality nodes from cortex to the hypothalamus. We did not observe changes in modular organization, nodal cartographic assignments, assortative mixing, rich club organization, and network resilience. We propose that experiential factors, perhaps involving associative or episodic memory, can exert a dramatic effect on functional network strength and efficiency when presented at a short temporal delay before imaging.

Compensatory functional connectome changes in a rat model of traumatic brain injury

“…MRI was carried out as previously reported. 21 Images were collected in an 11.1 T MRI scanner (Magnex Scientific Ltd, Oxford, UK) with a Resonance Research Inc. gradient set (RRI BFG-240/120-S6, maximum gradient strength of 1000 mT/m at 325 Amps and a 200 µs risetime; RRI, Billerica, MA) and controlled by a Bruker Paravision 6.01 console (Bruker BioSpin, Billerica, MA). A custom-made 2.5 cm × 3.5 cm quadrature radiofrequency (RF) surface transmit/receive coil tuned to 470.7 MHz ( 1 H resonance) was used for B1 excitation and signal detection (RF engineering lab, Advanced Magnetic Resonance Imaging and Spectroscopy Facility, Gainesville, FL).…”

Compensatory functional connectome changes in a rat model of traumatic brain injury

“…Magnetic resonance imaging was carried out as previously reported (Pompilus et al ., 2020). Images were collected in an 11.1 Tesla MRI scanner (Magnex Scientific Ltd., Oxford, UK) with a Resonance Research Inc. gradient set (RRI BFG-240/120-S6, maximum gradient strength of 1000 mT/m at 325 Amps and a 200 µs risetime; RRI, Billerica, MA) and controlled by a Bruker Paravision 6.01 console (Bruker BioSpin, Billerica, MA).…”

“…We updated a previous set of 144 node parcellations (Pompilus et al ., 2020) to include an additional 18 region of interest (ROI) masks covering voxels in the CCI region and the contralateral hemisphere (Figure 3A). These included 4 ROIs covering the primary visual cortex (Lesion ROI1, 3-5 in Figure 3A), one in the dorsal hippocampus centered on the dentate gyrus layer (Lesion ROI2), the rostral and caudal retrosplenial cortex (Lesion ROI9 and ROI6, respectively), and the barrel field and trunk regions of the primary somatosensory cortex (Lesion ROI7 and ROI8, respectively).…”

“…Individual ROI masks were generated using a previously published rat brain parcellation (Kenkel et al ., 2016). ROI seed generation was as previously reported (Pompilus et al ., 2020) and coordinates were used for 3D network visualizations in BrainNet viewer in MATLAB (Xia et al ., 2013). The principal eigenvector timeseries vector was extracted from preprocessed fMRI scans with the assistance of ROI mask overlays.…”

“…The procedure starts with a random grouping of nodes and iteratively moving nodes into groups which maximize the value of Q. The final number of modules and node assignments to each group (e.g., community affiliation assignments) was taken as the median of 1000 iterations of the modularity maximization procedure (Pompilus et al ., 2020). The number of communities per rat, population size of each community, and mean participation coefficient, eigenvector and betweenness centrality of the top four-most populated communities were assessed.…”