Network Neuroscience Untethered: Brain-Wide Immediate Early Gene Expression for the Analysis of Functional Connectivity in Freely Behaving Animals

Abstract

Studying how spatially discrete neuroanatomical regions across the brain interact is critical to advancing our understanding of the brain. Traditional neuroimaging techniques have led to many important discoveries about the nature of these interactions, termed functional connectivity. However, in animal models these traditional neuroimaging techniques have generally been limited to anesthetized or head-fixed setups or examination of small subsets of neuroanatomical regions. Using the brain-wide expression density of immediate early genes (IEG), we can assess brain-wide functional connectivity underlying a wide variety of behavioural tasks in freely behaving animal models. Here, we provide an overview of the necessary steps required to perform IEG-based analyses of functional connectivity. We also outline important considerations when designing such experiments and demonstrate the implications of these considerations using an IEG-based network dataset generated for the purpose of this review.

Sex-dependent impairments of parvalbumin expressing neurons in the retrosplenial cortex in Alzheimer’s disease

“…Using custom MATLAB analyses, outputs were organized to yield c-Fos expression densities across 90 neuroanatomical regions (for a detailed list of regions, see Supplementary Table 2). Regional c-Fos expression density was correlated across groups for all possible combinations of regions, generating correlation matrices of regional co-activation for each group 55 . From each co-activation matrix, the functional connectivity of the RSC was assessed using calculation derived from the Brain Connectivity Toolbox 56 .…”

Network-Level Changes in the Brain Underlie Fear Memory Strength

“…The distinctions between adaptive and maladaptive memories may not be solely attributed to changes within isolated structures or altered connectivity between pairs of structures, but rather to collective changes across multiple structures within the neural network. Combining the quantification of immediate-early genes expression linked to neural activation such as cfos 36 and network-based graph analysis [37][38][39] , it is possible to reveal the brain functional connectivity during active cognitive experiences, such as memory recall [40][41][42][43] . In network models of neural systems, brain regions are represented as nodes, and functional connections between nodes are represented as edges.…”

Network-level changes in the brain underlie fear memory strength

“…The distinctions between adaptive and maladaptive memories may not be solely attributed to changes within isolated structures or altered connectivity between pairs of structures, but rather to collective changes across multiple structures within the neural network. Combining the quantification of immediate-early genes expression linked to neural activation such as c-fos ( Terstege and Epp, 2023 ) and network-based graph analysis ( Bassett et al, 2018 ; Bullmore and Sporns, 2009 ; Roland et al, 2023 ), it is possible to reveal the brain functional connectivity during active cognitive experiences, such as memory recall ( Silva et al, 2019 ; Takeuchi et al, 2022 ; Vetere et al, 2017 ; Wheeler et al, 2013 ). In network models of neural systems, brain regions are represented as nodes, and functional connections between nodes are represented as edges.…”