Fine-grained mapping of mouse brain functional connectivity with resting-state fMRI

Highlights

• Mouse brain functional connectivity (MBFC) is thoroughly characterized via rsfMRI.

• Functional clusters are identified within large cortical and subcortical networks.

• Mouse brain functional hubs are revealed.

• Hippocampus is a central functional node within the MBFC network.

• MBFC shows typical features of small-worldness and community structures segregation.

Abstract

Understanding the intrinsic circuit-level functional organization of the brain has benefited tremendously from the advent of resting-state fMRI (rsfMRI). In humans, resting-state functional network has been consistently mapped and its alterations have been shown to correlate with symptomatology of various neurological or psychiatric disorders. To date, deciphering the mouse brain functional connectivity (MBFC) with rsfMRI remains a largely underexplored research area, despite the plethora of human brain disorders that can be modeled in this specie. To pave the way from pre-clinical to clinical investigations we characterized here the intrinsic architecture of mouse brain functional circuitry, based on rsfMRI data acquired at 7 T using the Cryoprobe technology. High-dimensional spatial group independent component analysis demonstrated fine-grained segregation of cortical and subcortical networks into functional clusters, overlapping with high specificity onto anatomical structures, down to single gray matter nuclei. These clusters, showing a high level of stability and reliability in their patterning, formed the input elements for computing the MBFC network using partial correlation and graph theory. Its topological architecture conserved the fundamental characteristics described for the human and rat brain, such as small-worldness and partitioning into functional modules. Our results additionally showed inter-modular interactions via “network hubs”. Each major functional system (motor, somatosensory, limbic, visual, autonomic) was found to have representative hubs that might play an important input/output role and form a functional core for information integration. Moreover, the rostro-dorsal hippocampus formed the highest number of relevant connections with other brain areas, highlighting its importance as core structure for MBFC.

Introduction

Detecting spontaneous, low-frequency fluctuations in the Blood Oxygen Level Dependent (BOLD) signal and their temporal correlations, resting-state fMRI (rsfMRI) has recently emerged as a powerful tool for non-invasive explorations of brain functional connectivity (FC) (Biswal et al., 1995, Smith et al., 2013). Considerable efforts have been devoted for mapping the human brain connectional networks (Sporns et al., 2005, Van Essen et al., 2013) and their reorganization under the influence of various pathological (Gillebert and Mantini, 2012, Lynall et al., 2010), environmental (Kang et al., 2013) or physiological conditions (Fan et al., 2012). Longitudinal rsfMRI investigations revealed important network fluctuations underlying neurological diseases (Brier et al., 2012) uncovering a research field with great potential for better understanding of disease mechanisms and developing targeted therapeutic interventions. Therefore, rsfMRI became an attractive non-invasive imaging biomarker for defining disease patterns or highlighting compensatory remodeling brain mechanisms (Park et al., 2011). Although extensively applied in human brain, rsfMRI in animals remains limited, mainly focused on deciphering the functional connectional architecture in non-human primates (Shen et al., 2012) and more recently in the cat (de Reus and van den Heuvel, 2013) and anesthetized (Hutchison et al., 2010, Kalthoff et al., 2011, Kalthoff et al., 2013, Liang et al., 2012a) and awake rat brains (Liang et al., 2011, Zhang et al., 2011). Using graph theory-based analysis, rat brain neural networks showed non-trivial organization, conserving fundamental topological properties of human brain networks (Liang et al., 2011), including small-world topology and high modularity (Bullmore and Sporns, 2009). Despite the various fundamental neurobiological questions possibly addressed using the rat, the most extensively used model in experimental neuroscience remains the mouse brain, especially through the availability of genetically-engineered mice. Consequently, it is of crucial importance for the translational research to probe the global topology of intrinsic architecture of mouse brain functional networks, bridging the gap between pre-clinical and clinical investigations and offering the unique possibility of finding functional correlates of genetic or drug related manipulations. Elementary clusters of mouse brain resting-state functional connectivity (RSFC) were previously identified (Jonckers et al., 2011, Sforazzini et al., 2013) using independent component analysis (ICA). This data driven method spatially separates the whole-brain rsfMRI signal into independent components (ICs) resulting from underlying sources (McKeown et al., 1998). However, the sole identification of elementary functional clusters is not addressing the issue of organizational principles and the degree of functional integration and efficiency of the global brain networks. Moreover, because of the stochastic nature of the ICA algorithm (Himberg et al., 2004, Hyvarinen and Oja, 2000), the identified functional patterns might change while modifying sampling and initial conditions. The reliability of these resulting components was not investigated for animal research before. Here we provide a systematic exploration of the mouse brain intrinsic connectional architecture by validating the stability pattern of ICA functional clusters and integrating them into the graph theory to reveal topological properties of the global brain networks. The “small-worldness” property was further investigated, as a criterion for the complexity and efficiency of the global network structure.