Resting state network topology of the ferret brain

Highlights

• The ferret brain exhibits sensory, motor, and default mode resting state networks (RSNs), comparable to those found in humans and non-human primates.

• Functional connectivity patterns in the ferret brain resemble that of a small-world network.

• These results support the idea of cross-species homologous RSNs.

Abstract

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4 T MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function.

Introduction

Behavior and cognition are supported by the brain's intrinsic anatomical and functional connectivity (i.e. correlated activity patterns between distinct brain regions) (Fox et al., 2012, Brusa et al., 2014, Zhou et al., 2016a). Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a powerful tool for measuring functional connectivity non-invasively across species (Biswal et al., 1995, Damoiseaux et al., 2006, Vincent et al., 2007, Belcher et al., 2013, Mechling et al., 2014, Kyathanahally et al., 2015). Groups of functionally connected brain regions measured using rsfMRI can be defined as networks that likely reflect the underlying structural organization and communication in the brain (Greicius et al., 2009, van den Heuvel and Hulshoff Pol, 2010). However, functional connectivity within such resting state networks (RSNs) is impaired in human psychiatric disorders such as schizophrenia, Alzheimer's disease, and autism spectrum disorders (Liu et al., 2008, Minshew and Keller, 2010, Sanz-Arigita et al., 2010, Zhang et al., 2011, Karbasforoushan and Woodward, 2012, Spetsieris et al., 2015, Wang et al., 2015). In particular, the default mode network (DMN), which is composed of hubs in the parietal and medial prefrontal cortices, exhibits elevated activity in neurotypical participants at rest, while the aforementioned disorders show either hypo- or hyperconnected DMNs (Raichle et al., 2001, Greicius et al., 2004, Minshew and Keller, 2010, Sheline et al., 2010, Wang et al., 2015). Resting state studies using graph theory analysis, a set of techniques that assesses information transfer among distributed brain regions, have suggested that human and animal brains exhibit small-world network topology (Watts and Strogatz, 1998, Achard et al., 2006, Bullmore and Sporns, 2012). This network attribute characterizes optimized trade-off between cost-efficient wiring and network resilience, and has shown to be altered in neuropsychiatric disorders (Liu et al., 2008, Zhang et al., 2011, Zhao et al., 2012).

Animal models bridge the gap between human psychiatric disorders and their underlying mechanisms. However, the study of these underlying mechanisms relies on accurate comparisons between animal model and human brain dynamics. Therefore, it is important to identify RSNs and their impairments across all research models. The ferret has a rich history of developmental research due to its early post-natal (P) ages corresponding to 25 weeks of gestation in the human (Barnette et al., 2009), as well as its short gestation time of ~42 days. Importantly, the ferret is born lissencephalic and the brain begins cortical folding (gyrification) at P10 (Sawada and Watanabe, 2012), allowing for translational study of early brain insults and impairments (Empie et al., 2015, Kou et al., 2015). Due to its well-developed sensory and higher-order brain structures, the ferret can be a useful model for studying sensory and cognitive impairments across a broad range of developmental time points (Basole et al., 2003, Fritz et al., 2010, Foxworthy et al., 2013a, Atiani et al., 2014). A map of neurotypical functional connectivity patterns would serve as a framework for translational studies in the ferret. Despite studies exploring the structure and function of individual brain regions, such large-scale brain network topology in the ferret has yet to be examined (Manger et al., 2002, Basole et al., 2003, Bizley and King, 2009, Fritz et al., 2010, Sellers et al., 2013, Bizley et al., 2015, Yu et al., 2015, Zhou et al., 2016b).

Here, we aimed to identify sensory and default mode RSNs in the ferret. We hypothesized that the ferret brain would exhibit RSNs comparable to those found in humans and non-human primates. To do this, we performed group-level independent component analysis (gICA), a data-driven approach to identify spatially independent functional networks, on anesthetized ferret resting state scans. Regions of interest composing the gICA functional networks were then compared to histological sections for identification of anatomical brain regions. Network connectivity measured by correlation analysis further validated within-network connectivity, and revealed the presence of interconnected somato-motor and putative default mode networks. We subsequently used graph theory analyses to demonstrate that the ferret brain network topology resembles that of a small-world network. As a whole, these findings add to the wealth of studies arguing for homologous cross-species RSNs (Vincent et al., 2007, Lu et al., 2012, Belcher et al., 2013, Hutchison et al., 2013, Mechling et al., 2014, Miranda-Dominguez et al., 2014, Sforazzini et al., 2014, Barks et al., 2015, Kyathanahally et al., 2015, Liang et al., 2015, Pan et al., 2015), and support the notion that ferrets are a robust animal model for translational research.