Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

Dóra Szabó,Ádám Kettinger,Attila Andics,Kálmán Czeibert,Ádám Miklósi,Márta Gácsi,Enikő Kubinyi

doi:10.1038/s41598-019-51752-2

Dóra Szabó, Ádám Kettinger + Show 5 more

Open Access

https://doi.org/10.1038/s41598-019-51752-2

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Abstract

Resting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained individuals of both sexes and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. In this exploratory study, using resting-state functional magnetic resonance imaging (rs-fMRI), we found several such networks: a network involving prefrontal, anterior cingulate, posterior cingulate and hippocampal regions; sensorimotor (SMN), auditory (AUD), frontal (FRO), cerebellar (CER) and striatal networks. The network containing posterior cingulate regions, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.

Highlights

Resting-state networks are spatially distributed, functionally connected brain regions
The structure and assumed tasks of Resting-state networks (RSNs) are of high interest as they have the potential to provide information about the brain’s large scale functional organization[1,3], and alternations in these networks have been found to correspond with various pathologies such as dementia or ADHD4
Our goal was to investigate whether applying the currently available methods yield interpretable results with our setup and if so, what kind of spatially distributed resting-state networks are detectable in a larger sample of awake, unrestrained family dogs in a resting-state functional magnetic resonance imaging (fMRI) setup, following up on previous reports[11,13]

Summary

Introduction

Resting-state networks are spatially distributed, functionally connected brain regions. To describe resting-state networks in dogs, we measured 22 awake, unrestrained individuals of both sexes and carried out group-level spatial independent component analysis to explore wholebrain connectivity patterns. In this exploratory study, using resting-state functional magnetic resonance imaging (rs-fMRI), we found several such networks: a network involving prefrontal, anterior cingulate, posterior cingulate and hippocampal regions; sensorimotor (SMN), auditory (AUD), frontal (FRO), cerebellar (CER) and striatal networks. ICA is appropriate to describe networks in case of a species which brain’s functional characteristics are yet to be determined, as it does not require selection of a priori seed regions This method attempts to discover statistically independent source signals from the measured observations, using non-linear transformations while looking for spatial independence[2]. Our goal was to investigate whether applying the currently available methods yield interpretable results with our setup (proof-of-concept) and if so, what kind of spatially distributed resting-state networks are detectable in a larger sample of awake, unrestrained family dogs in a resting-state fMRI setup, following up on previous reports[11,13]

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