Functional networks and network perturbations in rodents

Chuang, Kai‐Hsiang; Nasrallah, Fatima A.

doi:10.1016/j.neuroimage.2017.09.038

Cited by 59 publications

(48 citation statements)

References 228 publications

(298 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, RSN in dogs are anatomically similar to networks found in humans (Fig 3) and other animals [5,8,9,47,48,61]. Our results are in general agreement with the recent rs-fMRI study in awake dogs, with 6 out of 7 of our RSNs matching in both studies [17].…”

Section: Discussionsupporting

confidence: 91%

“…Due to the ability of acquiring data from animals and humans in the same manner, rs-fMRI has emerged as a translational method for bridging experimental animal and human studies. In conjunction with data-driven gICA analysis, rs-fMRI has proven to be a robust approach to identify and characterise similar brain networks across species [5,8,9,16,47,48].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Resting state networks of the canine brain under sevoflurane anaesthesia

et al. 2020

View full text Add to dashboard Cite

Resting-state functional Magnetic Resonance Imaging (rs-fMRI) has become an established technique in humans and reliably determines several resting state networks (RSNs) simultaneously. Limited data exist about RSN in dogs. The aim of this study was to investigate the RSNs in 10 healthy beagle dogs using a 3 tesla MRI scanner and subsequently perform group-level independent component analysis (ICA) to identify functionally connected brain networks. Rs-fMRI sequences were performed under steady state sevoflurane inhalation anaesthesia. Anaesthetic depth was titrated to the minimum level needed for immobilisation and mechanical ventilation of the patient. This required a sevoflurane MAC between 0.8 to 1.2. Group-level ICA dimensionality of 20 components revealed distributed sensory, motor and higher-order networks in the dogs' brain. We identified in total 7 RSNs (default mode, primary and higher order visual, auditory, two putative motor-somatosensory and one putative somatosensory), which are common to other mammals including humans. Identified RSN are remarkably similar to those identified in awake dogs. This study proves the feasibility of rs-fMRI in anesthetized dogs and describes several RSNs, which may set the basis for investigating pathophysiological characteristics of various canine brain diseases.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Resting state networks of the canine brain under sevoflurane anaesthesia

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Rodent rsfMRI has been a growing research field in neuroscience over the past 10 years (Chuang and Nasrallah, 2017;Gozzi and Schwarz, 2016;Hoyer et al, 2014;Jonckers et al, 2015Jonckers et al, , 2013Pan et al, 2015). The fast-paced development of the field has yielded a number of exciting results, yet the comparability of these findings remains unclear.…”

Section: Discussionmentioning

confidence: 99%

“…Since its onset in 2011 (Jonckers et al, 2011), mouse rsfMRI has developed in a number of centres and has grown to become a routine method with a number of applications, reviewed in (Chuang and Nasrallah, 2017;Gozzi and Schwarz, 2016;Hoyer et al, 2014;Jonckers et al, 2015Jonckers et al, , 2013Pan et al, 2015). Prominently, mouse rsfMRI has been used to investigate an extensive list of models, including Alzheimer's disease (Grandjean et al, 2014b, Shah et al, 2013, 2016cWiesmann et al, 2016;Zerbi et al, 2014), motor (DeSimone et al, 2016;Li et al, 2017), affective (Grandjean et al, 2016a), autism spectrum Haberl et al, 2015;Liska et al, 2018;Liska and Gozzi, 2016;Michetti et al, 2017;Sforazzini et al, 2016;Zerbi et al, 2018;Zhan et al, 2014), schizophrenia (Errico et al, 2015;Gass et al, 2016), pain (Buehlmann et al, 2018;Komaki et al, 2016), reward (Charbogne et al, 2017;Mechling et al, 2016), and demyelinating disorders (Hübner et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

Grandjean¹,

Canella

Anckaerts

et al. 2019

Preprint

View full text Add to dashboard Cite

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories world-wide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a murine default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

show abstract

“…Large-scale networks as detected by resting-state functional MRI (rsfMRI) provide exquisite information on the functional organization of the brain and associated dysfunction in mental or pathological processes. With similar resting-state networks also detectable in rodents, rsfMRI has become an attractive translational tool to understand the neural basis of resting-state networks, the mechanisms of disorders and the effects of treatments using widely available rodent models of diseases and transgenic animals (for review, see (Chuang and Nasrallah, 2017;Jonckers et al, 2015)). Functional connectivity is typically estimated from the correlated low-frequency oscillation of the blood oxygenation level dependent (BOLD) signals between brain regions.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of nuisance removal for functional MRI of rodent brain

Chuang

Lee

et al. 2019

NeuroImage

Self Cite

View full text Add to dashboard Cite

Functional MRI (fMRI) has become an important translational tool for studying brain activity and connectivity in animal models and humans. For accurate and reliable measurement of functional connectivity, nuisance removal strategies developed for human brain, such as regressing motion parameters, cerebrospinal fluid (CSF)/white matter-derived signals and the global signal, have been applied to rodent. However, due to the very different anatomy, with the majority of the rodent brain being gray matter, and experimental conditions, in which animals are anesthetized and head-fixed, these methods may not be suitable for rodent fMRI. In this study, we assessed various nuisance regression methods and the effects of motion correction on a large dataset of both task and resting fMRI of anesthetized rat brain. Sensitivity and specificity were assessed in the somatosensory pathway under forepaw stimulation and resting state. Reproducibility at various sample sizes was simulated by randomly subsampling the dataset. To overcome the difficulty in extracting nuisance from the brain, a method using principal components estimated from tissues outside the brain was evaluated. Our results showed that neither detrend, motion correction, motion regression nor CSF signal regression could improve specificity despite increasing temporal signal-to-noise ratios. Although global signal regression increased the specificity of task activation and functional connectivity, the sensitivity and connectivity strength was drastically reduced, likely due to its strong correlation with the cortical signal. Motion parameters also correlated with task activation and the global signal, indicating that motion correction detected intensity variations in the brain. The nuisance estimated from tissues outside the brain produced a moderate improvement in specificity. In conclusion, nuisance removal suitable for human fMRI may not be optimal for rodents. While further development is needed, estimating nuisance from tissues outside the brain may be an alternative.

show abstract

Functional networks and network perturbations in rodents

Cited by 59 publications

References 228 publications

Resting state networks of the canine brain under sevoflurane anaesthesia

Resting state networks of the canine brain under sevoflurane anaesthesia

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

Evaluation of nuisance removal for functional MRI of rodent brain

Contact Info

Product

Resources

About