Cortico-subcortical functional connectivity profiles of resting-state networks in marmosets and humans

AbstractUnderstanding the similarity of cortico-subcortical networks topologies between humans and nonhuman primate species is critical to study the origin of network alternations underlying human neurological and neuropsychiatric diseases. The New World common marmoset (Callithrix jacchus) has become popular as a non-human primate model for human brain function. Most marmoset connectomic research, however, has exclusively focused on cortical areas, with connectivity to subcortical networks less extensively explored. In this study, we aimed to first isolate patterns of subcortical connectivity with cortical resting-state networks (RSNs) in awake marmosets using resting-state functional magnetic resonance imaging (RS-fMRI), then to compare these networks to those in humans using connectivity fingerprinting. While we could match several marmoset and human RSNs based on their functional fingerprints, we also found a few striking differences, for example strong functional connectivity of the default mode network with the superior colliculus in marmosets that was much weaker in humans. Together, these findings demonstrate that many of the core cortico-subcortical networks in humans are also present in marmosets, but that small, potentially functionally relevant differences exist.

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Critique of Pure Marmoset

Brain Behavior and Evolution ◽

10.1159/000500500 ◽

2019 ◽

Vol 93 (2-3) ◽

pp. 92-107 ◽

Cited By ~ 1

Author(s):

Todd M. Preuss

Keyword(s):

Rapid Development ◽

Common Marmoset ◽

Macaque Monkey ◽

Careful Consideration ◽

Human Condition ◽

Primate Species ◽

Primate Model ◽

Short Lifespan ◽

Trade Offs ◽

Human Primate

The common marmoset, a New World (platyrrhine) monkey, is currently being fast-tracked as a non-human primate model species, especially for genetic modification but also as a general-purpose model for research on the brain and behavior bearing on the human condition. Compared to the currently dominant primate model, the catarrhine macaque monkey, marmosets are notable for certain evolutionary specializations, including their propensity for twin births, their very small size (a result of phyletic dwarfism), and features related to their small size (rapid development and relatively short lifespan), which result in these animals yielding experimental results more rapidly and at lower cost. Macaques, however, have their own advantages. Importantly, macaques are more closely related to humans (which are also catarrhine primates) than are marmosets, sharing approximately 20 million more years of common descent, and are demonstrably more similar to humans in a variety of genomic, molecular, and neurobiological characteristics. Furthermore, the very specializations of marmosets that make them attractive as experimental subjects, such as their rapid development and short lifespan, are ways in which marmosets differ from humans and in which macaques more closely resemble humans. These facts warrant careful consideration of the trade-offs between convenience and cost, on the one hand, and biological realism, on the other, in choosing between non-human primate models of human biology. Notwithstanding the advantages marmosets offer as models, prudence requires continued commitment to research on macaques and other primate species.

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Resting-State Functional Connectivity and Cognitive Impairment in Children with Perinatal Stroke

Neural Plasticity ◽

10.1155/2016/2306406 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Nigul Ilves ◽

Pilvi Ilves ◽

Rael Laugesaar ◽

Julius Juurmaa ◽

Mairi Männamaa ◽

...

Keyword(s):

Functional Connectivity ◽

Cognitive Functions ◽

Resting State ◽

Outcome Measurement ◽

Pediatric Stroke ◽

Resting State Networks ◽

Neurocognitive Deficits ◽

Resting State Functional Connectivity ◽

Perinatal Stroke ◽

Brain Functions

Perinatal stroke is a leading cause of congenital hemiparesis and neurocognitive deficits in children. Dysfunctions in the large-scale resting-state functional networks may underlie cognitive and behavioral disability in these children. We studied resting-state functional connectivity in patients with perinatal stroke collected from the Estonian Pediatric Stroke Database. Neurodevelopment of children was assessed by the Pediatric Stroke Outcome Measurement and the Kaufman Assessment Battery. The study included 36 children (age range 7.6–17.9 years): 10 with periventricular venous infarction (PVI), 7 with arterial ischemic stroke (AIS), and 19 controls. There were no differences in severity of hemiparesis between the PVI and AIS groups. A significant increase in default mode network connectivity (FDR 0.1) and lower cognitive functions (p<0.05) were found in children with AIS compared to the controls and the PVI group. The children with PVI had no significant differences in the resting-state networks compared to the controls and their cognitive functions were normal. Our findings demonstrate impairment in cognitive functions and neural network profile in hemiparetic children with AIS compared to children with PVI and controls. Changes in the resting-state networks found in children with AIS could possibly serve as the underlying derangements of cognitive brain functions in these children.

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Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

JAMA Psychiatry ◽

10.1001/jamapsychiatry.2015.0101 ◽

2015 ◽

Vol 72 (8) ◽

pp. 767 ◽

Cited By ~ 138

Author(s):

Leonardo Cerliani ◽

Maarten Mennes ◽

Rajat M. Thomas ◽

Adriana Di Martino ◽

Marc Thioux ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Resting State ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Resting State Networks

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Local GABA concentration is related to network-level resting functional connectivity

eLife ◽

10.7554/elife.01465 ◽

2014 ◽

Vol 3 ◽

Cited By ~ 86

Author(s):

Charlotte J Stagg ◽

Velicia Bachtiar ◽

Ugwechi Amadi ◽

Christel A Gudberg ◽

Andrei S Ilie ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Primary Motor Cortex ◽

Empirical Studies ◽

Network Connectivity ◽

Resting State Fmri ◽

Resonance Spectroscopy ◽

Resting State Networks ◽

Inhibitory Neurotransmitter ◽

Motor Network

Anatomically plausible networks of functionally inter-connected regions have been reliably demonstrated at rest, although the neurochemical basis of these ‘resting state networks’ is not well understood. In this study, we combined magnetic resonance spectroscopy (MRS) and resting state fMRI and demonstrated an inverse relationship between levels of the inhibitory neurotransmitter GABA within the primary motor cortex (M1) and the strength of functional connectivity across the resting motor network. This relationship was both neurochemically and anatomically specific. We then went on to show that anodal transcranial direct current stimulation (tDCS), an intervention previously shown to decrease GABA levels within M1, increased resting motor network connectivity. We therefore suggest that network-level functional connectivity within the motor system is related to the degree of inhibition in M1, a major node within the motor network, a finding in line with converging evidence from both simulation and empirical studies.

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The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

10.1101/634063 ◽

2019 ◽

Author(s):

Hannes Almgren ◽

Frederik Van de Steen ◽

Adeel Razi ◽

Karl Friston ◽

Daniele Marinazzo

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Effective Connectivity ◽

Network Connectivity ◽

Resting State Fmri ◽

Measurement Noise ◽

Resting State Networks ◽

Cross Sectional ◽

Global Signal ◽

Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

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