Muting, not fragmentation, of functional brain networks under general anesthesia

Areshenkoff, Corson N.; Nashed, Joseph Y.; Hutchison, R. Matthew; Hutchison, Melina; Lévy, Ron; Cook, Douglas J.; Menon, Ravi S.; Everling, Stefan; Gallivan, Jason P.

doi:10.1016/j.neuroimage.2021.117830

Cited by 22 publications

(12 citation statements)

References 58 publications

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“…As shown by Gratton et al (2018), intrinsic resting-state functional brain structure is stable over time, and highly individual-specific. Moreover, these static subject differences appear to dwarf any changes in activity associated with experimental conditions, temporary cognitive states or daily fluctuations, a finding we have replicated in several of our own analyses, Humans and non-Human primates (Areshenkoff et al, 2021(Areshenkoff et al, , 2022(Areshenkoff et al, , 2023.…”

Section: Discussionsupporting

confidence: 59%

Individual variability in sensorimotor learning reflects trait-like neurobehavioral subject factors

Areshenkoff,

de Brouwer,

Standage

et al. 2024

Preprint

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Models of Human motor behaviour often emphasize the computations performed by the motor system during learning. Yet, there is an emerging consensus that the learning of even simple motor actions can be augmented by sophisticated cognitive strategies, which rely upon executive functions implemented throughout the cortex. These executive functions, in turn, have been linked to stable subject differences in intrinsic brain organization function, observable even during rest. Here we show, using behavioural studies in humans, that individual differences in the rate of sensorimotor adaptation are linked to differences in executive function, as assessed using the classic trail-making task. Secondly, using separate train and test functional MRI datasets, we show that specific patterns of resting-state functional connectivity between higher-order cognitive brain networks, which have been previously linked to executive function, subsequently predict more rapid learning during sensorimotor adaptation. Importantly, this relationship was unique to cognitive brain networks, as the functional connectivity between sensorimotor brain networks did not predict subsequent motor learning performance. Together, these findings suggest that individual differences in motor learning reflect, to a substantial degree, trait-like subject differences in cognitive brain network structure. This perspective invites broader consideration as to the origins of motor learning ability, and links motor performance to an expansive literature implicating the coordinated functioning of the default mode, ventral attention, and frontoparietal networks in flexible behavioral control.

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Section: Discussionsupporting

confidence: 59%

Individual variability in sensorimotor learning reflects trait-like neurobehavioral subject factors

Areshenkoff,

de Brouwer,

Standage

et al. 2024

Preprint

Self Cite

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“…Because prior work 61,62 , including our own 39,63,64 , suggests that individual differences in functional connectivity can obscure any task-related effects, we centered the connectivity matrices using the Riemmanian manifold approach 39,63-65 (See Supplemental Figure 1 for an overview of the approach).…”

Section: Resultsmentioning

confidence: 99%

Reconfigurations of cortical manifold structure during reward-based motor learning

Nick,

Gale,

Areshenkoff

et al. 2023

Preprint

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Adaptive motor behavior depends on the coordinated activity of multiple neural systems distributed across cortex and subcortex. While the role of sensorimotor cortex in motor learning has been well-established, how higher-order brain systems interact with sensorimotor cortex to guide learning is less well understood. Using functional MRI, we examined human brain activity during a reward-based motor task where subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space and examined how regions expanded and contracted along the manifold during learning. During early learning, we found that several sensorimotor areas in the Dorsal Attention Network exhibited increased covariance with areas of the salience/ventral attention network and reduced covariance with areas of the default mode network (DMN). During late learning, these effects reversed, with sensorimotor areas now exhibiting increased covariance with DMN areas. However, areas in posteromedial cortex showed the opposite pattern across learning phases, with its connectivity suggesting a role in shifting from exploratory to exploitative modes of behavior. Our results identify the whole-brain neural changes supporting reward-based learning, and indicate distinct transitions in the coupling of sensorimotor to transmodal cortex when refining behavior over time.

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“…In part because of these refinements, we collected our data while animals were maintained under light (≤0.9%) isoflurane, allowing for maximal preservation of resting state network activity (Hutchison et al, 2014; Wu et al, 2016; Areshenkoff et al, 2021; Autio et al, 2021); we consider this particularly important given that cortical rs-FC is most strongly impacted by increasing doses of anesthesia (Hutchison et al, 2014; Lv et al, 2016; Giacometti et al, 2022). In light of work finding that DREADD constructs carrying an mCherry fusion protein are only sparsely expressed at the cell surface (Galvan et al, 2019), we chose to use a DREADD construct that instead contains an HA tag.…”

Section: Discussionmentioning

confidence: 99%

The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation

Elorette,

Fujimoto,

Stoll

et al. 2023

Preprint

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Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the macaque amygdala and activated them with a highly selective and potent DREADD agonist, deschloroclozapine. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Interestingly, activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5-14.5Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-disciplinary approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.

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Muting, not fragmentation, of functional brain networks under general anesthesia

Cited by 22 publications

References 58 publications

Individual variability in sensorimotor learning reflects trait-like neurobehavioral subject factors

Individual variability in sensorimotor learning reflects trait-like neurobehavioral subject factors

Reconfigurations of cortical manifold structure during reward-based motor learning

The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation

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