Functional MRI in Anesthesia and Resting-State Networks

This chapter reviews current knowledge of the effects of hypnotic anesthetic agents on brain resting-state networks (RSNs) that sustain consciousness. Although full exploration of the networks under anesthesia is not yet available, current evidence indicates that anesthetic agents with hypnotic properties dose-dependently modulate RSN functioning. Each anesthetic agent has specific effects that are not uniform within a given network and probably correlate with the specific clinical features observed when one agent or another is used. Observations made on RSNs during anesthesia are supplementary arguments to link the networks with specific aspects of consciousness and connectedness to the environment and to confirm their physiological functions. The precise link between observations made on RSNs during anesthesia and known biochemical targets of anesthetic agents, or their effects on systems that regulate the sleep–wake cycle, is not established yet. PET studies using radiolabeled probes that specifically target a neurotransmission system offer insights into the links. New technological advances and modes of functional data analysis, such as Granger causality and dynamic causal modeling, will help in obtaining a more in-depth exploration of the complex interactions between brain regions, their modulation by anesthesia, and their role in information processing by the brain. Effects of hypnosis on RSNs also have been studied. The hypnotic state is useful for performing surgical procedures and explorations without general anesthesia. The hypnotic state is associated with specific changes in the activity of RSNs that confirm hypnosis as a specific brain state, different from normal wakeful consciousness and anesthetic states.

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Executive control in bilinguals: A concise review on fMRI studies

Bilingualism Language and Cognition ◽

10.1017/s1366728916000249 ◽

2016 ◽

Vol 19 (4) ◽

pp. 699-705 ◽

Cited By ~ 37

Author(s):

CHRISTOS PLIATSIKAS ◽

GIGI LUK

Keyword(s):

Internal Control ◽

Executive Control ◽

Resting State ◽

Brain Regions ◽

Resting State Networks ◽

Dynamic Nature ◽

Concise Review ◽

Spatially Distributed ◽

Recent Developments ◽

Language Control

The investigation of bilingualism and cognition has been enriched by recent developments in functional magnetic resonance imaging (fMRI). Extending how bilingual experience shapes cognition, this review examines recent fMRI studies adopting executive control tasks with minimal or no linguistic demands. Across a range of studies with divergent ages and language pairs spoken by bilinguals, brain regions supporting executive control significantly overlap with brain regions recruited for language control (Abutalebi & Green). Furthermore, limited but emerging studies on resting-state networks are addressed, which suggest more coherent spatially distributed functional connectivity in bilinguals. Given the dynamic nature of bilingual experience, it is essential to consider both task-related functional networks (externally-driven engagement), and resting-state networks, such as default mode network (internal control). Both types of networks are important elements of bilingual language control, which relies on domain-general executive control.

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Abnormal Functional Resting-State Networks in ADHD: Graph Theory and Pattern Recognition Analysis of fMRI Data

BioMed Research International ◽

10.1155/2014/380531 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 41

Author(s):

Anderson dos Santos Siqueira ◽

Claudinei Eduardo Biazoli Junior ◽

William Edgar Comfort ◽

Luis Augusto Rohde ◽

João Ricardo Sato

Keyword(s):

Graph Theory ◽

Resting State ◽

Resting State Fmri ◽

Brain Regions ◽

Centrality Measures ◽

Support Vector ◽

Healthy Children ◽

Resting State Networks ◽

Predictive Values ◽

Predictive Information

The framework of graph theory provides useful tools for investigating the neural substrates of neuropsychiatric disorders. Graph description measures may be useful as predictor variables in classification procedures. Here, we consider several centrality measures as predictor features in a classification algorithm to identify nodes of resting-state networks containing predictive information that can discriminate between typical developing children and patients with attention-deficit/hyperactivity disorder (ADHD). The prediction was based on a support vector machines classifier. The analyses were performed in a multisite and publicly available resting-state fMRI dataset of healthy children and ADHD patients: the ADHD-200 database. Network centrality measures contained little predictive information for the discrimination between ADHD patients and healthy subjects. However, the classification between inattentive and combined ADHD subtypes was more promising, achieving accuracies higher than 65% (balance between sensitivity and specificity) in some sites. Finally, brain regions were ranked according to the amount of discriminant information and the most relevant were mapped. As hypothesized, we found that brain regions in motor, frontoparietal, and default mode networks contained the most predictive information. We concluded that the functional connectivity estimations are strongly dependent on the sample characteristics. Thus different acquisition protocols and clinical heterogeneity decrease the predictive values of the graph descriptors.

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Learning induces coordinated neuronal plasticity of metabolic demands and functional brain networks

10.1101/2021.11.12.468351 ◽

2021 ◽

Author(s):

Sebastian Klug ◽

Godber M Godbersen ◽

Lucas Rischka ◽

Wolfgang Wadsak ◽

Verena Pichler ◽

...

Keyword(s):

Glucose Metabolism ◽

Resting State ◽

Occipital Cortex ◽

Brain Regions ◽

Skill Learning ◽

Prediction Errors ◽

Task Execution ◽

Specific Effects ◽

Before And After ◽

Functional Brain Networks

The neurobiological basis of learning is reflected in adaptations of brain structure, network organization and energy metabolism. However, it is still unknown how different neuroplastic mechanisms act together and if cognitive advancements relate to general or task-specific changes. To address these questions, we tested how hierarchical network interactions contribute to improvements in the performance of a visuo-spatial processing task by employing simultaneous PET/MR neuroimaging before and after a 4-week learning period. We combined functional PET with metabolic connectivity mapping (MCM) to infer directional interactions across brain regions and subsequently performed simulations to disentangle the role of functional network dynamics and glucose metabolism. As a result, learning altered the top-down regulation of the salience network onto the occipital cortex, with increases in MCM at resting-state and decreases during task execution. Accordingly, a higher divergence between resting-state and task-specific effects was associated with better cognitive performance, indicating that these adaptations are complementary and both required for successful skill learning. Simulations further showed that changes at resting-state were dependent on glucose metabolism, whereas those during task performance were driven by functional connectivity between salience and visual networks. Referring to previous work, we suggest that learning establishes a metabolically expensive skill engram at rest, whose retrieval serves for efficient task execution by minimizing prediction errors between neuronal representations of brain regions on different hierarchical levels.

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Medial Temporal Lobe Contributions to Resting-State Networks

10.21203/rs.3.rs-681614/v1 ◽

2021 ◽

Author(s):

Sara Seoane ◽

Cristián Modroño ◽

José Luis González Mora ◽

Niels Janssen

Keyword(s):

Temporal Lobe ◽

Resting State ◽

Medial Temporal Lobe ◽

Brain Regions ◽

Resting State Networks ◽

Attention Networks ◽

Default Mode ◽

Parahippocampal Cortex ◽

Human Connectome Project ◽

Dorsal Attention Network

Abstract The medial temporal lobe (MTL) is a set of interconnected brain regions that have been shown to play a central role in behavior as well as in neurological disease. Recent studies using resting-state functional Magnetic Resonance Imaging (rsfMRI) have attempted to understand the MTL in terms of its functional connectivity with the rest of the brain. However, the exact characterization of the whole-brain networks that co-activate with the MTL as well as how the various sub-regions of the MTL are associated with these networks remains poorly understood. Here we attempted to advance these issues by exploiting the high spatial-resolution 7T rsfMRI dataset from the Human Connectome Project with a data-driven analysis approach that relied on Independent Component Analysis (ICA) restricted to the MTL. We found that four different well-known resting-state networks co-activated with a unique configuration of MTL subcomponents. Specifically, we found that different sections of the parahippocampal cortex were involved in the default mode, visual and dorsal attention networks, sections of the hippocampus in the somatomotor and default mode networks, and the lateral entorhinal cortex in the dorsal attention network. We replicated this set of results in a validation sample. These results provide new insight into how the MTL and its subcomponents contribute to known resting-state networks. The participation of the MTL in an expanded range of resting-state networks requires a rethink of its presumed role in behavior and disease.

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Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

10.1101/186098 ◽

2017 ◽

Cited By ~ 1

Author(s):

Giri P. Krishnan ◽

Oscar C. González ◽

Maxim Bazhenov

Keyword(s):

Computational Model ◽

Resting State ◽

Large Scale ◽

Brain Activity ◽

Brain Regions ◽

Brain Network ◽

Ion Concentration ◽

Brain State ◽

The Brain

AbstractResting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

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Activity flow over resting-state networks shapes cognitive task activations

10.1101/055194 ◽

2016 ◽

Cited By ~ 5

Author(s):

Michael W. Cole ◽

Takuya Ito ◽

Danielle S. Bassett ◽

Douglas H. Schultz

Keyword(s):

Resting State ◽

Large Scale ◽

Cognitive Task ◽

Brain Regions ◽

Resting State Networks ◽

Resting State Functional Connectivity ◽

Activation Patterns ◽

Distributed Processes ◽

Evoked Activity ◽

Task Activation

AbstractResting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-stateFC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allows prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

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Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

10.1101/2020.12.11.422071 ◽

2020 ◽

Author(s):

Behnaz Yousefi ◽

Shella Keilholz

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Spatial Organization ◽

Resting State Fmri ◽

Brain Regions ◽

Intrinsic Activity ◽

Resting State Networks ◽

Whole Brain ◽

Functional Connections ◽

Driving Mechanisms

The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as resting-state networks (RSNs) and functional connectivity gradients (FCGs). Dynamic analysis techniques have shown that the time-averaged maps captured by functional connectivity are mere summaries of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain intrinsic activity that cannot be inferred by functional connectivity, RSNs or FCGs. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale FCG, simultaneously across the cortical sheet and in most other brain regions. In some areas, like the thalamus, the propagation suggests previously-undescribed FCGs. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between brain regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between RSNs is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

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Functional Connectivity in Pediatric Sickle Cell Disease

Blood ◽

10.1182/blood-2021-151757 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 2049-2049

Author(s):

SaRah R. McNeely ◽

Xirui Hou ◽

Alicia D. Cannon ◽

Zixuan Lin ◽

Sophie M. Lanzkron ◽

...

Keyword(s):

Functional Connectivity ◽

Sickle Cell ◽

Resting State ◽

Test Scores ◽

Research Funding ◽

Brain Regions ◽

Resting State Networks ◽

And Control ◽

The Relationship ◽

Privately Held

Abstract Children with sickle cell disease (SCD) have a high risk of developing cerebrovascular complications, such as stroke and silent cerebral infarction (SCI). SCI is associated with increased risk of future infarction as well as neurocognitive deficits related to brain injury location and size; however, neurocognitive impairment may occur in the absence of neuroimaging abnormalities. Resting state functional magnetic resonance imaging (RS-fMRI) measures blood oxygen level dependent (BOLD) signal during rest to evaluate functional connectivity between brain regions. Functional connectivity is the temporal correlation between the BOLD signal in spatially distant brain regions, which reflects synchronous activity. For this study, we hypothesized that participants with SCI would have lower functional connectivity than participants without SCI and that specific resting state networks would be associated with specific cognitive tests in SCD and control participants. We recruited 26 participants from the local pediatric hematology and SCD clinics. Children with SCD were included in the study if they had a SCD diagnosis confirmed by laboratory studies and no known prior history of overt stroke or seizure. We obtained clinical history, laboratory tests, neuropsychological testing scores, and RS-fMRI scans in 21 participants with SCD and 5 control participants without SCD, 2 of who had sickle cell trait. Each participant received a resting state functional connectivity scan using a 3T MR scanner. Participants were asked to remain still, stay awake, and keep their eyes open during the resting state scan. The MRI study protocol included a BOLD scan and a T1-weighted magnetization-prepared rapid gradient-echo sequence (MPRAGE) with a scan duration of 8 minutes. We performed standard image pre-processing steps, including realignment, normalization to Montreal Neurologic Institute (MNI) standard brain space via MPRAGE image, spatial smoothing, and slice timing correction. Table 1 shows the characteristics of the study participants. Eight participants with SCD had SCI diagnosed as an incidental finding during the study. The average connectivity within 7 resting state networks (control, default mode, dorsal attention, limbic, salience ventral attention, somato-motor, and visual networks) was compared between all (both SCD and control) participants with SCI and without SCI (Table 2). Participants with SCI had significantly lower functional connectivity in the control network (p = 0.0231, 95% CI: 0.073- 0.144) in comparison to participants without SCI. We also analyzed the relationship between 4 clinical variables and functional connectivity within each resting state network for all of the participants, with and without SCD. After adjusting for age and sex, there was a significant association between 3 resting state networks (control, salience ventral attention, and visual networks) and both hemoglobin and hematocrit (Table 3). There was a significant association between functional connectivity in the visual network and hemoglobin when adjusting for age and sex among just the participants with SCD (p = 0.045, 95% CI: 0.001-0.082). We analyzed the relationship between functional connectivity within each resting state network and neuropsychological test scores and found multiple significant associations between control, default mode, dorsal attention, salience ventral attention, and visual networks and attention/executive functioning test scores for all participants as well as just participants with SCD. Our findings suggest that children with SCD and SCI have decreased functional connectivity in the control network in comparison to children with and without SCD without SCI, which may indicate abnormalities in brain regions underlying executive dysfunction. Our data also established a relationship between the degree of anemia and functional connectivity, showing increased functional connectivity in the control, salience ventral attention, and visual network in participants with higher hemoglobin and hematocrit levels. Neuropsychological data shows that select test scores are associated with changes in functional connectivity in resting state networks primarily involved with attention and executive functioning. This research supports the utility of RS-fMRI as an adjunct analysis for investigating neurocognitive abnormalities in pediatric SCD. Figure 1 Figure 1. Disclosures Lanzkron: Novartis: Research Funding; Imara: Research Funding; CSL Behring: Research Funding; Bluebird Bio: Consultancy; Shire: Research Funding; Novo Nordisk: Consultancy; Pfizer: Current holder of individual stocks in a privately-held company; Teva: Current holder of individual stocks in a privately-held company; GBT: Research Funding. Mirro: NOUS Imaging: Current Employment, Current holder of stock options in a privately-held company. Fields: Global Blood Therapeutics: Consultancy; Proclara Biosciences: Current equity holder in publicly-traded company. Lance: Novartis: Other: participated in research advisory board in 2020.

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The sensory and motor components of the cortical hierarchy are coupled to the rhythm of the stomach during rest

10.1101/2021.05.26.445829 ◽

2021 ◽

Author(s):

Ignacio Rebollo ◽

Catherine Tallon-Baudry

Keyword(s):

Resting State ◽

Brain Activity ◽

Piriform Cortex ◽

Brain Regions ◽

Cognitive Networks ◽

Hierarchical Organization ◽

Temporal Organization ◽

Resting State Networks ◽

Fine Grained ◽

Spatio Temporal

Bodily rhythms appear as novel scaffolding mechanisms orchestrating the spatio-temporal organization of spontaneous brain activity. Here, we follow up on the discovery of the gastric resting-state network (Rebollo et al, 2018), composed of brain regions in which the fMRI signal is phase-synchronized to the slow (0.05 Hz) electrical rhythm of the stomach. Using a larger sample size (n=63 human participants), we further characterize the anatomy and effect sizes of gastric-brain coupling across resting-state networks, a fine grained cortical parcellation, as well as along the main gradients of cortical organization. Most (67%) of the gastric network is included in the somato-motor-auditory (38%) and visual (29%) resting state networks. Gastric brain coupling also occurs in the granular insula and, to a lesser extent, in the piriform cortex. Thus, all sensory and motor cortices corresponding to both exteroceptive and interoceptive modalities are coupled to the gastric rhythm during rest. Conversely, little gastric-brain coupling occurs in cognitive networks and transmodal regions. These results suggest not only that gastric rhythm and sensory-motor processes are likely to interact, but also that gastric-brain coupling might be a mechanism of sensory and motor integration that mostly bypasses cognition, complementing the classical hierarchical organization of the human brain.

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Resting-state Network-specific Breakdown of Functional Connectivity during Ketamine Alteration of Consciousness in Volunteers

Anesthesiology ◽

10.1097/aln.0000000000001275 ◽

2016 ◽

Vol 125 (5) ◽

pp. 873-888 ◽

Cited By ~ 82

Author(s):

Vincent Bonhomme ◽

Audrey Vanhaudenhuyse ◽

Athena Demertzi ◽

Marie-Aurélie Bruno ◽

Oceane Jaquet ◽

...

Keyword(s):

Executive Control ◽

Resting State ◽

Brain Regions ◽

Network Effect ◽

Sensorimotor Network ◽

Control Networks ◽

Anesthetic Agents ◽

Connectivity Preservation ◽

Induced Changes ◽

Visual Network

Abstract Background Consciousness-altering anesthetic agents disturb connectivity between brain regions composing the resting-state consciousness networks (RSNs). The default mode network (DMn), executive control network, salience network (SALn), auditory network, sensorimotor network (SMn), and visual network sustain mentation. Ketamine modifies consciousness differently from other agents, producing psychedelic dreaming and no apparent interaction with the environment. The authors used functional magnetic resonance imaging to explore ketamine-induced changes in RSNs connectivity. Methods Fourteen healthy volunteers received stepwise intravenous infusions of ketamine up to loss of responsiveness. Because of agitation, data from six subjects were excluded from analysis. RSNs connectivity was compared between absence of ketamine (wake state [W1]), light ketamine sedation, and ketamine-induced unresponsiveness (deep sedation [S2]). Results Increasing the depth of ketamine sedation from W1 to S2 altered DMn and SALn connectivity and suppressed the anticorrelated activity between DMn and other brain regions. During S2, DMn connectivity, particularly between the medial prefrontal cortex and the remaining network (effect size β [95% CI]: W1 = 0.20 [0.18 to 0.22]; S2 = 0.07 [0.04 to 0.09]), and DMn anticorrelated activity (e.g., right sensory cortex: W1 = −0.07 [−0.09 to −0.04]; S2 = 0.04 [0.01 to 0.06]) were broken down. SALn connectivity was nonuniformly suppressed (e.g., left parietal operculum: W1 = 0.08 [0.06 to 0.09]; S2 = 0.05 [0.02 to 0.07]). Executive control networks, auditory network, SMn, and visual network were minimally affected. Conclusions Ketamine induces specific changes in connectivity within and between RSNs. Breakdown of frontoparietal DMn connectivity and DMn anticorrelation and sensory and SMn connectivity preservation are common to ketamine and propofol-induced alterations of consciousness.

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