δ-Oscillation Correlates of Anesthesia-induced Unconsciousness in Large-scale Brain Networks of Human Infants

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Functional brain connectivity studies can provide important information about changes in brain-state dynamics during general anesthesia. In adults, γ-aminobutyric acid–mediated agents disrupt integration of information from local to the whole-brain scale. Beginning around 3 to 4 months postnatal age, γ-aminobutyric acid–mediated anesthetics such as sevoflurane generate α-electroencephalography oscillations. In previous studies of sevoflurane-anesthetized infants 0 to 3.9 months of age, α-oscillations were absent, and power spectra did not distinguish between anesthetized and emergence from anesthesia conditions. Few studies detailing functional connectivity during general anesthesia in infants exist. This study’s aim was to identify changes in functional connectivity of the infant brain during anesthesia. Methods A retrospective cohort study was performed using multichannel electroencephalograph recordings of 20 infants aged 0 to 3.9 months old who underwent sevoflurane anesthesia for elective surgery. Whole-brain functional connectivity was evaluated during maintenance of a surgical state of anesthesia and during emergence from anesthesia. Functional connectivity was represented as networks, and network efficiency indices (including complexity and modularity) were computed at the sensor and source levels. Results Sevoflurane decreased functional connectivity at the δ-frequency (1 to 4 Hz) in infants 0 to 3.9 months old when comparing anesthesia with emergence. At the sensor level, complexity decreased during anesthesia, showing less whole-brain integration with prominent alterations in the connectivity of frontal and parietal sensors (median difference, 0.0293; 95% CI, −0.0016 to 0.0397). At the source level, similar results were observed (median difference, 0.0201; 95% CI, −0.0025 to 0.0482) with prominent alterations in the connectivity between default-mode and frontoparietal regions. Anesthesia resulted in fragmented modules as modularity increased at the sensor (median difference, 0.0562; 95% CI, 0.0048 to 0.1298) and source (median difference, 0.0548; 95% CI, −0.0040 to 0.1074) levels. Conclusions Sevoflurane is associated with decreased capacity for efficient information transfer in the infant brain. Such findings strengthen the hypothesis that conscious processing relies on an efficient system of integrated information transfer across the whole brain.

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Stroke-related alterations in inter-areal communication revealed via Granger causality analysis

10.1101/2021.01.04.425190 ◽

2021 ◽

Author(s):

Michele Allegra ◽

Chiara Favaretto ◽

Nicholas Metcalf ◽

Maurizio Corbetta ◽

Andrea Brovelli

Keyword(s):

Functional Connectivity ◽

Granger Causality ◽

Resting State ◽

Information Transfer ◽

Large Scale ◽

Functional Organization ◽

Information Flows ◽

Causality Analysis ◽

Stroke Patients ◽

Granger Causality Analysis

ABSTRACTNeuroimaging and neurological studies suggest that stroke is a brain network syndrome. While causing local ischemia and cell damage at the site of injury, stroke strongly perturbs the functional organization of brain networks at large. Critically, functional connectivity abnormalities parallel both behavioral deficits and functional recovery across different cognitive domains. However, the reasons for such relations remain poorly understood. Here, we tested the hypothesis that alterations in inter-areal communication underlie stroke-related modulations in functional connectivity (FC). To this aim, we used resting-state fMRI and Granger causality analysis to quantify information transfer between brain areas and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was strongly decreased with respect to healthy controls. Second, information transfer within the affected hemisphere, and from the affected to the intact hemisphere was reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they were correlated with impaired performance in several behavioral domains. Overall, our results support the hypothesis that stroke perturbs inter-areal communication within and across hemispheres, and suggest novel therapeutic approaches aimed at restoring normal information flow.SIGNIFICANCE STATEMENTA thorough understanding of how stroke perturbs brain function is needed to improve recovery from the severe neurological syndromes affecting stroke patients. Previous resting-state neuroimaging studies suggested that interaction between hemispheres decreases after stroke, while interaction between areas of the same hemisphere increases. Here, we used Granger causality to reconstruct information flows in the brain at rest, and analyze how stroke perturbs them. We showed that stroke causes a global reduction of inter-hemispheric communication, and an imbalance between the intact and the affected hemisphere: information flows within and from the latter are impaired. Our results may inform the design of stimulation therapies to restore the functional balance lost after stroke.

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Decoding Unattended Fearful Faces with Whole-Brain Correlations: An Approach to Identify Condition-Dependent Large-Scale Functional Connectivity

PLoS Computational Biology ◽

10.1371/journal.pcbi.1002441 ◽

2012 ◽

Vol 8 (3) ◽

pp. e1002441 ◽

Cited By ~ 30

Author(s):

Spiro P. Pantazatos ◽

Ardesheer Talati ◽

Paul Pavlidis ◽

Joy Hirsch

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Whole Brain ◽

Fearful Faces

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Regional, not global, functional connectivity contributes to isolated focal dystonia

Neurology ◽

10.1212/wnl.0000000000010791 ◽

2020 ◽

Vol 95 (16) ◽

pp. e2246-e2258 ◽

Cited By ~ 1

Author(s):

Scott A. Norris ◽

Aimee E. Morris ◽

Meghan C. Campbell ◽

Morvarid Karimi ◽

Babatunde Adeyemo ◽

...

Keyword(s):

Quality Control ◽

Functional Connectivity ◽

Sensorimotor Cortex ◽

Large Scale ◽

Focal Dystonia ◽

Whole Brain ◽

Correlation Matrices ◽

Body Regions ◽

And Control ◽

Rigorous Quality Control

ObjectiveTo test the hypothesis that there is shared regional or global functional connectivity dysfunction in a large cohort of patients with isolated focal dystonia affecting different body regions compared to control participants. In this case-control study, we obtained resting-state MRI scans (three or four 7.3-minute runs) with eyes closed in participants with focal dystonia (cranial [17], cervical [13], laryngeal [18], or limb [10]) and age- and sex-matched controls.MethodsRigorous preprocessing for all analyses was performed to minimize effect of head motion during scan acquisition (dystonia n = 58, control n = 47 analyzed). We assessed regional functional connectivity by computing a seed-correlation map between putamen, pallidum, and sensorimotor cortex and all brain voxels. We assessed significant group differences on a cluster-wise basis. In a separate analysis, we applied 300 seed regions across the cortex, cerebellum, basal ganglia, and thalamus to comprehensively sample the whole brain. We obtained participant whole-brain correlation matrices by computing the correlation between seed average time courses for each seed pair. Weighted object-oriented data analysis assessed group-level whole-brain differences.ResultsParticipants with focal dystonia had decreased functional connectivity at the regional level, within the striatum and between lateral primary sensorimotor cortex and ventral intraparietal area, whereas whole-brain correlation matrices did not differ between focal dystonia and control groups. Rigorous quality control measures eliminated spurious large-scale functional connectivity differences between groups.ConclusionRegional functional connectivity differences, not global network level dysfunction, contributes to common pathophysiologic mechanisms in isolated focal dystonia. Rigorous quality control eliminated spurious large-scale network differences between patients with focal dystonia and control participants.

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Linear Input as a Fundamental Motif of Brain Connectivity Organization

10.1101/549345 ◽

2019 ◽

Author(s):

Jonathan F. O’Rawe ◽

Hoi-Chung Leung

Keyword(s):

Functional Connectivity ◽

Information Transfer ◽

Brain Connectivity ◽

Brain Regions ◽

Fundamental Aspect ◽

Biased Sampling ◽

Whole Brain ◽

3 Dimensional ◽

Face Area ◽

Learning Techniques

AbstractDescribing the pattern of region-to-region functional connectivity is an important step towards understanding information transfer and transformation between brain regions. Although fMRI data are limited in spatial resolution, recent advances in technology afford more precise mapping. Here, we extended previous methods, connective field mapping, to 3 dimensions to provide a more concise estimate of the organization and potential information transformation from one region to another. We first replicated previous work with the 3 dimensional model by showing that the topology of functional connectivity between early visual regions maintained along their eccentricity axis or the anterior-posterior dimension. We then examined higher order visual regions (e,g, fusiform face area) and showed that their pattern of connectivity, the convergence and biased sampling, seem to contribute to some of their core receptive field properties. We further demonstrated that linearity of input is a fundamental aspect of functional connectivity of the whole brain, with higher linearity between regions within a network than across networks; that is, high connective linearity was evident between early visual areas, and between prefrontal areas, but less evident between them. By decomposing the whole brain linearity matrix with manifold learning techniques, we found that the principle mode of the linearity maps onto decompositions in both functional connectivity and genetic expression reported in previous studies. The current work provides evidence supporting that linearity of input is likely a fundamental motif of functional connectivity between regions for information processing across the brain, with high linearity preserving the integrity of information from one region to another within a network.

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Functional brain network reconfiguration during learning in a dynamic environment

10.1101/800284 ◽

2019 ◽

Cited By ~ 1

Author(s):

Chang-Hao Kao ◽

Ankit N. Khambhati ◽

Danielle S. Bassett ◽

Matthew R. Nassar ◽

Joseph T. McGuire ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Functional Integration ◽

Dynamic Environment ◽

Brain Network ◽

Uncertain Environments ◽

Whole Brain ◽

Functional Brain Network ◽

Brain Pattern ◽

Brain Functional Connectivity

AbstractWhen learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

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Heterogeneity of EEG resting-state brain networks in absolute pitch

10.1101/2020.05.03.063206 ◽

2020 ◽

Author(s):

Marielle Greber ◽

Carina Klein ◽

Simon Leipold ◽

Silvano Sele ◽

Lutz Jäncke

Keyword(s):

Functional Connectivity ◽

Auditory Cortex ◽

Resting State ◽

Large Scale ◽

Absolute Pitch ◽

Brain Regions ◽

Brain Network ◽

Higher Order ◽

Neural Basis ◽

Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

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Intra-session test-retest reliability of functional connectivity in infants

10.1101/2020.06.25.169524 ◽

2020 ◽

Author(s):

Yun Wang ◽

Walter Hinds ◽

Cristiane S Duarte ◽

Seonjoo Lee ◽

Catherine Monk ◽

...

Keyword(s):

Functional Connectivity ◽

Functional Organization ◽

List Type ◽

Whole Brain ◽

Retest Reliability ◽

Spatial Similarity ◽

Developmental Neuroscience ◽

Reliability Estimates ◽

Infant Brain ◽

Test Retest Reliability

AbstractResting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks – the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14 - 0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40 - 0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study studies. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientist may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success.HighlightsInfant functional connectivity (FC) shows poor edge-level reliability (ICCs)However, subject-level infant FC estimates show good-to-excellent ICCsSpatial reproducibility is better for some resting networks (DMN, SMN) than others (ECN)Reliability estimates differ across study site and MRI scannerConclusion - Infant FC can be a reliable measurement, but judicious use is needed

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Turbulent-like dynamics in the human brain

10.1101/865923 ◽

2019 ◽

Author(s):

Gustavo Deco ◽

Morten L. Kringelbach

Keyword(s):

Human Brain ◽

Brain Function ◽

Information Transfer ◽

Large Scale ◽

Brain Dynamics ◽

Whole Brain ◽

Large Scale Network ◽

Maximal Sensitivity ◽

Scale Network ◽

Best Fit

SummaryTurbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1003 healthy participants, we demonstrate Kuramoto’s amplitude turbulence in human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed amplitude turbulence, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the Exponential Distance Rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulence and optimal brain function. Overall, our results reveal a way of analysing and modelling whole-brain dynamics that suggests turbulence as the dynamic intrinsic backbone facilitating large scale network communication.

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Large-scale intrinsic connectivity is consistent across varying task demands

10.1101/407205 ◽

2018 ◽

Author(s):

Paulina Kieliba ◽

Sasidhar Madugula ◽

Nicola Filippini ◽

Eugene P. Duff ◽

Tamar R. Makin

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Brain Activation ◽

Whole Brain ◽

Activation Patterns ◽

Intrinsic Connectivity ◽

Connectivity Patterns ◽

Brain Functional Connectivity ◽

The Brain

AbstractMeasuring whole-brain functional connectivity patterns based on task-free (‘restingstate’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a ‘steadystates’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.New and NoteworthyDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

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Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity

10.1101/2021.08.28.458027 ◽

2021 ◽

Author(s):

Link Tejavibulya ◽

Hannah Peterson ◽

Abigail Sara Greene ◽

Siyuan Gao ◽

Max Rolison ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Functional Organization ◽

Regions Of Interest ◽

Data Driven ◽

Whole Brain ◽

Left And Right ◽

Using Data ◽

Brain Data ◽

Language Areas

Handedness influences differences in lateralization of language areas as well as dominance of motor and somatosensory cortices. However, differences in whole brain functional organization due to handedness have been relatively understudied beyond pre-specified networks of interest. Functional connectivity offers the ability to unravel differences in the functional organization of the whole brain. Here, we compared connectivity profiles of left- and right-handed individuals using data-driven parcellations of the whole brain. We explored differences in connectivity profiles of previously established regions of interest, and showed functional organization differences between primarily left- and primarily right-handed individuals in the motor, somatosensory, and language areas using functional connectivity. We then proceeded to investigate these differences in the whole brain and found that the functional organization of left- and right-handed individuals are not specific to regions of interest. In particular, we found that connections between and within-hemispheres and the cerebellum show distinct patterns of connectivity. Together these results shed light on regions of the brain beyond those traditionally explored that contribute to differences in the functional organization of left- and right-handed individuals.

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