scholarly journals Global brain dynamics during social exclusion predict subsequent behavioral conformity

2018 ◽  
Vol 13 (2) ◽  
pp. 182-191 ◽  
Author(s):  
Nick Wasylyshyn ◽  
Brett Hemenway Falk ◽  
Javier O Garcia ◽  
Christopher N Cascio ◽  
Matthew Brook O’Donnell ◽  
...  
Keyword(s):  
Social Exclusion ◽  
Brain Dynamics ◽  
Global Brain

scholarly journals Resting Brain Fluctuations Are Intrinsically Coupled to Visual Response Dynamics

2020 ◽  
Author(s):  
Michaël E Belloy ◽  
Jacob Billings ◽  
Anzar Abbas ◽  
Amrit Kashyap ◽  
Wen-Ju Pan ◽  
...  
Keyword(s):  
Reticular Formation ◽  
Visual Stimulation ◽  
Activity Patterns ◽  
Brain Dynamics ◽  
Brain State ◽  
Visual Response ◽  
Response Dynamics ◽  
Sensory Stimuli ◽  
Prominent Component ◽  
Global Brain

Abstract How do intrinsic brain dynamics interact with processing of external sensory stimuli? We sought new insights using functional magnetic resonance imaging to track spatiotemporal activity patterns at the whole brain level in lightly anesthetized mice, during both resting conditions and visual stimulation trials. Our results provide evidence that quasiperiodic patterns (QPPs) are the most prominent component of mouse resting brain dynamics. These QPPs captured the temporal alignment of anticorrelation between the default mode (DMN)- and task-positive (TPN)-like networks, with global brain fluctuations, and activity in neuromodulatory nuclei of the reticular formation. Specifically, the phase of QPPs prior to stimulation could significantly stratify subsequent visual response magnitude, suggesting QPPs relate to brain state fluctuations. This is the first observation in mice that dynamics of the DMN- and TPN-like networks, and particularly their anticorrelation, capture a brain state dynamic that affects sensory processing. Interestingly, QPPs also displayed transient onset response properties during visual stimulation, which covaried with deactivations in the reticular formation. We conclude that QPPs appear to capture a brain state fluctuation that may be orchestrated through neuromodulation. Our findings provide new frontiers to understand the neural processes that shape functional brain states and modulate sensory input processing.

scholarly journals Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis

2021 ◽  
Vol 14 ◽  
Author(s):  
Gabriella Tamburro ◽  
Pierpaolo Croce ◽  
Filippo Zappasodi ◽  
Silvia Comani
Keyword(s):  
Visual Inspection ◽  
Transition Probabilities ◽  
Brain Dynamics ◽  
Denoising Method ◽  
Eyes Closed ◽  
Eeg Recordings ◽  
Microstate Analysis ◽  
Global Brain ◽  
Automatic Removal

The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the expert classification of the ICs by visual inspection. Brain dynamics in two different groups of denoised EEG signals, reconstructed after having removed the artifactual ICs identified by either visual inspection or the automated methods, was assessed by calculating microstate topographies, microstate metrics (duration, occurrence, and coverage), and directional predominance (based on transition probabilities). No statistically significant differences between the expert and the automated classification of the artifactual ICs were found (p > 0.05). Cronbach’s α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. The total EEG signal variance explained by the sets of global microstate templates was about 80% for all denoised EEG datasets, with no significant differences between groups. For the differently denoised EEG datasets in the two recording conditions, we found that the global microstate templates and the sequences of global microstates were very similar (p < 0.01). Descriptive statistics and Cronbach’s α of microstate metrics highlighted no significant differences and excellent consistency between groups (p > 0.5). These results confirm the ability of the optimized fingerprint method and the ARCI approach to effectively remove physiological artifacts from EEG recordings while preserving global brain dynamics. They also suggest that microstate analysis could represent a novel approach for assessing the ability of an EEG denoising method to remove artifacts without altering brain dynamics.

scholarly journals Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans

Cell ◽  
2015 ◽  
Vol 163 (3) ◽  
pp. 656-669 ◽  
Author(s):  
Saul Kato ◽  
Harris S. Kaplan ◽  
Tina Schrödel ◽  
Susanne Skora ◽  
Theodore H. Lindsay ◽  
...  
Keyword(s):  
Caenorhabditis Elegans ◽  
Motor Command ◽  
Brain Dynamics ◽  
Command Sequence ◽  
Global Brain

scholarly journals A METHOD FOR INTEGRATIVE ANALYSIS OF LOCAL AND GLOBAL BRAIN DYNAMICS

2021 ◽  
Author(s):  
Robyn L. Miller ◽  
Victor M Vergara ◽  
Vince Calhoun
Keyword(s):  
Resting State ◽  
Network Connectivity ◽  
High Dimensional ◽  
Group Differences ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Time Resolved ◽  
Localized Patterns ◽  
Global Brain ◽  
The Brain

The most common pipelines for studying time-varying network connectivity in resting state functional magnetic resonance imaging (rs-fMRI) operate at the whole brain level, capturing a small discrete set of 'states' that best represent time-resolved joint measures of connectivity over all network pairs in the brain. This whole-brain hidden Markov model (HMM) approach 'uniformizes' the dynamics over what is typically more than 1000 pairs of networks, forcing each time-resolved high-dimensional observation into its best-matched high-dimensional state. While straightforward and convenient, this HMM simplification obscures functional and temporal nonstationarities that could reveal systematic, informative features of resting state brain dynamics at a more granular scale. We introduce a framework for studying functionally localized dynamics that intrinsically embeds them within a whole-brain HMM frame of reference. The approach is validated in a large rs-fMRI schizophrenia study where it identifies group differences in localized patterns of entropy and dynamics that help explain consistently observed differences between schizophrenia patients and controls in occupancy of whole-brain dFNC states more mechanistically.

scholarly journals Learning to read the imprints of consciousness on global brain dynamics: an application to intra-operative monitoring of anesthesia

2017 ◽  
Author(s):  
Leandro M. Alonso ◽  
Guillermo Solovey ◽  
Toru Yanagawa ◽  
Alex Proekt ◽  
Guillermo A. Cecchi ◽  
...  
Keyword(s):  
High Performance ◽  
Conscious State ◽  
Brain Dynamics ◽  
Marginal Stability ◽  
Depth Of Anesthesia ◽  
Single Trial ◽  
Learning To Read ◽  
Global Brain ◽  
Operative Monitoring ◽  
State Of Activity

In daily life, in the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness. A number of studies suggest that the awake, conscious state is not the default behavior of an assembly of neurons, but rather a very special state of activity that has to be actively maintained and curated to support its functional properties. Thus responsiveness is a feature that requires active maintenance, such as a homeostatic mechanism to balance excitation and inhibition. In this work we developed a method for monitoring such maintenance processes, focusing on a specific signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this effect to build a single-trial classifier which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a mean for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice.

scholarly journals Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network

PLoS Biology ◽  
2016 ◽  
Vol 14 (6) ◽  
pp. e1002469 ◽  
Author(s):  
Tianwen Chen ◽  
Weidong Cai ◽  
Srikanth Ryali ◽  
Kaustubh Supekar ◽  
Vinod Menon
Keyword(s):  
Brain Dynamics ◽  
Salience Network ◽  
Spatiotemporal Organization ◽  
Global Brain

scholarly journals A Method for Integrative Analysis of Local and Global Brain Dynamics

2021 ◽  
Author(s):  
Robyn L. Miller ◽  
Victor M. Vergara ◽  
Vince D. Calhoun
Keyword(s):  
Integrative Analysis ◽  
Brain Dynamics ◽  
Global Brain

scholarly journals The Control of Global Brain Dynamics: Opposing Actions of Frontoparietal Control and Default Mode Networks on Attention

2014 ◽  
Vol 34 (2) ◽  
pp. 451-461 ◽  
Author(s):  
P. J. Hellyer ◽  
M. Shanahan ◽  
G. Scott ◽  
R. J. S. Wise ◽  
D. J. Sharp ◽  
...  
Keyword(s):  
Brain Dynamics ◽  
Default Mode ◽  
Global Brain
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