scholarly journals Association between Scale-Free Brain Dynamics and Behavioral Performance: Functional MRI Study in Resting State and Face Processing Task

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Masato Kasagi ◽  
Zirui Huang ◽  
Kosuke Narita ◽  
Hitoshi Shitara ◽  
Tomokazu Motegi ◽  
...  
Keyword(s):  
Face Processing ◽  
Resting State ◽  
Cognitive Task ◽  
Temporal Structure ◽  
Power Laws ◽  
Posterior Cingulate Cortex ◽  
Scale Free ◽  
State Activity ◽  
Evoked Activity ◽  
And Task

The scale-free dynamics of human brain activity, characterized by an elaborate temporal structure with scale-free properties, can be quantified using the power-law exponent (PLE) as an index. Power laws are well documented in nature in general, particularly in the brain. Some previous fMRI studies have demonstrated a lower PLE during cognitive-task-evoked activity than during resting state activity. However, PLE modulation during cognitive-task-evoked activity and its relationship with an associated behavior remain unclear. In this functional fMRI study in the resting state and face processing + control task, we investigated PLE during both the resting state and task-evoked activities, as well as its relationship with behavior measured using mean reaction time (mRT) during the task. We found that (1) face discrimination-induced BOLD signal changes in the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), amygdala, and fusiform face area; (2) PLE significantly decreased during task-evoked activity specifically in mPFC compared with resting state activity; (3) most importantly, in mPFC, mRT significantly negatively correlated with both resting state PLE and the resting-task PLE difference. These results may lead to a better understanding of the associations between task performance parameters (e.g., mRT) and the scale-free dynamics of spontaneous and task-evoked brain activities.

Spontaneous Brain Activity Predicts Task-Evoked Activity During Animate Versus Inanimate Touch

2019 ◽  
Vol 29 (11) ◽  
pp. 4628-4645 ◽  
Author(s):  
Andrea Scalabrini ◽  
Sjoerd J H Ebisch ◽  
Zirui Huang ◽  
Simone Di Plinio ◽  
Mauro Gianni Perrucci ◽  
...  
Keyword(s):  
Anterior Cingulate Cortex ◽  
Spontaneous Activity ◽  
Brain Activity ◽  
Temporal Structure ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Scale Free ◽  
Spontaneous Brain Activity ◽  
Posterior Insula ◽  
Evoked Activity

Abstract The spontaneous activity of the brain is characterized by an elaborate temporal structure with scale-free properties as indexed by the power law exponent (PLE). We test the hypothesis that spontaneous brain activity modulates task-evoked activity during interactions with animate versus inanimate stimuli. For this purpose, we developed a paradigm requiring participants to actively touch either animate (real hand) or inanimate (mannequin hand) stimuli. Behaviorally, participants perceived the animate target as closer in space, temporally more synchronous with their own self, and more personally relevant, compared with the inanimate. Neuronally, we observed a modulation of task-evoked activity by animate versus inanimate interactions in posterior insula, in medial prefrontal cortex, comprising anterior cingulate cortex, and in medial superior frontal gyrus. Among these regions, an increased functional connectivity was shown between posterior insula and perigenual anterior cingulate cortex (PACC) during animate compared with inanimate interactions and during resting state. Importantly, PLE during spontaneous brain activity in PACC correlated positively with PACC task-evoked activity during animate versus inanimate stimuli. In conclusion, we demonstrate that brain spontaneous activity in PACC can be related to the distinction between animate and inanimate stimuli and thus might be specifically tuned to align our brain with its animate environment.

scholarly journals The role of neural flexibility in cognitive aging

2021 ◽  
Author(s):  
Eleanna Varangis ◽  
Weiwei Qi ◽  
Yaakov Stern ◽  
Seonjoo Lee
Keyword(s):  
Functional Connectivity ◽  
Cognitive Performance ◽  
Resting State ◽  
Cognitive Aging ◽  
Cognitive Task ◽  
Cognitive Networks ◽  
Cognitive Outcomes ◽  
Matrix Reasoning ◽  
Flexibility Measures ◽  
And Task

AbstractStudies assessing relationships between brain and cognitive changes in healthy aging have shown that a variety of aspects of brain structure and function explain a significant portion of the variability in cognitive outcomes throughout adulthood. Many studies assessing relationships between brain function and cognition have utilized time-averaged, or static functional connectivity methods to explore ways in which brain network organization may contribute to aspects of cognitive aging. However, recent studies in this field have suggested that time-varying, or dynamic measures of functional connectivity, which assess changes in functional connectivity throughout a scan session, may play a stronger role in explaining cognitive outcomes in healthy young adults. Further, both static and dynamic functional connectivity studies suggest that there may be differences in patterns of brain-cognition relationships as a function of whether or not the participant is performing a task during the scan. Thus, the goals of the present study were threefold: (1) assess whether dynamic connectivity (neural flexibility) during both resting as well as task-based scans is related to participant age and cognitive performance in a lifespan aging sample, (2) determine whether neural flexibility moderates relationships between age and cognitive performance, and (3) explore differences in neural flexibility between rest and task. Participants in the study were 423 healthy adults between the ages of 20-80 who provided resting state and/or task-based (Matrix Reasoning) functional magnetic resonance imaging (fMRI) scan data as part of their participation in two ongoing studies of cognitive aging. Neural flexibility measures from both resting and task-based scans reflected the number of times each node changed network assignment, and were averaged both across the whole brain (global neural flexibility) as well as within nine somatosensory/cognitive networks. Results showed that neural flexibility during the task was higher in older adults, and that neural flexibility in Default Mode and Visual networks was negatively related to performance on the Matrix Reasoning task. Resting state neural flexibility was not significantly related to either participant age or cognitive performance. Additionally, no neural flexibility measures that significantly moderated relationships between participant age and cognitive outcomes. Further, neural flexibility differed as a function of scan type, with resting state neural flexibility exhibiting significantly more variability than task-based neural flexibility. Thus, neural flexibility measures computed during a cognitive task may be more strongly related to cognitive performance across the adult lifespan, and are more sensitive to the effects of participant age on brain organization.

Activity flow over resting-state networks shapes cognitive task activations

2016 ◽  
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.

scholarly journals Self-similarity and multifractality in human brain activity: a wavelet-based analysis of scale-free brain dynamics☆

2018 ◽  
Author(s):  
Daria La Rocca ◽  
Nicolas Zilber ◽  
Patrice Abry ◽  
Virginie van Wassenhove ◽  
Philippe Ciuciu
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Temporal Structure ◽  
Estimation Methods ◽  
Brain Dynamics ◽  
List Type ◽  
Self Similarity ◽  
Scale Free

AbstractBackgroundThe temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed.New MethodHere, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a task.ResultsFirst, we report consistent infraslow (from 0.1 to 1.5 Hz) scalefree dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higherorder cortices; the anatomical gradient was more pronounced in task than in rest. Third, we observed a significant increase of multifractality during task as compared to rest. Additionally, the decrease in self-similarity and the increase in multifractality from rest to task were negatively correlated in regions involved in the task, suggesting a shift from structured global temporal dynamics in resting-state to locally bursty and non Gaussian scalefree structures during task.Comparison with Existing Method(s)We showed that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale-free brain dynamics.ConclusionsAltogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity.HighlightsWe estimated scale-free human brain dynamics using wavelet-leader formalism.High-to-low self-similarity defined a fronto-occipital gradient.The gradient was enhanced in task compared to resting-state.Scale-free brain dynamics showed multifractal properties.Self-similarity decreased whereas multifractality increased from rest to task.

scholarly journals Disrupted relationship between “resting state” connectivity and task-evoked activity during social perception in schizophrenia

2018 ◽  
Vol 193 ◽  
pp. 370-376 ◽  
Author(s):  
Sjoerd J.H. Ebisch ◽  
Vittorio Gallese ◽  
Anatolia Salone ◽  
Giovanni Martinotti ◽  
Giuseppe di Iorio ◽  
...  
Keyword(s):  
Social Perception ◽  
Resting State ◽  
Evoked Activity ◽  
Resting State Connectivity ◽  
And Task

scholarly journals The impact of hyperoxia on brain activity: A resting-state and task-evoked electroencephalography (EEG) study

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0176610 ◽  
Author(s):  
Min Sheng ◽  
Peiying Liu ◽  
Deng Mao ◽  
Yulin Ge ◽  
Hanzhang Lu
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
The Impact ◽  
And Task

scholarly journals Dynamic relationships between spontaneous and evoked electrophysiological activity

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Soren Wainio-Theberge ◽  
Annemarie Wolff ◽  
Georg Northoff
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
Behavioral Outcomes ◽  
Low Frequencies ◽  
Scale Free ◽  
Non Invasive ◽  
Spontaneous Neural Activity ◽  
Electrophysiological Signals ◽  
Evoked Activity ◽  
Dynamic Relationships

AbstractSpontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.

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