Brain activity at rest: a multiscale hierarchical functional organization

2011 ◽  
Vol 105 (6) ◽  
pp. 2753-2763 ◽  
Author(s):  
Gaëlle Doucet ◽  
Mikaël Naveau ◽  
Laurent Petit ◽  
Nicolas Delcroix ◽  
Laure Zago ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Organization ◽  
Brain Activity ◽  
Correlation Coefficients ◽  
Temporal Correlation ◽  
Hierarchical Level ◽  
Spontaneous Brain Activity ◽  
Dorsal Attentional Network

Spontaneous brain activity was mapped with functional MRI (fMRI) in a sample of 180 subjects while in a conscious resting-state condition. With the use of independent component analysis (ICA) of each individual fMRI signal and classification of the ICA-defined components across subjects, a set of 23 resting-state networks (RNs) was identified. Functional connectivity between each pair of RNs was assessed using temporal correlation analyses in the 0.01- to 0.1-Hz frequency band, and the corresponding set of correlation coefficients was used to obtain a hierarchical clustering of the 23 RNs. At the highest hierarchical level, we found two anticorrelated systems in charge of intrinsic and extrinsic processing, respectively. At a lower level, the intrinsic system appears to be partitioned in three modules that subserve generation of spontaneous thoughts (M1a; default mode), inner maintenance and manipulation of information (M1b), and cognitive control and switching activity (M1c), respectively. The extrinsic system was found to be made of two distinct modules: one including primary somatosensory and auditory areas and the dorsal attentional network (M2a) and the other encompassing the visual areas (M2b). Functional connectivity analyses revealed that M1b played a central role in the functioning of the intrinsic system, whereas M1c seems to mediate exchange of information between the intrinsic and extrinsic systems.

scholarly journals Abnormal Spontaneous Brain Activity and Functional Connectivity of the Default Mode Network in Patients with Bipolar Disorder

2021 ◽  
Author(s):  
Lei Zhao ◽  
Qijing Bo ◽  
Zhifang Zhang ◽  
Feng Li ◽  
Yuan Zhou ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Resting State ◽  
Brain Activity ◽  
Seed Region ◽  
Potential Candidate ◽  
Neural Basis ◽  
Default Mode ◽  
Spontaneous Brain Activity

Abstract Background: No consistent evidence on the specific brain regions is available in the default mode network (DMN), which show abnormal spontaneous activity in bipolar disorder (BD). We aim to identify this region that is particularly impaired in patients with BD by using several different indices measuring spontaneous brain activity and then investigate its functional connectivity (FC).Methods: A total of 56 patients with BD and 71 healthy controls (HC) underwent resting-state functional magnetic resonance imaging. Three commonly used functional indices were used to identify the brain region showing abnormal spontaneous brain activity in BD. Then, this region served as the seed region for resting-state FC analysis to identify its functional networks altered in BD.Results: The BD group exhibited decreased fALFF, ReHo, and DC values in the left precuneus. The BD group had decreased rsFC within the DMN, indicated by decreased resting-state FC within the left precuneus and between the left precuneus and the medial prefrontal cortex. The BD group had decreased negative connectivity between the left precuneus and the left putamen, extending to the left insula.Conclusions: The findings provide convergent evidence for the abnormalities in the DMN of BD, particularly located in the left precuneus. Decreased FC within the DMN and the disruptive anticorrelation between the DMN and the salience network are found in BD. These findings suggest that the DMN is a key aspect for understanding the neural basis of BD, and the altered functional patterns of DMN may be a potential candidate biomarker of BD.

scholarly journals Belief in Communism and Theory of Mind

2021 ◽  
Vol 12 ◽  
Author(s):  
Outong Chen ◽  
Fang Guan ◽  
Yu Du ◽  
Yijun Su ◽  
Hui Yang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Theory Of Mind ◽  
Resting State ◽  
Brain Activity ◽  
Neural Correlates ◽  
Resting State Functional Connectivity ◽  
Neural Basis ◽  
Spontaneous Brain Activity ◽  
Multiple Regions ◽  
Left Thalamus

A belief in communism refers to the unquestionable trust and belief in the justness of communism. Although former studies have discussed the political aim and social value of communism, the cognitive neural basis of a belief in communism remains largely unknown. In this study, we determined the behavioral and neural correlates between a belief in communism and a theory of mind (ToM). For study 1, questionnaire scores were measured and for study 2, regional homogeneity (ReHo) and resting-state functional connectivity (rsFC) were used as an index for resting-state functional MRI (rs-fMRI), as measured by the Belief in Communism Scale (BCS). The results showed that a belief in communism is associated with higher ReHo in the left thalamus and lower ReHo in the left medial frontal gyrus (MFG). Furthermore, the results of the rsFC analysis revealed that strength of functional connectivity between the left thalamus and the bilateral precuneus is negatively associated with a belief in communism. Hence, this study provides evidence that spontaneous brain activity in multiple regions, which is associated with ToM capacity, contributes to a belief in communism.

scholarly journals Spontaneously emerging patterns in human visual cortex and their functional connectivity are linked to the patterns evoked by visual stimuli

2019 ◽  
Author(s):  
DoHyun Kim ◽  
Tomer Livne ◽  
Nicholas V. Metcalf ◽  
Maurizio Corbetta ◽  
Gordon L. Shulman
Keyword(s):  
Visual Cortex ◽  
Functional Connectivity ◽  
Spontaneous Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Visual Stimuli ◽  
Correlation Coefficients ◽  
Temporal Correlation ◽  
Stimulus Category ◽  
Human Visual Cortex

AbstractThe function of spontaneous brain activity is an important issue in neuroscience. Here we test the hypothesis that patterns of spontaneous activity code representational patterns evoked by stimuli and tasks. We compared in human visual cortex multi-vertex patterns of spontaneous activity to patterns evoked by ecological visual stimuli (faces, bodies, scenes) and low-level visual features (e.g. phase-scrambled faces). Specifically, we identified regions that preferred particular stimulus categories during localizer scans (e.g. extra-striate body area for bodies), measured multi-vertex patterns for each category during event-related task scans, and then correlated over vertices these stimulus-evoked patterns to the pattern measured on each frame of resting-state scans. The mean correlation coefficient was essentially zero for all regions/stimulus categories, indicating that resting multi-vertex patterns were not biased toward particular stimulus-evoked patterns. However, the spread of correlation coefficients between stimulus-evoked and resting patterns, i.e. both positive and negative, was significantly greater for the preferred stimulus category of an ROI (e.g. body category in body-preferring ROIs). The relationship between spontaneous and stimulus-evoked multi-vertex patterns also governed the temporal correlation or functional connectivity of patterns of spontaneous activity between individual regions (pattern-based functional connectivity). Resting patterns related to an object category fluctuated preferentially between ROIs preferring the same category, and patterns related to different categories fluctuated independently within their respective preferred ROIs (e.g. body- and scene-related multi-vertex patterns within body- and scene-preferring ROIs). These results support the general proposal that spontaneous multi-vertex activity patterns are linked to stimulus-evoked patterns, consistent with a representational function for spontaneous activity.

scholarly journals Exploratory Analysis of Power Spectrum and Functional Connectivity During Resting State in Young Binge Drinkers: A MEG Study

2015 ◽  
Vol 25 (03) ◽  
pp. 1550008 ◽  
Author(s):  
A. Correas ◽  
S. Rodriguez Holguín ◽  
P. Cuesta ◽  
E. López-Caneda ◽  
L. M. García-Moreno ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Organization ◽  
Brain Activity ◽  
Power Spectra ◽  
First Year ◽  
Eyes Closed ◽  
Initial Sign ◽  
First Year University ◽  
The Brain

Binge Drinking (BD) is a pattern of intermittent intensive alcohol intake which has spread among young adults over the last decades. Adolescence constitutes a critical neuromaturation period in which the brain is particularly sensitive to the effects of alcohol. However, little is known about how BD affects the brain activity. This study aimed to characterize the brain's functional organization in BD and non-BD young population by means of analyzing functional connectivity (FC) and relative power spectra (PS) profiles measured with magnetoencephalography (MEG) during eyes-closed resting state. Our sample composed 73 first-year university students (35 BDs and 38 controls). Results showed that the BD subjects displayed a decreased alpha FC in frontal-parietal regions, and conversely, an enhanced FC in the delta, theta and beta bands in fronto-temporal networks. Besides the FC differences, the BD group showed a decreased PS within alpha range and an increased PS within theta range in the brain's occipital region. These differences in FC and PS measurements provide new evidence of the neurophysiological alterations related to the alcohol neurotoxicity and could represent an initial sign of an anomalous neural activity caused by a BD pattern of alcohol consumption during youth.

scholarly journals Normalisation of brain connectivity through compensatory behaviour, despite congenital hand absence

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Avital Hahamy ◽  
Stamatios N Sotiropoulos ◽  
David Henderson Slater ◽  
Rafael Malach ◽  
Heidi Johansen-Berg ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Adaptive Behaviour ◽  
Resting State Functional Connectivity ◽  
Spontaneous Brain Activity ◽  
White Matter Microstructure ◽  
Hand Region ◽  
Ecological Behaviour

Previously we showed, using task-evoked fMRI, that compensatory intact hand usage after amputation facilitates remapping of limb representations in the cortical territory of the missing hand (<xref ref-type="bibr" rid="bib15">Makin et al., 2013a</xref>). Here we show that compensatory arm usage in individuals born without a hand (one-handers) reflects functional connectivity of spontaneous brain activity in the cortical hand region. Compared with two-handed controls, one-handers showed reduced symmetry of hand region inter-hemispheric resting-state functional connectivity and corticospinal white matter microstructure. Nevertheless, those one-handers who more frequently use their residual (handless) arm for typically bimanual daily tasks also showed more symmetrical functional connectivity of the hand region, demonstrating that adaptive behaviour drives long-range brain organisation. We therefore suggest that compensatory arm usage maintains symmetrical sensorimotor functional connectivity in one-handers. Since variability in spontaneous functional connectivity in our study reflects ecological behaviour, we propose that inter-hemispheric symmetry, typically observed in resting sensorimotor networks, depends on coordinated motor behaviour in daily life.

scholarly journals Jumping over baselines with new methods to predict activation maps from resting-state fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Lacosse ◽  
Klaus Scheffler ◽  
Gabriele Lohmann ◽  
Georg Martius
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Behavioral Differences ◽  
Large Variability ◽  
Single Vertex ◽  
Spontaneous Brain Activity ◽  
New Methods ◽  
Group Averaging ◽  
Proposed Modification

AbstractCognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

scholarly journals Detecting functional connectivity disruptions in a translational pediatric traumatic brain injury porcine model using resting-state and task-based fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregory Simchick ◽  
Kelly M. Scheulin ◽  
Wenwu Sun ◽  
Sydney E. Sneed ◽  
Madison M. Fagan ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Connectivity ◽  
Resting State ◽  
Correlation Coefficients ◽  
Brain Network ◽  
Network Activity ◽  
Anatomical Structures ◽  
Healthy Control ◽  
And Task

AbstractFunctional magnetic resonance imaging (fMRI) has significant potential to evaluate changes in brain network activity after traumatic brain injury (TBI) and enable early prognosis of potential functional (e.g., motor, cognitive, behavior) deficits. In this study, resting-state and task-based fMRI (rs- and tb-fMRI) were utilized to examine network changes in a pediatric porcine TBI model that has increased predictive potential in the development of novel therapies. rs- and tb-fMRI were performed one day post-TBI in piglets. Activation maps were generated using group independent component analysis (ICA) and sparse dictionary learning (sDL). Activation maps were compared to pig reference functional connectivity atlases and evaluated using Pearson spatial correlation coefficients and mean ratios. Nonparametric permutation analyses were used to determine significantly different activation areas between the TBI and healthy control groups. Significantly lower Pearson values and mean ratios were observed in the visual, executive control, and sensorimotor networks for TBI piglets compared to controls. Significant differences were also observed within several specific individual anatomical structures within each network. In conclusion, both rs- and tb-fMRI demonstrate the ability to detect functional connectivity disruptions in a translational TBI piglet model, and these disruptions can be traced to specific affected anatomical structures.

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