scholarly journals Inter-relationships between changes in stress, mindfulness, and dynamic functional connectivity in response to a social stressor

2020 ◽  
Author(s):  
James Teng ◽  
Stijn A.A. Massar ◽  
Julian Lim
Keyword(s):  
Stress Response ◽  
Functional Connectivity ◽  
Stress Reactivity ◽  
Brain Connectivity ◽  
Affective Response ◽  
Trait Mindfulness ◽  
Dynamic Functional Connectivity ◽  
Before And After ◽  
Subjective Reports ◽  
Social Stressor

AbstractWe conducted a study to understand how dynamic functional brain connectivity contributes to the moderating effect of trait mindfulness on the stress response. 40 participants provided subjective reports of stress, cortisol assays, and functional MRI before and after undergoing a social stressor. Self-reported trait mindfulness was also collected. Experiencing stress led to significant decreases in the prevalence of a connectivity state previously associated with mindfulness, but no changes in two connectivity states with prior links to arousal. Connectivity did not return to baseline 30 minutes after stress. Higher trait mindfulness was associated with attenuated affective and neuroendocrine stress response, and smaller decreases in the mindfulness-related connectivity state. In contrast, we found no association between affective response and functional connectivity. Taken together, these data allow us to construct a preliminary brain-behaviour model of how mindfulness dampens stress reactivity.

scholarly journals Longitudinal Brain Functional Connectivity Changes of the Cortical Motor-Related Network in Subcortical Stroke Patients with Acupuncture Treatment

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yongxin Li ◽  
Ya Wang ◽  
Chenxi Liao ◽  
Wenhua Huang ◽  
Ping Wu
Keyword(s):  
Functional Connectivity ◽  
Western Medicine ◽  
Primary Motor Cortex ◽  
Brain Connectivity ◽  
Effective Means ◽  
Brain Network ◽  
Stroke Patients ◽  
Rehabilitation Therapy ◽  
Cortical Motor ◽  
Before And After

In clinical practice, the effectiveness of the rehabilitation therapy such as acupuncture combining conventional Western medicine (AG) on stroke people’s motor-related brain network and their behaviors has not been systematically studied. In the present study, seventeen adult ischemic patients were collected and divided into two groups: the conventional Western medicine treatment group (CG) and the AG. The neurological deficit scores (NDS) and resting-state functional MRI data were collected before and after treatment. Compared with the CG patients, AG patients exhibited a significant enhancement of the percent changes of NDS from pre- to posttreatment intervention. All patients showed significant changes of functional connectivity (FC) between the pair of cortical motor-related regions. After treatment, both patient groups showed a recovery of brain connectivity to the nearly normal level compared with the controls in these pairs. Moreover, a significant correlation between the percent changes of NDS and the pretreatment FC values of bilateral primary motor cortex (M1) in all patients was found. In conclusion, our results showed that AG therapy can be an effective means for ischemic stroke patients to recover their motor function ability. The FC strengths between bilateral M1 of stroke patients can predict stroke patients’ treatment outcome after rehabilitation therapy.

scholarly journals Nuisance Effects and the Limitations of Nuisance Regression in Dynamic Functional Connectivity fMRI

2018 ◽  
Author(s):  
Alican Nalci ◽  
Bhaskar D. Rao ◽  
Thomas T. Liu
Keyword(s):  
Functional Connectivity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Blood Oxygenation ◽  
Dynamic Functional Connectivity ◽  
Temporal Fluctuations ◽  
Before And After ◽  
The Difference ◽  
Time Courses ◽  
The Relationship

AbstractIn resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain’s intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors, with significant correlations observed in the majority (76%) of the cases examined. Significant correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed in the majority (71%) of the cases examined after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.

scholarly journals Effects of the antibiotic rifaximin on cortical functional connectivity are mediated through insular cortex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Davide Sometti ◽  
Chiara Ballan ◽  
Huiying Wang ◽  
Christoph Braun ◽  
Paul Enck
Keyword(s):  
Functional Connectivity ◽  
Animal Studies ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Insular Cortex ◽  
Controlled Study ◽  
Double Blind ◽  
Memory Processing ◽  
Before And After ◽  
The Central Nervous System

AbstractIt is well-known that antibiotics affect commensal gut bacteria; however, only recently evidence accumulated that gut microbiota (GM) can influence the central nervous system functions. Preclinical animal studies have repeatedly highlighted the effects of antibiotics on brain activity; however, translational studies in humans are still missing. Here, we present a randomized, double-blind, placebo-controlled study investigating the effects of 7 days intake of Rifaximin (non-absorbable antibiotic) on functional brain connectivity (fc) using magnetoencephalography. Sixteen healthy volunteers were tested before and after the treatment, during resting state (rs), and during a social stressor paradigm (Cyberball game—CBG), designed to elicit feelings of exclusion. Results confirm the hypothesis of an involvement of the insular cortex as a common node of different functional networks, thus suggesting its potential role as a central mediator of cortical fc alterations, following modifications of GM. Also, the Rifaximin group displayed lower connectivity in slow and fast beta bands (15 and 25 Hz) during rest, and higher connectivity in theta (7 Hz) during the inclusion condition of the CBG, compared with controls. Altogether these results indicate a modulation of Rifaximin on frequency-specific functional connectivity that could involve cognitive flexibility and memory processing.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

2021 ◽  
Author(s):  
Yue Cheng ◽  
Gaoyan Zhang ◽  
Xiaodong Zhang ◽  
Yuexuan Li ◽  
Jingli Li ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Functional Connectivity ◽  
Time Window ◽  
Brain Connectivity ◽  
Minimal Hepatic Encephalopathy ◽  
Classification Performance ◽  
Support Vector ◽  
Dynamic Features ◽  
Dynamic Functional Connectivity ◽  
Window Approach

Abstract To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n=30; noHE, n=32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary , DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

scholarly journals The transformation of hormonal stress responses throughout puberty and adolescence

2011 ◽  
Vol 210 (3) ◽  
pp. 391-398 ◽  
Author(s):  
Allison R Foilb ◽  
Patina Lui ◽  
Russell D Romeo
Keyword(s):  
Stress Response ◽  
Adolescent Development ◽  
Stress Responses ◽  
Stress Reactivity ◽  
Plasma Corticosterone ◽  
Male Rats ◽  
Before And After ◽  
Corticosterone Response ◽  
Acute Stressor ◽  
Adrenal Corticosterone

Prepubertal rats display heightened hormonal stress reactivity compared with adults in that levels of ACTH and corticosterone take twice as long (i.e. 40–60 min) to return to baseline following an acute stressor. Despite this substantial change in stress responsiveness, and the critical nature of the adolescence period of development, the maturation of the hormonal stress response from the time of pubertal onset to adulthood has not been thoroughly investigated. To examine this, we measured ACTH, corticosterone, and testosterone in 30-, 40-, 50-, 60-, and 70-day-old (i.e. spanning pubertal and adolescent development) male rats before and after a 30 min session of restraint stress. We found that the adult-like ACTH stress response develops between 50 and 60 days of age, while the corticosterone response changes between 30 and 40 days of age. We also found that adrenal corticosterone concentrations paralleled the plasma corticosterone response following restraint, suggesting that stress-induced adrenal corticosterone synthesis decreases during adolescent development and may, at least in part, contribute to the differential stress response observed before and after puberty. Finally, stress leads to increases in testosterone secretion, but only after 50 days of age. Collectively, these results indicate that shifts in hormonal stress responses occur throughout adolescent maturation and that these responses show distinct developmental profiles.

scholarly journals A simulation and comparison of dynamic functional connectivity methods

2017 ◽  
Author(s):  
William Hedley Thompson ◽  
Craig Geoffrey Richter ◽  
Pontus Plavén-Sigray ◽  
Peter Fransson
Keyword(s):  
Functional Connectivity ◽  
Wide Spectrum ◽  
Brain Connectivity ◽  
Sliding Window ◽  
Ground Truth ◽  
Spatial Distance ◽  
Dynamic Functional Connectivity ◽  
Benchmark Tests ◽  
Neuroimaging Data ◽  
Temporal Derivative

AbstractThere is a current interest in quantifying brain dynamic functional connectivity (DFC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brain’s dynamics. However, given that the ground truth for DFC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exists many DFC methods it is difficult to assess differences in dynamic brain connectivity between studies. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating DFC (sliding window, tapered sliding window, temporal derivative, spatial distance and jackknife correlation). In particular, we were interested in each methods’ ability to track changes in covariance over time, which is a key property in DFC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future DFC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. In this paper, we present dfcbenchmarker, which is a Python package where researchers can easily submit and compare their own DFC methods to evaluate its performance.

scholarly journals A common framework for the problem of deriving estimates of dynamic functional brain connectivity

2017 ◽  
Author(s):  
William Hedley Thompson ◽  
Peter Fransson
Keyword(s):  
Functional Connectivity ◽  
Analytical Methods ◽  
Brain Connectivity ◽  
Theoretical Framework ◽  
Research Field ◽  
Dynamic Functional Connectivity ◽  
Functional Brain ◽  
Temporal Properties ◽  
Common Framework ◽  
Key Features

AbstractThe research field of dynamic functional connectivity explores the temporal properties of brain connectivity. To date, many methods have been proposed, which are based on quite different assumptions. In order to understand in which way the results from different techniques can be compared to each other, it is useful to be able to formulate them within a common theoretical framework. In this study, we describe such a framework that is suitable for many of the dynamic functional connectivity methods that have been proposed. Our overall intention was to derive a theoretical framework that was constructed such that a wide variety of dynamic functional connectivity techniques could be expressed and evaluated within the same framework. At the same time, care was given to the fact that key features of each technique could be easily illustrated within the framework and thus highlighting critical assumptions that are made. We aimed to create a common framework which should serve to assist comparisons between different analytical methods for dynamic functional brain connectivity and promote an understanding of their methodological advantages as well as potential drawbacks.HighlightsDifferent approaches to compute dynamic functional brain connectivity have been proposed, each with their own assumptions.We present a theoretical framework that encompasses a large majority of proposed methods.Our common framework facilitates comparisons between different methods and illustrates their underlying assumptions.

scholarly journals Unsupervised clustering of track-weighted dynamic functional connectivity reveals white matter substrates of functional connectivity dynamics

2021 ◽  
Author(s):  
Gianpaolo Antonio Basile ◽  
Salvatore Bertino ◽  
Victor Nozais ◽  
Alessia Bramanti ◽  
Rosella Ciurleo ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Resting State ◽  
Time Course ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Dynamic Changes ◽  
Dynamic Functional Connectivity ◽  
Dynamic Connectivity

AbstractThe contribution of structural connectivity to functional connectivity dynamics is still far from being fully elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.

scholarly journals Dynamic functional connectivity markers of objective trait mindfulness

2018 ◽  
Author(s):  
Julian Lim ◽  
James Teng ◽  
Amiya Patanaik ◽  
Jesisca Tandi ◽  
Stijn A. A. Massar
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Brain State ◽  
Trait Mindfulness ◽  
Five Facet Mindfulness Questionnaire ◽  
Dispositional Mindfulness ◽  
Dynamic Functional Connectivity ◽  
Objectively Measured

AbstractWhile mindfulness is commonly viewed as a skill to be cultivated through practice, untrained individuals can also vary widely in dispositional mindfulness. Prior research has identified static neural connectivity correlates of this trait. Here, we use dynamic functional connectivity (DFC) analysis of resting-state fMRI to study time-varying connectivity patterns associated with naturally varying and objectively measured trait mindfulness. Participants were selected from the top and bottom tertiles of performers on a breath-counting task to form high trait mindfulness (HTM; N = 21) and low trait mindfulness (LTM; N = 18) groups. DFC analysis of resting state fMRI data revealed that the HTM group spent significantly more time in a brain state associated with task-readiness – a state characterized by high within-network connectivity and greater anti-correlations between task-positive networks and the default-mode network (DMN). The HTM group transitioned between brain states more frequently, but the dwell time in each episode of the task-ready state was equivalent between groups. These results persisted even after controlling for vigilance. Across individuals, certain connectivity metrics were weakly correlated with self-reported mindfulness as measured by the Five Facet Mindfulness Questionnaire, though these did not survive multiple comparisons correction. In the static connectivity maps, HTM individuals had greater within-network connectivity in the DMN and the salience network, and greater anti-correlations between the DMN and task-positive networks. In sum, DFC features robustly distinguish HTM and LTM individuals, and may be useful biological markers for the measurement of dispositional mindfulness.

scholarly journals Dissecting Static and Dynamic Functional Connectivity: Example From the Autism Spectrum

2019 ◽  
Vol 13 ◽  
pp. 117906951985180 ◽  
Author(s):  
Tonya White ◽  
Vince D. Calhoun
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Dynamic Functional Connectivity ◽  
Age Related ◽  
Autistic Symptoms ◽  
Magnetic Resonance Imaging Mri ◽  
Dynamic Measures ◽  
The Brain

The ability to measure the intrinsic functional architecture of the brain has grown exponentially over the last 2 decades. Measures of intrinsic connectivity within the brain, typically measured using resting-state functional magnetic resonance imaging (MRI), have evolved from primarily “static” approaches, to include dynamic measures of functional connectivity. Measures of dynamic functional connectivity expand the assumptions to allow brain regions to have temporally different patterns of communication between different regions. That is, connections within the brain can differentially fire between different regions at different times, and these differences can be quantified. Applying approaches that measure the dynamic characteristics of functional brain connectivity have been fruitful in identifying differences during brain development and psychopathology. We provide a brief overview of static and dynamic measures of functional connectivity and illustrate the synergy in applying these approaches to identify both age-related differences in children and differences between typically developing children and children with autistic symptoms.

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