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 Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 128 ◽  
Author(s):  
Aline Viol ◽  
Fernanda Palhano-Fontes ◽  
Heloisa Onias ◽  
Draulio de Araujo ◽  
Philipp Hövel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Structural Information ◽  
Brain Connectivity ◽  
Functional Brain ◽  
States Of Consciousness ◽  
Functional Brain Networks ◽  
Empirical Networks ◽  
Emergent Behaviors

With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.

scholarly journals Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

2022 ◽  
Vol 19 (1) ◽  
pp. 509
Author(s):  
Fangyuan Tian ◽  
Hongxia Li ◽  
Shuicheng Tian ◽  
Chenning Tian ◽  
Jiang Shao
Keyword(s):  
Functional Connectivity ◽  
Coal Mine ◽  
Brain Connectivity ◽  
Clustering Coefficient ◽  
Mine Safety ◽  
Functional Near Infrared Spectroscopy ◽  
Functional Brain ◽  
Unsafe Behavior ◽  
Brain Functional Connectivity ◽  
Mine Workers

(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.

scholarly journals The Influence of Heart Rate Variability Biofeedback on Cardiac Regulation and Functional Brain Connectivity

2021 ◽  
Vol 15 ◽  
Author(s):  
Andy Schumann ◽  
Feliberto de la Cruz ◽  
Stefanie Köhler ◽  
Lisa Brotte ◽  
Karl-Jürgen Bär
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Cingulate Cortex ◽  
Brain Regions ◽  
Control Group ◽  
Functional Brain ◽  
Middle Cingulate Cortex

BackgroundHeart rate variability (HRV) biofeedback has a beneficial impact on perceived stress and emotion regulation. However, its impact on brain function is still unclear. In this study, we aimed to investigate the effect of an 8-week HRV-biofeedback intervention on functional brain connectivity in healthy subjects.MethodsHRV biofeedback was carried out in five sessions per week, including four at home and one in our lab. A control group played jump‘n’run games instead of the training. Functional magnetic resonance imaging was conducted before and after the intervention in both groups. To compute resting state functional connectivity (RSFC), we defined regions of interest in the ventral medial prefrontal cortex (VMPFC) and a total of 260 independent anatomical regions for network-based analysis. Changes of RSFC of the VMPFC to other brain regions were compared between groups. Temporal changes of HRV during the resting state recording were correlated to dynamic functional connectivity of the VMPFC.ResultsFirst, we corroborated the role of the VMPFC in cardiac autonomic regulation. We found that temporal changes of HRV were correlated to dynamic changes of prefrontal connectivity, especially to the middle cingulate cortex, the left insula, supplementary motor area, dorsal and ventral lateral prefrontal regions. The biofeedback group showed a drop in heart rate by 5.2 beats/min and an increased SDNN as a measure of HRV by 8.6 ms (18%) after the intervention. Functional connectivity of the VMPFC increased mainly to the insula, the amygdala, the middle cingulate cortex, and lateral prefrontal regions after biofeedback intervention when compared to changes in the control group. Network-based statistic showed that biofeedback had an influence on a broad functional network of brain regions.ConclusionOur results show that increased heart rate variability induced by HRV-biofeedback is accompanied by changes in functional brain connectivity during resting state.

scholarly journals Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression

2018 ◽  
Author(s):  
Luigi A. Maglanoc ◽  
Nils Inge Landrø ◽  
Rune Jonassen ◽  
Tobias Kaufmann ◽  
Aldo Cordova-Palomera ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Total Sample ◽  
Individual Variability ◽  
Functional Brain Imaging ◽  
Depression Symptoms ◽  
Imaging Data ◽  
Functional Brain ◽  
Symptom Profiles ◽  
Connectivity Patterns

AbstractBackgroundDepression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data.MethodsWe assessed symptoms of depression and anxiety using Beck’s Depression and Beck’s Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imaging-based dynamic and static functional connectivity between subgroups in a subset of the total sample.ResultsWe identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a fronto-temporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures.ConclusionAdding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional connectivity patterns.

scholarly journals The relationship between spatial configuration and functional connectivity of brain regions revisited

2019 ◽  
Author(s):  
Janine D. Bijsterbosch ◽  
Christian F. Beckmann ◽  
Mark W. Woolrich ◽  
Stephen M. Smith ◽  
Samuel J. Harrison
Keyword(s):  
Functional Connectivity ◽  
Network Structure ◽  
Resting State ◽  
Spatial Configuration ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Spatial Effects ◽  
Functional Brain ◽  
Dual Regression ◽  
The Relationship

AbstractIn our previous paper (Bijsterbosch et al., 2018), we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.Impact statementWe show that functional connectivity network matrices as estimated from resting state functional MRI are biased by spatially overlapping network structure.

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 Abnormal thalamocortical network dynamics in migraine

Neurology ◽  
2019 ◽  
Vol 92 (23) ◽  
pp. e2706-e2716 ◽  
Author(s):  
Yiheng Tu ◽  
Zening Fu ◽  
Fang Zeng ◽  
Nasim Maleki ◽  
Lei Lan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Clinical Symptoms ◽  
Network Connectivity ◽  
Lower Efficiency ◽  
Dynamic Functional Connectivity ◽  
Correlation Clustering ◽  
Temporal Properties ◽  
Brain States ◽  
Global And Local

ObjectiveTo investigate the dynamic functional connectivity of thalamocortical networks in interictal migraine patients and whether clinical features are associated with abnormal connectivity.MethodsWe investigated dynamic functional network connectivity (dFNC) of the migraine brain in 89 interictal migraine patients and 70 healthy controls. We focused on the temporal properties of thalamocortical connectivity using sliding window cross-correlation, clustering state analysis, and graph-theory methods. Relationships between clinical symptoms and abnormal dFNC were evaluated using a multivariate linear regression model.ResultsFive dFNC brain states were identified to characterize and compare dynamic functional connectivity patterns. We demonstrated that migraineurs spent more time in a strongly interconnected between-network state, but they spent less time in a sparsely connected state. Interestingly, we found that abnormal posterior thalamus (pulvinar nucleus) dFNC with the visual cortex and the precuneus were significantly correlated with headache frequency of migraine. Further topologic measures revealed that migraineurs had significantly lower efficiency of information transfer in both global and local dFNC.ConclusionOur results demonstrated a transient pathologic state with atypical thalamocortical connectivity in migraineurs and extended current findings regarding abnormal thalamocortical networks and dysrhythmia in migraine.

scholarly journals Functional Brain Connectivity Patterns Associated with Visual Hallucinations in Dementia with Lewy Bodies

2021 ◽  
pp. 1-10
Author(s):  
Stefania Pezzoli ◽  
Matteo De Marco ◽  
Giovanni Zorzi ◽  
Annachiara Cagnin ◽  
Annalena Venneri
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Dementia With Lewy Bodies ◽  
Brain Connectivity ◽  
Lewy Bodies ◽  
Resting State Fmri ◽  
Group Differences ◽  
Visual Hallucinations ◽  
Functional Brain ◽  
Functional Brain Networks

Background: The presence of recurrent, complex visual hallucinations (VH) is among the core clinical features of dementia with Lewy bodies (DLB). It has been proposed that VH arise from a disrupted organization of functional brain networks. However, studies are still limited, especially investigating the resting-state functional brain features underpinning VH in patients with dementia. Objective: The aim of the present pilot study was to investigate whether there were any alterations in functional connectivity associated with VH in DLB. Methods: Seed-based analyses and independent component analysis (ICA) of resting-state fMRI scans were carried out to explore differences in functional connectivity between DLB patients with and without VH. Results: Seed-based analyses reported decreased connectivity of the lateral geniculate nucleus, the superior parietal lobule and the putamen with the medial frontal gyrus in DLB patients with VH. Visual areas showed a pattern of both decreased and increased functional connectivity. ICA revealed between-group differences in the default mode network (DMN). Conclusion: Functional connectivity analyses suggest dysfunctional top-down and bottom-up processes and DMN-related alterations in DLB patients with VH. These impairments might foster the generation of false visual images that are misinterpreted, ultimately resulting in VH.

scholarly journals Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes

2021 ◽  
Author(s):  
V. Zerbi ◽  
M. Pagani ◽  
M. Markicevic ◽  
M. Matteoli ◽  
D. Pozzi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Brain Mapping ◽  
Brain Connectivity ◽  
Autism Spectrum ◽  
Functional Brain ◽  
Mouse Mutants ◽  
Diagnostic Label ◽  
Syndromic Asd ◽  
Etiological Heterogeneity

AbstractAutism Spectrum Disorder (ASD) is characterized by substantial, yet highly heterogeneous abnormalities in functional brain connectivity. However, the origin and significance of this phenomenon remain unclear. To unravel ASD connectopathy and relate it to underlying etiological heterogeneity, we carried out a bi-center cross-etiological investigation of fMRI-based connectivity in the mouse, in which specific ASD-relevant mutations can be isolated and modeled minimizing environmental contributions. By performing brain-wide connectivity mapping across 16 mouse mutants, we show that different ASD-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse, often diverging, connectivity signatures are recognizable. Despite this heterogeneity, the identified connectivity alterations could be classified into four subtypes characterized by discrete signatures of network dysfunction. Our findings show that etiological variability is a key determinant of connectivity heterogeneity in ASD, hence reconciling conflicting findings in clinical populations. The identification of etiologically-relevant connectivity subtypes could improve diagnostic label accuracy in the non-syndromic ASD population and paves the way for personalized treatment approaches.

scholarly journals The relationship between spatial configuration and functional connectivity of brain regions revisited

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Janine Diane Bijsterbosch ◽  
Christian F Beckmann ◽  
Mark W Woolrich ◽  
Stephen M Smith ◽  
Samuel J Harrison
Keyword(s):  
Functional Connectivity ◽  
Spatial Configuration ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Spatial Effects ◽  
Functional Brain ◽  
Spatial Overlap ◽  
Exact Location ◽  
Dual Regression ◽  
The Relationship

Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.

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