scholarly journals Static and dynamic aspects of cerebro-cerebellar functional connectivity are associated with self-reported measures of impulsivity: A resting-state fMRI study

2020 ◽  
Vol 4 (3) ◽  
pp. 891-909
Author(s):  
Majd Abdallah ◽  
Nicolas Farrugia ◽  
Valentine Chirokoff ◽  
Sandra Chanraud
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Sensation Seeking ◽  
Large Scale ◽  
Resting State Fmri ◽  
Posterior Cingulate Cortex ◽  
Impulsive Behavior ◽  
Dynamic Features ◽  
Behavioral Correlates ◽  
Total Strength

Human and animal brain studies bring converging evidence of a possible role for the cerebellum and the cerebro-cerebellar system in impulsivity. However, the precise nature of the relation between cerebro-cerebellar coupling and impulsivity is far from understood. Characterizing functional connectivity (FC) patterns between large-scale brain networks that mediate different forms of impulsivity, and the cerebellum may improve our understanding of this relation. Here, we analyzed static and dynamic features of cerebro-cerebellar FC using a highly sampled resting-state functional magnetic resonance imaging (rs-fMRI) dataset and tested their association with two widely used self-reports of impulsivity: the UPPS-P impulsive behavior scale and the behavioral inhibition/approach systems (BIS/BAS) in a large group of healthy subjects ( N = 134, ≈ 1 hr of rs-fMRI/subject). We employed robust data-driven techniques to identify cerebral and cerebellar resting-state networks and extract descriptive summary measures of static and dynamic cerebro-cerebellar FC. We observed evidence linking BIS, BAS, sensation seeking, and lack of premeditation to the total strength and temporal variability of FC within networks connecting regions of the prefrontal cortex, precuneus, posterior cingulate cortex, basal ganglia, and thalamus with the cerebellum. Overall, our findings improve the existing knowledge of the neural correlates of impulsivity and the behavioral correlates of the cerebro-cerebellar system.

scholarly journals Static and Dynamic Aspects of Cerebro-Cerebellar Functional Connectivity are Associated with Self-reported Measures of Impulsivity: A Resting-State fMRI Study

2020 ◽  
Author(s):  
Majd Abdallah ◽  
Nicolas Farrugia ◽  
Valentine Chirokoff ◽  
Sandra Chanraud
Keyword(s):  
Basal Ganglia ◽  
Functional Connectivity ◽  
Resting State ◽  
Temporal Variability ◽  
Sensation Seeking ◽  
Animal Studies ◽  
Large Scale ◽  
Resting State Fmri ◽  
Posterior Cingulate Cortex ◽  
Fmri Study

AbstractConverging evidence from human and animal studies predict a possible role of the cerebellum in impulsivity. However, this hypothesis has not been thoroughly investigated within the framework of functional connectivity (FC). To address this issue, we employed resting-state fMRI data and two self-reports of impulsivity (UPPS-P and BIS/BAS) from a large group of healthy young individuals (N=134). We identified cerebral and cerebellar resting-state networks, and evaluated the association of static (strength) and dynamic (temporal variability) aspects of cerebro-cerebellar FC with different elements of self-reported impulsivity. Our results revealed that the behavioral inhibition and approach systems (BIS/BAS) were inversely associated with basal ganglia-cerebellar and fronto-cerebellar FC strength, respectively. In addition, we found that lack of premeditation was inversely associated with the temporal variability of FC between the cerebellum and top-down control networks that included sub-regions of the prefrontal cortex, precuneus, and posterior cingulate cortex. Moreover, we found that sensation seeking was associated with the temporal variability of FC between the cerebellum and networks that included cortical control regions and sub-cortical reward regions: the basal ganglia and the thalamus. Together, these findings indicate that the cerebellum may contribute to different forms of impulsivity through its connections to large-scale control and reward networks.

scholarly journals Large-scale DCMs for resting-state fMRI

2017 ◽  
Vol 1 (3) ◽  
pp. 222-241 ◽  
Author(s):  
Adeel Razi ◽  
Mohamed L. Seghier ◽  
Yuan Zhou ◽  
Peter McColgan ◽  
Peter Zeidman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Effective Connectivity ◽  
Directed Graphs ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Large Graphs ◽  
Model Inversion ◽  
Graph Theoretic

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

scholarly journals Heritability analysis with repeat measurements and its application to resting-state functional connectivity

2017 ◽  
Vol 114 (21) ◽  
pp. 5521-5526 ◽  
Author(s):  
Tian Ge ◽  
Avram J. Holmes ◽  
Randy L. Buckner ◽  
Jordan W. Smoller ◽  
Mert R. Sabuncu
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Transient Effects ◽  
Resting State Functional Connectivity ◽  
Linear Mixed Effects ◽  
Heritability Analysis ◽  
Test Retest Reliability ◽  
Different Levels

Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing heritability analysis methods do not discriminate between stable effects (e.g., due to the subject’s unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements—a prototypic data modality that exhibits variable levels of test–retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional heritability analyses.

Brain Functional Connectivity in OCD

2017 ◽  
Author(s):  
Carles Soriano-Mas ◽  
Ben J. Harrison
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Regulatory Systems ◽  
Variable Nature ◽  
Brain Functional Connectivity ◽  
Or Task

This chapter provides an overview of studies assessing alterations in brain functional connectivity in obsessive-compulsive disorder (OCD) as assessed by functional magnetic resonance imaging (fMRI). Although most of the reviewed studies relate to the analysis of resting-state fMRI data, the chapter also reviews studies that have combined resting-state with structural or task-based approaches, as well as task-based studies in which the analysis of functional connectivity was reported. The main conclusions to be drawn from this review are that patients with OCD consistently demonstrate altered patterns of brain functional connectivity in large-scale “frontostriatal” and “default mode” networks, and that the heterogeneity of OCD symptoms is likely to partly arise via distinct modulatory influences on these networks by broader disturbances of affective, motivational, and regulatory systems. The variable nature of some findings across studies as well as the influence of medications on functional connectivity measures is also discussed.

scholarly journals Evaluating denoising strategies in resting-state fMRI in traumatic brain injury (EpiBioS4Rx)

2021 ◽  
Author(s):  
Marina Weiler ◽  
Raphael Fernandes Casseb ◽  
Brunno Machado de Campos ◽  
Julia Sophia Crone ◽  
Evan S Lutkenhoff ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Clinical Population ◽  
Quality Data ◽  
Physiological Noise ◽  
Quality Control Metrics

Objective: Resting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity of traumatic brain injury patients. Methods: We applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 traumatic brain injury patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial (EpiBioS4Rx). Pipelines were evaluated by three quality control metrics across three exclusion regimes based on the participant's head movement profile. Results: While no pipeline eliminated noise effects on functional connectivity, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data driven methods performed comparatively worse. Conclusion: In this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related functional connectivity in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as traumatic brain injury datasets.

scholarly journals Networks of worry—towards a connectivity-based signature of late-life worry using higher criticism

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew R. Gerlach ◽  
Helmet T. Karim ◽  
Joseph Kazan ◽  
Howard J. Aizenstein ◽  
Robert T. Krafty ◽  
...  
Keyword(s):  
Older Adults ◽  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Anterior Cingulate ◽  
Resting State Functional Connectivity ◽  
Targeted Interventions ◽  
Higher Criticism ◽  
Large Scale Network

AbstractSevere worry is a complex transdiagnostic phenotype independently associated with increased morbidity, including cognitive impairment and cardiovascular diseases. We investigated the neurobiological basis of worry in older adults by analyzing resting state fMRI using a large-scale network-based approach. We collected resting fMRI on 77 participants (>50 years old) with varying worry severity. We computed region-wise connectivity across the default mode network (DMN), anterior salience network, and left executive control network. All 22,366 correlations were regressed on worry severity and adjusted for age, sex, race, education, disease burden, depression, anxiety, rumination, and neuroticism. We employed higher criticism, a second-level method of significance testing for rare and weak features, to reveal the functional connectivity patterns associated with worry. The analysis suggests that worry has a complex, yet distinct signature associated with resting state functional connectivity. Intra-connectivities and inter-connectivities of the DMN comprise the dominant contribution. The anterior cingulate, temporal lobe, and thalamus are heavily represented with overwhelmingly negative association with worry. The prefrontal regions are also strongly represented with a mix of positive and negative associations with worry. Identifying the most salient connections may be useful for targeted interventions for reducing morbidity associated with severe worry in older adults.

scholarly journals Characterizations of resting-state modulatory interactions in the human brain

2015 ◽  
Vol 114 (5) ◽  
pp. 2785-2796 ◽  
Author(s):  
Xin Di (邸新) ◽  
Bharat B. Biswal
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Interaction Analysis ◽  
Region Of Interest ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Processing Efficiency ◽  
Whole Brain ◽  
Positive Effects

Functional connectivity between two brain regions, measured using functional MRI (fMRI), has been shown to be modulated by other regions even in a resting state, i.e., without performing specific tasks. We aimed to characterize large-scale modulatory interactions by performing region-of-interest (ROI)-based physiophysiological interaction analysis on resting-state fMRI data. Modulatory interactions were calculated for every possible combination of three ROIs among 160 ROIs sampling the whole brain. Firstly, among all of the significant modulatory interactions, there were considerably more negative than positive effects; i.e., in more cases, an increase of activity in one region was associated with decreased functional connectivity between two other regions. Next, modulatory interactions were categorized as to whether the three ROIs were from one single network module, two modules, or three different modules (defined by a modularity analysis on their functional connectivity). Positive modulatory interactions were more represented than expected in cases in which the three ROIs were from a single module, suggesting an increase within module processing efficiency through positive modulatory interactions. In contrast, negative modulatory interactions were more represented than expected in cases in which the three ROIs were from two modules, suggesting a tendency of between-module segregation through negative modulatory interactions. Regions that were more likely to have modulatory interactions were then identified. The numbers of significant modulatory interactions for different regions were correlated with the regions' connectivity strengths and connection degrees. These results demonstrate whole-brain characteristics of modulatory interactions and may provide guidance for future studies of connectivity dynamics in both resting state and task state.

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