Altered Brain Signal Variability in Patients With Generalized Anxiety Disorder

Li, Liyuan; Wang, Yifeng; Ye, Liangkai; Chen, Wang; Huang, Xinju; Cui, Qian; He, Zongling; Liu, Dongfeng; Chen, Huafu

doi:10.3389/fpsyt.2019.00084

Cited by 10 publications

(9 citation statements)

References 68 publications

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“…The negative correlation between the SD of SSE and the SD of RT is in line with previous findings that larger temporal variability is associated with better behavioral performance [ 2 , 7 , 51 ]. Greater temporal variability may provide kinetic energy for brain function, enabling the brain to explore a variety of potential functional states.…”

Section: Discussionsupporting

confidence: 91%

Spatial variability of low frequency brain signal differentiates brain states

Wang

Yang

et al. 2020

PLoS ONE

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Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.

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Section: Discussionsupporting

confidence: 91%

Spatial variability of low frequency brain signal differentiates brain states

Wang

Yang

et al. 2020

PLoS ONE

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“…Patients with generalized anxiety disorder exhibited decreased brain signal variability in widespread regions, including the visual network, the sensorimotor network, the fronto-parietal network, the limbic system, and the thalamus (20). It should be noted that in our study, all subjects are young healthy populations.…”

Section: Discussionmentioning

confidence: 65%

White Matter Structural Brain Connectivity of Young Healthy Individuals With High Trait Anxiety

et al. 2020

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Results: Different connections were detected in fractional anisotropy-and fiber numberweighted networks. These connections were widely distributed in various regions, where relative significance was located in the inter-hemispheric frontal lobe, the frontallimbic lobe in the right intra-hemisphere, and the frontal-temporal lobe in the ipsilateral hemisphere. However, no significant difference was found in fiber length-weighted network and in graph properties among the three networks. Conclusions: The structural connectivity of white matter may be a vulnerability marker. Hence, healthy individuals with high trait anxiety levels are susceptible to anxiety-related psychopathology. The findings may help elucidate the pathological mechanism of anxiety and establish interventions for populations susceptible to anxiety disorders.

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“…Indeed, in the field of neuroimaging, interest in BOLD signal variability has been increasing with mounting evidence that it could be a promising correlate of cognitive performance with good measurement reliability and a more flexible brain state allowing more accurate processing, complementary to the traditional BOLD signal mean [52][53][54]. BOLD signal variability has also been reported to differ significantly between patients with generalized anxiety disorder healthy controls in widespread brain regions, with a nonlinear relationship between anxiety level and variability, showing promise as a potential clinical biomarker [55]. Critically, given the recent and sparse literature on the predictive value of BOLD variability both in resting-state and task-based fMRI in anxiety disorders [14,16], our study supports further investigation of BOLD variability as a predictive feature of clinical outcome.…”

Section: Discussionmentioning

confidence: 99%

Individual-Level Prediction of Exposure Therapy Outcome Using Structural and Functional MRI Data in Spider Phobia: A Machine-Learning Study

Chavanne,

Meinke,

Langhammer

et al. 2023

Depression and Anxiety

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Machine-learning prediction studies have shown potential to inform treatment stratification, but recent efforts to predict psychotherapy outcomes with clinical routine data have only resulted in moderate prediction accuracies. Neuroimaging data showed promise to predict treatment outcome, but previous prediction attempts have been exploratory and reported small clinical sample sizes. Herein, we aimed to examine the incremental predictive value of neuroimaging data in contrast to clinical and demographic data alone (for which results were previously published), using a two-level multimodal ensemble machine-learning strategy. We used pretreatment structural and task-based fMRI data to predict virtual reality exposure therapy outcome in a bicentric sample of N = 190 patients with spider phobia. First, eight 1st-level random forest classifications were conducted using separate data modalities (clinical questionnaire scores and sociodemographic data, cortical thickness and gray matter volumes, functional activation, connectivity, connectivity-derived graph metrics, and BOLD signal variance). Then, the resulting predictions were used to train a 2nd-level classifier that produced a final prediction. No 1st-level or 2nd-level classifier performed above chance level except BOLD signal variance, which showed potential as a contributor to higher-level prediction from multiple regions across the brain (1st-level balanced accuracy = 0.63 ). Overall, neuroimaging data did not provide any incremental accuracy for treatment outcome prediction in patients with spider phobia with respect to clinical and sociodemographic data alone. Thus, we advise caution in the interpretation of prediction performances from small-scale, single-site patient samples. Larger multimodal datasets are needed to further investigate individual-level neuroimaging predictors of therapy response in anxiety disorders.

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Altered Brain Signal Variability in Patients With Generalized Anxiety Disorder

Cited by 10 publications

References 68 publications

Spatial variability of low frequency brain signal differentiates brain states

Spatial variability of low frequency brain signal differentiates brain states

White Matter Structural Brain Connectivity of Young Healthy Individuals With High Trait Anxiety

Individual-Level Prediction of Exposure Therapy Outcome Using Structural and Functional MRI Data in Spider Phobia: A Machine-Learning Study

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