Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder

Yang, Xun; Liu, Jin; Meng, Yajing; Xia, Mingrui; Cui, Zaixu; Xi, Wu; Hu, Xinyu; Zhang, Wei; Gong, Gaolang; Gong, Qiyong; Sweeney, John A.; He, Yong

doi:10.1016/j.neuroimage.2017.12.011

Cited by 71 publications

(53 citation statements)

References 71 publications

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“…Due to factors inherent to the type of data, the risk of family‐wise errors is therefore high when true effects are present in noisy connectivity matrices. The probabilities given above for a fully‐connected matrix, however, describe the “worst case scenario.” When matrices are originally sparse or are thresholded prior to statistical testing, as in structural connectivity studies (Roberts, Perry, Roberts, Mitchell, & Breakspear, ; Zalesky et al, ) and sometimes in functional connectivity assessments (Yang et al, ), the reduced network density would decrease the risk of false‐positives. Even considering the probabilities described above, however, whenever any false‐positives occur, the total number of false‐positives should be small.…”

Section: Resultsmentioning

confidence: 99%

Statistical inference in brain graphs using threshold‐free network‐based statistics

Baggio

Abós

Segura

et al. 2018

Human Brain Mapping

114

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The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge-wise group-level statistical inference in brain graphs while controlling for multiple-testing associated false-positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold-free network-based statistics (TFNBS). The TFNBS combines threshold-free cluster enhancement, a method commonly used in voxel-wise statistical inference, and network-based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge-wise significance values and does not require the a priori definition of a hard cluster-defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false-positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.

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Section: Resultsmentioning

confidence: 99%

Statistical inference in brain graphs using threshold‐free network‐based statistics

Baggio

Abós

Segura

et al. 2018

Human Brain Mapping

114

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“…SAD is marked by a constant anxiety of facing negative judgement or evaluation in social or performance situations ( 86 ). Studies including one meta-analytic review revealed increased resting-state functional connectivity and activities, as well as task-related hyperactivation in the occipital cortex in SAD, which might underlie the enhanced environmental scanning for potentially threatening or feared stimuli in SAD ( 16 , 87 , 88 ). Treatment-related research provided evidence for a link between structural and functional alterations in SAD ( 45 ).…”

Section: Discussionmentioning

confidence: 99%

Gray Matter Structural Alterations in Social Anxiety Disorder: A Voxel-Based Meta-Analysis

et al. 2018

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The current insight into the neurobiological pathogenesis underlying social anxiety disorder (SAD) is still rather limited. We implemented a meta-analysis to explore the neuroanatomical basis of SAD. We undertook a systematic search of studies comparing gray matter volume (GMV) differences between SAD patients and healthy controls (HC) using a whole-brain voxel-based morphometry (VBM) approach. The anisotropic effect size version of seed-based d mapping (AES-SDM) meta-analysis was conducted to explore the GMV differences of SAD patients compared with HC. We included eleven studies with 470 SAD patients and 522 HC in the current meta-analysis. In the main meta-analysis, relative to HC, SAD patients showed larger GMVs in the left precuneus, right middle occipital gyrus (MOG) and supplementary motor area (SMA), as well as smaller GMV in the left putamen. In the subgroup analyses, compared with controls, adult patients (age ≥ 18 years) with SAD exhibited larger GMVs in the left precuneus, right superior frontal gyrus (SFG), angular gyrus, middle temporal gyrus (MTG), MOG and SMA, as well as a smaller GMV in the left thalamus; SAD patients without comorbid depressive disorder exhibited larger GMVs in the left superior parietal gyrus and precuneus, right inferior temporal gyrus, fusiform gyrus, MTG and superior temporal gyrus (STG), as well as a smaller GMV in the bilateral thalami; and currently drug-free patients with SAD exhibited a smaller GMV in the left thalamus compared with HC while no larger GMVs were found. For SAD patients with different clinical features, our study revealed directionally consistent larger cortical GMVs and smaller subcortical GMVs, including locationally consistent larger precuneus and thalamic deficits in the left brain. Age, comorbid depressive disorder and concomitant medication use of the patients might be potential confounders of SAD at the neuroanatomical level.

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“…These disruptions remained or even increased in severity over time, and within key cortical midline structures, local dysfunction predicted anxiety symptoms. While in recent years whole brain functional connectivity has been extensively examined in social anxiety disorder (e.g., Xing et al, 2016;Yang et al, 2017;Zhu et al, 2017), this is the first study to our knowledge that applied NBS and graph theory to address the question of whether network abnormalities are spatially restricted to the PFC-amygdala circuit, or if the integrity of brain function is globally disrupted in adults with GAD. An exception is represented by one recent study that tested whether parameters from dynamic functional connectivity could reliably classify adolescents with GAD from healthy controls in the absence of other clinical measures (Yao et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Network abnormalities in generalized anxiety pervade beyond the amygdala-pre-frontal cortex circuit: Insights from graph theory

Makovac

Mancini

Fagioli

et al. 2018

Psychiatry Research: Neuroimaging

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Generalized anxiety disorder (GAD) has excessive anxiety and uncontrollable worry as core symptoms. Abnormal cerebral functioning underpins the expression and perhaps pathogenesis of GAD:. Studies implicate impaired communication between the amygdala and the pre-frontal cortex (PFC). Our aim was to longitudinally investigate whether such network abnormalities are spatially restricted to this circuit or if the integrity of functional brain networks is globally disrupted in GAD. We acquired resting-state functional magnetic resonance imaging data from 16 GAD patients and 16 matched controls at baseline and after 1 year. Using network modeling and graph-theory, whole-brain connectivity was characterized from local and global perspectives. Overall lower global efficiency, indicating sub-optimal brain-wide organization and integration, was present in patients with GAD compared to controls. The amygdala and midline cortices showed higher betweenness centrality, reflecting functional dominance of these brain structures. Third, lower betweenness centrality and lower degree emerged for PFC, suggesting weakened inhibitory control. Overall, network organization showed impairments consistent with neurobiological models of GAD (involving amygdala, PFC, and cingulate cortex) and further pointed to an involvement of temporal regions. Such impairments tended to progress over time and predict anxiety symptoms. A graph-analytic approach represents a powerful approach to deepen our understanding of GAD.

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Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder

Cited by 71 publications

References 71 publications

Statistical inference in brain graphs using threshold‐free network‐based statistics

Statistical inference in brain graphs using threshold‐free network‐based statistics

Gray Matter Structural Alterations in Social Anxiety Disorder: A Voxel-Based Meta-Analysis

Network abnormalities in generalized anxiety pervade beyond the amygdala-pre-frontal cortex circuit: Insights from graph theory

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