Altered structural brain network topology in chronic migraine

DeSouza, Danielle D.; Woldeamanuel, Yohannes W.; Sanjanwala, Bharati; Bissell, Daniel A; Bishop, James; Peretz, Addie; Cowan, Robert P.

doi:10.1007/s00429-019-01994-7

Cited by 26 publications

(19 citation statements)

References 74 publications

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“…As for the discrepancy between DeSouza et al.’s study (3) and ours (2), differences in the above-mentioned factors possibly account for this. Furthermore, DeSouza et al.…”

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confidence: 74%

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Reply to letter to the editor: Insights into chronic migraine pathophysiology – what measures of gray matter reveal

et al. 2020

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“…As for the discrepancy between DeSouza et al.’s study (3) and ours (2), differences in the above-mentioned factors possibly account for this. Furthermore, DeSouza et al.…”

contrasting

confidence: 74%

“…Their finding of significant differences in structural network measures leads DeSouza et al. to suggest that such measures may provide a more sensitive means to detect structural differences in CM (3). While we agree with DeSouza et al.…”

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confidence: 99%

Reply to letter to the editor: Insights into chronic migraine pathophysiology – what measures of gray matter reveal

et al. 2020

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“…The high phenotypic covariance of brain morphology across brain areas can be interpreted as evidence advocating the existence of coordinated maturational, neurodevelopmental, and evolutionary processes in the brain [ 18 , 19 , 20 ]. Recently, a number of MRI and/or positron emission tomography studies have investigated structural covariance networks, based on graph theory [ 14 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Graph theory has been introduced as a method to construct human brain networks.…”

Section: Introductionmentioning

confidence: 99%

“…While these studies present significant advances in the investigation of anatomical connectivity, they mainly depended on group-level structural correlations of cortical morphology [ 14 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. These group-level structural covariance networks offer a statistical framework for investigating synchronized morphological alterations in brain regions across a group of subjects.…”

Section: Introductionmentioning

confidence: 99%

Altered Cortical Thickness-Based Individualized Structural Covariance Networks in Patients with Schizophrenia and Bipolar Disorder

Kim

Jeon

et al. 2020

JCM

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Structural covariance is described as coordinated variation in brain morphological features, such as cortical thickness and volume, among brain structures functionally or anatomically interconnected to one another. Structural covariance networks, based on graph theory, have been studied in mental disorders. This analysis can help in understanding the brain mechanisms of schizophrenia and bipolar disorder. We investigated cortical thickness-based individualized structural covariance networks in patients with schizophrenia and bipolar disorder. T1-weighted magnetic resonance images were obtained from 39 patients with schizophrenia, 37 patients with bipolar disorder type I, and 32 healthy controls, and cortical thickness was analyzed via a surface-based morphometry analysis. The structural covariance of cortical thickness was calculated at the individual level, and covariance networks were analyzed based on graph theoretical indices: strength, clustering coefficient (CC), path length (PL) and efficiency. At the global level, both patient groups showed decreased strength, CC and efficiency, and increased PL, compared to healthy controls. In bipolar disorder, we found intermediate network measures among the groups. At the nodal level, schizophrenia patients showed decreased CCs in the left suborbital sulcus and the right superior frontal sulcus, compared to bipolar disorder patients. In addition, patient groups showed decreased CCs in the right insular cortex and the left superior occipital gyrus. Global-level network indices, including strength, CCs and efficiency, positively correlated, while PL negatively correlated, with the positive symptoms of the Positive and Negative Syndrome Scale for patients with schizophrenia. The nodal-level CC of the right insular cortex positively correlated with the positive symptoms of schizophrenia, while that of the left superior occipital gyrus positively correlated with the Young Mania Rating Scale scores for bipolar disorder. Altered cortical structural networks were revealed in patients, and particularly, the prefrontal regions were more altered in schizophrenia. Furthermore, altered cortical structural networks in both patient groups correlated with core pathological symptoms, indicating that the insular cortex is more vulnerable in schizophrenia, and the superior occipital gyrus is more vulnerable in bipolar disorder. Our individualized structural covariance network indices might be promising biomarkers for the evaluation of patients with schizophrenia and bipolar disorder.

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“…Recently, we applied graph theory to surface- and voxel-based measures of gray matter structure to examine structural network alterations in patients with CM (3). Compared to controls, we showed that patients with CM had altered local and global network properties that were characterized as less integrated, less efficient, and abnormally segregated.…”

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confidence: 99%

Insights into chronic migraine pathophysiology – what measures of gray matter reveal

DeSouza

Cowan

2020

Cephalalgia

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Altered structural brain network topology in chronic migraine

Cited by 26 publications

References 74 publications

Reply to letter to the editor: Insights into chronic migraine pathophysiology – what measures of gray matter reveal

Reply to letter to the editor: Insights into chronic migraine pathophysiology – what measures of gray matter reveal

Altered Cortical Thickness-Based Individualized Structural Covariance Networks in Patients with Schizophrenia and Bipolar Disorder

Insights into chronic migraine pathophysiology – what measures of gray matter reveal

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