2021
DOI: 10.1016/j.nicl.2021.102871
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Potential structural trait markers of depression in the form of alterations in the structures of subcortical nuclei and structural covariance network properties

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Cited by 13 publications
(14 citation statements)
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References 93 publications
(156 reference statements)
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“…In the analysis of group-level SCN, we did not detect any significant differences in inter-group comparisons. Despite prior investigations indicating group-level differences in structural covariance aberrance (Chen et al, 2022a;Neufeld et al, 2020;Singh et al, 2013;Watanabe et al, 2020;Xiong et al, 2021;Yun & Kim, 2021), our current study did not reveal any significant differences between patients with MDD and HCs. The inconsistent findings across studies may be attributed to factors such as small sample sizes in previous single-center investigations, comorbidities, medication, age of onset (Han et al, 2021;Schmaal et al, 2017).…”
Section: Discussioncontrasting
confidence: 99%
See 1 more Smart Citation
“…In the analysis of group-level SCN, we did not detect any significant differences in inter-group comparisons. Despite prior investigations indicating group-level differences in structural covariance aberrance (Chen et al, 2022a;Neufeld et al, 2020;Singh et al, 2013;Watanabe et al, 2020;Xiong et al, 2021;Yun & Kim, 2021), our current study did not reveal any significant differences between patients with MDD and HCs. The inconsistent findings across studies may be attributed to factors such as small sample sizes in previous single-center investigations, comorbidities, medication, age of onset (Han et al, 2021;Schmaal et al, 2017).…”
Section: Discussioncontrasting
confidence: 99%
“…To date, the structural covariance network (SCN) has been commonly used to characterize the structural connectivity of gray matter morphology. Technically, traditional SCN, which was constructed from structural MRI by calculating interregional morphological similarity across a cohort of participants at the group level in a certain brain morphological measure, such as gray matter volume (GMV; Yao et al, 2010) or cortical thickness (He, Chen, & Evans, 2007), has been applied to identify abnormal alterations in the topological organization of the structural networks in MDD patients (Chen et al, 2022a; Singh et al, 2013; Xiong et al, 2021). Nevertheless, the population-based SCN not only neglects interindividual variability but cannot reveal the correlation between brain phenotype with clinical symptoms.…”
Section: Introductionmentioning
confidence: 99%
“…50,51 Our finding of altered pulvinar connectivity in this mixed (both amnestic and nonamnestic) group is particularly noteworthy since several studies of depression have highlighted the role of the pulvinar in the pathophysiology of depression and also in the response to antidepressant therapy and remission. 35,52 Xiong et al identified decreased gray matter volume in the anterior pulvinar as a trait-marker of depression, 14 a phenomenon which was subsequently reversed with antidepressant therapy. 35,50 This was also suggested by novel connectivity data showing that decreased functional connectivity between the pulvinar and the parietal cortex and precuneus may be associated with memory impairments after electroconvulsive therapy in depressive patients .…”
Section: Descriptivesmentioning
confidence: 99%
“…The results suggested that the remitted individuals demonstrated higher accuracy in identifying face emotions than the healthy individuals and the currently depressive individuals. On the other hand, trait-dependent abnormalities have also been reported for some psychophysical biomarkers (e.g., Chen et al, 2022;Douglas et al, 2012;Milders et al, 2010;Ruhe et al, 2019) and biological markers (e.g., Elliott et al, 2012;van der Vinne et al, 2019;van Eijndhoven et al, 2013;Xiong et al, 2021). For example, Milders et al (2010) investigated the stability of emotion recognition for patients with unipolar depression by a longitudinal design.…”
Section: Discussionmentioning
confidence: 99%