Disorganized cortical thickness covariance network in major depressive disorder implicated by aberrant hubs in large-scale networks

Wang, Tao; Wang, Kangcheng; Qu, Hang; Zhou, Jingjing; Li, Qi; Deng, Zhou; Du, Xue; Lv, Fajin; Ren, Gaoping; Guo, Jing; Qiu, Jiang; Xie, Peng

doi:10.1038/srep27964

Cited by 42 publications

(30 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The unique neuroimaging pattern that distinguished ELS involved the ICC, medial OFC, and insula, with the strongest post hoc predictive value in the medial OFC. This cluster of regions has been previously associated with negative affect and self‐referential processing, which is consistent with a higher degree of emotional dysregulation observed in the ELS + group.…”

Section: Discussionsupporting

confidence: 87%

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Salminen

Morey

Riedel

et al. 2019

Journal of Neuroimaging

View full text Add to dashboard Cite

BACKGROUND AND PURPOSE Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here, we used a novel machine learning method – evolving partitions to improve classification (EPIC) – to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat‐exposed military veterans. METHODS We used EPIC with repeated cross‐validation (CV) to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n = 40) versus controls (n = 57), and classification of ELS within the PTSD (ELS+ n = 16; ELS− n = 24) and control groups (ELS+ n = 16; ELS− n = 41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression. RESULTS On average, EPIC classified PTSD with 69% accuracy (SD = 5%), and ELS with 64% accuracy in the PTSD group (SD = 10%), and 62% accuracy in controls (SD = 6%). EPIC selected unique sets of individual features that classified each group with 75‐85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas‐defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS. CONCLUSIONS EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat‐exposed military veterans, which may represent distinct biotypes of stress‐related neuropathology.

show abstract

Section: Discussionsupporting

confidence: 87%

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Salminen

Morey

Riedel

et al. 2019

Journal of Neuroimaging

View full text Add to dashboard Cite

show abstract

“…[28][29] The unique neuroimaging pattern that distinguished ELS involved the ICC, medial OFC, and insula, with the strongest post hoc predictive value in the medial OFC. This cluster of regions has been previously associated with negative affect and self-referential processing, 30 which is consistent with a higher degree of emotional dysregulation observed in the ELS + group.…”

Section: Discussionsupporting

confidence: 86%

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Salminen

Morey

Riedel

et al. 2018

Preprint

View full text Add to dashboard Cite

Background and Purpose: Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here we used a novel machine learning method -evolving partitions to improve classification (EPIC) -to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans. Methods:We used EPIC with repeated cross-validation to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n=40) versus controls (n=57), and classification of ELS within the PTSD (ELS + n=16; ELS -n=24) and control groups (ELS + n=16; ELS -n=41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression.Results: On average, EPIC classified PTSD with 69% accuracy (SD=5%), and ELS with 64% accuracy in the PTSD group (SD=10%), and 62% accuracy in controls (SD=6%). EPIC selected unique sets of individual features that classified each group with 75-85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS.Conclusions: EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology.

show abstract

“…A few studies have reported significant alterations in global network architecture measures in individuals with various psychiatric disorders, particularly MDD. For example, lower small-worldness and clustering coefficient measures have been reported in MDD (Long et al, 2015; T. Wang et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Childhood maltreatment is associated with alteration in global network fiber-tract architecture independent of history of depression and anxiety

et al. 2017

View full text Add to dashboard Cite

Childhood maltreatment is a major risk factor for psychopathology. It is also associated with alterations in the network architecture of the brain, which we hypothesized may play a significant role in the development of psychopathology. In this study, we analyzed the global network architecture of physically healthy unmedicated 18–25 year old subjects (n=262) using diffusion tensor imaging (DTI) MRI and tractography. Anatomical networks were constructed from fiber streams interconnecting 90 cortical or subcortical regions for subjects with no-to-low (n=122) versus moderate-to-high (n=140) exposure to maltreatment. Graph theory analysis revealed lower degree, strength, global efficiency, and maximum Laplacian spectra, higher pathlength, small-worldness and Laplacian skewness, and less deviation from artificial networks in subjects with moderate-to-high exposure to maltreatment. On balance, local clustering was similar in both groups, but the different clusters were more strongly interconnected in the no-to-low exposure group. History of major depression, anxiety and attention deficit hyperactivity disorder did not have a significant impact on global network measures over and above the effect of maltreatment. Maltreatment is an important factor that needs to be taken into account in studies examining the relationship between network differences and psychopathology.

show abstract

Disorganized cortical thickness covariance network in major depressive disorder implicated by aberrant hubs in large-scale networks

Cited by 42 publications

References 70 publications

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Childhood maltreatment is associated with alteration in global network fiber-tract architecture independent of history of depression and anxiety

Contact Info

Product

Resources

About