Childhood trauma history is linked to abnormal brain connectivity in major depression

Yu, Meichen; Linn, Kristin A.; Shinohara, Russell T.; Oathes, Desmond J.; Cook, Philip A.; Duprat, Romain; Moore, Tyler M.; Oquendo, María A.; Phillips, Mary L.; McInnis, Melvin G.; Fava, Maurizio; Trivedi, Madhukar H.; McGrath, Patrick J.; Parsey, Ramin V.; Weissman, Myrna M.; Sheline, Yvette I.

doi:10.1073/pnas.1900801116

Cited by 175 publications

(138 citation statements)

References 79 publications

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“…Stress experienced early in life can change circuit development and response to later stress and reward, which are likely mediated in part by transcriptional changes in the brain early in life and sustained across the lifespan 7,[19][20][21][22] . Here, we present systematic mapping of enduring ELS-induced transcriptomic changes in three reward-associated brain regions key to depression-like behaviors 23 , in both male and female mice, and we further assess transcriptomic responses to a second stress in adulthood.…”

Section: Discussionmentioning

confidence: 99%

Early life stress alters transcriptomic patterning across reward circuitry in male and female mice

Peña

Smith

Ramakrishnan

et al. 2019

Preprint

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Abuse, neglect, and other forms of early life stress (ELS) significantly increase risk for psychiatric disorders including depression. In this study, we show that ELS in a postnatal sensitive period increases sensitivity to adult stress in female mice, consistent with our earlier findings in male mice. We used RNA-sequencing in the ventral tegmental area, nucleus accumbens, and prefrontal cortex of male and female mice to show that adult stress is distinctly represented in the brain's transcriptome depending on ELS history. We identify: 1) biological pathways disrupted after ELS and associated with increased behavioral stress sensitivity, 2) putative transcriptional regulators of the effect of ELS on adult stress response, and 3) subsets of primed genes specifically associated with latent behavioral changes. We also provide transcriptomic evidence that ELS increases sensitivity to future stress through enhancement of known programs of cortical plasticity.

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Section: Discussionmentioning

confidence: 99%

Early life stress alters transcriptomic patterning across reward circuitry in male and female mice

Peña

Smith

Ramakrishnan

et al. 2019

Preprint

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“…In addition, we reveal new brain regions connected to these well‐established regions. Further, we identify nodes in the DMN, executive control network, and SAL network that are well described in previous studies (Sheline et al, ; Sheline, Price, Yan, & Mintun, ; Yu et al, ). Our results also highlight new findings on hub regions in the association cortex that link to these established networks.…”

Section: Resultsmentioning

confidence: 89%

A difference degree test for comparing brain networks

Higgins

Kundu

Choi

et al. 2019

Human Brain Mapping

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Recently, there has been a proliferation of methods investigating functional connectivity as a biomarker for mental disorders. Typical approaches include massive univariate testing at each edge or comparisons of network metrics to identify differing topological features. Limitations of these methods include low statistical power due to the large number of comparisons and difficulty attributing overall differences in networks to local variation. We propose a method to capture the difference degree, which is the number of edges incident to each region in the difference network. Our difference degree test (DDT) is a two‐step procedure for identifying brain regions incident to a significant number of differentially weighted edges (DWEs). First, we select a data‐adaptive threshold which identifies the DWEs followed by a statistical test for the number of DWEs incident to each brain region. We achieve this by generating an appropriate set of null networks which are matched on the first and second moments of the observed difference network using the Hirschberger–Qi–Steuer algorithm. This formulation permits separation of the network's true topology from the nuisance topology induced by the correlation measure that alters interregional connectivity in ways unrelated to brain function. In simulations, the proposed approach outperforms competing methods in detecting differentially connected regions of interest. Application of DDT to a major depressive disorder dataset leads to the identification of brain regions in the default mode network commonly implicated in this ruminative disorder.

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“…We compared the performance of MSNR to two of the most commonly used single-scale network regression analyses, namely the individual edge model (Grillon et al 2013;Lewis et al 2009) and the community mean model (Yu et al 2019;Betzel et al 2014;King et al 2018;Yan et al 2019). These two approaches have been commonly used to study connectivity-phenotype relationships (Craddock, Tungaraza, and Milham 2015;Varoquaux and Craddock 2013) and differ primarily in the scale at which the brain network is examined ( Fig.3).…”

Section: Comparison To Single-scale Approachesmentioning

confidence: 99%

“…A community refers to a collection of nodes that are highly connected to each other and have little connection to nodes outside of the community. Prior work has demonstrated that brain network architecture present on these different scales is associated with development and aging (Power et al 2010;Gu et al 2015;Betzel et al 2014), cognition (Crossley et al 2013;Park and Friston 2013;Bressler and Menon 2010), and neuropsychiatric diseases (Yu et al 2019;Braun et al 2016;Fornito, Zalesky, and Breakspear 2015;Grillon et al 2013;Bassett, Xia, and Satterthwaite 2018;Xia et al 2018;Kernbach et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Given the importance of community structure in brain networks and its interpretability in the context of neural and cognitive computations (Sporns and Betzel 2016;Betzel, Medaglia, and Bassett 2018), it might be tempting to conduct a mass-univariate analysis at the mesoscale, considering within-and between-community connectivity as the input features (Yu et al 2019;Betzel et al 2014;Gu et al 2015;Braun et al 2016). Such an approach dramatically reduces the dimensionality of the data, which in turn decreases the burden of multiple comparisons correction.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Scale Network Regression for Brain-Phenotype Associations

Xia¹,

Cui³

et al. 2019

Preprint

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Complex brain networks are increasingly characterized at different scales, including global summary statistics, community connectivity, and individual edges. While research relating brain networks to demographic and behavioral measurements has yielded many insights into brain-phenotype relationships, common analytical approaches only consider network information at a single scale, thus failing to incorporate rich information present at other scales.Here, we designed, implemented, and deployed Multi-Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge-and community-level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant association between functional connectivity patterns with brain development, sex differences, and motion-related artifacts. Notably, compared to single-scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge-and community-1 level information, MSNR has the potential to yield novel insights into brain-behavior relationships.

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Childhood trauma history is linked to abnormal brain connectivity in major depression

Cited by 175 publications

References 79 publications

Early life stress alters transcriptomic patterning across reward circuitry in male and female mice

Early life stress alters transcriptomic patterning across reward circuitry in male and female mice

A difference degree test for comparing brain networks

Multi-Scale Network Regression for Brain-Phenotype Associations

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