Poster No:
1465
Submission Type:
Abstract Submission
Authors:
Haley Wang1, Zhen-Qi Liu2, Lauren Salminen3, Rajendra Morey4, Lianne Schmaal5, Laura Han5, Elena Pozzi5, ENIGMA MDD Working Group6, ENIGMA PTSD Working Group6, Tiffany Ho1
Institutions:
1UCLA, Los Angeles, CA, 2McGill University, Montreal, Quebec, 3University of Southern California, Los Angeles, CA, 4Duke University, Durham, NC, 5University of Melbourne, Melbourne, Australia, 6ENIGMA, International, International
First Author:
Co-Author(s):
Laura Han
University of Melbourne
Melbourne, Australia
Elena Pozzi
University of Melbourne
Melbourne, Australia
Introduction:
Early life adversity (ELA), including abuse and neglect, is strongly linked to stress-related mental disorders, such as major depressive disorder (MDD) and post-traumatic stress disorder (PTSD). Neuroimaging studies showed that individuals with more severe ELA have lower cortical thickness (CT), lower surface area (SA), and smaller subcortical volumes (SV). Case-control studies reported similar neuroanatomical abnormalities in MDD and PTSD, yet the extent to which MDD or PTSD-related brain alterations are attributable to ELA remains uncertain. Furthermore, neuroimaging studies of ELA showed heterogeneous effect sizes, and the regional distribution of structural brain abnormalities associated with ELA has been inconsistent. Here, we address these gaps by pooling together structural neuroimaging data from 25 international cohorts via the ENIGMA-MDD and PTSD Working Groups and leveraging data-driven methods to test whether neuroanatomical features (CT, SA, and SV) predict the severity of ELA.
Methods:
Of the included 3,668 subjects (mean age=35.1; age range: 8.3-73.7 years), 2027 (55.3%) were female. This study included 2,263 healthy control (HC) subjects, 890 MDD patients, and 515 PTSD patients. T1w structural MRI data were processed using harmonized FreeSurfer quality assurance protocols with neuroCombat to correct for site and scanner effects across cohorts. ELA severity was measured by the Childhood Trauma Questionnaire (CTQ). We examined diagnostic group differences in regional CT, SA, and SV, controlling for age, sex, and intracranial volume, correcting for multiple tests. Using Partial Least Square (PLS) correlation, we extracted latent components (LCs) that maximize covariance between the neuroanatomical features and CTQ scores across all subjects. Additionally, seven machine learning (ML) models (linear, tree-based, support vector regression, and ensemble methods) were trained on the neuroimaging data to assess top neural features contributing to predictions of ELA severity (i.e., the CTQ total and subscale scores separately) across all subjects.
Results:
The PTSD group had significantly higher total CTQ scores compared to the HC (d=1.47; p<.001) and MDD (d=0.73; p<.001) groups, with HC being lower than the MDD group (d=-0.64; p<.001). Compared with HC, MDD group exhibited widespread lower CT measures, with the largest effect sizes seen in the bilateral supramarginal, fusiform, and middle temporal regions (q<.05). MDD group also showed lower SA in the right superior temporal sulcus and bilateral superior temporal sulcus regions (q<.05). However, no differences in SV were observed. No structural brain measure differences were found between the PTSD as compared with the HC or MDD groups. Transdiagnostic PLS analyses revealed significant but weak LCs linking CT and SA with total CTQ (p<.001; r=.07), neglect (p<.001; r=.06), and abuse (p<.001; r=.06). No LCs were found correlating SV with CTQ scores. Consistent with the PLS analyses, all ML algorithms failed to predict CTQ scores based on neuroanatomical features reliably.
Conclusions:
Consistent with prior work, we found that MDD is associated with lower CT and SA in the supramarginal, fusiform, and middle temporal regions compared to HC, whereas PTSD showed no detectable neuroanatomical differences from MDD or HC after multiple tests correction. Interestingly, ELA severity did not correlate with the variances in macroscale neuroanatomical features transdiagnostically. Together, our results suggest that factors other than ELA, perhaps later environmental influences, may play a significant role in brain development and the neurophenotypes of MDD and PTSD. One possible interpretation is that ELA does not play an outsized role in shaping brain morphometry by adulthood and that these effects are more prominent during childhood and adolescence. To test this formulation, future work will include separate analyses for children, adolescents, and adults, within the inherent limitations of these consortia data.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Lifespan Development:
Early life, Adolescence, Aging
Modeling and Analysis Methods:
Classification and Predictive Modeling 1
Multivariate Approaches
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Keywords:
Affective Disorders
Cortex
Data Organization
Machine Learning
Multivariate
Psychiatric Disorders
STRUCTURAL MRI
Sub-Cortical
Trauma
Univariate
1|2Indicates the priority used for review
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