Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

EEG connectivity between the subgenual anterior cingulate and prefrontal cortices in response to antidepressant medication

Item does not contain fulltextAntidepressant medication is the most common treatment for major depressive disorder (MDD), however, the precise working mechanism underlying these treatments remains unclear. Recent neuromodulation treatments demonstrate that direct stimulation of the dorsolateral prefrontal cortex (DLPFC), dorsomedial prefrontal cortex (DMPFC), and subgenual anterior cingulate (sgACC) relate to clinical improvement, suggesting connectivity alterations of the DLPFC-DMPFC-sgACC network to mediate antidepressant response. The international Study to Predict Optimized Treatment in Depression (iSPOT-D) is an international multicentre study that collected EEG data for 1008 MDD patients, randomized to 3 different antidepressant medications (N=447 MDD with complete pre- and post-treatment data and N=336 non-MDD). Treatment response was defined by a decline of >50% on the Hamilton Rating Score for Depression (HRSD17). We investigated whether connectivity in alpha and theta frequencies of the DLPFC-DMPFC-sgACC network changed from pre- to post-treatment between: (i) patients and controls, and (ii) responders (R) and non-responders (NR). Women exhibited higher alpha and theta connectivity compared to males, both pre- and post-treatment. Furthermore, theta, but not alpha, hypo-connectivity was found for MDD patients. A decreased alpha connectivity after treatment was found only for male responders, while non-responders and females exhibited no changes in alpha connectivity. Decreasing alpha connectivity could potentially serve as a treatment emergent biomarker, in males only. Furthermore, it could be useful to a priori stratify by gender for future MDD studies

Magnetic Resonance Imaging Measures of Brain Structure to Predict Antidepressant Treatment Outcome in Major Depressive Disorder

10.1016/j.ebiom.2014.12.002EBioMedicine2137-4

Profound and reproducible patterns of reduced regional gray matter characterize major depressive disorder

10.1038/s41398-019-0512-8Translational Psychiatry9117

Cortical volume abnormalities in posttraumatic stress disorder: an ENIGMA-psychiatric genomics consortium PTSD workgroup mega-analysis

Studies of posttraumatic stress disorder (PTSD) report volume abnormalities in multiple regions of the cerebral cortex. However, findings for many regions, particularly regions outside commonly studied emotion-related prefrontal, insular, and limbic regions, are inconsistent and tentative. Also, few studies address the possibility that PTSD abnormalities may be confounded by comorbid depression. A mega-analysis investigating all cortical regions in a large sample of PTSD and control subjects can potentially provide new insight into these issues. Given this perspective, our group aggregated regional volumes data of 68 cortical regions across both hemispheres from 1379 PTSD patients to 2192 controls without PTSD after data were processed by 32 international laboratories using ENIGMA standardized procedures. We examined whether regional cortical volumes were different in PTSD vs. controls, were associated with posttraumatic stress symptom (PTSS) severity, or were affected by comorbid depression. Volumes of left and right lateral orbitofrontal gyri (LOFG), left superior temporal gyrus, and right insular, lingual and superior parietal gyri were significantly smaller, on average, in PTSD patients than controls (standardized coefficients = -0.111 to -0.068, FDR corrected P values < 0.039) and were significantly negatively correlated with PTSS severity. After adjusting for depression symptoms, the PTSD findings in left and right LOFG remained significant. These findings indicate that cortical volumes in PTSD patients are smaller in prefrontal regulatory regions, as well as in broader emotion and sensory processing cortical regions.Stress-related psychiatric disorders across the life spa

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Human brain structure changes throughout the lifespan. Brouwer et al. identified genetic variants that affect rates of brain growth and atrophy. The genes are linked to early brain development and neurodegeneration and suggest involvement of metabolic processes.Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.Stress-related psychiatric disorders across the life spa