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

A simplified, 2-question grading system for evaluating abstracts in orthopedic scientific meetings: a serial randomization study

Background and purpose: Efficient abstract scoring for congress presentation is important. Given the emergence of new study methodologies, a scoring system that accommodates all study designs is warranted. We aimed to assess the equivalence of a simplified, 2-question abstract grading system with a more complex currently used system in assessing abstracts submitted for orthopedic scientific meetings in a serial randomized study. Methods: Dutch Orthopedic Association Scientific Committee (DOASC) members were randomized to grade abstracts using either the current grading system, which includes up to 7 scoring categories, or the new grading system, which consists of only 2 questions. Pearson correlation coefficient and mean abstract score with 95% confidence intervals (CI) were calculated. Results: Analysis included the scoring of 195 abstracts by 12–14 DOASC members. The average score for an abstract using the current system was 60 points (CI 58–62), compared with 63 points (CI 62–64) using the new system. By using the new system, abstracts were scored higher by 3.3 points (CI 1.7–5.0). Pearson correlation was poor with coefficient 0.38 (P < 0.001). Conclusion: The simplified abstract grading system exhibited a poor correlation with the current scoring system, while the new system offers a more inclusive evaluation of varying study designs and is preferred by almost all DOASC members

Translation and validation of the Dutch Spine Oncology Study Group Outcomes Questionnaire (SOSGOQ2.0) to evaluate health-related quality of life in patients with symptomatic spinal metastases

Background: The primary goal of palliative treatment of spinal metastases is to maintain or improve health-related quality of life (HRQOL). We translated and validated a Dutch version of The Spine Oncology Study Group Outcome Questionnaire (SOSGOQ2.0), a valid and reliable 20-item questionnaire to evaluate HRQOL in patients with spinal metastases. Methods: After cross-cultural translation and adaptation, the questionnaire was pre-tested in fifteen patients referred for spine surgery and/or radiotherapy. This resulted in a final questionnaire that was sent to patients for assessment of internal consistency, construct (i.e., convergent and divergent) validity, discriminative power and test-retest reliability. Results: Overall, 147 patients (mean age 65.6 years, SD = 10.4) completed the questionnaire after a median time of 45.4 months (IQR = 18.9–72.9) after spine surgery and/or radiotherapy. Internal consistency was good for the Physical function, Pain, and Mental health domains (α = 0.87, 0.86, 0.72), but not for Social function (α = 0.04). Good convergent validity was demonstrated except for Social function (rs= 0.37 95%CI = 0.21–0.51). Discriminative power between patients with ECOG performance scores of 0–1 and 2–4 was found on all domains and Neurological function items. Test-retest reliability was acceptable for Physical function, Pain and Mental health (ICC = 0.89 95%CI = 0.81–0.94, ICC = 0.88 95%CI = 0.78–0.93, ICC = 0.68 95%CI = 0.48–0.81), whereas ICC = 0.45 (95%CI = 0.17–0.66) for Social function was below threshold. After removing item 20 from the Social function domain, internal consistency improved, and convergent validity and test-retest reliability were good. Conclusion: The Dutch version of the SOSGOQ2.0 questionnaire is a reliable and valid tool to measure HRQOL in patients with spinal metastases. Item 20 was removed to retain psychometric properties

Luciferase labeling for multipotent stromal cell tracking in spinal fusion versus ectopic bone tissue engineering in mice and rats

Tissue engineering of bone, by combining multipotent stromal cells (MSCs) with osteoconductive scaffolds, has not yet yielded any clinically useful applications so far. The fate and contribution of the seeded cells are not sufficiently clarified, especially at clinically relevant locations. Therefore, we investigated cell proliferation around the spine and at ectopic sites using noninvasive in vivo bioluminescence imaging (BLI) in relation to new bone formation. Goat MSCs were lentivirally transduced to express luciferase. After showing both correlation between MSC viability and BLI signal as well as survival and osteogenic capacity of these cells ectopically in mice, they were seeded on ceramic scaffolds and implanted in immunodeficient rats at two levels in the spine for spinal fusion as well as subcutaneously. Nontransduced MSCs were used as a control group. All rats were monitored at day 1 and after that weekly until termination at week 7. In mice a BLI signal was observed during the whole observation period, indicating survival of the seeded MSCs, which was accompanied by osteogenic differentiation in vivo. However, these same MSCs showed a different response in the rat model, where the BLI signal was present until day 14, both in the spine and ectopically, indicating that MSCs were able to survive at least 2 weeks of implantation. Only when the signal was still present after the total implantation period ectopically, which only occurred in one rat, new bone was formed extensively and the implanted MSCs were responsible for this bone formation. Ectopically, neither a reduced proliferative group (irradiated) nor a group in which the cells were devitalized by liquid nitrogen and the produced extracellular matrix remained (matrix group) resulted in bone formation. This suggests that the release of soluble factors or the presence of an extracellular matrix is not enough to induce bone formation. For the spinal location, the question remains whether the implanted MSCs contribute to the bone regeneration or that the principal mechanism of MSC activity is through the release of soluble mediators

Smaller Hippocampal Volume in Posttraumatic Stress Disorder : A Multisite ENIGMA-PGC Study: Subcortical Volumetry Results From Posttraumatic Stress Disorder Consortia

Background Many studies report smaller hippocampal and amygdala volumes in posttraumatic stress disorder (PTSD), but findings have not always been consistent. Here, we present the results of a large-scale neuroimaging consortium study on PTSD conducted by the Psychiatric Genomics Consortium (PGC)–Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) PTSD Working Group. Methods We analyzed neuroimaging and clinical data from 1868 subjects (794 PTSD patients) contributed by 16 cohorts, representing the largest neuroimaging study of PTSD to date. We assessed the volumes of eight subcortical structures (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, and lateral ventricle). We used a standardized image-analysis and quality-control pipeline established by the ENIGMA consortium. Results In a meta-analysis of all samples, we found significantly smaller hippocampi in subjects with current PTSD compared with trauma-exposed control subjects (Cohen's d = −0.17, p =.00054), and smaller amygdalae (d = −0.11, p =.025), although the amygdala finding did not survive a significance level that was Bonferroni corrected for multiple subcortical region comparisons (p <.0063). Conclusions Our study is not subject to the biases of meta-analyses of published data, and it represents an important milestone in an ongoing collaborative effort to examine the neurobiological underpinnings of PTSD and the brain's response to trauma

Smaller Hippocampal Volume in Posttraumatic Stress Disorder: A Multisite ENIGMA-PGC Study: Subcortical Volumetry Results From Posttraumatic Stress Disorder Consortia

Background Many studies report smaller hippocampal and amygdala volumes in posttraumatic stress disorder (PTSD), but findings have not always been consistent. Here, we present the results of a large-scale neuroimaging consortium study on PTSD conducted by the Psychiatric Genomics Consortium (PGC)–Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) PTSD Working Group. Methods We analyzed neuroimaging and clinical data from 1868 subjects (794 PTSD patients) contributed by 16 cohorts, representing the largest neuroimaging study of PTSD to date. We assessed the volumes of eight subcortical structures (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, and lateral ventricle). We used a standardized image-analysis and quality-control pipeline established by the ENIGMA consortium. Results In a meta-analysis of all samples, we found significantly smaller hippocampi in subjects with current PTSD compared with trauma-exposed control subjects (Cohen's d = −0.17, p =.00054), and smaller amygdalae (d = −0.11, p =.025), although the amygdala finding did not survive a significance level that was Bonferroni corrected for multiple subcortical region comparisons (p <.0063). Conclusions Our study is not subject to the biases of meta-analyses of published data, and it represents an important milestone in an ongoing collaborative effort to examine the neurobiological underpinnings of PTSD and the brain's response to trauma

Epigenome-wide meta-analysis of PTSD across 10 military and civilian cohorts identifies methylation changes in AHRR

Epigenetic differences may help to distinguish between PTSD cases and trauma-exposed controls. Here, we describe the results of the largest DNA methylation meta-analysis of PTSD to date. Ten cohorts, military and civilian, contribute blood-derived DNA methylation data from 1,896 PTSD cases and trauma-exposed controls. Four CpG sites within the aryl-hydrocarbon receptor repressor (AHRR) associate with PTSD after adjustment for multiple comparisons, with lower DNA methylation in PTSD cases relative to controls. Although AHRR methylation is known to associate with smoking, the AHRR association with PTSD is most pronounced in non-smokers, suggesting the result was independent of smoking status. Evaluation of metabolomics data reveals that AHRR methylation associated with kynurenine levels, which are lower among subjects with PTSD. This study supports epigenetic differences in those with PTSD and suggests a role for decreased kynurenine as a contributor to immune dysregulation in PTSD

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