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

Primary role of angiotensin converting enzyme 2 in cardiac production of angiotensin-(1-7) in transgenic Ren-2 hypertensive rats

Angiotensin-converting enzyme 2 (ACE2) converts angiotensin II (Ang II) to angiotensin-(1-7) [Ang-(1-7)] and this enzyme may serve as a key regulatory juncture in various tissues. Although the heart expresses ACE2, the extent that the enzyme participates in the cardiac processing of Ang II and Ang-(1-7) is equivocal. Therefore, we utilized the Langendorff preparation to characterize the ACE2 pathway in isolated hearts from male normotensive Sprague-Dawley [Tg((-))] and hypertensive [mRen2]27 [Tg((+))] rats. During a 60-minute recirculation period with 10 nM Ang II, the presence of Ang-(1-7) was assessed in the cardiac effluent. Ang-(1-7) generation from Ang II was similar in both the normal and hypertensive hearts (Tg((-)): 510 +/- 55 pM, n=20 versus Tg((+)): 497 +/- 63 pM, n=14) with peak levels occurring at 30 minutes after administration of the peptide. ACE2 inhibition (MLN-4760, 1microM) significantly reduced Ang-(1-7) production by 83% (57 +/- 19 pM, P < 0.01, n=7) in the Tg((+)) rats, whereas the inhibitor had no significant effect in the Tg((-)) rats (285 +/- 53 pM, P > 0.05, n=10). ACE2 activity was found in the effluent of perfused Tg((-)) and Tg((+)) hearts and it was highly associated with ACE2 protein expression (r =0.78). This study is the first demonstration for a direct role of ACE2 in the metabolism of cardiac Ang II in the hypertrophic heart of hypertensive rats. We conclude that predominant expression of cardiac ACE2 activity in the Tg((+)) may be a compensatory response to the extensive cardiac remodeling in this strain. Key words: angiotensin II, hypertension, isolated heart

Some antecedents and outcomes of brand love

Survey research is employed to test hypotheses involving brand love, a new marketing construct that assesses satisfied consumers’ passionate emotional attachment to particular brands. Findings suggest that satisfied consumers’ love is greater for brands in product categories perceived as more hedonic (as compared with utilitarian) and for brands that offer more in terms of symbolic benefits. Brand love, in turn, is linked to higher levels of brand loyalty and positive word-of-mouth. Findings also suggest that satisfied consumers tend to be less loyal to brands in more hedonic product categories and to engage in more positive word-of-mouth about self-expressive brands. Copyright Springer Science + Business Media, Inc. 2006Satisfaction, Delight, Love, Loyalty, Word-of-mouth, Consumer-brand relationships,