Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker

Ternary blends

International audienceTernary binders have become used more frequently for several reasons. In some cases, such as combining a rapidly reactive SCM such as silica fume with a more slowly reactive SCM such as fly ash or slag, the use of ternary binders can provide benefits for both early-age and later-age properties and durability of concrete. In other cases, high-alkali pozzolans have been combined with slag to both accelerate the slag hydration and bind the alkalis from the pozzolan. As well, two SCMs may be combined to improve economy of the concrete mixture. This chapter describes properties of various ternary binders. © RILEM 2018

Individual prediction of trauma-focused therapy outcome in veterans with posttraumatic stress disorder using neuroimaging data

Background: Trauma-focused psychotherapy is a first-line treatment for PTSD but 30–50% of patients do not benefit sufficiently (Bradley et al., 2005). Neuroimaging has been proposed as a potential biomarker predicting treatment-response in PTSD patients (Colvonen et al., 2017; Yuan et al., 2018). Objective: We investigated whether neuroimaging data could distinguish between treatment responders and non-responders on the group and single-subject level. Method: A total of 44 male veterans with PTSD underwent baseline structural and resting-state MRI followed by trauma-focused therapy (EMDR or TFCBT). Grey-matter volumes (GMV) were extracted from the MRI data and resting-state networks (RSN) were estimated using group-ICA of data from 28 matched trauma-exposed healthy controls. GMV and RSNs were used to find differences between responders and non-responders on the group and single-subject level. Treatment response was defined as 30% decrease in total Clinician-Administered PTSD Scale for the DSM-IV (CAPS-IV) score from pre- to post-treatment assessment. Gaussian process classifiers with 10 times repeated 10-fold cross-validation were used for classification. Results: An RSN centred on the pre-SMA could distinguish between responders and non-responders on an individual level with 81.4% accuracy (p < .001), 84.5% sensitivity, 78% specificity and AUC of 0.93, while an RSN centred on the bilateral superior frontal gyrus differed between groups (pFWE < .05). No significant singlesubject classification or group differences were 54 ESTSS 2019 Rotterdam Symposium Abstract Book observed for GMV. Conclusions: Rs-fMRI is capable of providing personalized predictions of treatment response in a sample of veterans with PTSD. It therefore has the potential to be useful as a biomarker of treatment response

Individual prediction of trauma-focused therapy outcome in veterans with posttraumatic stress disorder using neuroimaging data

Background: Trauma-focused psychotherapy is a first-line treatment for PTSD but 30–50% of patients do not benefit sufficiently (Bradley et al., 2005). Neuroimaging has been proposed as a potential biomarker predicting treatment-response in PTSD patients (Colvonen et al., 2017; Yuan et al., 2018). Objective: We investigated whether neuroimaging data could distinguish between treatment responders and non-responders on the group and single-subject level. Method: A total of 44 male veterans with PTSD underwent baseline structural and resting-state MRI followed by trauma-focused therapy (EMDR or TFCBT). Grey-matter volumes (GMV) were extracted from the MRI data and resting-state networks (RSN) were estimated using group-ICA of data from 28 matched trauma-exposed healthy controls. GMV and RSNs were used to find differences between responders and non-responders on the group and single-subject level. Treatment response was defined as 30% decrease in total Clinician-Administered PTSD Scale for the DSM-IV (CAPS-IV) score from pre- to post-treatment assessment. Gaussian process classifiers with 10 times repeated 10-fold cross-validation were used for classification. Results: An RSN centred on the pre-SMA could distinguish between responders and non-responders on an individual level with 81.4% accuracy (p < .001), 84.5% sensitivity, 78% specificity and AUC of 0.93, while an RSN centred on the bilateral superior frontal gyrus differed between groups (pFWE < .05). No significant singlesubject classification or group differences were 54 ESTSS 2019 Rotterdam Symposium Abstract Book observed for GMV. Conclusions: Rs-fMRI is capable of providing personalized predictions of treatment response in a sample of veterans with PTSD. It therefore has the potential to be useful as a biomarker of treatment response