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Toby Wise,1 Danilo Arnone,1 Lindsey Marwood,1 Roland Zahn,1–3 Karen E Lythe,2,3 Allan H Young1 1Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, King’s College London, London, 2Neuroscience and Aphasia Research Unit, School of Psychological Sciences, 3Manchester Academic Health Science Centre, University of Manchester, Manchester,UK Abstract: Successful recruitment is vital for any research study. Difficulties in recruitment are not uncommon and can have important implications. This is particularly relevant to research conducted in affective disorders due to the nature of the conditions and the clinical services that serve these patients. Recently, online public advertisements have become more generally accessible and may provide an effective way to recruit patient populations. However, there is paucity of evidence on their viability as a method of recruiting patients into studies of disease mechanisms in these disorders. Public advertisement methods can be useful when researchers require specific populations, such as those not receiving pharmacological treatment. This work describes our experience in successfully recruiting participants into neuroimaging research studies in affective disorders using online public advertisements. Results suggest that these online public advertisements are an effective method for successfully recruiting participants with affective disorders into research studies, particularly for research focusing on disease mechanisms in specific populations. Keywords: recruitment, affective disorders, advertising, depression, anxiety, bipolar

Converging evidence suggests that bipolar disorder (BD) is associated with white matter (WM) abnormalities. Meta-analyses of voxel based morphometry (VBM) data is commonly performed using published coordinates, however this method is limited since it ignores non-significant data. Obtaining statistical maps from studies (T-maps) as well as raw MRI datasets increases accuracy and allows for a comprehensive analysis of clinical variables. We obtained coordinate data (5-studies), T-Maps (12-studies, including unpublished data) and raw MRI datasets (5-studies) and analysed the 24 studies using Seed-based d Mapping (SDM). A VBM analysis was conducted to verify the results in an independent sample. The meta-analysis revealed decreased WM volume in the posterior corpus callosum extending to WM in the posterior cingulate cortex. This region was significantly reduced in volume in BD patients in the independent dataset (p=0.003) but there was no association with clinical variables. We identified a robust WM volume abnormality in BD patients that may represent a trait marker of the disease and used a novel methodology to validate the findings.

This paper provides a multi-objective optimization framework aimed at the management of a multi-carrier energy system involving both electricity and hydrogen. Using the concept of the multi-carrier hub, the proposed system has been modelled in order to define completely every energy flow inside the plant. After that, a heuristic multi-objective optimization algorithm, the Non-dominated Sorting Genetic Algorithm II, has been implemented for the energy management of the plant, taking into account simultaneously three different objective functions related to economic and technical goals. This optimization process provides the set point defining the working configuration of the plant for a daylong time horizon. The communication framework between the energy management system, the real plant and the monitoring tool has been developed too, using the Open Platform Communications (OPC) protocol for the data exchange. This has been presented along with the Decision Support System (DSS) provided by the optimizer and the Human Machine Interface (HMI) of the Supervisory Control And Data Acquisition (SCADA) monitoring the plant. All the presented applications are going to be deployed on a real plant demonstrator.

The INGRID FP7 European co-funded project studies several methodologies concerning hydrogen production and storage, aiming to provide services to electricity system operators for suitably balancing electrical supply and demand. In such a context, the problem of integrating different carriers into a single multi-hub optimiser represents a challenging topic for the research. This paper depicts the Energy Management System (EMS) of the plant which will be developed and built as a prototype of the INGRID system. The approach followed for the EMS design and development takes the cue from the matrix model presented in the rest of the paper, as well as the general optimisation problem formulation and the algorithm selected for its solution.

Finding robust brain substrates of mood disorders is an important target for research. The degree to which major depression and bipolar disorder are associated with common and/or distinct patterns of volumetric changes is nevertheless unclear. Furthermore the extant literature is heterogeneous with respect to the nature of these changes. We report a meta-analysis of voxel-based morphometry studies in major depression and bipolar disorder. We identified studies published up to January 2015 that compared grey matter in major depression (50 datasets including 4101 individuals) and bipolar disorder (36 datasets including 2407 individuals) using whole brain voxel-based morphometry. We used statistical maps from the studies included where available and reported peak coordinates otherwise. Group comparisons and conjunction analyses identified regions in which the disorders showed common and distinct patterns of volumetric alteration. Both disorders were associated with lower grey matter volume relative to healthy individuals in a number of areas. Conjunction analysis showed smaller volumes in both disorders in clusters in the dorsomedial and ventromedial prefrontal cortex, including the anterior cingulate cortex, and bilateral insula. Group comparisons indicated that findings of smaller grey matter volumes relative to controls in the right dorsolateral prefrontal cortex and left hippocampus, along with cerebellar, temporal, and parietal regions were more substantial in major depression. These results suggest that major depression and bipolar disorder are characterized by both common and distinct patterns of grey matter volume changes. This combination of differences and similarities has the potential to inform the development of diagnostic biomarkers for these conditions.

Objective: Several neuroimaging meta-analyses have summarized structural brain changes in major depression using coordinate-based methods. These methods might be biased toward brain regions where significant differences were found in the original studies. In this study, a novel voxel-based technique is implemented that estimates and meta-analyses between-group differences in grey matter from individual MRI studies, which are then applied to the study of major depression. Methods: A systematic review and meta-analysis of voxel-based morphometry studies were conducted comparing participants with major depression and healthy controls by using statistical parametric maps. Summary effect sizes were computed correcting for multiple comparisons at the voxel level. Publication bias and heterogeneity were also estimated and the excess of heterogeneity was investigated with metaregression analyses. Results: Patients with major depression were characterized by diffuse bilateral grey matter loss in ventrolateral and ventromedial frontal systems extending into temporal gyri compared to healthy controls. Grey matter reduction was also detected in the right parahippocampal and fusiform gyri, hippocampus, and bilateral thalamus. Other areas included parietal lobes and cerebellum. There was no evidence of statistically significant publication bias or heterogeneity. Conclusions: The novel computational meta-analytic approach used in this study identified extensive grey matter loss in key brain regions implicated in emotion generation and regulation. Results are not biased toward the findings of the original studies because they include all available imaging data, irrespective of statistically significant regions, resulting in enhanced detection of additional areas of grey matter loss. Hum Brain Mapp 37:1393-1404, 2016. (c) 2016 Wiley Periodicals, Inc.