An index test for intermediate grades on social studies books.

Thesis (Ed.M.)--Boston Universit

Focusing on Comorbidity A Novel Meta-Analytic Approach and Protocol to Disentangle the Specific Neuroanatomy of Co-occurring Mental Disorders

Background: In mental health, comorbidities are the norm rather than the exception. However, current meta-analytic methods for summarizing the neural correlates of mental disorders do not consider comorbidities, reducing them to a source of noise and bias rather than benefitting from their valuable information. Objectives: We describe and validate a novel neuroimaging meta-analytic approach that focuses on comorbidities. In addition, we present the protocol for a meta-analysis of all major mental disorders and their comorbidities. Methods: The novel approach consists of a modification of Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) in which the linear models have no intercept. As in previous SDM meta-analyses, the dependent variable is the brain anatomical difference between patients and controls in a voxel. However, there is no primary disorder, and the independent variables are the percentages of patients with each disorder and each pair of potentially comorbid disorders. We use simulations to validate and provide an example of this novel approach, which correctly disentangled the abnormalities associated with each disorder and comorbidity. We then describe a protocol for conducting the new meta-analysis of all major mental disorders and their comorbidities. Specifically, we will include all voxel-based morphometry (VBM) studies of mental disorders for which a meta-analysis has already been published, including at least 10 studies. We will use the novel approach to analyze all included studies in two separate single linear models, one for children/adolescents and one for adults. Discussion: The novel approach is a valid method to focus on comorbidities. The meta-analysis will yield a comprehensive atlas of the neuroanatomy of all major mental disorders and their comorbidities, which we hope might help develop potential diagnostic and therapeutic tools

The Life History and Cell Cycle of Kryptoperidinium foliaceum, A Dinoflagellate with Two Eukaryotic Nuclei

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Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners

descripción no proporcionada por scopusThis research has been conducted using the UK Biobank Resource (Project Number 40323) and has been supported by a Wellcome Trust’s Innovator Award (208519/Z/17/Z) to Andrea Mechelli. The present work was carried out within the scope of the research program Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua. The data from UCLA, LOSS AVERSION, EMOTIONREGULATION, FALSEBELIEFS, MATURATIONAL CHANGES, ASSOCIATIVE LEARNING, HARMAVOIDANCE, PLACEBO, MORAL JUDGEMENT, CYBERBALL, ROUTE LEARNING, SEQUENTIAL INFERENCE VBM, WASHINGTON UNIVERSITY datasets were obtained from the OpenfMRI database. Their accession numbers are ds000030, ds000053, ds000108, ds000109, ds000119, ds000168, ds000202, ds000208, ds000212, ds000214, ds000217, ds000222, and ds000243, respectively. The acquisition of dataset HMRRC was supported by the National Natural Science Foundation of China to Prof. Qiyong Gong (81220108013, 8122010801, 81621003, 81761128023 and 81227002). Part of the data used in this article (NITRC) have been funded in whole or in part with Federal funds from the Department of Health and Human Services, National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, under the following NIH grants: 1R43NS074540, 2R44NS074540, and 1U24EB023398and previously GSA Contract No. GS-00F-0034P, Order Number HHSN268200100090U. This research has been conducted using the UK Biobank Resource. Part of the data used in preparation of this article were obtained from the Alzheimer’s Disease Repository Without Borders (ARWiBo – www.arwibo.it). The overall goal of ARWiBo is to contribute, thorough synergy with neuGRID (https://neugrid2.eu), to global data sharing and analysis in order to develop effective therapies, prevention methods and a cure for Alzheimer’ and other neurodegenerative diseases. Part of the data used in this article was downloaded from the Collaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was performed at the Mind Research Network and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr. Vince Calhoun. Part of the data used for this study were downloaded from the Function BIRN Data Repository (http://fbirnbdr.birncommunity.org:8080/BDR/), supported by grants to the Function BIRN (U24-RR021992) Testbed funded by the National Center for Research Resources at the National Institutes of Health, U.S.A. Part of the data used in the preparation of this work were obtained from the Mind Clinical Imaging Consortium database through the Mind Research Network (www.mrn.org). The MCIC project was supported by the Department of Energy under Award Number DE-FG02-08ER64581. MCIC is the result of efforts of co-investigators from University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital. CLING/HMS: The CliNG study sample was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via the Clinical Research Group 241 ‘Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis’, TP2 (PI Gruber; http://www.kfo241.de; grant number GR 1950/5-1). Part of the data used in preparation of this article were obtained from the NU Schizophrenia Data and Software Tool (NUSDAST) database (http://central.xnat.org/REST/projects/NUDataSharing) As such, the investigators within NUSDAST contributed to the design and implementation of NUSDAST and/or provided data but did not participate in analysis or writing of this report. Part of the data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]. Part of the data used in preparation of this article were obtained from the SchizConnect database (http://schizconnect.org). As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by NIMH cooperative agreement 1U01 MH097435. João Sato is supported by Sao Paulo Research Foundation (FAPESP, Brazil) Grants 2018/04654-9 and 2018/21934-5

Influence of COMT val158met Genotype on the Depressed Brain during Emotional Processing and Working Memory

<p>Major depressive disorder (MDD) has been associated with abnormal prefrontal-limbic interactions and altered catecholaminergic neurotransmission. The val158met polymorphism on the catechol-O-methyltransferase (COMT) gene has been shown to influence prefrontal cortex (PFC) activation during both emotional processing and working memory (WM). Although COMT-genotype is not directly associated with MDD, it may affect MDD pathology by altering PFC activation, an endophenotype associated with both COMT and MDD. 125 participants, including healthy controls (HC, n=28) and MDD patients were genotyped for the COMT val158met polymorphism and underwent functional magnetic resonance imaging (fMRI-neuroimaging) during emotion processing (viewing of emotional facial expressions) and a WM task (visuospatial planning). Within HC, we observed a positive correlation between the number of met-alleles and right inferior frontal gyrus activation during emotional processing, whereas within patients the number of met-alleles was not correlated with PFC activation. During WM a negative correlation between the number of met-alleles and middle frontal gyrus activation was present in the total sample. In addition, during emotional processing there was an effect of genotype in a cluster including the amygdala and hippocampus. These results demonstrate that COMT genotype is associated with relevant endophenotypes for MDD. In addition, presence of MDD only interacts with genotype during emotional processing and not working memory.</p>