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Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information (vol 91, pg 626, 2022)

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BIOLOGICAL PSYCHIATRY 2022年第7期91卷 690-690页

作者： Maihofer, Adam X. Vinkers, Christiaan H. Department of Psychiatry University of California San Diego La Jolla California Department of Family Medicine and Public Health University of California San Diego La Jolla California Center of Excellence for Stress and Mental Health Veterans Affairs Healthcare System San Diego California. Electronic address: amaihofer@health.ucsd.edu. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Department of Psychiatry Massachusetts General Hospital Boston Massachusetts. Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom National Institute of Health Research Maudsley Biomedical Research Centre King's College London London United Kingdom. Department of Psychiatry Harvard Medical School Boston Massachusetts Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts Center of Excellence in Depression and Anxiety Disorders Belmont Massachusetts. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts. Department of Psychiatry University of California San Diego La Jolla California Center of Excellence for Stress and Mental Health Veterans Affairs Healthcare System San Diego California Research Service Veterans Affairs Healthcare System San Diego California. Duke Molecular Physiology Institute Duke University School of Medicine Durham North Carolina. Department of Psychiatry Veterans Affairs Connecticut Healthcare System West Haven Connecticut Department of Psychiatry Yale University School of Medicine New Haven Connecticut. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Department of Epidemiology Columbia University Mailman School of Public Health New York. Division of Mental Health Atlanta Veterans Affairs Medical Center

BACKGROUND: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). METHODS: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran program GWAS of PTSD total symptoms. RESULTS: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran program. CONCLUSIONS: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.

关键词： GWAS Genetics Heritability PTSD PheWAS Trauma

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE neuroscience 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

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The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia program

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Schizophrenia 2025年第1期11卷 1-14页

作者： Harms, Michael P. Cho, Kang-Ik K. Anticevic, Alan Bolo, Nicolas R. Bouix, Sylvain Campbell, Dylan Cannon, Tyrone D. Cecchi, Guillermo Goncalves, Mathias Haidar, Anastasia Hughes, Dylan E. Izyurov, Igor John, Omar Kapur, Tina Kim, Nicholas Kotler, Elana Kubicki, Marek Kuperman, Joshua M. Laulette, Kristen Lindberg, Ulrich Markiewicz, Christopher Ning, Lipeng Poldrack, Russell A. Rathi, Yogesh Romo, Paul A. Tamayo, Zailyn Wannan, Cassandra Wickham, Alana Yassin, Walid Zhou, Juan Helen Addington, Jean Alameda, Luis Arango, Celso Breitborde, Nicholas J. K. Broome, Matthew R. Cadenhead, Kristin S. Calkins, Monica E. Chen, Eric Yu Hai Choi, Jimmy Conus, Philippe Corcoran, Cheryl M. Cornblatt, Barbara A. Diaz-Caneja, Covadonga M. Ellman, Lauren M. Fusar-Poli, Paolo Gaspar, Pablo A. Gerber, Carla Glenthøj, Louise Birkedal Horton, Leslie E. Hui, Christy Lai Ming Kambeitz, Joseph Kambeitz-Ilankovic, Lana Keshavan, Matcheri S. Kim, Sung-Wan Koutsouleris, Nikolaos Kwon, Jun Soo Langbein, Kerstin Mamah, Daniel Mathalon, Daniel H. Mittal, Vijay A. Nordentoft, Merete Pearlson, Godfrey D. Perez, Jesus Perkins, Diana O. Powers, Albert R. Rogers, Jack Sabb, Fred W. Schiffman, Jason Shah, Jai L. Silverstein, Steven M. Smesny, Stefan Stone, William S. Strauss, Gregory P. Thompson, Judy L. Upthegrove, Rachel Verma, Swapna K. Wang, Jijun Wolf, Daniel H. Kahn, Rene S. Kane, John M. McGorry, Patrick D. Nelson, Barnaby Woods, Scott W. Shenton, Martha E. Wood, Stephen J. Bearden, Carrie E. Pasternak, Ofer Department of Psychiatry Washington University School of Medicine St. Louis MO United States Department of Psychiatry Brigham and Women’s Hospital Harvard Medical School Boston MA United States Department of Psychiatry Yale University School of Medicine New Haven CT United States Department of Psychiatry Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States Department of Software Engineering and Information Technology École de technologie supérieure Montréal QC Canada Department of Psychology Yale University New Haven CT United States T.J. Watson Research Laboratory IBM Research Yorktown Heights NY United States Department of Psychology Stanford University Stanford CA United States Department of Psychology University of California Los Angeles CA United States Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Massachusetts General Hospital Harvard Medical School Boston MA United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston MA United States Department of Radiology University of California San Diego CA United States Department of Clinical Physiology and Nuclear Medicine Copenhagen University Hospital - Rigshospitalet Glostrup Denmark Seaman Family MR Research Centre Calgary AB Canada Orygen Parkville VIC Australia Centre for Sleep and Cognition and Centre for Translational MR Research Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Psychiatry Hotchkiss Brain Institute University of Calgary Calgary AB Canada General Psychiatry Service Treatment and Early Intervention in Psychosis Program Lausanne University Hospital and University of Lausanne Lausanne Switzerland Department of Child and Adolescent Psychiatry Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón Instituto de Salud Carlos III School of Medicine Universidad Complutense Madrid Spain

Neuroimaging with MRI has been a frequent component of studies of individuals at clinical high risk (CHR) for developing psychosis, with goals of understanding potential brain regions and systems impacted in the CHR state and identifying prognostic or predictive biomarkers that can enhance our ability to forecast clinical outcomes. To date, most studies involving MRI in CHR are likely not sufficiently powered to generate robust and generalizable neuroimaging results. Here, we describe the prospective, advanced, and modern neuroimaging protocol that was implemented in a complex multi-site, multi-vendor environment, as part of the large-scale Accelerating Medicines Partnership® Schizophrenia program (AMP® SCZ), including the rationale for various choices. This protocol includes T1- and T2-weighted structural scans, resting-state fMRI, and diffusion-weighted imaging collected at two time points, approximately 2 months apart. We also present preliminary variance component analyses of several measures, such as signal- and contrast-to-noise ratio (SNR/CNR) and spatial smoothness, to provide quantitative data on the relative percentages of participant, site, and platform (i.e., scanner model) variance. Site-related variance is generally small (typically <10%). For the SNR/CNR measures from the structural and fMRI scans, participant variance is the largest component (as desired;40–76%). However, for SNR/CNR in the diffusion scans, there is substantial platform-related variance (>55%) due to differences in the diffusion imaging hardware capabilities of the different scanners. Also, spatial smoothness generally has a large platform-related variance due to inherent, difficult to control, differences between vendors in their acquisitions and reconstructions. These results illustrate some of the factors that will need to be considered in analyses of the AMP SCZ neuroimaging data, which will be the largest CHR cohort to date. Watch Dr. Harms discuss this article at https://vimeo.

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Author Correction: Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort

Nature Mental Health

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Nature Mental Health 2024年 1543页

作者： Dirk H. M. Pelt Lannie Ligthart Catharina E. M. van Beijsterveldt René Pool Meike Bartels Christiaan H. Vinkers Philippe C. Habets Department of Biological Psychology Faculty of Behavioral and Movement Sciences Vrije Universiteit Amsterdam Amsterdam The Netherlands Amsterdam Public Health Research Institute Amsterdam University Medical Center Amsterdam The Netherlands Department of Psychiatry and Anatomy and Neurosciences Amsterdam University Medical Center location Vrije Universiteit Amsterdam Amsterdam The Netherlands Amsterdam Neuroscience Mood Anxiety Psychosis Sleep and Stress Program Amsterdam The Netherlands GGZ inGeest Mental Health Care Amsterdam The Netherlands

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Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

arXiv

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arXiv 2022年

作者： Sun, Shaoxiong Folarin, Amos A. Zhang, Yuezhou Cummins, Nicholas Garcia-Dias, Rafael Stewart, Callum Ranjan, Yatharth Rashid, Zulqarnain Conde, Pauline Laiou, Petroula Sankesara, Heet Matcham, Faith Leightley, Daniel White, Katie M. Oetzmann, Carolin Ivan, Alina Lamers, Femke Siddi, Sara Simblett, Sara Nica, Raluca Rintala, Aki Mohr, David C. Myin-Germeys, Inez Wykes, Til Haro, Josep Maria Penninx, Brenda W.J.H. Vairavan, Srinivasan Narayan, Vaibhav A. Annas, Peter Hotopf, Matthew Dobson, Richard J.B. Department of Biostatistics & Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Institute of Health Informatics University College London London United Kingdom NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust London United Kingdom Health Data Research UK London University College London London United Kingdom NIHR Biomedical Research Centre University College London Hospitals NHS Foundation Trust London United Kingdom Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom School of Psychology University of Sussex Falmer United Kingdom Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Mental Health Program Amsterdam Public Health Research Institute Amsterdam Netherlands Teaching Research and Innovation Unit Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Madrid Spain Faculty of Medicine and Health Sciences Universitat de Barcelona Barcelona Spain Department of Psychology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom RADAR-CNS Patient Advisory Board King's College London London United Kingdom The Romanian League for Mental Health Bucharest Romania Department of Neurosciences Center for Contextual Psychiatry Katholieke Universiteit Leuven Leuven Belgium Physical Activity and Functional Capacity Research Group Faculty of Health Care and Social Services LAB University of Applied Sciences Lahti Finland Center for Behavioral Intervention Technologies Department of Preventive Medicine Northwestern University ChicagoIL United States South London and Maudsley NHS Foundation Trust London United Ki

Background: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data;distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk;and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. Objective: We aimed to address these 3 challenges to inform future work in stratified analyses. Methods: Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and

关键词： Smartphones

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The short version of the Sexual Distress Scale (SDS-3): Measurement invariance across countries, gender identities, and sexual orientations

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International Journal of Clinical and Health psychology 2024年第2期24卷 100461-100461页

作者： Lin, Chung-Ying Tsai, Meng-Che Koós, Mónika Nagy, Léna Kraus, Shane W. Demetrovics, Zsolt Potenza, Marc N. Ballester-Arnal, Rafael Batthyány, Dominik Bergeron, Sophie Billieux, Joël Briken, Peer Cárdenas-López, Georgina Carvalho, Joana Castro-Calvo, Jesús Chen, Lijun Ciocca, Giacomo Corazza, Ornella Csako, Rita I. Fernandez, David P. Fernandez, Elaine F. Fujiwara, Hironobu Fuss, Johannes Gabrhelík, Roman Gewirtz-Meydan, Ateret Gjoneska, Biljana Gola, Mateusz Grubbs, Joshua B. Hashim, Hashim T. Islam, Md. Saiful Ismail, Mustafa Jiménez-Martínez, Martha Jurin, Tanja Kalina, Ondrej Klein, Verena Költő, András Lee, Sang-Kyu Lewczuk, Karol Lochner, Christine López-Alvarado, Silvia Lukavská, Kateřina Mayta-Tristán, Percy Milea, Ion Miller, Dan J. Orosová, Oľga Orosz, Gábor Ponce, Fernando P. Quintana, Gonzalo R. Garzola, Gabriel C. Quintero Ramos-Diaz, Jano Rigaud, Kévin Rousseau, Ann Scanavino, Marco De Tubino Schulmeyer, Marion K. Sharan, Pratap Shibata, Mami Shoib, Sheikh Sigre-Leirós, Vera Sniewski, Luke Spasovski, Ognen Steibliene, Vesta Stein, Dan J. Strizek, Julian Štulhofer, Aleksandar Ünsal, Berk C. Vaillancourt-Morel, Marie-Pier Van Hout, Marie Claire Bőthe, Beáta Institute of Allied Health Sciences College of Medicine National Cheng Kung University Tainan Taiwan Biostatistics Consulting Center National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Department of Pediatrics National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Department of Psychology University of Nevada Las Vegas NV United States Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar United Kingdom College of Education Psychology and Social Work Flinders University Adelaide Australia Yale University School of Medicine New Haven CT United States Connecticut Council on Problem Gambling Wethersfield CT United States Departamento de Psicología Básica Clínica y Psicobiología University Jaume I Castelló Spain Institute for Behavioural Addictions Sigmund Freud University Vienna Austria Département de Psychologie Université de Montréal Montréal Canada Centre de recherche interdisciplinaire sur les problèmes conjugaux et les agressions sexuelles Montréal Canada Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland Institute for Sex Research Sexual Medicine & Forensic Psychiatry University Medical Center Hamburg-Eppendorf (UKE) Hamburg Germany Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico Mexico William James Center for Research Department of Education and Psychology University of Aveiro Aveiro Portugal Department of Personality Assessment and Psychological Treatments University of Valencia Valencia Spain Department of Psychology College of Humanity and Social Science Fuzhou University

Background: The three-item Sexual Distress Scale (SDS-3) has been frequently used to assess distress related to sexuality in public health surveys and research on sexual wellbeing. However, its psychometric properties and measurement invariance across cultural, gender and sexual subgroups have not yet been examined. This multinational study aimed to validate the SDS-3 and test its psychometric properties, including measurement invariance across language, country, gender identity, and sexual orientation groups. Methods: We used global survey data from 82,243 individuals (Mean _age=32.39 years;40.3 % men, 57.0 % women, 2.8 % non-binary, and 0.6 % other genders) participating in the International Sexual Survey (ISS;https://***/) across 42 countries and 26 languages. Participants completed the SDS-3, as well as questions regarding sociodemographic characteristics, including gender identity and sexual orientation. Results: Confirmatory factor analysis (CFA) supported a unidimensional factor structure for the SDS-3, and multi-group CFA (MGCFA) suggested that this factor structure was invariant across countries, languages, gender identities, and sexual orientations. Cronbach's α for the unidimensional score was 0.83 (range between 0.76 and 0.89), and McDonald's ω was 0.84 (range between 0.76 and 0.90). Participants who did not experience sexual problems had significantly lower SDS-3 total scores (M = 2.99;SD=2.54) compared to those who reported sexual problems (M = 5.60;SD=3.00), with a large effect size (Cohen's d = 1.01 [95 % CI=-1.03, -0.98];p < 0.001). Conclusion: The SDS-3 has a unidimensional factor structure and appears to be valid and reliable for measuring sexual distress among individuals from different countries, gender identities, and sexual orientations. © 2024 The Author(s)

关键词： Confirmatory factor analysis (CFA) International sex survey Psychometric analysis Sexual distress

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Fear of falling and concern about falling in individuals with relapsing-remitting MS and progressive MS

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Multiple Sclerosis and Related Disorders 2023年 73卷 104638-104638页

作者： Taylor N. Takla Patricia N. Matsuda Tracy E. Herring Ana M. Daugherty Nora E. Fritz Neuroimaging and Neurorehabilitation Laboratory Wayne State University Translational Neuroscience Program Wayne State University Department of Psychiatry and Behavioral Neurosciences Wayne State School of Medicine Department of Rehabilitation Medicine Division of Physical Therapy University of Washington Department of Rehabilitation Medicine University of Washington Department of Psychology Wayne State University Detroit MI USA Institute of Gerontology Wayne State University Detroit MI USA Department of Health Care Sciences Wayne State University Department of Neurology Wayne State University

Background and Purpose Falls and fear of falling (FOF) in multiple sclerosis (MS) are highly prevalent issues. Previously, FOF and concern about falling (CAF) have been used interchangeably; however, persons with MS report that FOF is different than CAF. Therefore, our objective was to examine prevalence and relations of FOF and CAF to function in relapsing-remitting MS (RRMS) and progressive MS (PMS). Methods In an online survey, 1025 participants with MS indicated their FOF (yes/no) and CAF (yes/no), rated how they felt on a scale of no concern (0) to fearful (4) during 28 activities (the CF-Fall), and completed physical, cognitive, and psychological measures. Pearson's correlations were used to examine relations among the CF-Fall with other measures. In persons endorsing FOF and CAF, independent t-tests were used to compare functioning between subtypes. Results Overall, 60.2% of subjects reported that FOF and CAF are different constructs and 47.2% reported FOF (yes/no) and 64.6% reported CAF (yes/no). PMS reported greater FOF (59.1%) and CAF (80.1%) than RRMS (41.6% and 57.0%, respectively). Avoidance behavior ( r  = .82, p < .001), walking impairment ( r  = .83, p < .001), physical function ( r  = -.84, p < .001), and lower extremity function ( r  = -.85, p < .001) were most correlated to the CF-Fall. People with PMS endorsing FOF and CAF demonstrated greater FOF on the CF-Fall, greater avoidance behavior, poorer walking ability, physical function, life-space mobility, lower extremity function, but greater cognitive function than RRMS (all, p < .001). Discussion These findings underscore the disparity between FOF and CAF, emphasize the importance of evaluating FOF and CAF in MS subtypes separately, as rates for the whole sample may underestimate the prevalence in PMS, and highlight non-motor factors contributing to FOF and CAF. Future work should focus on interventions that incorporate motor, cognitive, and psychological components to address FOF and CAF.

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Sleep across the first year of life is prospectively associated with brain volume in 12-months old infants

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Neurobiology of Sleep and Circadian Rhythms 2023年 14卷 100091页

作者： Pittner, Katharina Rasmussen, Jerod Lim, Miranda M. Gilmore, John H. Styner, Martin Entringer, Sonja Wadhwa, Pathik D. Buss, Claudia Charité Universitaetsmedizin Berlin Corporate Member of Freie Universität Berlin Humboldt-Universität zu Berlin Berlin Institute of Health Department of Medical Psychology Berlin Germany Development Health and Disease Research Program University of California Irvine CA United States Department of Pediatrics University of California Irvine CA United States VA Portland Health Care System Portland OR United States Oregon Health & Science University Department of Neurology Portland OR United States Oregon Health & Science University Department of Behavioral Neuroscience Portland OR United States Oregon Health & Science University Oregon Institute of Occupational Health Sciences Portland OR United States VA Portland Health Care System National Center for Rehabilitative Auditory Research Portland OR United States Department of Psychiatry University of North Carolina Chapel Hill United States Departments of Psychiatry and Human Behavior Obstetrics & Gynecology Epidemiology University of California Irvine CA United States

Objective: Longer sleep duration in infancy supports cognitive and affective functioning – likely through effects on brain development. From childhood through old age, there is evidence for a close link between sleep and brain volume. However, little is known about the association between sleep duration and brain volume in infancy, a developmental period of unprecedented brain maturation. This study aimed to close this gap by assessing sleep duration across the first year of life and gray and white matter volume at 12-mo age. Method: Infant sleep duration trajectories across the first year of life were based on maternal reports at 1, 3, 6, 9, and 12 months of age. Infant specific trajectories were generated by running a logarithmic regression for each infant and residualizing the resulting slopes for their intercept. Structural magnetic resonance imaging (MRI) scans were acquired at 12-mo age. Gray and white matter volume estimates were residualized for intracranial volume and age at scan. Results: Data to calculate sleep trajectories was available for 112 infants. Overall, sleep duration decreased over the course of the first year of life and was best described by a logarithmic function. Of these infants, data on brain volume was available for 45 infants at 12-mo age. Infants whose sleep duration decreased less during the first year of life relative to their intercept had, on average, greater white matter volume (β = .36, p = .02). Furthermore, average sleep duration across the first year of life, and sleep duration specifically at 6 and 9 months were positively associated with white matter volume. Sleep duration during the first year of life was not significantly associated with gray matter volume at 12-mo age. Conclusion: Sufficient sleep duration may benefit infant white matter development – possibly by supporting myelination. The fact that sleep duration was not associated with gray matter volume is in line with preclinical studies suggesting that sleep may be

关键词： Brain development Gray matter volume Infancy Sleep White matter volume

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Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model

arXiv

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arXiv 2023年

作者： Goya-Maldonado, Roberto Erwin-Grabner, Tracy Zeng, Ling-Li Ching, Christopher R.K. Aleman, Andre Amod, Alyssa R. Basgoze, Zeynep Benedetti, Francesco Besteher, Bianca Brosch, Katharina Bülow, Robin Colle, Romain Connolly, Colm G. Corruble, Emmanuelle Couvy-Duchesne, Baptiste Cullen, Kathryn Dannlowski, Udo Davey, Christopher G. Dols, Annemiek Ernsting, Jan Evans, Jennifer W. Fisch, Lukas Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Grabe, Hans J. Groenewold, Nynke A. Grotegerd, Dominik Hahn, Tim Hamilton, J. Paul Han, Laura K.M. Harrison, Ben J. Ho, Tiffany C. Jahanshad, Neda Jamieson, Alec J. Karuk, Andriana Kircher, Tilo Klimes-Dougan, Bonnie Koopowitz, Sheri-Michelle Lancaster, Thomas Leenings, Ramona Li, Meng Linden, David E.J. MacMaster, Frank P. Mehler, David M.A. Meinert, Susanne Melloni, Elisa Mueller, Bryon A. Mwangi, Benson Nenadić, Igor Ojha, Amar Okamoto, Yasumasa Oudega, Mardien L. Penninx, Brenda W.J.H. Poletti, Sara Pomarol-Clotet, Edith Portella, Maria J. Pozzi, Elena Radua, Joaquim Rodríguez-Cano, Elena Sacchet, Matthew D. Salvador, Raymond Schrantee, Anouk Sim, Kang Soares, Jair C. Solanes, Aleix Stein, Dan J. Stein, Frederike Stolicyn, Aleks Thomopoulos, Sophia I. Toenders, Yara J. Uyar-Demir, Aslihan Vieta, Eduard Vives-Gilabert, Yolanda Völzke, Henry Walter, Martin Whalley, Heather C. Whittle, Sarah Winter, Nils Wittfeld, Katharina Wright, Margaret J. Wu, Mon-Ju Yang, Tony T. Zarate, Carlos Veltman, Dick J. Schmaal, Lianne Thompson, Paul M. Georg-August University Göttingen Germany College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China Imaging Genetics Center Mark & Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del ReyCA90274 United States Department of Biomedical Sciences of Cells and Systems University Medical Center Groningen University of Groningen Groningen Netherlands Department of Psychiatry & Mental Health Neuroscience Institute University of Cape Town Cape Town South Africa Department of Psychiatry and Behavioral Science University of Minnesota Medical School MinneapolisMN United States Division of Neuroscience IRCCS Scientific Institute Ospedale San Raffaele Milano Italy Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Department of Psychiatry and Psychotherapy University of Marburg Rudolf Bultmann Str. 8 Marburg35039 Germany Institute for Radiology and Neuroradiology University Medicine Greifswald Greifswald Germany MOODS Team CESP INSERM U1018 Faculté de Médecine Univ Paris-Saclay Le Kremlin Bicêtre94275 France Service Hospitalo Universitaire de Psychiatrie de Bicêtre Hôpitaux Universitaires Paris-Saclay Assistance Publique-Hôpitaux deParis Hôpital de Bicêtre Le Kremlin BicêtreF-94275 France Department of Biomedical Sciences Florida State University TallahasseeFL United States Sorbonne University Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière ParisF-75013 France Institute for Molecular Bioscience the University of Queensland St LuciaQLD Australia Institute for Translational Psychiatry University of Münster Münster Germany Melbourne Neuropsychiatry Centre Department of Psychiatry the University of Melbourne ParkvilleVIC Australia Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam Neuroscience Amsterdam Public Health Research Institute Am

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%;SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%;SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible. Future studies are needed to determine whether more sophisticated integration of information from ot

关键词： Support vector machines

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Reliability of resting-state EEG modulation by continuous and intermittent theta burst stimulation of the primary motor cortex: a sham-controlled study

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Scientific Reports 2023年第1期13卷 1-12页

作者： Rodionov, Andrei Ozdemir, Recep A. Benwell, Christopher S. Y. Fried, Peter J. Boucher, Pierre Momi, Davide Ross, Jessica M. Santarnecchi, Emiliano Pascual-Leone, Alvaro Shafi, Mouhsin M. Berenson-Allen Center for Noninvasive Brain Stimulation Department of Neurology Beth Israel Deaconess Medical Center Boston MA United States Department of Neurology Harvard Medical School Boston MA United States Division of Psychology School of Humanities Social Sciences and Law University of Dundee Dundee United Kingdom Krembil Centre for Neuroinformatics Centre for Addiction and Mental Health Toronto Canada Research Education and Clinical Center Veterans Affairs Palo Alto Healthcare System and the Sierra Pacific Mental Illness Palo Alto CA United States Department of Psychiatry and Behavioral Sciences Stanford Medical School Stanford CA United States Precision Neuroscience and Neuromodulation Program Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Boston MA United States Deanna and Sidney Wolk Center for Memory Health Hebrew Senior Life Hinda and Arthur Marcus Institute for Aging Research Boston MA United States

Theta burst stimulation (TBS) is a form of repetitive transcranial magnetic stimulation designed to induce changes of cortical excitability that outlast the period of TBS application. In this study, we explored the effects of continuous TBS (cTBS) and intermittent TBS (iTBS) versus sham TBS stimulation, applied to the left primary motor cortex, on modulation of resting state electroencephalography (rsEEG) power. We first conducted hypothesis-driven region-of-interest (ROI) analyses examining changes in alpha (8–12 Hz) and beta (13–21 Hz) bands over the left and right motor cortex. Additionally, we performed data-driven whole-brain analyses across a wide range of frequencies (1–50 Hz) and all electrodes. Finally, we assessed the reliability of TBS effects across two sessions approximately 1 month apart. None of the protocols produced significant group-level effects in the ROI. Whole-brain analysis revealed that cTBS significantly enhanced relative power between 19 and 43 Hz over multiple sites in both hemispheres. However, these results were not reliable across visits. There were no significant differences between EEG modulation by active and sham TBS protocols. Between-visit reliability of TBS-induced neuromodulatory effects was generally low-to-moderate. We discuss confounding factors and potential approaches for improving the reliability of TBS-induced rsEEG modulation. © 2023, The Author(s).

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