MétaCan
Menu
Back to cohort
Record W2995065313 · doi:10.1038/s41380-019-0631-x

Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium

2019· review· en· W2995065313 on OpenAlexafffund
Emily L. Dennis, Seth G. Disner, Negar Fani, Lauren E. Salminen, Mark W. Logue, Emily K. Clarke‐Rubright, Courtney C. Haswell, Christopher L. Averill, Lee A. Baugh, Jessica Bomyea, Steven E. Bruce, Jiook Cha, Kyle Choi, Nicholas D. Davenport, Maria Densmore, Stéfan du Plessis, Gina L. Forster, Jessie L. Frijling, A. Gönenç, Staci A. Gruber, Daniel W. Grupe, Jeffrey P. Guenette, Jasmeet P. Hayes, David Hofmann, Jonathan Ipser, Tanja Jovanović, Sinéad Kelly, Mitzy Kennis, Philipp Kinzel, Saskia B.J. Koch, Inga K. Koerte, Sheri‐Michelle Koopowitz, Mayuresh S. Korgaonkar, John H. Krystal, Lauren A. M. Lebois, Gen Li, Vincent A. Magnotta, Antje Manthey, Geoff J. May, Deleene S. Menefee, Laura Nawijn, Richard W. J. Neufeld, Jack B. Nitschke, Daniel C.M. O'Doherty, Matthew Peverill, Kerry J. Ressler, Annerine Roos, Margaret A. Sheridan, Anika Sierk, Alan N. Simmons, Raluca M. Simons, Jeffrey S. Simons, Jennifer S. Stevens, Benjamin Suarez‐Jimenez, Danielle R. Sullivan, Jean Théberge, Jana K. Tran, Leigh L. van den Heuvel, Steven J.A. van der Werff, Sanne J.H. van Rooij, Mirjam van Zuiden, Carmen Vélez, Mieke Verfaellie, Robert Vermeiren, Benjamin Wade, Tor D. Wager, Henrik Walter, Sherry Winternitz, Jonathan D. Wolff, Gerald E. York, Ye Zhu, Xi Zhu, Chadi G. Abdallah, Richard A. Bryant, Judith K. Daniels, Richard J. Davidson, Kelene A. Fercho, Carol E. Franz, Elbert Geuze, Evan M. Gordon, Milissa L. Kaufman, William S. Kremen, Jim Lagopoulos, Ruth A. Lanius, Michael J. Lyons, Stephen R. McCauley, Regina E. McGlinchey, Katie A. McLaughlin, William Milberg, Yuval Neria, Miranda Olff, Soraya Seedat, Martha E. Shenton, Scott R. Sponheim, Dan J. Stein, Murray B. Stein, Thomas Straube, David F. Tate, Nic J.A. van der Wee, Dick J. Veltman, Li Wang, Elisabeth A. Wilde, Paul M. Thompson, Peter Kochunov, Neda Jahanshad, Rajendra A. Morey

Bibliographic record

VenueMolecular Psychiatry · 2019
Typereview
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaLawson Health Research InstituteWestern University
FundersNational Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and StrokeNational Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesNational Institute on Drug AbuseNational Institute of Mental HealthNational Institute on AgingNational Institute on Alcohol Abuse and AlcoholismNational Health and Medical Research CouncilNational Center for Advancing Translational SciencesMedical Research CouncilNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchClinical Science Research and DevelopmentNational Institutes of HealthCongressionally Directed Medical Research ProgramsEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBill and Melinda Gates FoundationZonMwNational Alliance for Research on Schizophrenia and DepressionCanadian Institute for Military and Veteran Health ResearchChinese Academy of SciencesDivision of Research Capacity DevelopmentDeutsche ForschungsgemeinschaftNational Research FoundationYale Center for Clinical Investigation, Yale School of MedicineInstitute for Clinical and Translational Research, University of Wisconsin, MadisonMedical Research and Materiel CommandNational Natural Science Foundation of ChinaGeorgia Clinical and Translational Science AllianceWaisman CenterU.S. Department of Veterans AffairsYale UniversityOffice of Research and DevelopmentNational Center for PTSD, U.S. Department of Veterans AffairsMichael J. Fox Foundation for Parkinson's ResearchTraumatic Brain Injury Center of ExcellenceSouth African Medical Research CouncilU.S. Department of Defense
KeywordsFractional anisotropyWhite matterCorpus callosumPsychologyNeuroimagingPsychiatryDiffusion MRIBrain Structure and FunctionDepression (economics)Clinical psychologyNeuroscienceMedicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.357
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations117
Published2019
Admission routes2
Has abstractno

Explore more

Same venueMolecular PsychiatrySame topicAdvanced Neuroimaging Techniques and ApplicationsFrench-language works237,207