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Record W2266075323 · doi:10.1038/mp.2015.227

Subcortical volumetric abnormalities in bipolar disorder

2016· article· en· W2266075323 on OpenAlexafffund
Derrek P. Hibar, Lars T. Westlye, Theo G.M. van Erp, Jerod M. Rasmussen, Cassandra D. Leonardo, Joshua Faskowitz, Unn K. Haukvik, Cecilie B. Hartberg, Nhat Trung Doan, Ingrid Agartz, Anders M. Dale, Oliver Gruber, Bernd Krämer, Sarah Trost, Benny Liberg, Christoph Abé, Carl Johan Ekman, Martin Ingvar, Mikael Landén, Scott C. Fears, Nelson B. Freimer, Carrie E. Bearden, Emma Sprooten, David C. Glahn, Godfrey D. Pearlson, Louise Emsell, Joanne Kenney, Catherine E Scanlon, Colm McDonald, Dara M. Cannon, Jorge Almeida, Amelia Versace, Xavier Caseras, Natalia Lawrence, Mary L. Phillips, Danai Dima, Giuseppe Delvecchio, Sophia Frangou, Theodore D. Satterthwaite, Daniel H. Wolf, Josselin Houenou, Chantal Henry, Ulrik Fredrik Malt, Erlend Bøen, Torbjørn Elvsåshagen, Allan H. Young, Adrian J. Lloyd, Guy M. Goodwin, Clare E. Mackay, Corin Bourne, Amy C. Bilderbeck, Lucija Abramovic, Marco P. Boks, Neeltje E.M. van Haren, Roel A. Ophoff, René S. Kahn, Michael Bauer, Andrea Pfennig, Martin Alda, Tomáš Hájek, Benson Mwangi, Jair C. Soares, Thomas E. Nickson, Ralica Dimitrova, J. E. Sussmann, Saskia P. Hagenaars, Heather C. Whalley, Andrew M. McIntosh, Paul M. Thompson, Ole A. Andreassen

Bibliographic record

VenueMolecular Psychiatry · 2016
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsDalhousie University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Center for Mental HealthMedical Research CouncilFondation FondaMentalNational Institutes of HealthDalhousie UniversityNorges ForskningsrådStiftelsen för Strategisk ForskningCanadian Institutes of Health ResearchNational Institute of Neurological Disorders and StrokeNational Institute for Health and Care ResearchNational Cancer InstituteNova Scotia Health Research FoundationNational Institute of Mental HealthAustralian GovernmentCHIST-ERANational Center for Advancing Translational SciencesWellcome TrustStiftelsen Kristian Gerhard Jebsen
KeywordsBipolar disorderPsychologyNeuroscienceMedicineCognition

Abstract

fetched live from OpenAlex

Considerable uncertainty exists about the defining brain changes associated with bipolar disorder (BD). Understanding and quantifying the sources of uncertainty can help generate novel clinical hypotheses about etiology and assist in the development of biomarkers for indexing disease progression and prognosis. Here we were interested in quantifying case–control differences in intracranial volume (ICV) and each of eight subcortical brain measures: nucleus accumbens, amygdala, caudate, hippocampus, globus pallidus, putamen, thalamus, lateral ventricles. In a large study of 1710 BD patients and 2594 healthy controls, we found consistent volumetric reductions in BD patients for mean hippocampus (Cohen’s d =−0.232; P =3.50 × 10 −7 ) and thalamus ( d =−0.148; P =4.27 × 10 −3 ) and enlarged lateral ventricles ( d =−0.260; P =3.93 × 10 −5 ) in patients. No significant effect of age at illness onset was detected. Stratifying patients based on clinical subtype (BD type I or type II) revealed that BDI patients had significantly larger lateral ventricles and smaller hippocampus and amygdala than controls. However, when comparing BDI and BDII patients directly, we did not detect any significant differences in brain volume. This likely represents similar etiology between BD subtype classifications. Exploratory analyses revealed significantly larger thalamic volumes in patients taking lithium compared with patients not taking lithium. We detected no significant differences between BDII patients and controls in the largest such comparison to date. Findings in this study should be interpreted with caution and with careful consideration of the limitations inherent to meta-analyzed neuroimaging comparisons.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.242
Teacher spread0.236 · 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
GenreEmpirical

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

Citations524
Published2016
Admission routes2
Has abstractyes

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