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Record W4307829684 · doi:10.1101/2022.10.28.514160

Linking Enlarged Choroid Plexus with Plasma Analyte and Structural Phenotypes in Clinical High Risk for Psychosis: A Multisite Neuroimaging Study

2022· preprint· en· W4307829684 on OpenAlexaff
Deepthi Bannai, Martin Reuter, Rachal Hegde, Dung Hoang, Iniya Adhan, Swetha Gandu, Sovannarath Pong, Nick Raymond, Victor Zeng, Yoonho Chung, George He, Daqiang Sun, Theo G.M. van Erp, Jean Addington, Carrie E. Bearden, Kristin S. Cadenhead, Barbara A. Cornblatt, Daniel H. Mathalon, Thomas H. McGlashan, Clark Jeffries, William S. Stone, Ming T. Tsuang, Elaine F. Walker, Scott W. Woods, Tyrone D. Cannon, Diana O. Perkins, Matcheri S. Keshavan, Paulo Lizano

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychosisChoroid plexusVentricleNeuroimagingCerebrospinal fluidInternal medicineProdromeMedicineLateral ventriclesWhite matterCardiologyEndocrinologyPathologyMagnetic resonance imagingPsychiatryRadiologyCentral nervous system

Abstract

fetched live from OpenAlex

Abstract Background Choroid plexus (ChP) enlargement exists in first-episode and chronic psychosis, but whether enlargement occurs before psychosis onset is unknown. This study investigated whether ChP volume is enlarged in individuals with clinical high-risk (CHR) for psychosis and whether these changes are related to clinical, neuroanatomical, and plasma analytes. Methods Clinical and neuroimaging data from the North American Prodrome Longitudinal Study 2 (NAPLS2) was used for analysis. 509 participants (169 controls, 340 CHR) were recruited. Conversion status was determined after 2-years of follow-up, with 36 psychosis converters. The lateral ventricle ChP was manually segmented from baseline scans. A subsample of 31 controls and 53 CHR had plasma analyte and neuroimaging data. Results Compared to controls, CHR ( d=0 . 23, p=0 . 017 ) and non-converters (d=0 . 22, p=0 . 03) demonstrated higher ChP volumes, but not in converters. In CHR, greater ChP volume correlated with lower cortical (r=-0 . 22, p<0 . 001) , subcortical gray matter (r=-0 . 21, p<0 . 001) , and total white matter volume (r=-0 . 28,p<0 . 001) , as well as larger lateral ventricle volume (r=0 . 63,p<0 . 001) . Greater ChP volume correlated with makers functionally associated with the lateral ventricle ChP in CHR [CCL1 (r=-0 . 30, p=0 . 035) , ICAM1 (r=0 . 33, p=0 . 02)] , converters [IL1β (r=0 . 66, p=0 . 004 )], and non-converters [BMP6 (r=-0 . 96, p<0 . 001) , CALB1 (r=-0 . 98, p<0 . 001) , ICAM1 (r=0 . 80, p=0 . 003) , SELE (r=0 . 59, p=0 . 026) , SHBG (r=0 . 99, p<0 . 001) , TNFRSF10C (r=0 . 78, p=0 . 001) ]. Conclusions CHR and non-converters demonstrated significantly larger ChP volumes compared to controls. Enlarged ChP was associated with neuroanatomical alterations and analyte markers functionally associated with the ChP. These findings suggest that the ChP may be a key explanatory biomarker in CHR for psychosis.

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)0.002
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.038
GPT teacher head0.333
Teacher spread0.295 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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