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Record W4409148814 · doi:10.1038/s41537-025-00581-6

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

2025· article· en· W4409148814 on OpenAlexafffund
Michael P. Harms, Kang Ik K. Cho, Alan Anticevic, Nicolas R. Bolo, Sylvain Bouix, Tyrone D. Cannon, Guillermo Cecchi, Mathias Goncalves, Anastasia Haidar, Dylan Hughes, Igor Izyurov, Omar John, Tina Kapur, Nicholas Kim, Elana Kotler, Marek Kubicki, Joshua Kuperman, Kristen Laulette, Ulrich Lindberg, Christopher J. Markiewicz, Lipeng Ning, Russell A. Poldrack, Yogesh Rathi, Paul Romo, Zailyn Tamayo, Cassandra Wannan, Alana Wickham, Walid Yassin, Juan Zhou, Jean Addington, Luis Alameda, Celso Arango, Nicholas J. K. Breitborde, Matthew R. Broome, Kristin S. Cadenhead, Monica E. Calkins, Eric Chen, Jimmy Choi, Philippe Conus, Cheryl M. Corcoran, Barbara A. Cornblatt, Covadonga M. Díaz‐Caneja, Lauren M. Ellman, Paolo Fusar‐Poli, Pablo A. Gaspar, Carla Gerber, Louise Birkedal Glenthøj, Leslie E. Horton, Christy Lai Ming Hui, Joseph Kambeitz, Lana Kambeitz‐Ilankovic, Matcheri S. Keshavan, Sung‐Wan Kim, Nikolaos Koutsouleris, Jun Soo Kwon, Kerstin Langbein, Daniel H. Mathalon, Vijay A. Mittal, Merete Nordentoft, Godfrey D. Pearlson, Jesús Pérez, Diana O. Perkins, Albert R. Powers, Jack Rogers, Fred W. Sabb, Jason Schiffman, Jai Shah, Steven M. Silverstein, Stefan Smesny, William S. Stone, Gregory P. Strauss, Judy L. Thompson, Rachel Upthegrove, Swapna Verma, Jijun Wang, Daniel H. Wolf, René S. Kahn, John M. Kane, Patrick D. McGorry, Barnaby Nelson, Scott W. Woods, Martha E. Shenton, Stephen J. Wood, Carrie E. Bearden

Bibliographic record

VenueSchizophrenia · 2025
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityHotchkiss Brain InstituteUniversity of CalgaryÉcole de Technologie Supérieure
FundersCilagH. Lundbeck A/SWellcome TrustBiogenGedeon RichterWellcomeSunovionNational Institute of Neurological Disorders and StrokeTeva Pharmaceutical IndustriesServierNational Institute of Mental HealthPfizerIndiviorBristol-Myers SquibbAllerganNational Institutes of HealthHLS TherapeuticsU.S. Department of Health and Human Services
KeywordsNeuroimagingSchizophrenia (object-oriented programming)Protocol (science)General partnershipMedicinePsychologyPsychiatryNeuroscienceBusinessAlternative medicinePathology

Abstract

fetched live from OpenAlex

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.com/1059777228?share=copy#t=0 .

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)0.012
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.089
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0890.041

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 head0.074
GPT teacher head0.367
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations7
Published2025
Admission routes2
Has abstractyes

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