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Record W4416931838 · doi:10.1093/brain/awaf455

Immune alterations in schizophrenia and the effects of a therapeutic antibody: a neuroimaging study

2025· article· en· W4416931838 on OpenAlexafffund
Yuya Mizuno, Inês Figueiredo, Toby Pillinger, Guy Hindley, Luke Baxter, Sita Parmar, Maria Lobo, J Donocik, Ivana Rosenzweig, Anish Gupta, Ilaria Callegari, Sami Jeljeli, Joel Dunn, Alexander Hammers, Ramla Awais, Kerstin Sander, Erik Årstad, Marios Politis, Julia Schubert, Mattia Veronese, Federico Turkheimer, Tiago Reis Marques, Oliver Howes

Bibliographic record

VenueBrain · 2025
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsSt. Thomas Hospital
FundersMedical Research CouncilNIHR Great Ormond Street Hospital Biomedical Research CentreCentro Singular de Investigación de GaliciaUniversity College LondonKing's College LondonDepartment of Health and Social CareNational Institute for Health and Care ResearchWellcome TrustSouth London and Maudsley NHS Foundation TrustNIHR BioResourceMaudsley CharityMedical Research Council CanadaNHS Blood and Transplant
KeywordsNatalizumabTranslocator proteinNeuroinflammationNeuroimagingSchizophrenia (object-oriented programming)PsychosisGrey matterTemporal lobePlacebo

Abstract

fetched live from OpenAlex

Immune dysfunction is implicated in the pathophysiology of schizophrenia. The 18 kDa translocator protein (TSPO), expressed by various cell types, including microglia and astrocytes, is widely used as a marker for neuroinflammation and can be quantified in vivo using PET. However, findings from TSPO PET studies in recent-onset psychosis have been inconsistent, and it remains unknown whether TSPO levels can be modified in schizophrenia. We addressed these questions with a baseline case-control comparison of patients with a first-episode psychotic disorder who were symptomatic despite antipsychotic treatment and healthy volunteers, and a longitudinal study testing the effects of natalizumab (a monoclonal antibody previously shown to reduce TSPO levels in neuroinflammatory conditions) on TSPO levels and symptoms in patients. Baseline and 3-month follow-up brain imaging was carried out using 18F-DPA-714 TSPO PET, quantified as the distribution volume ratio (DVR) in total, frontal lobe and temporal lobe grey matter. A total of 103 volunteers (62 patients and 41 healthy controls) received baseline brain imaging, and 47 patients completed follow-up imaging after receiving natalizumab (n = 31) or placebo (n = 16) infusions. Natalizumab was well tolerated, with no serious treatment-related adverse events. The patient group also received clinical assessments with the Positive and Negative Syndrome Scale at baseline and follow-up. At baseline, DVR was significantly higher in patients relative to controls in total (η2 = 0.04) and temporal lobe (η2 = 0.06) grey matter. However, there was no significant change in DVR across these regions following natalizumab or placebo treatment. Mean ± standard deviation (SD) CSF levels of natalizumab after treatment were 10.7 ± 27.2 ng/ml, indicating that the monoclonal antibody crossed the blood-brain barrier. Patients receiving natalizumab showed a modest but statistically significant improvement in Positive and Negative Syndrome Scale total scores (mean ± SD change: -3.7 ± 9.1, Cohen's d = 0.40, P = 0.017), although there was no relationship between change in DVR and change in symptom severity (P > 0.05). These findings are consistent with elevated grey matter TSPO levels in first-episode psychosis relative to healthy controls. Although natalizumab treatment was associated with a modest reduction in symptoms, the absence of corresponding changes in DVR suggests that higher grey matter TSPO might reflect expression by non-microglial cells. The lack of significant changes in the placebo group indicates that it is a stable trait biomarker. Further work is needed to clarify the functional relevance and cellular specificity of TSPO alterations in psychosis. ClinicalTrials.gov: NCT03093064.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.288
Teacher spread0.278 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2025
Admission routes2
Has abstractyes

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