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Record W4407418161 · doi:10.1093/ijnp/pyae059.388

EVALUATION OF NEUROMELANIN IN PATIENTS WITH TREATMENT- RESISTANT SCHIZOPHRENIA

2025· article· en· W4407418161 on OpenAlexaff
Ryosuke Tarumi, Shiori Honda, Takahide Etani, Satoki Homma, Yuka Kaneko, Karin Matsushita, Yui Tobari, Fumihiko Ueno, Guillermo Horga, Clifford Cassidy, Sakiko Tsugawa, Hiroyuki Uchida, Ariel Graff‐Guerrero, Yoshihiro Noda, Shinichiro Nakajima

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldMedicine
TopicChemokine receptors and signaling
Canadian institutionsUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsNeuromelaninSchizophrenia (object-oriented programming)MedicinePsychiatryPsychologyNeuroscienceDopamineDopaminergicSubstantia nigra

Abstract

fetched live from OpenAlex

Abstract Background Approximately 30% of patients with schizophrenia (SZ) do not respond to antipsychotic treatment. Although abnormalities of the dopamine (DA) function are implicated in the pathophysiology of SZ, reports on striatal DA function and treatment responsiveness are inconsistent.The striatum is modified by dopamine released from the substantia nigra (SN). Neuromelanin (NM) is a product of monoamine metabolism including DA. NM-sensitive MRI sequences allow in vivo quantification of NM levels in the SN. NM-MRI signal is thought to serve as a biomarker for SN DA neuron integrity, and in turn, striatal DA functioning. A recent meta-analysis (1) reported that NM levels were higher in patients with SZ than healthy controls (HCs), but no studies have reported on the relationship between NM levels and treatment responsiveness in this population. Aims The purpose of this study was to investigate the relationship between midbrain DA function and treatment responsiveness in patients with schizophrenia. Methods This study was approved by the Ethics Committee of Keio University School of Medicine and Komagino Hospital (approval numbers: 20170313, 20230003). We recruited patients with treatment- resistant schizophrenia (TRS) and patients with non-TRS from Komagino Hospital (Tokyo, Japan). We used a 3T GE MRI with a 8-channel head coil and applied NM sensitive MRI (2D GRE MT, TR=260ms) to measure NM signals in the SN. We also evaluated the severity of symptoms using the Positive and Negative Symptom Scale (PANSS). First, we conducted an analysis of covariance to compare the levels of NM signals between the TRS and non-TRS groups controlling for age and sex as covariates. Subsequently, we performed correlation analyses to explore relationships between severity of symptoms and NM signals. Results Forty-nine participants (TRS: n=17; non-TRS: n=21) completed the study. Overall group differences were found in contrast-to-noise ratio (CNR)-NM ((F(1, 34)=3.04, p=0.082, Adjusted R2=23.9%)). Specifically, the TRS group showed higher CNR-NM compared to the non-TRS group (p =0.03, Cohen’ s d=2.46). Correlations were not found between CNR-NM and age or between CNR-NM and PANSS scores in each group. In the whole patient group, CNR-NM was higher in women (t (36) = 2.5, p = 0.017) and associated with PANSS positive scores (r=0.45, p=0.005). Conclusion The present study demonstrated elevated SN DA function in TRS compared to non-TRS. PET studies have shown inconsistent relationships between treatment response and striatal DA function. Further research is needed to examine the relationship between antipsychotic treatment response and dopamine function in the nigrostriatal pathway of schizophrenia. References (1)Ueno, F. et al. (2022) 'Neuromelanin accumulation in patients with schizophrenia: A systematic review and meta-analysis,' Neuroscience &Biobehavioral Reviews, 132, pp. 1205–1213. https://doi.org/10.1016/j.neubiorev.2021.10.028.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.336
Teacher spread0.317 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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