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Record W4413284393 · doi:10.1093/ijnp/pyaf052.187

221. NEUROMELANIN ACCUMULATION AND TREATMENT RESPONSIVENESS IN PATIENTS WITH SCHIZOPHRENIA: A CROSS-SECTIONAL STUDY WITH NEUROMELANIN-SENSITIVE MRI

2025· article· en· W4413284393 on OpenAlexaff
Fumihiko Ueno, Yusuke Iwata, Shiori Honda, Guillermo Horga, Clifford Cassidy, Edgardo Torres‐Carmona, Jian Song, Vincenzo De Luca, Sakiko Tsugawa, Yoshihiro Noda, Mohit Agarwal, G Remington, Philip Gerretsen, Shinichiro Nakajima, Ariel Graff‐Guerrero

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsNeuromelaninMedicineSchizophrenia (object-oriented programming)Cross-sectional studyPsychiatryPsychologyNeuroscienceInternal medicinePathologyParkinson's diseaseDiseaseSubstantia nigra

Abstract

fetched live from OpenAlex

Abstract Background Neuromelanin (NM), a byproduct of monoamine metabolism, reflects dopamine and norepinephrine activity in the brain. NM-sensitive MRI sequences enable in vivo quantification of NM levels in the substantia nigra (SN) and locus coeruleus (LC), which correspond to dopamine and norepinephrine neuron activity, respectively. Striatal dopamine dysfunction is a hallmark of schizophrenia: increased striatal dopamine synthesis has been associated with responsiveness to first-line antipsychotics (first-line responders [FLR]), whereas normal striatal dopamine synthesis characterizes treatment-resistant schizophrenia (TRS). Clozapine is the only approved treatment for TRS; however, its relationship with NM-MRI-derived dopamine markers remains unexplored. Additionally, norepinephrine dysregulation, originating in the LC, has been implicated in delusions and cognitive impairments in schizophrenia, yet its role in treatment response remains unvalidated using NM-MRI. Aims & Objectives This study aims to elucidate the relationship between NM accumulation and treatment responsiveness in schizophrenia. Specifically, we sought to: Method We conducted a cross-sectional study involving four groups: URS (n = 16), non-URS (n = 16), FLR (n = 20), and HCs (n = 26). NM-MRI was used to measure NM signals in the SN and LC, quantified as contrast ratios (CR). Group comparisons were performed, controlling for age and sex, with Benjamini–Hochberg correction applied for multiple comparisons. Associations between CR and clinical characteristics were also analyzed. Results Of the 78 participants, two (1 URS, 1 FLR) were excluded due to insufficient data quality. Significant group differences were observed in CR for both the SN and LC (SN: F(3,70) = 4.45, η² = 0.16, p = 0.01; LC: F(3,70) = 2.87, η² = 0.11, p = 0.04). In the SN, both URS (Cohen’s d = 1.01, p = 0.01) and FLR (Cohen’s d = 0.94, p = 0.01) showed elevated CR compared to HCs. In the LC, URS demonstrated higher CR than FLR (Cohen’s d = 0.99, p = 0.04). No significant associations were found between CR and clinical characteristics or symptom severity. Discussion & Conclusions This study highlighted distinct dopaminergic and noradrenergic activity patterns in schizophrenia subgroups. Elevated dopaminergic activity (SN CR) was observed in both URS and FLR, whereas heightened noradrenergic activity (LC CR) differentiated URS from FLR. These findings suggest NM-MRI’s potential in predicting treatment response in schizophrenia, underscoring the need for longitudinal studies to establish its clinical utility.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.333
Teacher spread0.319 · 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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