Associations of Neuromelanin in the Substantia Nigra with Antipsychotic Response in Schizophrenia

Associations of Neuromelanin in the Substantia Nigra with Antipsychotic Response in Schizophrenia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Associations of Neuromelanin in the Substantia Nigra with Antipsychotic Response in Schizophrenia Shinichiro Nakajima, Ryosuke Tarumi, Shiori Honda, Clifford Cassidy, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6323439/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Approximately 30% of patients with schizophrenia do not respond to antipsychotics. While schizophrenia has been primarily explained by the dopamine dysfunction hypothesis, treatment-resistant schizophrenia (TRS) may involve a different pathophysiology. Neuromelanin (NM), a product of dopamine metabolism in the substantia nigra (SN), indirectly measures long-term dopamine synthesis capacity. Few studies have examined SN NM levels in TRS. Therefore, we investigated the relationship between SN NM levels and treatment responsiveness in schizophrenia. We included age- and sex-matched TRS, patients with schizophrenia in remission of positive symptoms (SZ-R), and healthy controls (HCs). Neuromelanin-sensitive magnetic resonance imaging was used to measure SN NM signals. We also evaluated clinical symptoms and cognitive impairment. We conducted voxel-wise analyses of NM contrast-to-noise ratio (CNR) to compare groups pairwise. Correlation analyses examined relationships between NM signals and symptom severity. Seventy-two participants (n = 24 per group) completed the study. The TRS group had higher dorsal SN CNR than the HC group (510 out of 1948 voxels at p < 0.05, corrected p = 0.005, permutation test). In contrast, no significant differences were observed in the other comparisons. No significant correlations were found between NM CNR and clinical severity. Our findings contrast with previous positron emission tomography studies on dorsal striatal dopamine function. Since the dorsal SN contributes to both the mesolimbic and nigrostriatal pathways, with a relatively greater role in the former, dopamine functions in these pathways may play different roles for treatment responsiveness. Further research with multimodal imaging is needed to examine dopamine function and antipsychotic treatment responsiveness in schizophrenia. Health sciences/Biomarkers/Diagnostic markers Health sciences/Diseases/Psychiatric disorders/Schizophrenia Figures Figure 1 Figure 2 Figure 3 Introduction Antipsychotic medications have brought significant advances in the treatment of schizophrenia. However, approximately 30% of patients do not respond adequately to these medications and are deemed to have treatment-resistant schizophrenia (TRS). Resistance to treatment impacts their quality of life, productivity, and healthcare costs [ 1 ]. Thus, elucidating the neurophysiological bases of treatment response to antipsychotics is imperative for a better understanding of the pathology of schizophrenia. The dopamine hypothesis posits that an abnormal dopamine pathway is crucial to the pathophysiological mechanism underlying schizophrenia [ 2 ]. In support, PET studies have revealed that endogenous dopamine levels [ 3 , 4 ] and dopamine synthesis and release capacity [ 5 ] are elevated in the dorsal striatum of patients with schizophrenia, compared with healthy controls (HCs), suggesting that presynaptic dopamine function is increased in the nigrostriatal pathway of schizophrenia [ 6 ]. However, dopamine function in the nigrostriatal pathway varies depending on the antipsychotic response in this population. More specifically, some studies showed that striatal dopamine synthesis capacity following antipsychotic treatment is decreased in patients with TRS in comparison with patients with non-TRS [ 7 , 8 ]. Similar findings have also been reported in a subgroup of non-TRS patients whose positive symptoms were in remission (patients with schizophrenia in remission from positive symptoms; SZ-R) [ 9 ]. In contrast, other reports suggested no significant difference in presynaptic dopamine function between patients with TRS and patients with non-TRS [ 10 ] or HCs [ 7 , 9 ] and between patients with non-TRS and HCs [ 8 ]. Furthermore, it was noted that presynaptic dopamine function in the striatum after antipsychotic treatment was lower in the SZ-R group compared with HCs [ 11 ]. In summary, while the comparable level of presynaptic dopamine function in the striatum observed between patients with TRS and HCs seems to be consistent, the results for those with non-TRS, including remission of positive symptoms, are inconsistent. However, few studies have examined the relationship between remission of positive symptoms and presynaptic dopaminergic function in patients with schizophrenia [ 9 , 11 , 12 ], which needs further investigation. While PET is a powerful tool for assessing dopamine function, neuromelanin-sensitive MRI (NM-MRI) holds promise as a complementary, non-invasive alternative to measure midbrain dopamine function. PET imaging is not widely applied in the clinical setting of psychiatry due to its invasive and costly nature, the short half-time of radioactive tracers, and relatively limited spatial resolution. NM is a pigment produced by the synthesis of monoamine neurotransmitters such as dopamine, which are deposited with age in specific brain regions such as dopamine neurons in the substantia nigra (SN) [ 13 ]. NM is bound to iron, forming paramagnetic complexes, which can non-invasively be measured by NM-MRI. A recent meta-analysis demonstrated that NM levels in the SN are increased in patients with schizophrenia compared with HCs, which also supports elevated presynaptic dopamine function in the nigrostriatal pathway of schizophrenia [ 14 ]. However, there is only one NM-MRI study that examined the relationship between dopamine function in the SN and treatment responsiveness to antipsychotics in patients with schizophrenia [ 15 ]. They included patients with less than one year of antipsychotic exposure and found that baseline NM levels were higher in responders at the 6-month follow-up compared to non-responders and HC[ 15 ], which is consistent with some of the aforementioned PET studies. They also showed no changes in SN NM levels between baseline and follow-up either in the responder or non-responder group. In addition, to the best of our knowledge, no study, employing NM-MRI, has compared dopamine function between patients with TRS and patients with SZ-R. As such, further research is required to investigate the potential of NM-MRI in understanding the mechanism underlying antipsychotic treatment responsiveness in schizophrenia. The aim of this study was to compare dopamine function in the SN measured by NM-MRI among patients with TRS, patients with SZ-R, and HCs. We included the remission group to avoid the influence of positive symptoms on SN NM levels since a previous study noted that SN NM levels were associated with the severity of positive symptoms in this population [ 13 ]. The SN was selected as a region of interest since previous NM-MRI studies demonstrated that SN NM is highly accumulated in patients with schizophrenia compared to HCs [ 14 ]. We also explored the relationships between the SN NM levels and the severity of clinical symptoms in patients with schizophrenia. Based on the aforementioned PET studies [ 7 , 9 , 11 ], we hypothesized that SN NM levels would be comparable between patients with TRS and HCs, and would be decreased in patients with SZ-R in comparison with HCs. Materials and methods Participants This single-center cross-sectional trial was carried out at Komagino Hospital. Data analysis was conducted at Keio University School of Medicine and Komagino Hospital, with approval granted by the ethics committees of these constitutions (approval numbers: 20170313 and 20230003). All participants were included after completing the informed consent procedure. All participants, with the exception of the HCs, were patients receiving clinical care at Komagino Hospital. These participants met the criteria for schizophrenia or schizoaffective disorder based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [ 16 ]. Antipsychotic treatment responsiveness was defined by the modified Treatment Response and Resistance in Psychosis Working Group Consensus criteria [ 17 ]. We defined standard antipsychotic treatment, antipsychotic treatment response, and failure based on the previous study (Supplementary Material l) [ 18 ]. The criteria for TRS included the following: (a) a history of failure of the standard treatment with at least two previous antipsychotics excluding clozapine, the only approved medication for TRS and (b) current severity defined as a score of ≥ 5 (moderate-severe) on 2 positive symptom items or 4 (moderate) on 3 positive symptom items of the Positive and Negative Syndrome Scale (PANSS). The criteria for SZ-R included the following: (a) current use of a non-clozapine antipsychotic and (b) successful treatment response to this antipsychotic. They also met the criteria of remission of positive symptoms by Andeasen et al.[ 19 ]. The inclusion criteria for HCs are no history of psychiatric illness confirmed by qualified psychiatrists. The exclusion criteria for all groups are detailed in Supplementary Material 2. The sample size was calculated based on an effect size reported from a previous study (Supplementary Material 3). Clinical Assessment Clinical assessments included the following: the PANSS [ 20 ], Clinical Global Impression Severity (CGI-S) [ 21 ], and Global Assessment of Functioning (GAF) [ 22 ]. Cognitive functioning was assessed using the following scales: the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) [ 23 , 24 ] and the Executive Interview (EXIT) [ 25 , 26 ] for cognitive function, and the Japanese Adult Reading Test (JART) [ 27 ] for estimating premorbid intelligence levels. NM-MRI acquisition and preprocessing Magnetic resonance (MR) images were acquired for all participants on a 3T GE Signa HDxt scanner with an 8-channel head coil. Participants underwent a 3D inversion recovery prepared T1-weighted magnetic resonance imaging (MRI) scan and NM-MRI imaging. Detailed scan parameters are provided in Supplementary Material 4. During the preprocessing phase, we used ANTs [ 28 ] and SPM12 running on MATLAB (R2022b). And the preprocessing steps were conducted following the methodology of previous studies [ 13 , 29 ]. NM-MRI scans were co-registered to each participant's T1-weighted images and normalized to the MNI space using ANTs. All images were visually inspected after each preprocessing step. After these steps, intensity normalization and spatial smoothing were performed sequentially using custom MATLAB scripts. The contrast-to-noise ratio (CNR) for each participant and voxel( v ) was calculated as the relative difference in NM-MRI signal intensity ( I ) from a reference region ( RR ) in white matter tracts, known to have minimal neuromelanin content (i.e., the crus cerebri), as follows: CNR v = (I v − mode(I RR )) / mode(I RR ) The reference region mask for the SN was derived from a previous study [ 13 ], and the SN mask was also adapted from the same paper with modifications (the masks overlaid on the CNR mean image for all participants are shown in Fig. 1 ). The mode ( I RR ) was computed for each participant using a kernel-smoothing function applied to a histogram of the voxel intensity distribution within the mask. The resulting NM-MRI CNR maps were further spatially smoothed with a Gaussian kernel of 1mm full width at half maximum. Statistical Analysis All analyses were conducted in MATLAB 2022a, R version 4.3.1, SPSS29. The analysis involved a voxel-wise examination of CNR values within the SN mask. The primary analysis assessed group differences using robust linear regression to predict CNR at each voxel within the whole SN mask, following the method described by Cassidy et al. [ 13 ]. We conducted pairwise comparisons among all the groups to examine differences between each pair. Age and sex were included as covariates because of the following reasons: age and sex were reported to be associated with NM accumulation [ 30 , 31 ]. To correct for multiple comparisons, we used a permutation test in which diagnosis labels were randomly shuffled relative to individual NM signal maps described in a previous study [ 13 ] (Supplemental Material 5). In addition, a similar analysis was performed to compare NM-MRI CNR between the whole schizophrenia group and the HC group. Moreover, we also presented results of exploratory region-of-interest (ROI) analyses for the whole SN and two SN subregions (SN pars compacta, SNc; SN pars reticulata, SNr). The average CNR from these three masks were compared using the one-way ANCOVA, controlling for age and sex. Furthermore, we examined the association between NM-MRI CNR, defined as the average voxel signal showing significant differences between the whole patient group and HC group in the voxel-wise analysis, and participants’ symptom severity and cognitive function measures using Spearman and Pearson’s methods (detailed in Supplementary Material 6). Results Clinico-demographic data The clinico-demographic information is shown in Table 1 a, and clinical severity is shown in Table 1 b. A total of 100 participants were initially recruited (33 patients with TRS, 35 patients with SZ-R, and 32 HCs). After excluding participants due to technical issues and discomfort during MRI scanning (detailed in Supplementary Material 7), a total of seventy-two age- and sex-matched participants (24 per group) remained for analyses. Table 1 a clinico-demographic characteristics TRS (n = 24) SZ-R (n = 24) HCs (n = 24) ANOVA, t-tests, or x^2 df F or t-value p-value mean ± SD or n (%) Age, year 47.42 ± 11.50 45.96 ± 10.73 46.88 ± 12.43 (2, 69) 0.093 0.91 Sex, female 9 (37.50) 9 (37.50) 9 (37.50) 2 1.00 Education, year 12.08 ± 2.14 13.25 ± 1.94 15.58 ± 3.08 (2, 69) 12.28 < 0.0001 Age of onset, year 23.46 ± 5.68 25.04 ± 6.90 - (1, 46) 0.85 0.40 Duration of illness, year 23.88 ± 10.71 20.58 ± 11.12 - (1, 46) −1.023 0.31 CPZ dose, mg/day 738.27 ± 419.96 493.30 ± 350.84 - 0.038 ANOVA analysis of variance, CPZ chlorpromazine, HCs healthy controls, SZ-R schizophrenia with remission from positive symptoms, TRS treatment-resistant Schizophrenia Table 1 b Clinical severity TRS (n = 24) SZ-R (n = 24) HCs (n = 24) ANOVA or t-test df F or t-value p-value mean ± SD PANSS Total 84.96 ± 19.94 56.71 ± 12.84 - (1, 46) 5.71 < 0.001 Positive 20.95 ± 4.94 11.50 ± 3.66 - (1, 46) 7.38 < 0.001 Negative 24.00 ± 6.54 16.79 ± 4.68 - (1, 46) 4.30 < 0.001 General 39.58 ± 11.35 28.42 ± 6.58 - (1, 46) 4.08 < 0.001 CGI-S 4.71 ± 0.45 2.92 ± 0.28 - (1, 46) 16.15 < 0.001 RBANS Total 72.50 ± 22.30 89.04 ± 15.95 106.04 ± 11.87 (2, 69) 21.75 < 0.001 Immediate memory 74.79 ± 26.05 85.67 ± 19.32 95.04 ± 14.13 (2, 69) 5.66 0.005 Visuospatial/constructional 85.92 ± 26.67 107.17 ± 13.90 115.83 ± 10.87 (2, 69) 165.99 < 0.001 Language 79.83 ± 19.53 89.29 ± 13.82 99.54 ± 12.09 (2, 69) 9.331 0.003 Attention 71.75 ± 22.39 85.25 ± 20.44 108.83 ± 13.85 (2, 69) 21.72 < 0.001 Delayed memory 75.08 ± 27.14 91.13 ± 15.69 102.96 ± 11.24 (2, 69) 12.18 < 0.001 EXIT 10.88 ± 5.73 8.50 ± 5.16 4.67 ± 2.48 (2, 69) 10.32 < 0.001 JART 97.54 ± 10.90 101.92 ± 10.68 109.54 ± 6.45 (2, 69) 9.27 < 0.001 GAF 42.41 ± 9.39 68.29 ± 6.49 88.46 ± 5.90 (2, 69) 222.7 < 0.001 Values are mean ± SD or n (%). ANOVA analysis of variance, CGI-S Clinical Global Impression Severity scale, EXIT Executive Interview, GAF Global Assessment for Functioning, JART Japanese Adult Reading Test, HCs Healthy Controls, PANSS Positive and Negative Syndromes Scale, RBANS Repeatable Battery for Assessment of Neuropsychological Status, SZ-R Shrizophrenia with Remission from Positive Symptoms, TRS Treatment-resistant Schizophrenia RBANS total scores were lower in TRS compared to SZ-R (p = 0.0053) and HCs (p < 0.001) and in SZ-R compared to HCs (p = 0.004) RBANS immediate memory index scores were lower in TRS compared to HCs (p = 0.004). RBANS visuospatial/constructional index scores were lower in TRS compared to SZ-R (p < 0.001) and HCs (p < 0.001) RBANS language index scores were lower in TRS compared to HCs (p < 0.001) RBANS attention index scores were higher in HCs compared to SZ-R (p < 0.001) and TRS (p < 0.001) RBANS delayed memory index scores were lower in TRS compared to SZ-R (p = 0.018) and HCs (p < 0.001), EXIT scores were higher in HCs compared to SZ-R (p = 0.021) and TRS (p < 0.001) JART scores were higher in HCs compared to SZ-R (p = 0.026) and TRS (p < 0.001) GAF scores were lower in TRS compared to SZ-R (p < 0.001) and HCs (p < 0.001) and in SZ-R compared to HCs (p < 0.001) Voxel-wise analysis for the NM-MRI The only significant finding was an increase in NM signal in the TRS group compared with the HC group, with no other significant differences observed (TRS vs. HC: 510 out of 1948 voxels at p < 0.05, corrected p = 0.005, permutation test; TRS vs. SZ-R: 97 out of 1948 voxels; SZ-R vs. HC, corrected p = 0.332: 203 out of 1948 voxels, corrected p = 0.128) (Fig. 2 ). The significant voxels were located in the dorsal region of the SN (Fig. 3 ). An exploratory analysis found increases in NM signal in the whole schizophrenia group in comparison with the HC group (441 out of 1948 voxels at p < 0.05, corrected p = 0.015, permutation test). ROI analysis for the NM-MRI No significant difference in average CNR in the whole SN was found among the TRS, SZ-R, and HC groups (F 2,67 =4.71, p = 0.22). We also did not find any significant differences in average CNR within the subregion masks among the groups (SNc, F 2,67 = 2.53, p = 0.09; SNr, F 2,67 =1.15, p = 0.32). Correlations between SN NM levels and clinical measures The result of correlation analyses is shown in Table 2 . For the TRS group, we found no significant correlations between NM-MRI CNR in the dorsal SN and CPZ equivalent dose, PANSS total scores, PANSS positive, negative, or general subscale scores, CGI-S score, or GAF score. Similarly, for the SZ-R group, we found no significant correlations between them. For both TRS and SZ-R groups, we found no significant correlations between NM-MRI CNR in the SN and any cognitive function measures. Even for the entire schizophrenia group, there were no significant correlations between NM-MRI CNR and clinical measures or cognitive measures. Similarly, for HCs, we found no significant correlations between them. Table 2 Results of correlation between the clinical assessments or cognitive assessments and neuromelanin level in substantia nigra TRS (n = 24) SZ-R (n = 24) HCs (n = 24) SZ (n = 48) PANSS Total Coefficient 0.16 0.04 - 0.19 p value 0.46 0.84 - 0.20 Positive Coefficient 0.04 −0.05 - 0.11 p value 0.87 0.82 - 0.46 Negative Coefficient 0.06 −0.14 - 0.08 p value 0.77 0.50 - 0.57 General Coefficient 0.14 0.02 - 0.16 p value 0.51 0.91 - 0.27 CGI-S Coefficient −0.11 0.26 - 0.24 p value 0.60 0.22 - 0.09 RBANS Total Coefficient 0.33 0.04 −0.23 0.07 p value 0.12 0.85 0.28 0.62 Immediate memory Coefficient 0.16 0.01 −0.29 0.05 p value 0.45 0.97 0.17 0.76 Visuospatial/constructional Coefficient 0.21 0.03 0.04 0.10 p value 0.33 0.90 0.86 0.51 Language Coefficient 0.15 0.06 −0.31 −0.03 p value 0.49 0.77 0.14 0.84 Attention Coefficient 0.17 0.07 −0.06 0.04 p value 0.44 0.76 0.80 0.80 Delayed memory Coefficient 0.31 0.05 −0.08 0.14 p value 0.15 0.82 0.72 0.35 EXIT Coefficient −0.13 −0.14 0.41 −0.05 p value 0.54 0.51 0.05 0.75 JART Coefficient 0.24 0.19 −0.06 0.15 p value 0.26 0.38 0.79 0.32 GAF Coefficient 0.15 −0.21 −0.36 −0.28 p value 0.49 0.32 0.09 0.05 CGI-S Clinical Global Impression Severity scale, EXIT Executive Interview, GAF Global Assessment for Functioning, JART Japanese Adult Reading Test, HCs Healthy Controls, PANSS Positive and Negative Syndromes Scale, RBANS Repeatable Battery for Assessment of Neuropsychological Status, SZ-R Remitted Schizophrenia, TRS Treatment-resistant Schizophrenia Significant correlation* (p < 0.005 is significant after Bonferroni correction for HCs. p < 0.0017 is significant after Bonferroni correction for patients' group) Discussion This is a cross-sectional study comparing SN NM levels among patients with TRS, patients with SZ-R, and HCs using NM-MRI to examine the relationship between treatment responsiveness and dopamine function in the SN of schizophrenia. Our findings are three-fold. First, the voxel-wise analysis found that NM levels in the dorsal SN were higher in the TRS group than in the HC group while no significant difference was found in the other comparisons. Second, ROI analyses demonstrated no significant group difference in SN NM levels. Third, there was no significant correlation between PANSS, RBANS, EXIT, or JART scores and SN NM levels in the entire patient group, indicating that the severity of clinical symptoms or cognitive impairment may not correlate with dopamine function in the SN of patients with schizophrenia undergoing long-term antipsychotic treatment. Based on a previous PET study that noted a decrease in striatal dopamine synthesis capacity in patients with SZ-R in comparison with HCs [ 11 ], we hypothesized that SN NM levels would be lower in patients with SZ-R than in HCs. However, contrary to our hypothesis and inconsistent with the results of the previous study [ 11 ], SN NM levels were not significantly different between the SZ-R group and HC groups, suggesting that dopamine function in the SN of patients with SZ-R following long-term antipsychotic treatment may be similar to that of HCs. There are several possible explanations for the discrepancy between the results of our study and those of the previous study [ 11 ]. Firstly, the difference in neuroimaging modalities used to assess dopamine function between the two studies—specifically, NM-MRI versus 18F-DOPA PET—may have contributed to the inconsistent results. NM is produced during the process of dopamine synthesis and gradually accumulates in neurons over a long period of time [ 30 ], a process that depends on cytosolic dopamine excess in dopamine cells [ 32 , 33 ]. As such, the NM levels measured by MRI likely reflect long-term dopamine function. In contrast, PET for dopamine synthesis capacity measures the uptake and decarboxylation of DOPA into dopamine by aromatic acid decarboxylase (AADC) in dopaminergic neurons [ 34 ]. Consequently, these two modalities offer different temporal resolutions for assessing dopamine function. In support, a study [ 35 ] reported a moderate correlation between striatal kicer measured by 18F-DOPA PET (an index of presynaptic dopamine synthesis capacity) and NM-MRI CNR in the SN and VTA of patients with schizophrenia. Furthermore, another study found a negative correlation between kicer values and NM levels in the SN of HCs [ 36 ]. Therefore, while these indices may correlate with each other in patients with schizophrenia, they are not identical and reflect different aspects of dopamine function. Considering this, while short-term dopamine function, as reflected in kicer measured by 18F-DOPA PET, may be decreased in patients with SZ-R compared with HCs, this alteration may not have been captured by NM-MRI signals, which reflect long-term dopamine function, in our study. Supporting this assumption, NM levels were not significantly different between baseline and a 6-month follow-up for both responders and non-responders [ 15 ], which indicates that NM levels may not decrease even after antipsychotic treatment. However, it is also possible that NM levels might decrease further, potentially dropping below those of HCs similar to the results of Avram et al., after several years of follow-up [ 11 ]. Nevertheless, it is important to note that van der Pluijm et al. [ 15 ] noted that responders exhibited high NM-MRI signals compared to HCs in the voxel-wise analysis, whereas responders in our study did not. One possible explanation for this discrepancy is the difference in the characteristics or definition of treatment responders. In van der Pluijm et al., treatment responders were primarily first-episode cases, with a short duration of illness (an average of 38.85 weeks) and minimal antipsychotic exposure [ 15 ]. In contrast, treatment responders in our study had a much longer duration of illness (an average of 20.58 years) and extensive antipsychotic exposure. Secondly, the difference in the severity of positive symptoms may have contributed to the inconsistent results regarding dopamine function in patients with SZ-R [ 19 ]. Previous studies have suggested that SN NM levels may be associated with the severity of positive symptoms in patients with schizophrenia [ 13 , 31 ]. Cassidy et al. [ 13 ] showed that severely symptomatic patients with schizophrenia (i.e., a positive subscale score in the PANSS ≥ 19) had higher SN NM levels than HCs, whereas patients with milder symptoms (i.e., a positive subscale score in the PANSS < 19) showed no significant difference in SN NM levels compared to HCs. In addition, they found a positive correlation between the PANSS positive scores and SN NM levels [ 13 ], a finding that was also confirmed by another study [ 31 ]. Although our study found no significant correlation between PANSS positive scores and SN NM levels in the whole patient group, these results suggest that SN NM levels may be related to the severity of positive symptoms in schizophrenia, where SN NM levels decrease as symptom severity alleviates. However, it is important to note that our study included patients on antipsychotic medication, while previous studies involved antipsychotic-free patients [ 13 , 31 ], impeding a direct comparison. On the other hand, the only prospective NM-MRI study noted that the non-responder group, who underwent less than one year of antipsychotic treatment at baseline, had lower levels of SN NM levels compared with the responder group [ 15 ]. One reason could be that they included those with first-episode schizophrenia and a shorter duration of illness [ 15 ], while we enrolled those with a duration of illness of over 20 years. Avram et al. also included patients with a duration of illness of 15 years and demonstrated lower dopamine synthesis capacity in the SZ-R group than in the HC group [ 11 ]. Moreover, clinical outcomes have been reported to improve with increasing age in patients with early-onset schizophrenia [ 37 ], including diminished psychotic symptoms and improved psychosocial function [ 38 ]. These findings suggest that dopamine function in the nigrostriatal pathway may be normalized with aging, which is supported by the evidence for age-related improvement in clinical outcomes of schizophrenia, including diminished psychotic symptoms and improved psychosocial function [ 39 – 41 ]. Given the limited number of studies focusing on remission, further research is warranted to explore the relationship between dopamine function and the remission of positive symptoms in schizophrenia by administering standardized treatments and utilizing both PET and NM-MRI to regularly monitor the nigrostriatal dopamine system. To the best of our knowledge, this is the first study to compare SN NM levels between the SZ-R group and the TRS groups. We found no significant difference in SN NM levels between them, suggesting that dopamine function in the SN may be comparable between them. However, while it has been suggested that the underlying neurobiology of the two conditions is likely different, one study measuring d-amphetamine-induced dopamine release in first-episode psychosis using [11C]-(+)-PHNO PET demonstrated that there was no significant difference in dopamine release of the regions of interest in the nigrostriatal pathway measured before the start of treatment between patients with response and patients without response following 3-month treatment [ 42 ]. Another PET study noted that dopamine synthesis capacity in the striatum at baseline correlated with improvement in positive symptoms of patients with first-episode psychosis after 6-week treatment but not after six months [ 43 ]. Of note, regarding the duration of illness, our study and the previous study [ 11 ] included patients who suffered over 20 years and 15 years, respectively. This difference in illness duration and a corresponding trial number of antipsychotic treatment could have contributed to the inconsistent results on the relationship between treatment responsiveness and dopamine function in the nigrostriatal pathway between previous studies and the present study [ 11 , 42 , 43 ]. On the other hand, a meta-analysis revealed that there are greater variabilities in dopamine D2/3 receptor or transporter availability of patients with schizophrenia in comparison with those of HCs with no significant group difference in their mean availability [ 44 ]. In addition, they also noted that while there is higher dopamine synthesis capacity in the patient group, there is no significant group difference in its variability [ 44 ]. This suggests the existence of two distinct subgroups within the patient population: one with high receptor and/or transporter availability and one with low availability. Although the meta-analysis did not directly compare mean D2/3 receptor or dopamine transporter availability between treatment-responsive and treatment-resistant patients [ 44 ], their variability may explain their distinct underlying neurobiology. Mounting evidence suggests no significant difference in presynaptic dopamine synthesis capacity in the striatum between patients with TRS and HCs [ 7 , 9 , 44 ]. Unexpectedly, however, we found higher SN NM levels in the TRS group in comparison with the HC group. This discrepancy may be attributable to the aforementioned difference in the neuroimaging methodology between NM-MRI versus 18F-DOPA PET and in the regions of interest between the SN versus the striatum. On the other hand, few studies have examined SN NM levels in patients with TRS. Consistent with the previous PET findings, a recent NM-MRI study reported that antipsychotic non-responders had SN NM levels comparable to those of HCs both before and at the 6-month follow-up scant [ 15 ] They also found that NM levels in the ventral SN were negatively associated with antipsychotic responsiveness and that nonresponders had lower NM-MRI signal than responders [ 15 ]. Of note, while van der Pluijm et al. reported abnormalities in the ventral region, our study found significant results concentrated in the dorsal region. The dorsal SN contributes to both the mesolimbic and nigrostriatal pathways, with a relatively greater role in the former [ 45 – 47 ], while the dorsal striatum and ventral SN compose the nigrostriatal dopamine pathway. These findings suggest that dopamine functions in the mesolimbic dopamine pathway and nigrostriatal pathway including different subregions of the SN may play different roles for antipsychotic treatment responsiveness in schizophrenia. Further research is warranted to examine dopaminergic function in both pathways in patients with TRS employing multimodal imaging for the long term. While several studies have identified correlations between NM-MRI metrics and clinical symptoms [ 13 , 31 ], a meta-analysis by Ueno et al. [ 14 ] did not find a significant association between them. Consistent with this, the present study found no significant correlations between any clinical scores and SN NM levels in patients with schizophrenia. It is possible that antipsychotic treatment may alter symptomatic presentation regardless of treatment response. However, both PET and NM-MRI studies consistently report stable presynaptic dopamine function after antipsychotic treatment, which may explain the absence of the correlation between any symptomatic severity and SN NM levels in patients with schizophrenia in cross-sectional studies conducted follow-up measurement [ 15 ]. On the other hand, we found no significant correlations between SN NM levels and any cognitive function scores. It has been demonstrated that dopamine function is implicated in cognitive functions [ 48 ]. Consistent with our result, however, a previous study also found no significant negative correlation between processing speed and dopamine synthesis capacity in the striatum [ 11 ] Overall, our results suggest that dopamine function in the SN measured with NM-MRI may not be related to cognitive functions in patients with schizophrenia regardless of antipsychotic treatment responsiveness. There are several limitations to our study. First, due to the nature of a cross-sectional design, we were not able to collect the detailed treatment history including the duration of untreated psychosis. As such, it is difficult to elucidate whether the null finding of the measured NM levels existed before the antipsychotic treatment, illness progression, and aging. More specifically, our study lacks long-term data, which is necessary to investigate how NM-MRI CNR changes over time and how treatments and the progression of the disorder influence these changes. As a result, the TRS group may have included those who showed positive symptoms because of partial nonadherence, which we were unable to assess in our study. Second, while NM-MRI CNR has been shown to be related to NM concentration [ 13 ], the underlying mechanisms are not fully understood. For instance, it remains unclear how factors such as free water, proton density, and other tissue characteristics influence the CNR [ 13 , 49 ]. Third, it is possible that even though patients share the same diagnosis, their underlying pathophysiology may differ, as psychiatric disorders, including schizophrenia, are highly heterogeneous. Fourth, since we were unable to assess adherence in this study, it is possible that this may have influenced the results of the correlational analyses between NM levels and symptom severity. Fifth, we could not consider differences in types of antipsychotic medication due to the limited sample size. Additionally, since none of the patients was taking clozapine, its potential impact remains unclear. Finally, we did not perform voxel-wise analyses of cognitive or clinical measures, for which the relationships with NM-MRI signals could exist in SN subregions that were not found in the SN ROI analyses performed. Conclusion To the best of our knowledge, this is the first to compare SN NM levels measured with NM-MRI in patients with TRS, patients with SZ-R, and HCs to examine the relationship between dopamine function in the SN and antipsychotic treatment responsiveness in patients with schizophrenia. Our voxel-wise analyses found the TRS group showed higher NM levels in the dorsal SN, which contributes to both the mesolimbic and nigrostriatal pathways, with a relatively greater role in the former, than the HC groups with no significant differences in the other comparisons. Our results of SN NM levels measured with NM-MRI are inconsistent with previous studies on dopamine function in the dorsal striatum, which belongs to the nigrostriatal pathway, measured with PET in relation to antipsychotic responsiveness. These findings suggest that dopamine functions in the mesolimbic pathway and nigrostriatal pathway may play different roles in antipsychotic treatment responsiveness in schizophrenia. Given the limited number of NM-MRI studies on antipsychotic treatment responsiveness in schizophrenia, especially those focusing on remission, further research with multimodal imaging is warranted to examine the relationship between antipsychotic treatment responsiveness and dopamine function in schizophrenia. Declarations Data availability Statement The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Acknowledgments This study was supported by the Japan Society for the Promotion of Science (18H02755, 22H03002), Japan Agency for Medical Research and Development (AMED: JP24wm0625302), Japan Research Foundation for Clinical Pharmacology, Naito Foundation, Watanabe Foundation, and Takeda Science Foundation. We thank Mr. Nishikata for his technical support. We thank the participants and their families for their cooperation in this research. Competing Interests The authors have nothing to disclose. Funding SH has received the JSPS Research Fellowship for Young Scientists (DC1), and The Keio University Doctorate Student Grant-in-Aid Program from Ushioda Memorial Fund. FU has received grants from Discovery Fund, Nakatani Foundation, Canadian Institutes of Health Research (CIHR), and Brain & Behavior Research Foundation (BBRF); manuscript fees from Dainippon Sumitomo Pharma; and consultant fees from VeraSci, and Uchiyama Underwriting within the past three years. SN has received grants from Japan Society for the Promotion of Science (18H02755, 22H03002), Japan Agency for Medical Research and development (AMED: JP24wm0625302, JP24wm0625307), Japan Research Foundation for Clinical Pharmacology, Naito Foundation, Takeda Science Foundation, Watanabe Foundation, Osakeno-Kagaku Foundation, and Astellas Foundation within the past three years. SN has also received research support, manuscript fees or speaker’s honoraria from Asahi Quality & Innovations, Ltd., Teijin Pharma, Sumitomo Pharma, Meiji Seika Pharma, Otsuka, PDR pharma, and MSD within the past three years. YN has received Grants-in-Aid for Scientific Research (B) (18H02755; 20H04092; 21H02813) from the Japan Society for the Promotion of Science (JSPS), research grants (a) from Japan Agency for Medical Research and Development (AMED), investigator-initiated clinical study grants from TEIJIN PHARMA LIMITED (Tokyo, Japan) and Inter Reha Co., Ltd. (Tokyo, Japan). YN also receives research grants from Japan Health Foundation, Meiji Yasuda Mental Health Foundation, Mitsui Life Social Welfare Foundation, Takeda Science Foundation, SENSHIN Medical Research Foundation, Health Science Center Foundation, Mochida Memorial Foundation for Medical and Pharmaceutical Research, Taiju Life Social Welfare Foundation, and Daiichi Sankyo Scholarship Donation Program. YN has received speaker’s honoraria from Dainippon Sumitomo Pharma, MOCHIDA PHARMACEUTICAL CO., LTD. (Tokyo, Japan), and Yoshito-miyakuhin Corporation within the past three years. AG-G received the grants from Canadian Institutes of Health Research (MOP-141968, MOP 14249). AG-G also receives research grants from Ministry of Economic Development and Innovation Ontario, Ontario Mental Health Foundation Type A grant, and NARSAD Independent Investigator (AG-G). Other authors do not have any conflict of interest to declare. References Kennedy JL, Altar CA, Taylor DL, Degtiar I, Hornberger JC. The social and economic burden of treatment-resistant schizophrenia: a systematic literature review. Int Clin Psychopharmacol. 2014;29:63–76. Wada M, Noda Y, Iwata Y, Tsugawa S, Yoshida K, Tani H, et al. Dopaminergic dysfunction and excitatory/inhibitory imbalance in treatment-resistant schizophrenia and novel neuromodulatory treatment. Mol Psychiatry. 2022;27:2950–2967. 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Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files Tarumietal2025SupplementaryMaterialMP.docx supplementary materials Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6323439","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":436854132,"identity":"5bda493e-0942-4b5e-bc9e-f93b7d017d61","order_by":0,"name":"Shinichiro 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22:25:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6323439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6323439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79910439,"identity":"0c39dbef-0198-4c8f-939b-aee315c13e2c","added_by":"auto","created_at":"2025-04-04 11:31:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31234,"visible":true,"origin":"","legend":"\u003cp\u003eThe substantia nigra (SN) and reference region mask (crus cerebri) overlaid on the contrast-to-noise ratio (CNR) mean image for all participants. The cyan indicates the reference region, and the yellow represents the SN.\u003c/p\u003e","description":"","filename":"Tarumietal2025Fig1MP.png","url":"https://assets-eu.researchsquare.com/files/rs-6323439/v1/f424042fde00431bdcbf76b6.png"},{"id":79911398,"identity":"096ce5b3-079b-4106-b888-dd11cad8031b","added_by":"auto","created_at":"2025-04-04 11:39:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20026,"visible":true,"origin":"","legend":"\u003cp\u003eThe signal from significant voxels where TRS showed higher values than HC was averaged and visualized across the three groups (while these voxels were statistically significant for TRS vs. HC [permutation test p=0.005], SZ-R was not included in the statistical comparison).\u003c/p\u003e","description":"","filename":"Tarumietal2025Fig2MP.png","url":"https://assets-eu.researchsquare.com/files/rs-6323439/v1/b2f7be45aeef4ba095751e5c.png"},{"id":79910441,"identity":"38a92e22-9b19-4149-9bfa-5a7b670ddb4b","added_by":"auto","created_at":"2025-04-04 11:31:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39519,"visible":true,"origin":"","legend":"\u003cp\u003eA map of significant voxels where TRS exhibited a higher neuromelanin-MRI contrast-to-noise ratio (CNR) than HCs (thresholded at p \u0026lt; 0.05; permutation test p=0.005).\u003c/p\u003e","description":"","filename":"Tarumietal2025Fig3MP.png","url":"https://assets-eu.researchsquare.com/files/rs-6323439/v1/8909b632a0a4ce6f57da0b25.png"},{"id":85951463,"identity":"90bc6f7c-d35a-442c-9235-3ee0e9c37713","added_by":"auto","created_at":"2025-07-03 13:55:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1078335,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6323439/v1/b955ca36-5723-4e3d-ad77-be330c82e26d.pdf"},{"id":79910440,"identity":"2eb2be1d-24d6-4478-931c-65b7dd0712a2","added_by":"auto","created_at":"2025-04-04 11:31:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16970,"visible":true,"origin":"","legend":"\u003cp\u003esupplementary materials\u003c/p\u003e","description":"","filename":"Tarumietal2025SupplementaryMaterialMP.docx","url":"https://assets-eu.researchsquare.com/files/rs-6323439/v1/b35151cac1ab6f4488f2b612.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Associations of Neuromelanin in the Substantia Nigra with Antipsychotic Response in Schizophrenia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntipsychotic medications have brought significant advances in the treatment of schizophrenia. However, approximately 30% of patients do not respond adequately to these medications and are deemed to have treatment-resistant schizophrenia (TRS). Resistance to treatment impacts their quality of life, productivity, and healthcare costs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Thus, elucidating the neurophysiological bases of treatment response to antipsychotics is imperative for a better understanding of the pathology of schizophrenia.\u003c/p\u003e \u003cp\u003eThe dopamine hypothesis posits that an abnormal dopamine pathway is crucial to the pathophysiological mechanism underlying schizophrenia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In support, PET studies have revealed that endogenous dopamine levels [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and dopamine synthesis and release capacity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] are elevated in the dorsal striatum of patients with schizophrenia, compared with healthy controls (HCs), suggesting that presynaptic dopamine function is increased in the nigrostriatal pathway of schizophrenia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, dopamine function in the nigrostriatal pathway varies depending on the antipsychotic response in this population. More specifically, some studies showed that striatal dopamine synthesis capacity following antipsychotic treatment is decreased in patients with TRS in comparison with patients with non-TRS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similar findings have also been reported in a subgroup of non-TRS patients whose positive symptoms were in remission (patients with schizophrenia in remission from positive symptoms; SZ-R) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In contrast, other reports suggested no significant difference in presynaptic dopamine function between patients with TRS and patients with non-TRS [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] or HCs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and between patients with non-TRS and HCs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, it was noted that presynaptic dopamine function in the striatum after antipsychotic treatment was lower in the SZ-R group compared with HCs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In summary, while the comparable level of presynaptic dopamine function in the striatum observed between patients with TRS and HCs seems to be consistent, the results for those with non-TRS, including remission of positive symptoms, are inconsistent. However, few studies have examined the relationship between remission of positive symptoms and presynaptic dopaminergic function in patients with schizophrenia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which needs further investigation.\u003c/p\u003e \u003cp\u003eWhile PET is a powerful tool for assessing dopamine function, neuromelanin-sensitive MRI (NM-MRI) holds promise as a complementary, non-invasive alternative to measure midbrain dopamine function. PET imaging is not widely applied in the clinical setting of psychiatry due to its invasive and costly nature, the short half-time of radioactive tracers, and relatively limited spatial resolution. NM is a pigment produced by the synthesis of monoamine neurotransmitters such as dopamine, which are deposited with age in specific brain regions such as dopamine neurons in the substantia nigra (SN) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. NM is bound to iron, forming paramagnetic complexes, which can non-invasively be measured by NM-MRI. A recent meta-analysis demonstrated that NM levels in the SN are increased in patients with schizophrenia compared with HCs, which also supports elevated presynaptic dopamine function in the nigrostriatal pathway of schizophrenia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, there is only one NM-MRI study that examined the relationship between dopamine function in the SN and treatment responsiveness to antipsychotics in patients with schizophrenia [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. They included patients with less than one year of antipsychotic exposure and found that baseline NM levels were higher in responders at the 6-month follow-up compared to non-responders and HC[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which is consistent with some of the aforementioned PET studies. They also showed no changes in SN NM levels between baseline and follow-up either in the responder or non-responder group. In addition, to the best of our knowledge, no study, employing NM-MRI, has compared dopamine function between patients with TRS and patients with SZ-R. As such, further research is required to investigate the potential of NM-MRI in understanding the mechanism underlying antipsychotic treatment responsiveness in schizophrenia.\u003c/p\u003e \u003cp\u003eThe aim of this study was to compare dopamine function in the SN measured by NM-MRI among patients with TRS, patients with SZ-R, and HCs. We included the remission group to avoid the influence of positive symptoms on SN NM levels since a previous study noted that SN NM levels were associated with the severity of positive symptoms in this population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The SN was selected as a region of interest since previous NM-MRI studies demonstrated that SN NM is highly accumulated in patients with schizophrenia compared to HCs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We also explored the relationships between the SN NM levels and the severity of clinical symptoms in patients with schizophrenia. Based on the aforementioned PET studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], we hypothesized that SN NM levels would be comparable between patients with TRS and HCs, and would be decreased in patients with SZ-R in comparison with HCs.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003eThis single-center cross-sectional trial was carried out at Komagino Hospital. Data analysis was conducted at Keio University School of Medicine and Komagino Hospital, with approval granted by the ethics committees of these constitutions (approval numbers: 20170313 and 20230003). All participants were included after completing the informed consent procedure. All participants, with the exception of the HCs, were patients receiving clinical care at Komagino Hospital. These participants met the criteria for schizophrenia or schizoaffective disorder based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Antipsychotic treatment responsiveness was defined by the modified Treatment Response and Resistance in Psychosis Working Group Consensus criteria [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. We defined standard antipsychotic treatment, antipsychotic treatment response, and failure based on the previous study (Supplementary Material l) [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe criteria for TRS included the following: (a) a history of failure of the standard treatment with at least two previous antipsychotics excluding clozapine, the only approved medication for TRS and (b) current severity defined as a score of \u0026ge;\u0026thinsp;5 (moderate-severe) on 2 positive symptom items or 4 (moderate) on 3 positive symptom items of the Positive and Negative Syndrome Scale (PANSS). The criteria for SZ-R included the following: (a) current use of a non-clozapine antipsychotic and (b) successful treatment response to this antipsychotic. They also met the criteria of remission of positive symptoms by Andeasen et al.[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. The inclusion criteria for HCs are no history of psychiatric illness confirmed by qualified psychiatrists. The exclusion criteria for all groups are detailed in Supplementary Material 2.\u003c/p\u003e\n \u003cp\u003eThe sample size was calculated based on an effect size reported from a previous study (Supplementary Material 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eClinical Assessment\u003c/h3\u003e\n\u003cp\u003eClinical assessments included the following: the PANSS [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], Clinical Global Impression Severity (CGI-S) [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], and Global Assessment of Functioning (GAF) [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Cognitive functioning was assessed using the following scales: the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] and the Executive Interview (EXIT) [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] for cognitive function, and the Japanese Adult Reading Test (JART) [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] for estimating premorbid intelligence levels.\u003c/p\u003e\n\u003ch3\u003eNM-MRI acquisition and preprocessing\u003c/h3\u003e\n\u003cp\u003eMagnetic resonance (MR) images were acquired for all participants on a 3T GE Signa HDxt scanner with an 8-channel head coil. Participants underwent a 3D inversion recovery prepared T1-weighted magnetic resonance imaging (MRI) scan and NM-MRI imaging. Detailed scan parameters are provided in Supplementary Material 4. During the preprocessing phase, we used ANTs [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] and SPM12 running on MATLAB (R2022b). And the preprocessing steps were conducted following the methodology of previous studies [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. NM-MRI scans were co-registered to each participant\u0026apos;s T1-weighted images and normalized to the MNI space using ANTs. All images were visually inspected after each preprocessing step. After these steps, intensity normalization and spatial smoothing were performed sequentially using custom MATLAB scripts. The contrast-to-noise ratio (CNR) for each participant and voxel(\u003csub\u003e\u003cem\u003ev\u003c/em\u003e\u003c/sub\u003e) was calculated as the relative difference in NM-MRI signal intensity (\u003cem\u003eI\u003c/em\u003e) from a reference region (\u003csub\u003e\u003cem\u003eRR\u003c/em\u003e\u003c/sub\u003e) in white matter tracts, known to have minimal neuromelanin content (i.e., the crus cerebri), as follows:\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003eCNR\u003csub\u003ev\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;(I\u003csub\u003ev\u003c/sub\u003e \u0026minus; mode(I\u003csub\u003eRR\u003c/sub\u003e))\u0026thinsp;/\u0026thinsp;mode(I\u003csub\u003eRR\u003c/sub\u003e)\u0026thinsp;\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eThe reference region mask for the SN was derived from a previous study [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], and the SN mask was also adapted from the same paper with modifications (the masks overlaid on the CNR mean image for all participants are shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The mode (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003eRR\u003c/em\u003e\u003c/sub\u003e) was computed for each participant using a kernel-smoothing function applied to a histogram of the voxel intensity distribution within the mask. The resulting NM-MRI CNR maps were further spatially smoothed with a Gaussian kernel of 1mm full width at half maximum.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll analyses were conducted in MATLAB 2022a, R version 4.3.1, SPSS29. The analysis involved a voxel-wise examination of CNR values within the SN mask. The primary analysis assessed group differences using robust linear regression to predict CNR at each voxel within the whole SN mask, following the method described by Cassidy et al. [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. We conducted pairwise comparisons among all the groups to examine differences between each pair. Age and sex were included as covariates because of the following reasons: age and sex were reported to be associated with NM accumulation [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. To correct for multiple comparisons, we used a permutation test in which diagnosis labels were randomly shuffled relative to individual NM signal maps described in a previous study [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] (Supplemental Material 5). In addition, a similar analysis was performed to compare NM-MRI CNR between the whole schizophrenia group and the HC group. Moreover, we also presented results of exploratory region-of-interest (ROI) analyses for the whole SN and two SN subregions (SN pars compacta, SNc; SN pars reticulata, SNr). The average CNR from these three masks were compared using the one-way ANCOVA, controlling for age and sex.\u003c/p\u003e\n \u003cp\u003eFurthermore, we examined the association between NM-MRI CNR, defined as the average voxel signal showing significant differences between the whole patient group and HC group in the voxel-wise analysis, and participants\u0026rsquo; symptom severity and cognitive function measures using Spearman and Pearson\u0026rsquo;s methods (detailed in Supplementary Material 6).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinico-demographic data\u003c/h2\u003e \u003cp\u003eThe clinico-demographic information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, and clinical severity is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. A total of 100 participants were initially recruited (33 patients with TRS, 35 patients with SZ-R, and 32 HCs). After excluding participants due to technical issues and discomfort during MRI scanning (detailed in Supplementary Material 7), a total of seventy-two age- and sex-matched participants (24 per group) remained for analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ea\u003c/b\u003e clinico-demographic characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRS (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSZ-R (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHCs (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANOVA, t-tests, or x^2 df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF or t-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.42\u0026thinsp;\u0026plusmn;\u0026thinsp;11.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.96\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.88\u0026thinsp;\u0026plusmn;\u0026thinsp;12.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, female\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (37.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (37.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (37.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation, year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of onset, year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.46\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.04\u0026thinsp;\u0026plusmn;\u0026thinsp;6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of illness, year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.88\u0026thinsp;\u0026plusmn;\u0026thinsp;10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.58\u0026thinsp;\u0026plusmn;\u0026thinsp;11.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCPZ dose, mg/day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e738.27\u0026thinsp;\u0026plusmn;\u0026thinsp;419.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e493.30\u0026thinsp;\u0026plusmn;\u0026thinsp;350.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eANOVA analysis of variance, CPZ chlorpromazine, HCs healthy controls, SZ-R schizophrenia with remission from positive symptoms, TRS treatment-resistant Schizophrenia\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e Clinical severity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTRS (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSZ-R (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHCs (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eANOVA or t-test df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF or t-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePANSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.96\u0026thinsp;\u0026plusmn;\u0026thinsp;19.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.71\u0026thinsp;\u0026plusmn;\u0026thinsp;12.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.58\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.42\u0026thinsp;\u0026plusmn;\u0026thinsp;6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCGI-S\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eRBANS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.50\u0026thinsp;\u0026plusmn;\u0026thinsp;22.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.04\u0026thinsp;\u0026plusmn;\u0026thinsp;15.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.04\u0026thinsp;\u0026plusmn;\u0026thinsp;11.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmediate memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.79\u0026thinsp;\u0026plusmn;\u0026thinsp;26.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.67\u0026thinsp;\u0026plusmn;\u0026thinsp;19.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.04\u0026thinsp;\u0026plusmn;\u0026thinsp;14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisuospatial/constructional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.92\u0026thinsp;\u0026plusmn;\u0026thinsp;26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.17\u0026thinsp;\u0026plusmn;\u0026thinsp;13.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e165.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLanguage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.83\u0026thinsp;\u0026plusmn;\u0026thinsp;19.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.29\u0026thinsp;\u0026plusmn;\u0026thinsp;13.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.54\u0026thinsp;\u0026plusmn;\u0026thinsp;12.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.75\u0026thinsp;\u0026plusmn;\u0026thinsp;22.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.25\u0026thinsp;\u0026plusmn;\u0026thinsp;20.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.83\u0026thinsp;\u0026plusmn;\u0026thinsp;13.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.08\u0026thinsp;\u0026plusmn;\u0026thinsp;27.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.13\u0026thinsp;\u0026plusmn;\u0026thinsp;15.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102.96\u0026thinsp;\u0026plusmn;\u0026thinsp;11.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEXIT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJART\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109.54\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGAF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.46\u0026thinsp;\u0026plusmn;\u0026thinsp;5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e222.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%). ANOVA analysis of variance, CGI-S Clinical Global Impression Severity scale, EXIT Executive Interview, GAF Global Assessment for Functioning, JART Japanese Adult Reading Test, HCs Healthy Controls, PANSS Positive and Negative Syndromes Scale, RBANS Repeatable Battery for Assessment of Neuropsychological Status, SZ-R Shrizophrenia with Remission from Positive Symptoms, TRS Treatment-resistant Schizophrenia\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eRBANS total scores were lower in TRS compared to SZ-R (p\u0026thinsp;=\u0026thinsp;0.0053) and HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in SZ-R compared to HCs (p\u0026thinsp;=\u0026thinsp;0.004)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eRBANS immediate memory index scores were lower in TRS compared to HCs (p\u0026thinsp;=\u0026thinsp;0.004).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eRBANS visuospatial/constructional index scores were lower in TRS compared to SZ-R (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eRBANS language index scores were lower in TRS compared to HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eRBANS attention index scores were higher in HCs compared to SZ-R (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TRS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eRBANS delayed memory index scores were lower in TRS compared to SZ-R (p\u0026thinsp;=\u0026thinsp;0.018) and HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001),\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eEXIT scores were higher in HCs compared to SZ-R (p\u0026thinsp;=\u0026thinsp;0.021) and TRS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eJART scores were higher in HCs compared to SZ-R (p\u0026thinsp;=\u0026thinsp;0.026) and TRS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eGAF scores were lower in TRS compared to SZ-R (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in SZ-R compared to HCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVoxel-wise analysis for the NM-MRI\u003c/h3\u003e\n\u003cp\u003eThe only significant finding was an increase in NM signal in the TRS group compared with the HC group, with no other significant differences observed (TRS vs. HC: 510 out of 1948 voxels at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, corrected p\u0026thinsp;=\u0026thinsp;0.005, permutation test; TRS vs. SZ-R: 97 out of 1948 voxels; SZ-R vs. HC, corrected p\u0026thinsp;=\u0026thinsp;0.332: 203 out of 1948 voxels, corrected p\u0026thinsp;=\u0026thinsp;0.128) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The significant voxels were located in the dorsal region of the SN (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). An exploratory analysis found increases in NM signal in the whole schizophrenia group in comparison with the HC group (441 out of 1948 voxels at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, permutation test).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eROI analysis for the NM-MRI\u003c/h2\u003e \u003cp\u003eNo significant difference in average CNR in the whole SN was found among the TRS, SZ-R, and HC groups (F\u003csub\u003e2,67\u003c/sub\u003e=4.71, p\u0026thinsp;=\u0026thinsp;0.22). We also did not find any significant differences in average CNR within the subregion masks among the groups (SNc, F\u003csub\u003e2,67\u003c/sub\u003e= 2.53, p\u0026thinsp;=\u0026thinsp;0.09; SNr, F\u003csub\u003e2,67\u003c/sub\u003e=1.15, p\u0026thinsp;=\u0026thinsp;0.32).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between SN NM levels and clinical measures\u003c/h2\u003e \u003cp\u003eThe result of correlation analyses is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For the TRS group, we found no significant correlations between NM-MRI CNR in the dorsal SN and CPZ equivalent dose, PANSS total scores, PANSS positive, negative, or general subscale scores, CGI-S score, or GAF score. Similarly, for the SZ-R group, we found no significant correlations between them. For both TRS and SZ-R groups, we found no significant correlations between NM-MRI CNR in the SN and any cognitive function measures. Even for the entire schizophrenia group, there were no significant correlations between NM-MRI CNR and clinical measures or cognitive measures. Similarly, for HCs, we found no significant correlations between them.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of correlation between the clinical assessments or cognitive assessments and neuromelanin level in substantia nigra\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTRS (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSZ-R (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHCs (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSZ (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003ePANSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCGI-S\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cb\u003eRBANS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eImmediate memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVisuospatial/constructional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLanguage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAttention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDelayed memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEXIT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eJART\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGAF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCGI-S Clinical Global Impression Severity scale, EXIT Executive Interview, GAF Global Assessment for Functioning, JART Japanese Adult Reading Test, HCs Healthy Controls, PANSS Positive and Negative Syndromes Scale, RBANS Repeatable Battery for Assessment of Neuropsychological Status, SZ-R Remitted Schizophrenia, TRS Treatment-resistant Schizophrenia\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignificant correlation* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005 is significant after Bonferroni correction for HCs. p\u0026thinsp;\u0026lt;\u0026thinsp;0.0017 is significant after Bonferroni correction for patients' group)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is a cross-sectional study comparing SN NM levels among patients with TRS, patients with SZ-R, and HCs using NM-MRI to examine the relationship between treatment responsiveness and dopamine function in the SN of schizophrenia. Our findings are three-fold. First, the voxel-wise analysis found that NM levels in the dorsal SN were higher in the TRS group than in the HC group while no significant difference was found in the other comparisons. Second, ROI analyses demonstrated no significant group difference in SN NM levels. Third, there was no significant correlation between PANSS, RBANS, EXIT, or JART scores and SN NM levels in the entire patient group, indicating that the severity of clinical symptoms or cognitive impairment may not correlate with dopamine function in the SN of patients with schizophrenia undergoing long-term antipsychotic treatment.\u003c/p\u003e \u003cp\u003eBased on a previous PET study that noted a decrease in striatal dopamine synthesis capacity in patients with SZ-R in comparison with HCs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], we hypothesized that SN NM levels would be lower in patients with SZ-R than in HCs. However, contrary to our hypothesis and inconsistent with the results of the previous study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], SN NM levels were not significantly different between the SZ-R group and HC groups, suggesting that dopamine function in the SN of patients with SZ-R following long-term antipsychotic treatment may be similar to that of HCs. There are several possible explanations for the discrepancy between the results of our study and those of the previous study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Firstly, the difference in neuroimaging modalities used to assess dopamine function between the two studies\u0026mdash;specifically, NM-MRI versus 18F-DOPA PET\u0026mdash;may have contributed to the inconsistent results. NM is produced during the process of dopamine synthesis and gradually accumulates in neurons over a long period of time [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], a process that depends on cytosolic dopamine excess in dopamine cells [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As such, the NM levels measured by MRI likely reflect long-term dopamine function. In contrast, PET for dopamine synthesis capacity measures the uptake and decarboxylation of DOPA into dopamine by aromatic acid decarboxylase (AADC) in dopaminergic neurons [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Consequently, these two modalities offer different temporal resolutions for assessing dopamine function. In support, a study [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] reported a moderate correlation between striatal kicer measured by 18F-DOPA PET (an index of presynaptic dopamine synthesis capacity) and NM-MRI CNR in the SN and VTA of patients with schizophrenia. Furthermore, another study found a negative correlation between kicer values and NM levels in the SN of HCs [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, while these indices may correlate with each other in patients with schizophrenia, they are not identical and reflect different aspects of dopamine function. Considering this, while short-term dopamine function, as reflected in kicer measured by 18F-DOPA PET, may be decreased in patients with SZ-R compared with HCs, this alteration may not have been captured by NM-MRI signals, which reflect long-term dopamine function, in our study. Supporting this assumption, NM levels were not significantly different between baseline and a 6-month follow-up for both responders and non-responders [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which indicates that NM levels may not decrease even after antipsychotic treatment. However, it is also possible that NM levels might decrease further, potentially dropping below those of HCs similar to the results of Avram et al., after several years of follow-up [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Nevertheless, it is important to note that van der Pluijm et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] noted that responders exhibited high NM-MRI signals compared to HCs in the voxel-wise analysis, whereas responders in our study did not. One possible explanation for this discrepancy is the difference in the characteristics or definition of treatment responders. In van der Pluijm et al., treatment responders were primarily first-episode cases, with a short duration of illness (an average of 38.85 weeks) and minimal antipsychotic exposure [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In contrast, treatment responders in our study had a much longer duration of illness (an average of 20.58 years) and extensive antipsychotic exposure. Secondly, the difference in the severity of positive symptoms may have contributed to the inconsistent results regarding dopamine function in patients with SZ-R [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous studies have suggested that SN NM levels may be associated with the severity of positive symptoms in patients with schizophrenia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Cassidy et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] showed that severely symptomatic patients with schizophrenia (i.e., a positive subscale score in the PANSS\u0026thinsp;\u0026ge;\u0026thinsp;19) had higher SN NM levels than HCs, whereas patients with milder symptoms (i.e., a positive subscale score in the PANSS\u0026thinsp;\u0026lt;\u0026thinsp;19) showed no significant difference in SN NM levels compared to HCs. In addition, they found a positive correlation between the PANSS positive scores and SN NM levels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], a finding that was also confirmed by another study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Although our study found no significant correlation between PANSS positive scores and SN NM levels in the whole patient group, these results suggest that SN NM levels may be related to the severity of positive symptoms in schizophrenia, where SN NM levels decrease as symptom severity alleviates. However, it is important to note that our study included patients on antipsychotic medication, while previous studies involved antipsychotic-free patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], impeding a direct comparison. On the other hand, the only prospective NM-MRI study noted that the non-responder group, who underwent less than one year of antipsychotic treatment at baseline, had lower levels of SN NM levels compared with the responder group [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. One reason could be that they included those with first-episode schizophrenia and a shorter duration of illness [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while we enrolled those with a duration of illness of over 20 years. Avram et al. also included patients with a duration of illness of 15 years and demonstrated lower dopamine synthesis capacity in the SZ-R group than in the HC group [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, clinical outcomes have been reported to improve with increasing age in patients with early-onset schizophrenia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], including diminished psychotic symptoms and improved psychosocial function [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings suggest that dopamine function in the nigrostriatal pathway may be normalized with aging, which is supported by the evidence for age-related improvement in clinical outcomes of schizophrenia, including diminished psychotic symptoms and improved psychosocial function [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Given the limited number of studies focusing on remission, further research is warranted to explore the relationship between dopamine function and the remission of positive symptoms in schizophrenia by administering standardized treatments and utilizing both PET and NM-MRI to regularly monitor the nigrostriatal dopamine system.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to compare SN NM levels between the SZ-R group and the TRS groups. We found no significant difference in SN NM levels between them, suggesting that dopamine function in the SN may be comparable between them. However, while it has been suggested that the underlying neurobiology of the two conditions is likely different, one study measuring d-amphetamine-induced dopamine release in first-episode psychosis using [11C]-(+)-PHNO PET demonstrated that there was no significant difference in dopamine release of the regions of interest in the nigrostriatal pathway measured before the start of treatment between patients with response and patients without response following 3-month treatment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Another PET study noted that dopamine synthesis capacity in the striatum at baseline correlated with improvement in positive symptoms of patients with first-episode psychosis after 6-week treatment but not after six months [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Of note, regarding the duration of illness, our study and the previous study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] included patients who suffered over 20 years and 15 years, respectively. This difference in illness duration and a corresponding trial number of antipsychotic treatment could have contributed to the inconsistent results on the relationship between treatment responsiveness and dopamine function in the nigrostriatal pathway between previous studies and the present study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. On the other hand, a meta-analysis revealed that there are greater variabilities in dopamine D2/3 receptor or transporter availability of patients with schizophrenia in comparison with those of HCs with no significant group difference in their mean availability [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In addition, they also noted that while there is higher dopamine synthesis capacity in the patient group, there is no significant group difference in its variability [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This suggests the existence of two distinct subgroups within the patient population: one with high receptor and/or transporter availability and one with low availability. Although the meta-analysis did not directly compare mean D2/3 receptor or dopamine transporter availability between treatment-responsive and treatment-resistant patients [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], their variability may explain their distinct underlying neurobiology.\u003c/p\u003e \u003cp\u003eMounting evidence suggests no significant difference in presynaptic dopamine synthesis capacity in the striatum between patients with TRS and HCs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Unexpectedly, however, we found higher SN NM levels in the TRS group in comparison with the HC group. This discrepancy may be attributable to the aforementioned difference in the neuroimaging methodology between NM-MRI versus 18F-DOPA PET and in the regions of interest between the SN versus the striatum. On the other hand, few studies have examined SN NM levels in patients with TRS. Consistent with the previous PET findings, a recent NM-MRI study reported that antipsychotic non-responders had SN NM levels comparable to those of HCs both before and at the 6-month follow-up scant [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] They also found that NM levels in the ventral SN were negatively associated with antipsychotic responsiveness and that nonresponders had lower NM-MRI signal than responders [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Of note, while van der Pluijm et al. reported abnormalities in the ventral region, our study found significant results concentrated in the dorsal region. The dorsal SN contributes to both the mesolimbic and nigrostriatal pathways, with a relatively greater role in the former [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], while the dorsal striatum and ventral SN compose the nigrostriatal dopamine pathway. These findings suggest that dopamine functions in the mesolimbic dopamine pathway and nigrostriatal pathway including different subregions of the SN may play different roles for antipsychotic treatment responsiveness in schizophrenia. Further research is warranted to examine dopaminergic function in both pathways in patients with TRS employing multimodal imaging for the long term.\u003c/p\u003e \u003cp\u003eWhile several studies have identified correlations between NM-MRI metrics and clinical symptoms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], a meta-analysis by Ueno et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] did not find a significant association between them. Consistent with this, the present study found no significant correlations between any clinical scores and SN NM levels in patients with schizophrenia. It is possible that antipsychotic treatment may alter symptomatic presentation regardless of treatment response. However, both PET and NM-MRI studies consistently report stable presynaptic dopamine function after antipsychotic treatment, which may explain the absence of the correlation between any symptomatic severity and SN NM levels in patients with schizophrenia in cross-sectional studies conducted follow-up measurement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. On the other hand, we found no significant correlations between SN NM levels and any cognitive function scores. It has been demonstrated that dopamine function is implicated in cognitive functions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Consistent with our result, however, a previous study also found no significant negative correlation between processing speed and dopamine synthesis capacity in the striatum [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Overall, our results suggest that dopamine function in the SN measured with NM-MRI may not be related to cognitive functions in patients with schizophrenia regardless of antipsychotic treatment responsiveness.\u003c/p\u003e \u003cp\u003eThere are several limitations to our study. First, due to the nature of a cross-sectional design, we were not able to collect the detailed treatment history including the duration of untreated psychosis. As such, it is difficult to elucidate whether the null finding of the measured NM levels existed before the antipsychotic treatment, illness progression, and aging. More specifically, our study lacks long-term data, which is necessary to investigate how NM-MRI CNR changes over time and how treatments and the progression of the disorder influence these changes. As a result, the TRS group may have included those who showed positive symptoms because of partial nonadherence, which we were unable to assess in our study. Second, while NM-MRI CNR has been shown to be related to NM concentration [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the underlying mechanisms are not fully understood. For instance, it remains unclear how factors such as free water, proton density, and other tissue characteristics influence the CNR [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Third, it is possible that even though patients share the same diagnosis, their underlying pathophysiology may differ, as psychiatric disorders, including schizophrenia, are highly heterogeneous. Fourth, since we were unable to assess adherence in this study, it is possible that this may have influenced the results of the correlational analyses between NM levels and symptom severity. Fifth, we could not consider differences in types of antipsychotic medication due to the limited sample size. Additionally, since none of the patients was taking clozapine, its potential impact remains unclear. Finally, we did not perform voxel-wise analyses of cognitive or clinical measures, for which the relationships with NM-MRI signals could exist in SN subregions that were not found in the SN ROI analyses performed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo the best of our knowledge, this is the first to compare SN NM levels measured with NM-MRI in patients with TRS, patients with SZ-R, and HCs to examine the relationship between dopamine function in the SN and antipsychotic treatment responsiveness in patients with schizophrenia. Our voxel-wise analyses found the TRS group showed higher NM levels in the dorsal SN, which contributes to both the mesolimbic and nigrostriatal pathways, with a relatively greater role in the former, than the HC groups with no significant differences in the other comparisons. Our results of SN NM levels measured with NM-MRI are inconsistent with previous studies on dopamine function in the dorsal striatum, which belongs to the nigrostriatal pathway, measured with PET in relation to antipsychotic responsiveness. These findings suggest that dopamine functions in the mesolimbic pathway and nigrostriatal pathway may play different roles in antipsychotic treatment responsiveness in schizophrenia. Given the limited number of NM-MRI studies on antipsychotic treatment responsiveness in schizophrenia, especially those focusing on remission, further research with multimodal imaging is warranted to examine the relationship between antipsychotic treatment responsiveness and dopamine function in schizophrenia.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eData availability Statement\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Japan Society for the Promotion of Science (18H02755, 22H03002), Japan Agency for Medical Research and Development (AMED: JP24wm0625302), Japan Research Foundation for Clinical Pharmacology, Naito Foundation, Watanabe Foundation, and Takeda Science Foundation. We thank Mr. Nishikata for his technical support. We thank the participants and their families for their cooperation in this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSH has received the JSPS Research Fellowship for Young Scientists (DC1), and The Keio University Doctorate Student Grant-in-Aid Program from Ushioda Memorial Fund. FU has received grants from Discovery Fund, Nakatani Foundation, Canadian Institutes of Health Research (CIHR), and Brain \u0026amp; Behavior Research Foundation (BBRF); manuscript fees from Dainippon Sumitomo Pharma; and consultant fees from VeraSci, and Uchiyama Underwriting within the past three years. SN has received grants from Japan Society for the Promotion of Science (18H02755, 22H03002), Japan Agency for Medical Research and development (AMED: JP24wm0625302, JP24wm0625307), Japan Research Foundation for Clinical Pharmacology, Naito Foundation, Takeda Science Foundation, Watanabe Foundation, Osakeno-Kagaku Foundation, and Astellas Foundation within the past three years. SN has also received research support, manuscript fees or speaker\u0026rsquo;s honoraria from Asahi Quality \u0026amp; Innovations, Ltd., Teijin Pharma, Sumitomo Pharma, Meiji Seika Pharma, Otsuka, PDR pharma, and MSD within the past three years. YN has received Grants-in-Aid for Scientific Research (B) (18H02755; 20H04092; 21H02813) from the Japan Society for the Promotion of Science (JSPS), research grants (a) from Japan Agency for Medical Research and Development (AMED), investigator-initiated clinical study grants from TEIJIN PHARMA LIMITED (Tokyo, Japan) and Inter Reha Co., Ltd. (Tokyo, Japan). YN also receives research grants from Japan Health Foundation, Meiji Yasuda Mental Health Foundation, Mitsui Life Social Welfare Foundation, Takeda Science Foundation, SENSHIN Medical Research Foundation, Health Science Center Foundation, Mochida Memorial Foundation for Medical and Pharmaceutical Research, Taiju Life Social Welfare Foundation, and Daiichi Sankyo Scholarship Donation Program. YN has received speaker\u0026rsquo;s honoraria from Dainippon Sumitomo Pharma, MOCHIDA PHARMACEUTICAL CO., LTD. (Tokyo, Japan), and Yoshito-miyakuhin Corporation within the past three years. AG-G received the grants from Canadian Institutes of Health Research (MOP-141968, MOP 14249). AG-G also receives research grants from Ministry of Economic Development and Innovation Ontario, Ontario Mental Health Foundation Type A grant, and NARSAD Independent Investigator (AG-G). Other authors do not have any conflict of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKennedy JL, Altar CA, Taylor DL, Degtiar I, Hornberger JC. The social and economic burden of treatment-resistant schizophrenia: a systematic literature review. Int Clin Psychopharmacol. 2014;29:63\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eWada M, Noda Y, Iwata Y, Tsugawa S, Yoshida K, Tani H, et al. Dopaminergic dysfunction and excitatory/inhibitory imbalance in treatment-resistant schizophrenia and novel neuromodulatory treatment. Mol Psychiatry. 2022;27:2950\u0026ndash;2967.\u003c/li\u003e\n\u003cli\u003eAbi-Dargham A, Rodenhiser J, Printz D, Zea-Ponce Y, Gil R, Kegeles LS, et al. Increased baseline occupancy of D2 receptors by dopamine in schizophrenia. Proc Natl Acad Sci U S A. 2000;97:8104\u0026ndash;8109.\u003c/li\u003e\n\u003cli\u003eCaravaggio F, Borlido C, Wilson A, Graff-Guerrero A. 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Neuroscience. 2014;282:248\u0026ndash;257.\u003c/li\u003e\n\u003cli\u003eMatsuda W, Furuta T, Nakamura KC, Hioki H, Fujiyama F, Arai R, et al. Single nigrostriatal dopaminergic neurons form widely spread and highly dense axonal arborizations in the neostriatum. J Neurosci. 2009;29:444\u0026ndash;453.\u003c/li\u003e\n\u003cli\u003ePrensa L, Gim\u0026eacute;nez-Amaya JM, Parent A, Bern\u0026aacute;cer J, Cebri\u0026aacute;n C. The nigrostriatal pathway: axonal collateralization and compartmental specificity. J Neural Transm Suppl. 2009:49\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eNakajima S, Gerretsen P, Takeuchi H, Caravaggio F, Chow T, Le Foll B, et al. The potential role of dopamine D₃ receptor neurotransmission in cognition. Eur Neuropsychopharmacol. 2013;23:799\u0026ndash;813.\u003c/li\u003e\n\u003cli\u003eLakhani DA, Zhou X, Tao S, Patel V, Wen S, Okromelidze L, et al. Diagnostic utility of 7T neuromelanin imaging of the substantia nigra in Parkinson\u0026rsquo;s disease. NPJ Parkinsons Dis. 2024;10:13.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6323439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6323439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eApproximately 30% of patients with schizophrenia do not respond to antipsychotics. While schizophrenia has been primarily explained by the dopamine dysfunction hypothesis, treatment-resistant schizophrenia (TRS) may involve a different pathophysiology. Neuromelanin (NM), a product of dopamine metabolism in the substantia nigra (SN), indirectly measures long-term dopamine synthesis capacity. Few studies have examined SN NM levels in TRS. Therefore, we investigated the relationship between SN NM levels and treatment responsiveness in schizophrenia.\u003c/p\u003e \u003cp\u003eWe included age- and sex-matched TRS, patients with schizophrenia in remission of positive symptoms (SZ-R), and healthy controls (HCs). Neuromelanin-sensitive magnetic resonance imaging was used to measure SN NM signals. We also evaluated clinical symptoms and cognitive impairment. We conducted voxel-wise analyses of NM contrast-to-noise ratio (CNR) to compare groups pairwise. Correlation analyses examined relationships between NM signals and symptom severity.\u003c/p\u003e \u003cp\u003eSeventy-two participants (n\u0026thinsp;=\u0026thinsp;24 per group) completed the study. The TRS group had higher dorsal SN CNR than the HC group (510 out of 1948 voxels at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, corrected p\u0026thinsp;=\u0026thinsp;0.005, permutation test). In contrast, no significant differences were observed in the other comparisons. No significant correlations were found between NM CNR and clinical severity.\u003c/p\u003e \u003cp\u003eOur findings contrast with previous positron emission tomography studies on dorsal striatal dopamine function. Since the dorsal SN contributes to both the mesolimbic and nigrostriatal pathways, with a relatively greater role in the former, dopamine functions in these pathways may play different roles for treatment responsiveness. Further research with multimodal imaging is needed to examine dopamine function and antipsychotic treatment responsiveness in schizophrenia.\u003c/p\u003e","manuscriptTitle":"Associations of Neuromelanin in the Substantia Nigra with Antipsychotic Response in Schizophrenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-04 11:31:33","doi":"10.21203/rs.3.rs-6323439/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd3caed0-1a21-4e10-8154-b1bb4183d23b","owner":[],"postedDate":"April 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46516424,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":46516425,"name":"Health sciences/Diseases/Psychiatric disorders/Schizophrenia"}],"tags":[],"updatedAt":"2025-07-03T13:47:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-04 11:31:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6323439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6323439","identity":"rs-6323439","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}