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. 2024 Apr 3;42:103603. doi: 10.1016/j.nicl.2024.103603

Right superior frontal gyrus: A potential neuroimaging biomarker for predicting short-term efficacy in schizophrenia

Yongfeng Yang a,b,c,d,1, Xueyan Jin b,c,d,1, Yongjiang Xue i, Xue Li b,c,d,1, Yi Chen b,c,d, Ning Kang b,c,d, Wei Yan a, Peng Li a, Xiaoge Guo b,c,d, Binbin Luo b,c,d, Yan Zhang b,c,d, Qing Liu b,c,d, Han Shi b,c,d, Luwen Zhang b,c,d, Xi Su b,c,d, Bing Liu e,f, Lin Lu a,g,h, Luxian Lv b,c,d,⁎, Wenqiang Li b,c,d,⁎
PMCID: PMC11015154  PMID: 38588618

Highlights

  • •

    A prospective longitudinal study to examine structural and functional changes in schizophrenia.

  • •

    A relatively objective sample size and data utilization from a multi-dimensional perspective.

  • •

    The findings suggest that the right superior frontal gyrus (SFG) is a critical target of antipsychotic drugs.

  • •

    The right SFG could be used as potential neuroimaging biomarkers in predicting the treatment outcome of antipsychotics in schizophrenia.

Keywords: Schizophrenia, Functional connectivity, White matter density, Grey matter density, Before and after treatment

Abstract

Antipsychotic drug treatment for schizophrenia (SZ) can alter brain structure and function, but it is unclear if specific regional changes are associated with treatment outcome. Therefore, we examined the effects of antipsychotic drug treatment on regional grey matter (GM) density, white matter (WM) density, and functional connectivity (FC) as well as associations between regional changes and treatment efficacy. SZ patients (n = 163) and health controls (HCs) (n = 131) were examined by structural magnetic resonance imaging (sMRI) at baseline, and a subset of SZ patients (n = 77) were re-examined after 8 weeks of second-generation antipsychotic treatment to assess changes in regional GM and WM density. In addition, 88 SZ patients and 81 HCs were examined by resting-state functional MRI (rs-fMRI) at baseline and the patients were re-examined post-treatment to examine FC changes. The Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery (MCCB) were applied to measure psychiatric symptoms and cognitive impairments in SZ. SZ patients were then stratified into response and non-response groups according to PANSS score change (≥50 % decrease or <50 % decrease, respectively). The GM density of the right cingulate gyrus, WM density of the right superior frontal gyrus (SFG) plus 5 other WM tracts were reduced in the response group compared to the non-response group. The FC values between the right anterior cingulate and paracingulate gyrus and left thalamus were reduced in the entire SZ group (n = 88) after treatment, while FC between the right inferior temporal gyrus (ITG) and right medial superior frontal gyrus (SFGmed) was increased in the response group. There were no significant changes in regional FC among the non-response group after treatment and no correlations with symptom or cognition test scores. These findings suggest that the right SFG is a critical target of antipsychotic drugs and that WM density and FC alterations within this region could be used as potential indicators in predicting the treatment outcome of antipsychotics of SZ.

1. Introduction

Schizophrenia (SZ) is a severe chronic mental disorder with a lifetime prevalence of about 1 % (McCutcheon et al., 2020). Symptoms are classified into 5 domains, positive, negative, cognitive, depression and excitement which may vary markedly in prominence across individuals, suggesting diverse aetiologies (Marder et al., 2019, Richetto and Meyer, 2021). Indeed, current evidence suggests that SZ risk is associated with neurodevelopmental abnormalities and neurodegenerative changes (Singh et al., 2004) as well as multiple genetic and epigenetic factors (Jaaro-Peled and Sawa, 2020). Potential neurodevelopmental deficits in SZ include regional malformations and insufficient or ineffective communication between brain areas, so recent studies have attempted to identify differences in regional structure and functional connectivity (FC) utilizing specialized neuroimaging modalities such as voxel-based morphometry (VBM) and resting-state functional magnetic resonance imaging (rs-fMRI) (Kubicki and Shenton, 2014).

Numerous studies had identified differences in grey matter (GM) and white matter (WM) density compared to matched controls (Laskaris et al., 2016). For instance, VBM studies had identified GM density reductions across extensive brain regions as well as significant reductions in WM density in the genu and truncus of the corpus callosum (Hulshoff Pol et al., 2004, Robyn Honea et al., 2005) of chronic SZ patients. Further, changes in WM and GM density were found to be correlated with illness severity (Hulshoff Pol et al., 2004). Collectively, these findings indicate that the pathogenic processes underlying SZ disrupt a wide range of neural network functions during development, ultimately leading to symptom expression in late adolescence and early adulthood (Cao et al., 2016). Consistent with this notion, a few studies have recorded abnormal resting-state (rs) FC in SZ patients (Dietsche et al., 2017, Dong et al., 2018, Wang et al., 2014, Zhang et al., 2021). Antipsychotic drugs targeting dopamine D2 receptors are the first-line treatments for SZ, in addition to influencing behavior and brain function at the cellular level(Fusar-Poli et al., 2013), it is also speculated that long-term antipsychotic drug treatment can alter brain structure and FC (Fusar-Poli et al., 2013). We hypothesize that SZ has key brain region with both structural and functional change, and that is closely related to treatment. If so, such changes could provide valuable clues to disease or symptom etiology and biomarkers for treatment evaluation.

However, little is known about the effects of antipsychotics on brain tissue density or FC (Wang et al., 2017). Therefore, we conducted a prospective longitudinal study to examine the relationships between antipsychotic drug treatment response and changes in WM density, GM density, and FC.

2. Materials and methods

2.1. Participants

Patients with SZ were enrolled from the Second Affiliated Hospital of Xinxiang Medical University between April 2021 and January 2023, while health controls (HCs) were recruited from the communities surrounding Xinxiang City during the same period. Common inclusion criteria for all subjects were written informed consent, both biological parents of Han ethnicity, 18–55 years of age, and normal intelligence. Patient inclusion criteria were meeting the diagnostic criteria of SZ according to the structured clinical interview for DSM-IV-TR disorders and no electroconvulsive therapy for 3 months prior to enrollment. Common exclusion criteria were as follows: meeting the DSM-IV-TR diagnostic criteria for treatment-resistant SZ, alcohol and drug dependence, schizoaffective disorder, delirium, or dementia; uncontrolled physical diseases, including thyroid disease, diabetes, hypertension, and heart disease; paternal or maternal relatives within three generations meeting DSM-IV diagnostic criteria for Axis I disorders; history of epilepsy; current or past drug-induced malignant syndrome, including severe tardive dyskinesia; pregnancy or lactation; history of head injury associated with disturbance of consciousness for more than 5 min; MRI scanning contraindications.

For subgroup analysis of subjects in our study, they were divided into response and non-response groups according to the Positive and Negative Syndrome Scale (PANSS) reduction rate (RR) (Leucht et al., 2005), with patients showing a ≥50 % decrease in PANSS total score classified as response group (Jauhar et al., 2019), in contrast, was the non-response group.

2.2. Sample size calculation

The sample size was analyzed using the GPower software package(Faul et al., 2007), when using the independent samples t-test, the presence of a medium effect size (0.5) was preset and the statistical test power was preset to be 0.8 with a significance level of 0.05, which indicated that a minimum of 64 subjects were required in each group; when using the paired t-test, the presence of a medium effect size (0.5) was preset and the statistical test power was preset to be 0.8 with a significance level of 0.05, the results indicated that at least 34 subjects were needed.

2.3. Ethics

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The study protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Xinxiang Medical University (approval number XYEFYLL-2021-02).

2.4. Evaluation tools

PANSS was used to evaluate the severity of positive symptoms, negative symptoms, cognitive dysfunction, depression, and agitation (Kim et al., 2012). In addition, the MATRICS Consensus Cognitive Battery (MCCB) was used to assess six cognitive domains in SZ (Chen et al., 2020, Lai et al., 2022): attention/vigilance, information processing speed, verbal learning, visual learning, working memory, reasoning, and problem solving.

2.5. Data acquisition and processing

MRI data were obtained by a 3.0-T magnetic resonance scanner (Siemens, Verio, Germany) with a 12-channel closed head scanning coil. Before scanning, the participants were instructed to keep relax, remain still, and stay awake. Foam pads and earplugs were used to reduce head motion and scanner noise.

Following are structure data scanning parameters: T1-weighted three-dimension turbo field echo (T1W_3D_TFE) sequence sagittal scanning, repetition time = 2530 ms, echo time = 2.43 ms, inversion time = 1100 ms, layer thickness = 1 mm, no interval scanning, a total of 192 layers, matrix size = 256 × 256 × 192, voxel size = 1 × 1 × 1 mm, and flip angle = 7°. The collection time was 5 min and 53 s (192 time points). Resting-state fMRI data were acquired using an echo planar imaging (EPI) sequence sensitive to BOLD signal. The rs-fMRI scanning parameters were as follows: repetition time = 2000 ms, echo time = 30 ms, matrix size = 64 × 64, resolution of axial slice 3.4 × 3.4 mm2, gap between slices = 0.6 mm, flip angle = 90°, slice number = 33, slice thickness = 4 mm. Resting-state data were collected for 8 min 8 s (240 time points).

Grey and white matter density maps were obtained from T1-weighted magnetic resonance (MR) images of each subject using the Computational Anatomy Toolbox (CAT12, https://www.neuro.uni-jena.de/cat/) of Statistical Parametric Mapping (SPM, https://www.fil.ion.ucl.ac.uk/spm/). Density values were then extracted based on the 246 partitions of the Brainnetome atlas as regions of interest (ROIs) (Fan et al., 2016).

Resting-state fMRI data were processed using the RESTplus v1.24 toolkit (https://restfmri.net/forum/restplus). Participants with head motion exceeding 3 mm in translation or 3° in rotation were eliminated. A seed-based approach first proposed by Di Martino et al. was used to analyze FC (Chen et al., 2022). The ROIs were constructed as spheres of 6-mm radius with central coordinates corresponding to brain regions of the Anatomical Automatic Labeling (AAL) brain map showing significant results in GM/WM density analysis (Supplementary Table 1). The RESTplus v1.24 toolkit was then used to construct whole-brain FC maps.

2.6. GM/WM density analyses

A large MRI dataset acquired from 163 SZ patients and 131 HCs at baseline was used to examine disease-related changes in GM and WM density. Structural MRI data were also acquired from 77 of these patients after 8 weeks of treatment with a second-generation antipsychotic. These patients were then divided into response (n = 44) and non-response (n = 33) groups according to the RR of PANSS.

Regional grey and white matter density values at baseline were compared between SZ and HC groups and between response and non-response groups at post-treatment by independent samples t-tests. Individual years of education, sex, and age were treated as covariates in group comparisons. The Gaussian random field (GRF) theory was applied to correct for multiple comparisons and significance was set at p < 0.05 (GRF theory, voxel-level p < 0.001, cluster-level p < 0.05).

2.7. FC analysis

Eight-eight SZ patients and 81 HCs were also examined by rs-MRI. In the patient group, scans were acquired at baseline and after 8 weeks of treatment with a second-generation antipsychotic. Patients were again stratified into response (n = 47) and non-response groups (n = 41) depending on the RR of PANSS. Independent samples t-tests were applied to evaluate FC differences in SZ and HCs group at baseline, while paired-sample t-tests were used to compare FC after treatment using the same covariates and correction methods described for GM/WM density comparisons.

2.8. Analyses of demographic variables and clinical scale scores

Age and education duration were compared by independent-samples t-tests, while sex ratio was compared by Chi-square test. Partial correlation analyses were conducted between regional FC changes post-treatment and changes in PANSS and MCCB scores with age, sex, illness duration, and years of education as covariates. The False Discovery Rate (FDR) theory was applied to correct for the FC and clinical scale correlation analyses, and the significance level was set at p < 0.05. All statistical analyses were conducted using the Statistical Package for Social Science version 26.0 (SPSS 26.0).

3. Results

3.1. Analyses of demographic and clinical characteristics

The flowchart of the study subjects is shown in Fig. 1. The demographic and clinical characteristics of the 163 SZ patients and 131 HCs are summarized in Table 1. There were no significant differences in age or sex ratio between the total SZ and HC cohorts, while years of education was significantly lower in the patient cohort. There were no significant differences in age, sex ratio, and years of education between response and non-response SZ subgroups examined by sMRI (Table 2). The demographic and clinical characteristics between SZ patients and HCs examined by rs-MRI in Supplementary Table 2, or between response and non-response patients examined by rs-MRI in Supplementary Table 3. These latter datasets appeared in our previous article (Li et al., 2023). Antipsychotic drugs and medications taken for comorbid conditions among SZ patients examined using rs-MRI are also shown in Supplementary Table 2. Antipsychotic drug doses were converted to chlorpromazine equivalents (Andreasen et al., 2010).

Fig. 1.

Fig. 1

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The Overview of data processing and the results of the study. SZ, schizophrenia; HC, healthy controls; ROI, region of interest.

Table 1.

Demographic data and clinical characteristics of SZ patients and HCs.

Variables Level SZ (n=163) HC (n=131) t/χ2 p
Age(years) Mean (SD) 29.09 (7.59)85
(52.1 %)78
(47.9 %)11.05
(2.91)61.68
(66.76)		 28.92 (6.74) 0.199 0.727
Male n (%) 71 (54.2 %) 0.349 0.843
Female n (%) 60 (45.8 %)
Education(years) Mean (SD) 13.10 (3.69) −5.325 0.000
Duration(months) Mean (SD) – – –
PANSS Baseline scores Mean (SD) 8.38
(15.51)13.60
(3.64)24.14
(7.54)19.64
(4.28)8.04
(3.36)7.45
(5.12)
Total – – –
Positive – – –
Negative – – –
Cognitive
Depression
Excitement – – –
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Note: SZ, Schizophrenia; HCs, Healthy Controls; SD, Standard Deviation; -, not applicable; PANSS, Positive and Negative Syndrome Scale.

Table 2.

Demographic data and clinical characteristics of the SZ response and non-response group (n = 77).

Variables Level response group (n=44) non-response group (n=33) t/χ2 p
Age(years) Mean (SD) 31.11 (8.98)23
(52.3 %)
21 (47.7 %)10.70
(3.08)77.64
(64.79)		 28.85 (8.62) 1.114 0.269
Male n (%) 19 (57.6 %) 0.214 0.817
Female n (%) 14 (42.4 %)
Education(years) Mean (SD) 11.00 (3.03) −0.419 0.676
Duration(months) Mean (SD) 82.95 (83.18) −0.315 0.754



PANSS scores Mean (SD) Baseline 8 Week Baseline 8 Week
Total 94.82 (16.36) 50.64 (10.53) 93.48 (14.71) 71.09 (10.96) **
Positive 13.84 (4.49) 5.77 (2.55) 12.48 (3.79) 8.79 (3.31) **
Negative 27.73 (8.73) 15.98 (5.3) 26.85 (5.81) 22 (5.12) **
Cognitive 19.98 (5.37) 11.61 (2.25) 19.27 (6.46) 16.64 (3.48) **
Depression 20.05 (4.55) 4.82 (1.4) 20.67 (5.21) 6.21 (2.87) **
Excitement 8.32 (3.63) 8.82 (2.45) 8.55 (3.74) 12.97 (3.98) **



MCCB scores Mean (SD)
TMT 77.72 (47.09) 56.46 (28.80) 64.50 (33.40) 50.69 (26.28)
BACS-SC 34.59 (12.43) 36.36 (13.49) 40.61 (12.60) 39.31 (14.20)
HVLT-R 15.41 (5.78) 18.95 (5.48) 17.34 (7.10) 21.52 (7.87)
WMS-SS 11.13 (2.76) 11.38 (3.33) 12.00 (4.02) 13.17 (3.41) *
Mazes 7.62 (6.29) 9.26 (6.53) 9.59 (5.15) 10.21 (6.24)
BVMT-R 15.43 (8.58) 21.21 (10.26) 19.07 (10.27) 21.76 (12.13)
CFAN 13.21 (6.33) 15.59 (5.33) 17.00 (5.32) * 18.45 (8.26)
CPT-IP 1.09 (0.77) 1.16 (0.84) 1.41 (0.99) 1.33 (0.79)
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Note: SZ, Schizophrenia; SD, Standard Deviation; *:p < 0. 05; **:p < 0.001, PANSS, Positive and Negative Syndrome Scale; MCCB, MATRICS Consensus Cognitive Battery;TMT, Trail Making Test; BACS-SC, Brief Assessment of Cognition in Schizophrenia, Symbol Coding; HVLT-R, The revised Hopkins Verbal Learning Test; WMS-SS, Wechsler Memory Scale, Third Edition, Spatial Span; BVMT-R, The revised Brief Visuospatial Memory Test; CFAN, Category Fluency Test, Animal naming; CPT-IP, The Continuous Performance Test Identical Pairs.

3.2. Differences in baseline GM and WM densities between SZ patients and HCs

A comparison of whole-brain GM and WM density maps between the 163 SZ patients and 131 HCs at baseline revealed 38 ROIs with lower GM density and 32 with lower WM density in SZ patients (Supplementary Tables 4 and 5). Regions exhibiting reductions included the inferior frontal gyrus, orbital gyrus, inferior temporal gyrus, parahippocampal gyrus, cingulate, medioventral occipital cortex, lateral occipital cortex, basal ganglia, and thalamus.

3.3. Changes in GM and WM density following antipsychotic drug treatment

Grey matter density was significantly decreased in the right cingulate gyrus (CG) following treatment in response group compared to the non-response group (ROI 178, p = 0.020) (Fig. 2A, Supplementary Table 6) while WM was significantly decreased in six region post-treatments: right superior frontal gyrus (SFG, ROI 008, p = 0.031), right middle frontal gyrus (MFG, ROI 026, p = 0.016), left superior temporal gyrus (STG, ROI 077, p = 0.026), right middle temporal gyrus (MTG, ROI 084, p = 0.041), right inferior temporal gyrus (ITG, ROI 096, p = 0.045), and right fusiform gyrus (FuG, ROI 107, p = 0.038) (Fig. 2B, Supplementary Table 6).

Fig. 2.

Fig. 2

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GM and WM density Changes before and after treatment in the SZ response and non-response group. A, GM density Changes before and after treatment in the SZ response and non-response group; B, WM density Changes before and after treatment in the SZ response and non-response group. Cool color indicates that density values was decreased. GM, gray matter; WM, white matter; SZ, schizophrenia; CG, Cingulate Gyrus; SFG, Superior Frontal Gyrus; MFG, Middle Frontal Gyrus; STG, Superior Temporal Gyrus; MTG, Middle Temporal Gyrus; ITG, Inferior Temporal Gyrus; FuG, Fusiform Gyrus; L, left; R, right.

3.4. Differences in regional FC between SZ patients and HCs

The FC map of patients at baseline included both pathways stronger than in HCs and weaker than in HCs (Fig. 3) (GRF corrected, p < 0.05) (Supplementary Table 7). FC values from right dorsolateral SFG (SFGdor) to the inferior region of the cerebellum, middle frontal gyrus, inferior frontal gyrus, and paracingulate gyrus were significantly greater compared to HCs, while FC values from the right SFGdor to parahippocampal gyrus and precuneus were significantly reduced compared to HCs.

Fig. 3.

Fig. 3

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FC Changes between the SZ patients and HCs. Warm color indicates that FC was higher in SZ patients than in HCs, and vice versa. SZ, schizophrenia; FC, functional connectivity; HCs, healthy controls, L, left; R, right.

3.5. Changes in FC following antipsychotic drug treatment

After antipsychotic treatment for 8 weeks, the FC values between the right anterior cingulate and anterior cingulate gyrus and left thalamus (THA) were reduced in the entire SZ group (n = 88) (GRF corrected, p < 0.05, t = -5.477) (Fig. 4A, Supplementary Table 8), while the FC between the ITG and the right medial SFG (SFGmed) was strengthened in the response group (GRF corrected, p < 0.05, t = 5.238) (Fig. 4B, Supplementary Table 8). In contrast, there were no significant FC changes after treatment in the non-response group.

Fig. 4.

Fig. 4

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FC Changes before and after treatment in the SZ patients. A, Significant differences in FC before and after treatment in the entire SZ group (n = 88); B, Significant differences in FC before and after treatment in the SZ response group. Warm color indicates that FC was increased, and vice versa. SZ, schizophrenia; FC, functional connectivity; L, left; R, right.

3.6. Correlation analysis of FC with MCCB and PANSS

Partial correlation analyses with age, sex, illness duration, and years of education as covariates yielded no significant relationships between clinical scores and individual FC changes (FDR corrected, p < 0.05) (Supplementary Table 9).

4. Discussion

This prospective longitudinal study with a relatively objective sample size and data utilization from a multi-dimensional perspective to examine structural and functional changes in SZ. More specifically, a comparison of changes in regional GM density, WM density, and FC between antipsychotic drug responsive and non-responsive SZ groups revealed associations between drug efficacy and both reduced WM density in right SFG and increased FC between right ITG and right SFGmed. Thus, structural and functional changes in the right SFG may be related to the curative effects of antipsychotic drugs for SZ. These changes in right SFG after drug treatment are consistent with the changes in neural activity reported in our previous study (Li et al., 2023), further supporting the SFG as an essential target of antipsychotic drugs and a potential predictive marker for therapeutic efficacy.

The reductions in baseline GM and WM density reported previously among first-episode SZ patients were confirmed by our sMRI analyses. Many such studies have reported more extensive reductions in GM density than WM density (Yin et al., 2021), including within the superior temporal lobe, medial temporal lobe, inferior frontal lobe, and medial frontal cortex (Leung et al., 2011, Robyn Honea et al., 2005, Smieskova et al., 2010). These differences may be related to disease stage or duration. For instance, Chan and colleagues found more extensive GM abnormalities in chronic SZ patients than first-episode SZ patients (Chan et al., 2011). Similarly, a 5-year longitudinal study of 96 patients by van Haren and coworkers found progressively reduced GM density in the left superior frontal gyrus, left superior temporal gyrus, right caudate nucleus, and right thalamus (Van Haren et al., 2007). Further, these changes were associated with the number of episodes. The less widespread GM atrophy observed in the present study may reflect the relatively brief treatment period. Nonetheless, our results suggest that antipsychotic drug treatment may also contribute to GM density changes during disease progression.

Accumulating evidence also implicates WM abnormalities in SZ (Cocchi et al., 2009). A meta-analysis of 17 VBM studies concluded that SZ is associated with WM reductions primarily in four brain regions, including bilateral frontal lobes and bilateral internal bursae. Subjects with SZ also exhibited reduced WM density in the anterior limb of the internal capsule compared to unaffected relatives (McIntosh et al., 2005). In addition, reduced frontal lobe WM density has been found among healthy individuals with a family history of SZ, suggesting that these abnormalities are a prerequisite for disease onset (McIntosh et al., 2005). Consistent with these studies on SZ patients, we found substantial changes in frontal WM density.

Disruptions in FC have also been reported in SZ, especially between the frontal lobe and cerebellum, parietal lobe, and occipital lobe (Alex Fornito et al., 2011, Li et al., 2012). In the current study, there were significant FC changes in multiple brain regions at baseline. Consistent with our results, decreased FC has been reported between the left SFG, right hippocampus, right parahippocampal gyrus, left thalamus, left caudate, insula, and right superior parietal lobule, and increased FC between the left SFG and right middle frontal gyrus in first-episode untreated SZ (Qiu et al., 2021). Furthermore, SZ patients with auditory hallucinations exhibited increased FC between dorsal medial prefrontal and insular cortex, and decreased FC between ventromedial prefrontal and cingulate cortex (Alonso-Solis et al., 2015). In addition to local FC disruption, abnormalities within and between brain networks have been reported (Woodward et al., 2011), which may be probed in our future research. Furthermore, we found that these differences in FC after drug treatment were associated with drug response, as only the responsive group exhibited reduced FC with the right SFG. These FC changes are associated with changes in neural activity patterns (Zhao et al., 2018). For instance, our previous study revealed abnormal functional activities in a wide range of brain regions and increased regional brain activity in right SFGmed associated with the curative effect of drugs, especially negative symptoms improvement (Li et al., 2023). Moreover, a previous study found that the most significant FC changes were associated with negative symptom severity (Nawaz et al., 2021). In the current study, however, no such associations were found with specific symptom improvement, possibly because we did not stratify participants by disease stage (first episode or relapsed) or by duration. Nonetheless, few studies have examined changes in brain FC using a longitudinal design, so we can make a strong case for short-term changes caused by drug treatment. In both of our studies, the right SFG appears to be a reliable predictor of antipsychotic drug efficacy in SZ, highlighting the potential importance of this region in the pathophysiology of SZ.

There was a study suggest that there were neurochemical metabolite abnormalities in the prefrontal cortex in early onset SZ patients, atypical antipsychotic treatment can effectively relieve the symptoms and restore the reduced N-acetylaspartate in the prefrontal cortex (Gan et al., 2014). Miranda et al. found that postsynaptic alpha-adrenergic receptor density was enhanced in the dorsolateral prefrontal cortex of schizophrenic subjects treated with antipsychotics (Brocos-Mosquera et al., 2021). From this, we could speculate that antipsychotic drugs may act by altering chemical neurotransmitters in the SFG cortical region in relation to treatment response.

This study has several limitations. First, the drug treatment course may have been too brief (8 weeks) for detectable changes in GM density, WM density, or FC. Second, this was a real-world study that did not strictly limit the specific drugs used or dosages, so distinct mechanisms of action may confound the results, particularly associations between tissue density or FC changes and symptoms. Finally, as mentioned, subjects were not stratified according to disease stage or duration.

5. Conclusion

We report that relatively short-term antipsychotic drug treatment can differentially alter the structure and function of the right SFG depending on treatment response, indicating that this region is a key treatment target for SZ and contributor to SZ pathogenesis as well as a biomarker for antipsychotic drug efficacy.

Funding

This work was supported by the National Natural Science Foundation of China (81971252, U22A20304 to LL; U1904130 and 82171498 to WL), Major Science and Technology Projects of Henan Province (201300310200 to WY), Natural Science Foundation of Henan (232300421190 to YY), Medical Science and Technology Research Project of Henan Province (LHGJ20210541 to XW).

CRediT authorship contribution statement

Yongfeng Yang: Writing – original draft, Funding acquisition. Xueyan Jin: Software, Data curation. Yongjiang Xue: Software. Xue Li: Visualization. Yi Chen: Validation. Ning Kang: Validation. Wei Yan: Data curation. Peng Li: Data curation. Xiaoge Guo: Conceptualization. Binbin Luo: Conceptualization. Yan Zhang: Supervision. Qing Liu: Validation. Han Shi: Conceptualization. Luwen Zhang: Formal analysis. Xi Su: Investigation. Bing Liu: Validation. Lin Lu: Validation. Luxian Lv: Writing – review & editing, Resources. Wenqiang Li: Writing – review & editing, Resources.

Acknowledgments

The authors acknowledge the participation of patients, their family members, and healthy volunteers, as well as the clinical doctors and technicians of the Second Affiliated Hospital of Xinxiang Medical University who helped us to collect clinical data. We also showing great gratitude to the International Science Editing (http://www.internationalscienceediting.com) for editing our manuscript.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2024.103603.

Contributor Information

Luxian Lv, Email: lvx928@126.com.

Wenqiang Li, Email: lwq781603@163.com.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary data 1
mmc1.docx (15.2KB, docx)
Supplementary data 2
mmc2.docx (24.1KB, docx)
Supplementary data 3
mmc3.docx (24.7KB, docx)
Supplementary data 4
mmc4.docx (18.6KB, docx)
Supplementary data 5
mmc5.docx (17.9KB, docx)
Supplementary data 6
mmc6.docx (13.2KB, docx)
Supplementary data 7
mmc7.docx (16KB, docx)
Supplementary data 8
mmc8.docx (13.3KB, docx)
Supplementary data 9
mmc9.docx (19.4KB, docx)

Data availability

Data will be made available on request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data 1
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Supplementary data 2
mmc2.docx (24.1KB, docx)
Supplementary data 3
mmc3.docx (24.7KB, docx)
Supplementary data 4
mmc4.docx (18.6KB, docx)
Supplementary data 5
mmc5.docx (17.9KB, docx)
Supplementary data 6
mmc6.docx (13.2KB, docx)
Supplementary data 7
mmc7.docx (16KB, docx)
Supplementary data 8
mmc8.docx (13.3KB, docx)
Supplementary data 9
mmc9.docx (19.4KB, docx)

Data Availability Statement

Data will be made available on request.

Articles from NeuroImage : Clinical are provided here courtesy of Elsevier

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