Decreased Broca-Left Supplementary Motor Area Connectivity underlying Auditory Verbal Hallucination: A Resting-State NIRS Study

Decreased Broca-Left Supplementary Motor Area Connectivity underlying Auditory Verbal Hallucination: A Resting-State NIRS Study | 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 Decreased Broca-Left Supplementary Motor Area Connectivity underlying Auditory Verbal Hallucination: A Resting-State NIRS Study Wentian Dong, Zetao HUANG, Yingding Ma, Jiuju Wang, Yanping Song, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7173755/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Translational Psychiatry → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Despite decades of research, the underlying mechanism of auditory verbal hallucinations (AVH), a core symptom of schizophrenia, is still unrevealed. Previous studies have tried to capture the neural activity during AVH episodes while the trait features of AVH were less investigated. To address this gap, we employed a resting-state functional Near Infrared Spectroscopy (fNIRS) to investigate the neuroimaging patterns in schizophrenia patients with AVH history (AVHh+). We hypothesized that significant differences of network activity modality may be observed in AVHh+. Method: We recruited 23 AVHh+, 16 schizophrenia patients without AVH history (AVHh-), and 17 matched healthy controls (HCs). Participants underwent an 8-minute resting-state fNIRS scanning. Data processing and analysis were conducted by the NirSpark software (HuiChuang, China) package and R Studio. Result: A significant lower bilateral functional connectivity (FC) strength in a range of frontal-temporal regions was observed in schizophrenic patients. Compared to the AVHh- group, the AVHh+ group showed significantly lower FC in the Broca's area and the left supplementary motor area (SMA). Conclusion: Schizophrenia demonstrated a widespread reduced FC in frontal and temporal regions. The hypoconnectivity of Broca-left SMA circuit might serve as a trait marker specific to AVH. Health sciences/Diseases/Psychiatric disorders/Schizophrenia Health sciences/Biomarkers/Diagnostic markers Figures Figure 1 Figure 2 Figure 3 1. Introduction Schizophrenia remains a major psychiatric disorder affecting approximately 1% of the global population, despite decades of extensive research. It is characterized by severe disturbances in cognition, perception, and emotion (Charlson et al., 2018 ; Ha et al., 2013 ; Y et al., 2019 ). Among its core symptoms, auditory verbal hallucination (AVH) is a hallmark feature, with 60–80% of patients experiencing the sensation of "hearing voices" at some point in their illness (Andreasen & Flaum, 1991 ; Sartorius et al., 1972 ; Schneider, 1957 ). These voices are typically perceived as originating from distinct external agents, are often negative or derogatory in content, and are associated with high levels of distress (Baumeister et al., 2017 ; de Leede-Smith & Barkus, 2013 ). Indeed, Persistent AVHs significantly impair cognitive functions, elevate the risk of self-harm, and reduce quality of life (F.Y. Chen et al., 2023 ; Han et al., 2023 ). Although numerous theoretical models have been proposed to explain AVH, its neurobiological mechanisms remain incompletely understood. Among these models, the inner speech monitoring deficit hypothesis has garnered substantial support (Friston & Frith, 1995 ; Frith, 2005 ; Alderson-Day & Fernyhough, 2015 ). Defined as silent, self-directed verbal thought, inner speech plays a critical role in higher-order cognition task, self-awareness, and mental planning (Alderson-Day & Fernyhough, 2015 ; Langland-Hassan, 2021 ). Neuroimaging studies have identified a complex network underpinning inner speech, involving the left inferior frontal gyrus (Broca’s area), supplementary motor area (SMA), dorsolateral prefrontal cortex (DLPFC), superior temporal gyrus (STG), supramarginal gyrus, temporoparietal junction (TPJ), insular cortex, and the cerebellum (McGuire et al., 1995 ; McGuire, Silbersweig, Murray, et al., 1996 ; McGuire, Silbersweig, Wright, et al., 1996 ; Shergill et al., 2001 ). Early work by McGuire and Shergill et al. reported increased blood flow in Broca’s area during AVH episodes (McGuire et al., 1993 ; Shergill et al., 2000 ), implying that AVH and inner speech might share the similar neural basis. Later on, meta-analyses by Jardri et al. further demonstrated that the left IFG, insula, bilateral STG, bilateral medial temporal gyrus (MTG), Broca’s area, and left parahippocampal gyrus are consistently hyperactivated during active hallucinations (Jardri et al., 2011 ), which are overlapped with the regions associated with inner speech. Another report from Ford et al. demonstrated that during auditory verbal hallucinations, the left primary auditory cortex shows reduced responsiveness to external auditory probes, suggesting a competition between internally generated verbal content and external stimuli. To date, the findings accumulated support the view that AVHs may result from a failure in monitoring internally generated verbal content (Barber et al., 2021 ). Despite growing evidence on regional activations associated with AVH in diverse tasks, the FC in resting state has received far less attention . However, disruptions in connectivity, especially at resting state, may reflect more trait-like features of AVH (Alderson-Day et al., 2015 ; Northoff, 2014 ). Previous studies have reported aberrant FC changes between distributed frontal-temporal regions at resting state (Panikratova et al., 2023 ; Marino et al., 2022 ; Clos et al., 2014 ), but results are mixed for several confusing factors such as sample selected (duration of illness, sex, drug dose, etc.), study design and statistics analysis. On the other hand, existing researches are more focused on the dynamic neural activity during AVH episodes, dedicated to capture the temporal changes when AVH appear and to reveal the status-biomarkers of AVH (Jardri et al., 2013 ; Shergill et al., 2000 ). Noteworthy, as Kühn and his colleagues stated, the pathology of AVH is probably more than the overactivation of language and auditory related network, but also involves the imbalance of perception and monitoring related regions (Kühn & Gallinat, 2012 ). The vulnerability of AVH may vary across schizophrenia patients, which denotes that FC may differ significantly between schizophrenia patients with and without AVH history ever. The distinction, if can be located, could imply the underlying mechanism of AVH. To address this gap, we hypothesized that the functional coupling of brain regions associated with inner speech would be significantly altered in AVHh + patients compared to those without an AVH history (AVHh−) and healthy controls, even if during non-hallucinatory periods. To test this, we employed functional near-infrared spectroscopy (fNIRS) , a noninvasive imaging technique well-suited for detecting cortical hemodynamics under naturalistic or low-burden conditions. Unlike fMRI or EEG, fNIRS offers quiet operation, greater portability, and superior motion tolerance, making it particularly advantageous for studies involving psychotic populations (Ferrari & Quaresima, 2012 ; Kim et al., 2017 ; Wang et al., 2017 ). In recent years, fNIRS has gained recognition as a feasible tool for mapping resting-state brain networks (Husain et al., 2023 ; Zhang et al., 2021 ). By applying resting-state fNIRS, we aimed to identify specific patterns that underlies AVH vulnerability in schizophrenia. 2. Methods and materials 2.1 Subjects and experimental procedure In total, thirty-nine schizophrenia patients were recruited from the in-patient ward of Peking University Sixth Hospital (Beijing, China) and met the following criteria : 1) diagnosed with schizophrenia/schizoaffective disorder conducted by an experienced psychiatrist, which were based on the comprehensive interviews of the Structured Clinical Interview of the DSM-IV (SCID); 2) Han Chinese in origin; 3) right-handed; 4) between 18 and 65 years old. Detailed information regarding past symptomatology were acquired in patient interviews and examination of the patients' medical records, according to which these patients were further divided into two subgroups [n(AVHh+) vs. n(AVHh-) = 23:16]. Exclusion criteria were as follows : 1) a history of other psychotic disorders; 2) substance abuse or dependence; 3) severe medical disorders; 4) traumatic brain injury; 5) electroconvulsive therapy within the past 6 months; 6) intellectual disability or neurological impairment. 7) other factors against fNIRS scanning, such as the patients who were too agitated to allow evaluation. Seventeen healthy controls were enrolled from neighboring communities, which meet the following criteria: 1) had no history of neurological mental or physical diseases; 2) right-handed; 3) between 18 and 65 years old. All subjects provided written informed consent to participate following the review of a complete study description. All experiments were performed in accordance with relevant guidelines and regulations of the Ethics Committee of Peking University Sixth Hospital, which abide with the Helsinki Declaration. A resting-state fNIRS scanning was lasted for at least 8 min in a dimly lit, isolated room. During the recording, the participants were instructed to sit still and focus on a fixation cross on the computer without falling asleep. Such resting-state recording did not require overt perceptual input or behavioral output. 2.2 NIRS data acquisition In this experiment, the NirSmart-6000A equipment (Danyang Huichuang Medical Equipment Co., Ltd., China) was used to continuously measure and record the concentration changes of brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) during the resting state. The system consists of a near-infrared light source (light emitting diodes, LED) and an avalanche photodiode (APD) as detectors, the sampling frequency was 11 Hz; 730 nm and 850 nm were the major wavelengths. The experiment used 21 emitters and 16 detectors to form 54 channels (Fig. 1), equally distributed on the bilateral hemispheres. According to the obtained spatial coordinates, these channels were displayed (See supplementary 1).The average distance between the emitter and the detector is 3 cm (range 2.7–3.3 cm), with reference to the international 10/20 system for positioning. 2.3 NIRS data analysis We used the NirSpark software package (Liu et al., 2022 ) to analyze NIRS data. Data were preprocessed via the following steps. The motion artifact interferences irrelevant to the experimental data were eliminated. A bandpass filter with cut-off frequencies of 0.01–0.20 Hz was used for resting state data and a 0.2 Hz low-pass filter was used for resting-state data to remove physiological noise (e.g., respiration, cardiac activity, and low-frequency signal drift). The modified Beer-Lambert law was used to convert optical densities into changes in the HbO and HbR concentrations (Niu et al., 2012 ). Then we extracted 7 min stable hemoglobin time series for each participant. Motion artifacts were corrected by a moving SD and a cubic spline interpolation method. Once the noise components were identified, the concentration signal was reconstructed with these particular components eliminated from the original hemoglobin time course. The filtered concentration signal was used for further analysis. In this study, we used oxy-hemoglobin signal to present the following results because the HbO signal generally has a better signal-to-noise ratio than the HbR signal (Strangman et al., 2002 ). 2.4 Statistical analysis For each participant, FC analysis on the changes in HbO concentration between channels was calculated by conducting Pearson correlation analysis was performed on the HbO values obtained by sampling to get the correlation coefficient between channels. This procedure generated a 54 × 54 correlation matrix for each participant. The formula is as follows: $$\:{R}_{x,y}=\frac{cov(X,\:Y)}{{\sigma\:}_{X}\:{\sigma\:}_{Y}}=\frac{E\left(XY\right)-E\left(X\right)E\left(Y\right)}{\sqrt{E\left({X}^{2}\right)-{E}^{2}\left(X\right)}\sqrt{E\left({Y}^{2}\right)-{E}^{2}\left(Y\right)}}$$ Where cov (x, y) represents the covariance of X and Y; E (x) and E (y) represent the mean values of X and Y; and σ X and σ Y represent the standard deviations of X and Y. Of note, these correlation coefficients (r) were normalized to z-values with Fisher’s r-to-z transformation, and the formula is as follows: $$\:z=\frac{1}{2}\text{ln}\frac{1+r}{1-r}={\text{tanh}}^{-1}r$$ Based on prior studies investigating the pathology basis of AVH, we defined regions of interest (ROIs) by averaging the amplitudes of the included channels (see Table 1). All statistical analyses and data visualizations were performed using R version 4.5.0 ( Team, 2016 ) . The following packages were used: ggplot2, tidyverse, readxl, igraph and brainconn. One-way analyses of variance (ANOVAs) were conducted for every ROI, with p < 0.05 being the significance threshold for group comparisons. Post hoc S-N-K t -tests were then used to assess sources of differences among groups in one-way ANOVAs. Statistical significance was set as corrected p below 0.05, and multiple comparisons were adjusted using the Benjamini-Hochberg false discovery rate (FDR) correction (Benjamini & Hochberg, 1995 ). Demographic information was compared among the three groups using ANOVAs for continuous variables, and categorical variables were compared using the chi-square test; Clinical characteristics between patient groups were analysed (Chou et al., 2021) using two-sample t -test. In cases where the assumption of normality was violated (tested for normal distribution using Kolmogorov-Smirnov test), we used two-tailed Mann-Whitney U-tests. 3. Result 3.1 Demographic and clinical characteristics There were no significant differences observed in age (F = 0.40, P = 0.675), education level (F = 0.98, P = 0.384), or gender (χ²=2.75, P = 0.253) in three groups, suggesting they are statistically matched. Additionally, there were no significant differences between the AVHh + and AVHh- group participants in terms of illness duration (t(32.32) = 0.10, P = 0.92, 95% CI [–6.66, 7.38]) or antipsychotic dosage measured in chlorpromazine equivalents (t(31.36) = 0.69, P = 0.49, 95% CI [–135.17, 274.47]), suggesting that the two patient groups were also comparable in clinical variables (see Table 2). 3.2 Decreased Functional Connectivity in Patients with Schizophrenia No statistically significant group difference was observed in average FC strength between the HCs and patients (t (18.77) = 1.04, p = 0.31, 95% CI [–0.12, 0.34]) [Fig. 2(a)]. Quantitatively, the mean values of FC strength and its standard deviations were 0.43 ± 0.12 for HC group, 0.39 ± 0.10 for AVHh- group, and 0.36 ± 0.10 for AVHh + group [Fig. 2(b)]. The difference of average FC strength was also insignificant between AVHh + and AVHh- group (t (33.05) = -0.09, p = 0.93, 95% CI [–0.23, 0.21]), but the number of FC strength ranged (0.2, 0.3] was much larger in AVHh + group compared to that in AVHh- group [Fig. 2(c)]. Additionally, the three groups discriminate themselves from a range of frontal-temporal regions in the brain area level [e.g. frontopolar area and right subcentral area, left Wernicke’s area and right subcentral area, Fig. 2(d)]. 3.3 Broca area-left SMA FC decline in AVHh + group In the comparison of FC strength between AVHh + and AVHh- groups, a significant difference was observed specifically in the connectivity between Broca’s area and the left SMA (Fig. 3), with the difference reaching statistical significance after FDR correction (p = 0.0435, p ≤ 0.05). Notably, none of other examined FCs (including Broca’s area–left STG, Broca’s area–Wernicke’s area, left STG–Wernicke’s area, and left SMA–left STG) showed significant differences (all FDR corrected p > 0.83), indicating the specificity of the Broca’s area–left SMA circuit in relation to the presence of AVH symptoms in schizophrenia. 4. Discussion In this study we tried to investigate the trait feature of AVH in schizophrenia by the means of resting-state fNIRS, and we found that: 1) a diffuse reduction of FC across frontal–temporal regions in patients with schizophrenia relative to HCs. Specifically, the FC strength of three groups saw a significant difference in a range of region pairs; 2) Crucially, the AVHh + group showed significantly lower FC strength between Broca’s area and the left SMA compared to the AVHh- group. The main finding that schizophrenia demonstrate a distributed decline in frontal-temporal region is consistent with the disconnection hypothesis of schizophrenia (Friston & Frith, 1995 ). Such widespread hypoconnectivity aligns with prior evidence that schizophrenia involves network-level dysconnectivity across multiple brain systems (Hoffman & Hampson, 2012 ; Li et al., 2019 ). Noteworthy, recent meta-analysis also pointed out that the vulnerability of AVH in schizophrenia is associated with FC across bilateral Broca’s area, MTG and caudate nucleus, which is also in line with our finding on the whole (Mo et al., 2024 ). Our crucial finding, the reduced resting-state functional connectivity between Broca’s area and the left SMA in schizophrenia patients with AVHh+, adds to a growing body of literature examining dysconnectivity in language-related networks in AVH. Broca’s area and SMA are key nodes in the generation and monitoring of inner speech (Alderson-Day & Fernyhough, 2015 ; Langland-Hassan, 2021 ). Prior work has observed the reduced SMA response in AVH compared with deliberate imagining alien speech (Shergill et al., 2001 ), and in verbal imagery in schizophrenic patients with AVH compared with healthy controls and schizophrenic patients without AVH (McGuire, Silbersweig, Wright, et al., 1996 ). Therefore, our result that the decline of FC strength between the left SMA and Broca’s area is generally in accordance with these previous studies, suggesting a dysfunction of inner speech generation and monitoring. On the other hand, our result can be contrasted with that of Clos et al. ( 2014 ), who examined resting-state fMRI connectivity in psychotic patients with frequent AVHs (Clos et al., 2014 ). Intriguingly, Clos and colleagues reported an increase in connectivity between Broca’s area and the SMA in psychotic patients with AVH relative to HCs. They interpretated that the reduction of SMA activation was observed in the given tasks, while they have observed the increased coupling between the left SMA and Broca’s region in resting state, underlying an increased generation of inner speech in psychotic patients. The discrepancy maybe caused by differences in subjects selection, as patients in Clos et al.’s study are hallucinated several times a day for at least one year, and they compared a AVH group with healthy controls, without including a non-AVH schizophrenia group, so the results may be confounded by differences between schizophrenia patients and healthy individuals. In contrast, our study compared AVH and non-AVH groups, thereby more cleanly revealing trait markers specific to AVH. A comprehensive review of functional and anatomical connectivity studies in AVH conducted by Curčić-Blake et al. (2017) concluded that, aberrant coupling within the broader language–auditory–memory network is a hallmark of hallucination-prone patients (Ćurčić-Blake et al., 2017 ). Our current result is in line with this general framework, underscoring that trait-like connectivity disruptions extend beyond the classic fronto-temporal loop to include frontal speech-motor integration circuits. Specifically, whereas Curčić-Blake et al. highlighted ongoing debates about fronto-temporal disconnection in schizophrenia, our finding pinpoints that a weakened Broca–SMA coupling could represent a stable marker of AVH vulnerability. This complements prior observations of fronto-temporal dysconnectivity by suggesting that the speech generation network itself may be fundamentally impaired in those susceptible to hallucinations. Our results also complement findings by Storchak et al. ( 2021 ), who showed that reduced activation in motor-related regions correlates with higher hallucination proneness in non-clinical populations (Storchak et al., 2021 ). They emphasized deficits in monitoring rather than generating inner speech, implicating the SMA as crucial for self-attribution processes. This supports our interpretation that diminished Broca–SMA connectivity could impair the brain’s ability to correctly label internally generated speech as self-originated, contributing to the misattribution characteristic of AVH. Several limitations temper the interpretation of our findings. First, the cross-sectional design prevents us from determining causality or temporal precedence – we cannot ascertain whether Broca–SMA disconnection is an antecedent risk factor for AVH or a consequence of experiencing recurrent hallucinations. Prospective longitudinal studies (e.g. following patients from early illness or prodromal stages) are warranted to address this question. Second, although our patient groups were matched on demographics and clinical variables like illness duration, we did not fully control for antipsychotic medication effects. Future research should attempt to replicate these results in medication-naïve or first-episode patients. Third, the sample size, while comparable to similar fNIRS studies, was modest. Additionally, our fNIRS optode coverage focused on frontal regions, which constrained analysis to select cortical networks; combining fNIRS with other modalities or expanding coverage could reveal connectivity with auditory cortex or deeper structures implicated in AVH. Despite these caveats, our results provide a coherent picture that AVH in schizophrenia are associated with aberrant connectivity in an inner speech generation-monitoring network. 5. Conclusion In conclusion, the reduced Broca–SMA connectivity observed in AVHh + patients may represent a neurophysiological mechanism underlying the misattribution of inner speech and a potential trait biomarker of AVH predisposition. Future studies using longitudinal designs, task-based paradigms (e.g. explicit inner speech tasks), and multimodal imaging are needed to establish the stability, specificity, and clinical utility of this connectivity marker for AVHs. Declarations Funding The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Beijing Municipal Health Commission Research Ward Programme (3rd batch) Fund and the Peking University Sixth Hospital Research Ward Independent Innovation. References Alderson-Day, B., & Fernyhough, C. (2015). Inner Speech: Development, Cognitive Functions, Phenomenology, and Neurobiology. Psychological Bulletin , 141 (5), 931–965. https://doi.org/10.1037/bul0000021 Alderson-Day, B., McCarthy-Jones, S., & Fernyhough, C. (2015). Hearing voices in the resting brain: A review of intrinsic functional connectivity research on auditory verbal hallucinations. 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PloS One , 7 (9), e45771. https://doi.org/10.1371/journal.pone.0045771 Northoff, G. (2014). Are Auditory Hallucinations Related to the Brain’s Resting State Activity? A “Neurophenomenal Resting State Hypothesis.” Clinical Psychopharmacology and Neuroscience: The Official Scientific Journal of the Korean College of Neuropsychopharmacology , 12 (3), 189–195. https://doi.org/10.9758/cpn.2014.12.3.189 Panikratova, Y. R., Lebedeva, I. S., Akhutina, T. V., Tikhonov, D. V., Kaleda, V. G., & Vlasova, R. M. (2023). Executive control of language in schizophrenia patients with history of auditory verbal hallucinations: A neuropsychological and resting-state fMRI study. Schizophrenia Research , 262 , 201–210. https://doi.org/10.1016/j.schres.2023.10.026 Raij, T. T., & Riekki, T. J. J. (2012). Poor supplementary motor area activation differentiates auditory verbal hallucination from imagining the hallucination. NeuroImage : Clinical , 1 (1), 75–80. https://doi.org/10.1016/j.nicl.2012.09.007 Sartorius, N., Shapiro, R., Kimura, M., & Barrett, K. (1972). WHO international pilot study of schizophrenia. Psychological Medicine , 2 (4), 422–425. https://doi.org/10.1017/s0033291700045244 Schneider, K. (1957). [Primary & secondary symptoms in schizophrenia]. Fortschritte Der Neurologie, Psychiatrie, Und Ihrer Grenzgebiete , 25 (9), 487–490. Shergill, S. S., Brammer, M. J., Williams, S. C., Murray, R. M., & McGuire, P. K. (2000). Mapping auditory hallucinations in schizophrenia using functional magnetic resonance imaging. Archives of General Psychiatry , 57 (11), 1033–1038. https://doi.org/10.1001/archpsyc.57.11.1033 Shergill, S. S., Bullmore, E. T., Brammer, M. J., Williams, S. C., Murray, R. M., & McGuire, P. K. (2001). A functional study of auditory verbal imagery. Psychological Medicine , 31 (2), 241–253. https://doi.org/10.1017/s003329170100335x Storchak, H., Hudak, J., Dresler, T., Haeussinger, F. B., Fallgatter, A. J., & Ehlis, A.-C. (2021). Monitoring Processes and Their Neuronal Correlates as the Basis of Auditory Verbal Hallucinations in a Non-clinical Sample. Frontiers in Psychiatry , 12 , 644052. https://doi.org/10.3389/fpsyt.2021.644052 Strangman, G., Culver, J. P., Thompson, J. H., & Boas, D. A. (2002). A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. NeuroImage , 17 (2), 719–731. Team, R. C. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Http://Www.R-Project.Org/ . https://cir.nii.ac.jp/crid/1574231874043578752 Wang, J., Dong, Q., & Niu, H. (2017). The minimum resting-state fNIRS imaging duration for accurate and stable mapping of brain connectivity network in children. Scientific Reports , 7 (1), 6461. https://doi.org/10.1038/s41598-017-06340-7 Waters, F., Woodward, T., Allen, P., Aleman, A., & Sommer, I. (2012). Self-recognition deficits in schizophrenia patients with auditory hallucinations: A meta-analysis of the literature. Schizophrenia Bulletin , 38 (4), 741–750. https://doi.org/10.1093/schbul/sbq144 Y, H., Y, W., H, W., Z, L., X, Y., J, Y., Y, Y., C, K., X, X., J, L., Z, W., S, H., Y, X., Y, H., T, L., W, G., H, T., G, X., X, X., … Y, W. (2019). Prevalence of mental disorders in China: A cross-sectional epidemiological study. The Lancet. Psychiatry , 6 (3). https://doi.org/10.1016/S2215-0366(18)30511-X Zhang, N., Yuan, X., Li, Q., Wang, Z., Gu, X., Zang, J., Ge, R., Liu, H., Fan, Z., & Bu, L. (2021). The effects of age on brain cortical activation and functional connectivity during video game-based finger-to-thumb opposition movement: A functional near-infrared spectroscopy study. Neuroscience Letters , 746 , 135668. https://doi.org/10.1016/j.neulet.2021.135668 Chen, F.-Y., Pu, X.-F., Fan, X.-X., & Huang, G.-Y. (2023). Current situation and related factors of self-injurious behavior among 315 patients with schizophrenia. Practical Preventive Medicine, 30(5), 589–592. Han, D., Deng, Y.-Z., & Wang, X.-P. (2023). A bibliometric and knowledge mapping analysis of research on aggressive and violent behavior in patients with schizophrenia. Chinese Journal of Clinical Psychology, 31(5), 1132–1139. https://doi.org/10.16128/j.cnki.1005-3611.2023.05.021 Tables Tables are available in the Supplementary Files section. 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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-7173755","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":493200069,"identity":"42a78203-850f-4786-8c7a-ef331e26026a","order_by":0,"name":"Wentian 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1","description":"","filename":"supplementalmaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7173755/v1/989ceb5a3dc93c9d5b25ac6f.xlsx"},{"id":88238188,"identity":"0aab34cd-b508-4fdc-bcc1-3ad47d424529","added_by":"auto","created_at":"2025-08-04 10:42:23","extension":"svg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10499,"visible":true,"origin":"","legend":"Tab.1","description":"","filename":"Tab.1.svg","url":"https://assets-eu.researchsquare.com/files/rs-7173755/v1/e574059c7ffec0fe7b8105f8.svg"},{"id":88239483,"identity":"009c1af4-0d24-41cc-88f3-97a93c1d0183","added_by":"auto","created_at":"2025-08-04 10:58:23","extension":"svg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":25455,"visible":true,"origin":"","legend":"Tab.2","description":"","filename":"Tab.2.svg","url":"https://assets-eu.researchsquare.com/files/rs-7173755/v1/9d9420afa90e295c7d01373c.svg"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Decreased Broca-Left Supplementary Motor Area Connectivity underlying Auditory Verbal Hallucination: A Resting-State NIRS Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSchizophrenia remains a major psychiatric disorder affecting approximately 1% of the global population, despite decades of extensive research. It is characterized by severe disturbances in cognition, perception, and emotion (Charlson et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ha et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Y et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Among its core symptoms, auditory verbal hallucination (AVH) is a hallmark feature, with 60\u0026ndash;80% of patients experiencing the sensation of \"hearing voices\" at some point in their illness (Andreasen \u0026amp; Flaum, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Sartorius et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Schneider, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). These voices are typically perceived as originating from distinct external agents, are often negative or derogatory in content, and are associated with high levels of distress (Baumeister et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; de Leede-Smith \u0026amp; Barkus, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Indeed, Persistent AVHs significantly impair cognitive functions, elevate the risk of self-harm, and reduce quality of life (F.Y. Chen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough numerous theoretical models have been proposed to explain AVH, its neurobiological mechanisms remain incompletely understood. Among these models, the \u003cb\u003einner speech monitoring deficit hypothesis\u003c/b\u003e has garnered substantial support (Friston \u0026amp; Frith, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Frith, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Alderson-Day \u0026amp; Fernyhough, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Defined as silent, self-directed verbal thought, inner speech plays a critical role in higher-order cognition task, self-awareness, and mental planning (Alderson-Day \u0026amp; Fernyhough, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Langland-Hassan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Neuroimaging studies have identified a complex network underpinning inner speech, involving the left inferior frontal gyrus (Broca\u0026rsquo;s area), supplementary motor area (SMA), dorsolateral prefrontal cortex (DLPFC), superior temporal gyrus (STG), supramarginal gyrus, temporoparietal junction (TPJ), insular cortex, and the cerebellum (McGuire et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; McGuire, Silbersweig, Murray, et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; McGuire, Silbersweig, Wright, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Shergill et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Early work by McGuire and Shergill et al. reported increased blood flow in Broca\u0026rsquo;s area during AVH episodes (McGuire et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Shergill et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), implying that AVH and inner speech might share the similar neural basis. Later on, meta-analyses by Jardri et al. further demonstrated that the left IFG, insula, bilateral STG, bilateral medial temporal gyrus (MTG), Broca\u0026rsquo;s area, and left parahippocampal gyrus are consistently hyperactivated during active hallucinations (Jardri et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which are overlapped with the regions associated with inner speech. Another report from Ford et al. demonstrated that during auditory verbal hallucinations, the left primary auditory cortex shows reduced responsiveness to external auditory probes, suggesting a competition between internally generated verbal content and external stimuli. To date, the findings accumulated support the view that AVHs may result from \u003cb\u003ea failure in monitoring internally generated verbal content\u003c/b\u003e (Barber et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite growing evidence on regional activations associated with AVH in diverse tasks, \u003cb\u003ethe FC in resting state has received far less attention\u003c/b\u003e. However, disruptions in connectivity, especially at resting state, may reflect more trait-like features of AVH (Alderson-Day et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Northoff, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Previous studies have reported aberrant FC changes between distributed frontal-temporal regions at resting state (Panikratova et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Marino et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Clos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), but results are mixed for several confusing factors such as sample selected (duration of illness, sex, drug dose, etc.), study design and statistics analysis. On the other hand, existing researches are more focused on the dynamic neural activity during AVH episodes, dedicated to capture the temporal changes when AVH appear and to reveal the status-biomarkers of AVH (Jardri et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Shergill et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Noteworthy, as K\u0026uuml;hn and his colleagues stated, the pathology of AVH is probably more than the overactivation of language and auditory related network, but also involves the imbalance of perception and monitoring related regions (K\u0026uuml;hn \u0026amp; Gallinat, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The vulnerability of AVH may vary across schizophrenia patients, which denotes that FC may differ significantly between schizophrenia patients with and without AVH history ever. The distinction, if can be located, could imply the underlying mechanism of AVH.\u003c/p\u003e\u003cp\u003eTo address this gap, we hypothesized that the \u003cb\u003efunctional coupling of brain regions associated with inner speech\u003c/b\u003e would be significantly altered in AVHh\u0026thinsp;+\u0026thinsp;patients compared to those without an AVH history (AVHh\u0026minus;) and healthy controls, even if during non-hallucinatory periods. To test this, we employed \u003cb\u003efunctional near-infrared spectroscopy (fNIRS)\u003c/b\u003e, a noninvasive imaging technique well-suited for detecting cortical hemodynamics under naturalistic or low-burden conditions. Unlike fMRI or EEG, fNIRS offers quiet operation, greater portability, and superior motion tolerance, making it particularly advantageous for studies involving psychotic populations (Ferrari \u0026amp; Quaresima, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In recent years, fNIRS has gained recognition as a feasible tool for mapping resting-state brain networks (Husain et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By applying resting-state fNIRS, we aimed to identify specific patterns that underlies AVH vulnerability in schizophrenia.\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Subjects and experimental procedure\u003c/h2\u003e\u003cp\u003eIn total, thirty-nine schizophrenia patients were recruited from the in-patient ward of Peking University Sixth Hospital (Beijing, China) and met \u003cb\u003ethe following criteria\u003c/b\u003e: 1) diagnosed with schizophrenia/schizoaffective disorder conducted by an experienced psychiatrist, which were based on the comprehensive interviews of the Structured Clinical Interview of the DSM-IV (SCID); 2) Han Chinese in origin; 3) right-handed; 4) between 18 and 65 years old. Detailed information regarding past symptomatology were acquired in patient interviews and examination of the patients' medical records, according to which these patients were further divided into two subgroups [n(AVHh+) vs. n(AVHh-)\u0026thinsp;=\u0026thinsp;23:16]. \u003cb\u003eExclusion criteria were as follows\u003c/b\u003e: 1) a history of other psychotic disorders; 2) substance abuse or dependence; 3) severe medical disorders; 4) traumatic brain injury; 5) electroconvulsive therapy within the past 6 months; 6) intellectual disability or neurological impairment. 7) other factors against fNIRS scanning, such as the patients who were too agitated to allow evaluation. Seventeen healthy controls were enrolled from neighboring communities, which meet the following criteria: 1) had no history of neurological mental or physical diseases; 2) right-handed; 3) between 18 and 65 years old. All subjects provided written informed consent to participate following the review of a complete study description. All experiments were performed in accordance with relevant guidelines and regulations of the Ethics Committee of Peking University Sixth Hospital, which abide with the Helsinki Declaration.\u003c/p\u003e\u003cp\u003eA resting-state fNIRS scanning was lasted for at least 8 min in a dimly lit, isolated room. During the recording, the participants were instructed to sit still and focus on a fixation cross on the computer without falling asleep. Such resting-state recording did not require overt perceptual input or behavioral output.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 NIRS data acquisition\u003c/h2\u003e\u003cp\u003eIn this experiment, the NirSmart-6000A equipment (Danyang Huichuang Medical Equipment Co., Ltd., China) was used to continuously measure and record the concentration changes of brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) during the resting state. The system consists of a near-infrared light source (light emitting diodes, LED) and an avalanche photodiode (APD) as detectors, the sampling frequency was 11 Hz; 730 nm and 850 nm were the major wavelengths. The experiment used 21 emitters and 16 detectors to form 54 channels (Fig.\u0026nbsp;1), equally distributed on the bilateral hemispheres. According to the obtained spatial coordinates, these channels were displayed (See supplementary 1).The average distance between the emitter and the detector is 3 cm (range 2.7\u0026ndash;3.3 cm), with reference to the international 10/20 system for positioning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 NIRS data analysis\u003c/h2\u003e\u003cp\u003eWe used the NirSpark software package (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to analyze NIRS data. Data were preprocessed via the following steps. The motion artifact interferences irrelevant to the experimental data were eliminated. A bandpass filter with cut-off frequencies of 0.01\u0026ndash;0.20 Hz was used for resting state data and a 0.2 Hz low-pass filter was used for resting-state data to remove physiological noise (e.g., respiration, cardiac activity, and low-frequency signal drift). The modified Beer-Lambert law was used to convert optical densities into changes in the HbO and HbR concentrations (Niu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Then we extracted 7 min stable hemoglobin time series for each participant. Motion artifacts were corrected by a moving SD and a cubic spline interpolation method. Once the noise components were identified, the concentration signal was reconstructed with these particular components eliminated from the original hemoglobin time course. The filtered concentration signal was used for further analysis. In this study, we used oxy-hemoglobin signal to present the following results because the HbO signal generally has a better signal-to-noise ratio than the HbR signal (Strangman et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\u003cp\u003eFor each participant, FC analysis on the changes in HbO concentration between channels was calculated by conducting Pearson correlation analysis was performed on the HbO values obtained by sampling to get the correlation coefficient between channels. This procedure generated a 54 \u0026times; 54 correlation matrix for each participant. The formula is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{R}_{x,y}=\\frac{cov(X,\\:Y)}{{\\sigma\\:}_{X}\\:{\\sigma\\:}_{Y}}=\\frac{E\\left(XY\\right)-E\\left(X\\right)E\\left(Y\\right)}{\\sqrt{E\\left({X}^{2}\\right)-{E}^{2}\\left(X\\right)}\\sqrt{E\\left({Y}^{2}\\right)-{E}^{2}\\left(Y\\right)}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere cov (x, y) represents the covariance of X and Y; E (x) and E (y) represent the mean values of X and Y; and σ\u003csub\u003eX\u003c/sub\u003e and σ\u003csub\u003eY\u003c/sub\u003e represent the standard deviations of X and Y. Of note, these correlation coefficients (r) were normalized to z-values with Fisher\u0026rsquo;s r-to-z transformation, and the formula is as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:z=\\frac{1}{2}\\text{ln}\\frac{1+r}{1-r}={\\text{tanh}}^{-1}r$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on prior studies investigating the pathology basis of AVH, we defined regions of interest (ROIs) by averaging the amplitudes of the included channels (see Table\u0026nbsp;1). All statistical analyses and data visualizations were performed using \u003cb\u003eR version 4.5.0 (\u003c/b\u003eTeam, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The following packages were used: ggplot2, tidyverse, readxl, igraph and brainconn. One-way analyses of variance (ANOVAs) were conducted for every ROI, with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 being the significance threshold for group comparisons. Post hoc S-N-K \u003cem\u003et\u003c/em\u003e-tests were then used to assess sources of differences among groups in one-way ANOVAs. Statistical significance was set as corrected \u003cem\u003ep\u003c/em\u003e below 0.05, and multiple comparisons were adjusted using the Benjamini-Hochberg false discovery rate (FDR) correction (Benjamini \u0026amp; Hochberg, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Demographic information was compared among the three groups using ANOVAs for continuous variables, and categorical variables were compared using the chi-square test; Clinical characteristics between patient groups were analysed (Chou et al., 2021) using two-sample \u003cem\u003et\u003c/em\u003e-test. In cases where the assumption of normality was violated (tested for normal distribution using Kolmogorov-Smirnov test), we used two-tailed Mann-Whitney U-tests.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic and clinical characteristics\u003c/h2\u003e\u003cp\u003eThere were no significant differences observed in age (F\u0026thinsp;=\u0026thinsp;0.40, P\u0026thinsp;=\u0026thinsp;0.675), education level (F\u0026thinsp;=\u0026thinsp;0.98, P\u0026thinsp;=\u0026thinsp;0.384), or gender (χ\u0026sup2;=2.75, P\u0026thinsp;=\u0026thinsp;0.253) in three groups, suggesting they are statistically matched. Additionally, there were no significant differences between the AVHh\u0026thinsp;+\u0026thinsp;and AVHh- group participants in terms of illness duration (t(32.32)\u0026thinsp;=\u0026thinsp;0.10, P\u0026thinsp;=\u0026thinsp;0.92, 95% CI [\u0026ndash;6.66, 7.38]) or antipsychotic dosage measured in chlorpromazine equivalents (t(31.36)\u0026thinsp;=\u0026thinsp;0.69, P\u0026thinsp;=\u0026thinsp;0.49, 95% CI [\u0026ndash;135.17, 274.47]), suggesting that the two patient groups were also comparable in clinical variables (see Table\u0026nbsp;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Decreased Functional Connectivity in Patients with Schizophrenia\u003c/h2\u003e\u003cp\u003eNo statistically significant group difference was observed in average FC strength between the HCs and patients (t (18.77)\u0026thinsp;=\u0026thinsp;1.04, p\u0026thinsp;=\u0026thinsp;0.31, 95% CI [\u0026ndash;0.12, 0.34]) [Fig.\u0026nbsp;2(a)]. Quantitatively, the mean values of FC strength and its standard deviations were 0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 for HC group, 0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 for AVHh- group, and 0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 for AVHh\u0026thinsp;+\u0026thinsp;group [Fig.\u0026nbsp;2(b)]. The difference of average FC strength was also insignificant between AVHh\u0026thinsp;+\u0026thinsp;and AVHh- group (t (33.05) = -0.09, p\u0026thinsp;=\u0026thinsp;0.93, 95% CI [\u0026ndash;0.23, 0.21]), but the number of FC strength ranged (0.2, 0.3] was much larger in AVHh\u0026thinsp;+\u0026thinsp;group compared to that in AVHh- group [Fig.\u0026nbsp;2(c)]. Additionally, the three groups discriminate themselves from a range of frontal-temporal regions in the brain area level [e.g. frontopolar area and right subcentral area, left Wernicke\u0026rsquo;s area and right subcentral area, Fig.\u0026nbsp;2(d)].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Broca area-left SMA FC decline in AVHh\u0026thinsp;+\u0026thinsp;group\u003c/h2\u003e\u003cp\u003eIn the comparison of FC strength between AVHh\u0026thinsp;+\u0026thinsp;and AVHh- groups, a significant difference was observed specifically in the connectivity between Broca\u0026rsquo;s area and the left SMA (Fig.\u0026nbsp;3), with the difference reaching statistical significance after FDR correction (p\u0026thinsp;=\u0026thinsp;0.0435, p\u0026thinsp;\u0026le;\u0026thinsp;0.05). Notably, none of other examined FCs (including Broca\u0026rsquo;s area\u0026ndash;left STG, Broca\u0026rsquo;s area\u0026ndash;Wernicke\u0026rsquo;s area, left STG\u0026ndash;Wernicke\u0026rsquo;s area, and left SMA\u0026ndash;left STG) showed significant differences (all FDR corrected p\u0026thinsp;\u0026gt;\u0026thinsp;0.83), indicating the specificity of the Broca\u0026rsquo;s area\u0026ndash;left SMA circuit in relation to the presence of AVH symptoms in schizophrenia.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study we tried to investigate the trait feature of AVH in schizophrenia by the means of resting-state fNIRS, and we found that: 1) a diffuse reduction of FC across frontal\u0026ndash;temporal regions in patients with schizophrenia relative to HCs. Specifically, the FC strength of three groups saw a significant difference in a range of region pairs; 2) Crucially, the AVHh\u0026thinsp;+\u0026thinsp;group showed significantly lower FC strength between Broca\u0026rsquo;s area and the left SMA compared to the AVHh- group.\u003c/p\u003e\u003cp\u003eThe main finding that schizophrenia demonstrate a distributed decline in frontal-temporal region is consistent with the disconnection hypothesis of schizophrenia (Friston \u0026amp; Frith, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Such widespread hypoconnectivity aligns with prior evidence that schizophrenia involves network-level dysconnectivity across multiple brain systems (Hoffman \u0026amp; Hampson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Noteworthy, recent meta-analysis also pointed out that the vulnerability of AVH in schizophrenia is associated with FC across bilateral Broca\u0026rsquo;s area, MTG and caudate nucleus, which is also in line with our finding on the whole (Mo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur crucial finding, the reduced resting-state functional connectivity between Broca\u0026rsquo;s area and the left SMA in schizophrenia patients with AVHh+, adds to a growing body of literature examining dysconnectivity in language-related networks in AVH. Broca\u0026rsquo;s area and SMA are key nodes in the generation and monitoring of inner speech (Alderson-Day \u0026amp; Fernyhough, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Langland-Hassan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Prior work has observed the reduced SMA response in AVH compared with deliberate imagining alien speech (Shergill et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and in verbal imagery in schizophrenic patients with AVH compared with healthy controls and schizophrenic patients without AVH (McGuire, Silbersweig, Wright, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Therefore, our result that the decline of FC strength between the left SMA and Broca\u0026rsquo;s area is generally in accordance with these previous studies, suggesting a dysfunction of inner speech generation and monitoring. On the other hand, our result can be contrasted with that of Clos et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who examined resting-state fMRI connectivity in psychotic patients with frequent AVHs (Clos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Intriguingly, Clos and colleagues reported an increase in connectivity between Broca\u0026rsquo;s area and the SMA in psychotic patients with AVH relative to HCs. They interpretated that the reduction of SMA activation was observed in the given tasks, while they have observed the increased coupling between the left SMA and Broca\u0026rsquo;s region in resting state, underlying an increased generation of inner speech in psychotic patients. The discrepancy maybe caused by differences in subjects selection, as patients in \u003cb\u003eClos et al.\u0026rsquo;s\u003c/b\u003e study are hallucinated several times a day for at least one year, and they compared a AVH group with healthy controls, without including a non-AVH schizophrenia group, so the results may be confounded by differences between schizophrenia patients and healthy individuals. In contrast, our study compared AVH and non-AVH groups, thereby more cleanly revealing trait markers specific to AVH.\u003c/p\u003e\u003cp\u003eA comprehensive review of functional and anatomical connectivity studies in AVH conducted by \u003cb\u003eCurčić-Blake et al. (2017)\u003c/b\u003e concluded that, aberrant coupling within the broader language\u0026ndash;auditory\u0026ndash;memory network is a hallmark of hallucination-prone patients (Ćurčić-Blake et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our current result is in line with this general framework, underscoring that trait-like connectivity disruptions extend beyond the classic fronto-temporal loop to include frontal speech-motor integration circuits. Specifically, whereas Curčić-Blake et al. highlighted ongoing debates about fronto-temporal disconnection in schizophrenia, our finding pinpoints that a weakened Broca\u0026ndash;SMA coupling could represent a stable marker of AVH vulnerability. This complements prior observations of fronto-temporal dysconnectivity by suggesting that the speech generation network itself may be fundamentally impaired in those susceptible to hallucinations. Our results also complement findings by Storchak et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who showed that reduced activation in motor-related regions correlates with higher hallucination proneness in non-clinical populations (Storchak et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They emphasized deficits in monitoring rather than generating inner speech, implicating the SMA as crucial for self-attribution processes. This supports our interpretation that diminished Broca\u0026ndash;SMA connectivity could impair the brain\u0026rsquo;s ability to correctly label internally generated speech as self-originated, contributing to the misattribution characteristic of AVH.\u003c/p\u003e\u003cp\u003eSeveral limitations temper the interpretation of our findings. First, the cross-sectional design prevents us from determining causality or temporal precedence \u0026ndash; we cannot ascertain whether Broca\u0026ndash;SMA disconnection is an antecedent risk factor for AVH or a consequence of experiencing recurrent hallucinations. Prospective longitudinal studies (e.g. following patients from early illness or prodromal stages) are warranted to address this question. Second, although our patient groups were matched on demographics and clinical variables like illness duration, we did not fully control for antipsychotic medication effects. Future research should attempt to replicate these results in medication-na\u0026iuml;ve or first-episode patients. Third, the sample size, while comparable to similar fNIRS studies, was modest. Additionally, our fNIRS optode coverage focused on frontal regions, which constrained analysis to select cortical networks; combining fNIRS with other modalities or expanding coverage could reveal connectivity with auditory cortex or deeper structures implicated in AVH. Despite these caveats, our results provide a coherent picture that AVH in schizophrenia are associated with aberrant connectivity in an inner speech generation-monitoring network.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the \u003cb\u003ereduced Broca\u0026ndash;SMA connectivity\u003c/b\u003e observed in AVHh\u0026thinsp;+\u0026thinsp;patients may represent a neurophysiological mechanism underlying the misattribution of inner speech and a potential trait biomarker of AVH predisposition. Future studies using longitudinal designs, task-based paradigms (e.g. explicit inner speech tasks), and multimodal imaging are needed to establish the stability, specificity, and clinical utility of this connectivity marker for AVHs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Beijing Municipal Health Commission Research Ward Programme (3rd batch) Fund and the Peking University Sixth Hospital Research Ward Independent Innovation. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlderson-Day, B., \u0026amp; Fernyhough, C. (2015). 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Psychiatry\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3). https://doi.org/10.1016/S2215-0366(18)30511-X\u003c/li\u003e\n\u003cli\u003eZhang, N., Yuan, X., Li, Q., Wang, Z., Gu, X., Zang, J., Ge, R., Liu, H., Fan, Z., \u0026amp; Bu, L. (2021). The effects of age on brain cortical activation and functional connectivity during video game-based finger-to-thumb opposition movement: A functional near-infrared spectroscopy study. \u003cem\u003eNeuroscience Letters\u003c/em\u003e, \u003cem\u003e746\u003c/em\u003e, 135668. https://doi.org/10.1016/j.neulet.2021.135668\u003c/li\u003e\n\u003cli\u003eChen, F.-Y., Pu, X.-F., Fan, X.-X., \u0026amp; Huang, G.-Y. (2023). Current situation and related factors of self-injurious behavior among 315 patients with schizophrenia. Practical Preventive Medicine, 30(5), 589\u0026ndash;592.\u003c/li\u003e\n\u003cli\u003eHan, D., Deng, Y.-Z., \u0026amp; Wang, X.-P. (2023). A bibliometric and knowledge mapping analysis of research on aggressive and violent behavior in patients with schizophrenia. Chinese Journal of Clinical Psychology, 31(5), 1132\u0026ndash;1139. https://doi.org/10.16128/j.cnki.1005-3611.2023.05.021\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7173755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7173755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Despite decades of research, the underlying mechanism of auditory verbal hallucinations (AVH), a core symptom of schizophrenia, is still unrevealed. Previous studies have tried to capture the neural activity during AVH episodes while the trait features of AVH were less investigated. To address this gap, we employed a resting-state functional Near Infrared Spectroscopy (fNIRS) to investigate the neuroimaging patterns in schizophrenia patients with AVH history (AVHh+). We hypothesized that significant differences of network activity modality may be observed in AVHh+.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethod: We recruited 23 AVHh+, 16 schizophrenia patients without AVH history (AVHh-), and 17 matched healthy controls (HCs). Participants underwent an 8-minute resting-state fNIRS scanning. Data processing and analysis were conducted by the NirSpark software (HuiChuang, China) package and R Studio.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResult: A significant lower bilateral functional connectivity (FC) strength in a range of frontal-temporal regions was observed in schizophrenic patients. Compared to the AVHh- group, the AVHh+ group showed significantly lower FC in the Broca's area and the left supplementary motor area (SMA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Schizophrenia demonstrated a widespread reduced FC in frontal and temporal regions. The hypoconnectivity of Broca-left SMA circuit might serve as a trait marker specific to AVH.\u003c/p\u003e","manuscriptTitle":"Decreased Broca-Left Supplementary Motor Area Connectivity underlying Auditory Verbal Hallucination: A Resting-State NIRS Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 10:42:18","doi":"10.21203/rs.3.rs-7173755/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-10-22T09:33:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-25T16:23:59+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-07T16:57:43+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-08-18T09:07:23+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-08-06T07:20:07+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-07-30T13:12:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T09:41:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-23T09:29:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2025-07-22T20:37:35+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-07-21T11:33:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af4f9610-681f-47ec-bd42-4e4c4ca090cc","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52373557,"name":"Health sciences/Diseases/Psychiatric disorders/Schizophrenia"},{"id":52373558,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2026-03-06T08:05:15+00:00","versionOfRecord":{"articleIdentity":"rs-7173755","link":"https://doi.org/10.1038/s41398-026-03839-4","journal":{"identity":"translational-psychiatry","isVorOnly":false,"title":"Translational Psychiatry"},"publishedOn":"2026-03-02 05:00:00","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-08-04 10:42:18","video":"","vorDoi":"10.1038/s41398-026-03839-4","vorDoiUrl":"https://doi.org/10.1038/s41398-026-03839-4","workflowStages":[]},"version":"v1","identity":"rs-7173755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7173755","identity":"rs-7173755","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}