Postoperative Neurochemical Changes in GABA and Glx by 1H-Magnetic Resonance Spectroscopy analysis of elderly patients

Postoperative Neurochemical Changes in GABA and Glx by 1H-Magnetic Resonance Spectroscopy analysis of elderly patients | 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 Research Article Postoperative Neurochemical Changes in GABA and Glx by 1 H-Magnetic Resonance Spectroscopy analysis of elderly patients Haoli Mao, Fengwei Zhang, Huan He, Ren Zhou, Chao Suo, Mengda Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9364712/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The underlying mechanisms by which general anesthesia and surgery affect brain function remain unclear, particularly in the elderly. Investigating their impact on cerebral metabolism may be key to understanding these processes. At present, research on brain metabolism mainly relies on cerebrospinal fluid and animal models. Magnetic Resonance Spectroscopy (MRS) can explore changes in metabolic products in specific regions of the brain, providing a new non-invasive in vivo detection method for studying the effects of general anesthesia and surgery on brain metabolism. We employed single-voxel brain 1 H-MRS for the longitudinal study. 30 patients with oral and maxillofacial surgery were randomly selected and evaluated with a cognitive function scale before surgery to ensure that they have no neurocognitive dysfunction. Head MRS scans were performed on the elderly patients one day before and one day after surgery. A total of 23 patients were finally included. The detection sequences included MEGA-PRESS optimsed for γ- Aminobutyric acid detection, and short echo-time (TE = 30 ms) as well as long echo-time (TE = 144 ms) PRESS sequences at left prefrontal area. MRS data were analyzed and compared using LCModel and TARQUIN. Paired t-test was conducted to detecting metabolites’ change before and after the surgery. We found a significant decrease of γ- Aminobutyric acid concentration using MEGA-PRESS sequence, and a significant increase of Glutamate/Glutamine concentration detected by both short TE and long TE PRESS sequences. In conclusion, the study confirmed the presence of postoperative neurochemical changes characterized by decreased GABA and increased Glx in elderly patients. These metabolic changes may be a potential mechanism underlying postoperative cognitive dysfunction. One sentence summary 1 HMRS and cerebral metabolite in elderly after surgery anesthesia elderly Magnetic Resonance Spectroscopy metabolite prefrontal cortex Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The impact of anesthesia and surgery on neural function, including the in vivo concentration alteration on brain metabolism, has already become a hot research topic (Deng et al., 2024 ; Goettel et al., 2017 ; Zhang et al., 2025 ) in general population. However, there’s a lack of attention and research evidence on aging cohort. Given for the differences of brain metabolites in elderly cohort, and aging related brain degeneration, this cohort is particular vulnerable to the influence of anesthesia and surgery (Brown and Purdon, 2013 ). It makes it more pressed to understand the metabolic mechanism of anesthesia and surgery for elderly people. The impact of anesthesia and surgery on brain function extends beyond the intraoperative loss of consciousness and may also induce postoperative cognitive dysfunction (POCD) by disrupting neurotransmitters and energy metabolism, especially in the elderly populatio. Studies show that the occurrence of postoperative delirium (POD) in elderly patients is associated with elevated cerebral lactate levels, indicating anesthesia surgery leads to activation of glycolysis and metabolic dysfunction (Taylor et al., 2022 ). However, the current research has obvious limitations: animal models inadequately replicate the metabolic characteristics of human aging; invasive methods like cerebrospinal fluid (CSF) analysis have restricted clinical applicability, and conventional MRS lacking sensitivity for certain metabolites. Crucially, there remains a paucity of in-depth research specifically investigating the effects of anesthesia and surgery on cerebral metabolism in elderly patients before and after surgery. In recent years, with the continuous development of 1 H-Magnetic Resonance Spectroscopy ( 1 HMRS) technology, its potential applications in the field of anesthesia have gradually attracted attention. MRS has significant advantages such as being non-invasive and highly repeatable. It can directly detect the concentration of various brain metabolites, including N-acetylaspartic acid (NAA), Creatine (Cr), glutamate (Glu), glutamine (Gln), γ- Aminobutyric acid (GABA) and etc. (Henning, 2018 ). The concentration levels of these metabolites in the brain are closely related to neuronal activity and various brain functions. They play a key role in maintaining neurotransmitter balance, energy metabolism, and cellular structural stability (Tomiyasu et al., 2022 ). A clinical study based on 1H-MRS data shows that in elderly patients, the ratios of hippocampal Glu/Cr and Glx/Cr (glutamate + glutamine over Creatine increase after surgery, while the NAA/Cr ratio decreases (Li et al., 2023 ). These metabolic changes correlate with postoperative sleep disturbance, highlighting the importance and application prospects of MRS in non-invasive monitoring of brain metabolic changes. Thus, it is of great significance to deeply understand the impact of anesthesia and surgery on brain function in the elderly cohort. Filling these research gaps will not only enhance our understanding of the mechanism of anesthesia-induced neurotoxicity, but also facilitate the optimization of individualized anesthesia strategies for the elderly, ultimately alleviating the significant public health burden caused by perioperative neurocognitive disorders. However, due to the limitation of single voxel MRS, other brain region especially those heavily involved in cognitive function and aging, e.g., frontal area, need to be investigate to complete the full theory behind. GABA, as the main inhibitory neurotransmitter in the brain, plays a critical role in regulating the excitability of neurons and is indispensable for maintaining the stable state of the nervous system. Glutamate is the main excitatory neurotransmitter, which can facilitate the signal transmission between neurons and is crucial for cognitive functions such as learning and memory. Glutamine, as a storage form and precursor of glutamate, is vital in the metabolic cycle between glutamatergic neurons and astrocytes, ensuring glutamate homeostasis. Many studies show that GABA and glutamate are important for cognitive function (Koh et al., 2023 ; Zhang et al., 2022 ). For instance, in AD patients, GABA and Glx levels are often significantly reduced, which may contribute to the early diagnosis of AD (Carello-Collar et al., 2023 ; Huang et al., 2017 ). Therefore, conducting 1 H-MRS studies on elderly surgical populations is critically urgent. By monitoring of the metabolites such as GABA and Glx on the day before and the day after surgery, we can understand the impact of anesthesia and surgery on brain, focusing on the frontal area, then analyze the potential links between metabolic alterations and POCD. This not only helps improve anesthesia management for the elderly and reduces the potential negative impact of anesthesia surgeries on the brain but also presents a new research direction for slowing down or improving aging - related neurodegenerative diseases. Anesthesia and surgery are inseparable, sharing an indivisible connection in clinical practice. Therefore, the study focuses on elderly patients undergoing oral and maxillofacial surgery. We detected dynamic changes in cerebral metabolites by MRS before surgery (one day prior) and on the first day after surgery, in order to explore the potential mechanisms by which general anesthesia influences brain metabolism. This study aims to provide a theoretical basis for optimizing anesthesia protocols and preventing postoperative cognitive dysfunction in elderly patients, and offer new perspectives and research ideas for the development of geriatric anesthesia medicine. 2. Materials and Methods This study was conducted at Shanghai Ninth People’s Hospital at Shanghai Jiao Tong University School of Medicine in Shanghai, China, in 2021–2022. After receiving approval from the Ethics Committee of Shanghai Ninth People’s Hospital (SH9H-2022-T133-2). All procedures involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. Written informed consent was obtained from all individual participants included in the study. This study is a prospective observational study, involving patients who underwent oral and maxillofacial surgery at the Ninth People's Hospital of Shanghai from July 2022 to December 2022. Patients were selected to undergo Magnetic Resonance Imaging (MRI) scans including single voxel Magnetic Resonance Spectroscopy (MRS) scans one day before surgery, and then performed post-surgery MRS scans around one day after surgery. Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research. 2.1 Subjects Elderly patients (aged ≥ 65 years) who undergo head, neck, maxillofacial surgery under general anesthesia are the target participant group with inclusion criteria detailed as below: ASA level I-III; No history of allergy to anesthetic drugs; No severe liver and kidney dysfunction; No severe anemia; No history of cardiovascular or cerebrovascular diseases or related surgeries; No neurological or psychiatric disorders; No history of long-term continuous use of sedatives or antidepressants; No alcohol dependence or drug abuse: No hearing or speech impairment. The exclusion criteria include left-handed; Those who use immunosuppressive agents or immunomodulatory therapy; Known chemotherapy drugs that affect cognitive function; Individuals who have experienced severe head injuries before and during testing; Those who do not cooperate in participating in cognitive function tests during the perioperative period; Intraoperative blood loss > 1000ml; Intraoperative mean arterial pressure<60mmHg; Perioperative rescue history; Patients with serious complications such as postoperative cerebral embolism, cerebral hemorrhage, and pulmonary infection. 2.2 Imaging protocol The subjects underwent two baseline magnetic resonance spectroscopy scans of patients before surgery and the day after surgery (with an average interval of 24 hours) in shanghai 9th hospital. A 3T Philip (Ingenia CX, Philips Medical Systems) magnetic resonance imaging system with a 32-channel head coil housed at shanghai 9th hospital (Shanghai, China) was used for collecting MRS and T1-weighted structural imaging data. T1-weighted images were acquired for localisation of the MRS voxel at left Dorsal Lateral Prefrontal cortex (DLPFC) with these detailed parameters: isotropic 1 mm 3 voxel, 172 sagittal slices, voxel resolution = 1.0 x 1.0 x 1.0 mm 3 , TR = 1900ms, TE = 2.52ms, flip angle = 9°, field of view 256 x 256 mm, acquisition time = 3 minutes). MRS voxel of interest (VOI) size is 40 × 20 × 20 mm (Fig. 1 ), covering the left dorsolateral prefrontal cortex (DLPFC), the position and size of each voxel are carefully adjusted to ensure accurate data collection. Once the VOI was chosen, three single-voxel MRS scanning sequences were used to assess the metabolite concentration of the same VOI at the same location. First, GABA concentration was measured using a Mescher-Garwood point-resolved spectral sequence (MEGA-PRESS), with the parameters as below: TE= 68ms, TR = 2000ms, edit frequency = 1.95 ppm, average = 32, each average including one edit-on (at 1.95 ppm) and one edit-off measurement, scanning time = ~ 5 min. Then, a short-echo PRESS sequence (TE = 30, TR = 2000, Ave = 96, ~ 4 min) followed by a long-echo PRESS sequence (TE = 144, TR = 2000, Ave = 96, ~ 4 min) were conducted to quantify the five major metabolites such as N-acetylaspartate (NAA), Creatine (Crn), Choline, Glutamate/Glutamine (Glx) and Myo-Inositol (MI) at the same location. While performing the MEGA-PRESS, short-echo PRESS and long-echo PRESS sequences, the corresponding water signals (water unsuppressed spectra) were collected at the same location as reference data for the subsequent quantification process. 2.3 MRS Data Processing MEGA-PRESS. For GABA concentration, TARQUIN (Version 4.3.10, https://tarquin.sourceforge.net/ ) was applied with standard MEGA-PRESS GABA setup for data preprocessing and quality checks, including automatic phase correction, automatic reference, and eddy current correction. The fitting was also performed using TARQUIN, using the default option of MEGA-PRESS GABA basis (Fig. 2 A). Exclude data that did not meet quality inspection standards (Cramer-Rà o lower bounds based Standard Deviation (i.e., S.D.) 10, lined with < 0.1). Partial volume corrections (e.g., voxel splitting) were performed to prevent quantitative data inflation from gray and white matter variance(Ganji et al., 2014 ). Specifically, the partial volume effect of the GABA concentration was approximated and corrected by considering the gray and white matter volume ratio of the voxel of interest (Fig. 2 B) (Kirkovski et al., 2018 ). The corrected concentrations were transformed using the following equation, where GMV, WMV and CSF denote the gray matter, white matter and CSF volumes. $$\:{GABA}_{corrected}={GABA}_{RAW}÷\frac{GMV}{GMV+WMV+CSFV}\times\:100\%$$ PRESS. Both short TE (30 ms) PRESS and long TE (144 ms) PRESS data were analyzed using the LCModel toolbox (Version 6.3-1R; Provencher, 1993 ; 2001)(Provencher, 1993 ), a widely used automated program for 1H-MRS analysis. Default spectral preprocessing, including baseline correction, phase correction, and eddy current correction, was performed in LCModel. The standard base-sets were used for the corresponding MRS data with matched echo times (TE) provided by LCmodel for the further metablites fitting. Estimated quality check parameters were set at SNR > 10, FWHM (line with) 10% for NAA, Crn, choline, MI and Glx (S.D. > 30% for other metabolites). Similar as above, partial volume corrections (e.g., voxel splitting) were performed to prevent quantitative data inflation from gray and white matter variance. Specifically, the partial volume effects of the metabolite concentration were approximated and corrected by considering the gray and white matter volume ratio of the voxel of interest (Fig. 2 C) (Kirkovski et al., 2018 ). The corrected concentrations were transformed using the following equation, where GMV, WMV and CSF denote the gray matter, white matter and CSF volumes. $$\:{Concentration}_{corrected}={Concentration}_{LCModel}÷\frac{GMV+WMV}{GMV+WMV+CSFV}\times\:100\%$$ 2.4 Statistical analysis methods The data analysis was conducted using the IBM Social Science Statistics package (SPSS; Version 22, Armonk, U.S.A, https://www.ibm.com/products/spss-statistics ). Paired sample t -test is used to determine whether there is a significant increase or decrease between baseline and follow-up. The effect sizes were reported for significant findings with R-squared (R 2 ). To correct the false positive effect of perform the independent tests on five different major metabolites, Bonferroni correction was applied. And p-value smaller than 0.05 after Bonferroni correction was considered significant for PRESS data. 3. Results 3.1 Baseline clinical and demographic information The study initially included 30 patients scheduled for oral and maxillofacial surgery. One patient's anesthesia method was changed to local anesthesia and was therefore excluded. Among the 29 included patients, the surgical duration was (67.42 ± 31.18) minutes, and the anesthesia duration was (92.11 ± 31.93) minutes (Table 1 ). 30 patients underwent MRS scans one day before surgery. However, 7 patients declined the postoperative MRS follow-up (including 1 patient's local anesthesia). Consequently, a total of 23 patients completed both the preoperative and postoperative 1 HMRS image. The time between the postoperative scan and the end of the surgery was (1414.87 ± 232.23) minutes. Table 1 Baseline of oral statistics patients Project name Result Gender (number, percent) Male 14, 48.3% Female 15, 51.7% Age (years), mean ± SD 70.75 ± 4.10 BMI (kg/m 2 ), mean ± SD 23.01 ± 3.00 Academic qualifications (number, percentage) Illiteracy 2, 6.9% Elementary school 8, 27.6% Junior high school 8, 27.6% High school 6, 20.7% College 5, 17.2% ASA classification (number, percentage) I 1, 3.4% II 28, 96.6% Underlying diseases (number, percentage) Yes 22, 75.9% No 7, 24.1% Laboratory indicators, mean ± SD Hemoglobin (g/l) 135.89 ± 17.07 Red blood cell (×10 12 ) 4.37 ± 0.56 White blood cell (×10 9 ) 4.72 ± 1.05 Platelet (×10 9 ) 209.33 ± 55.90 Total albumin (g/l) 69.63 ± 5.66 Creatinine (µmol/L) 66.78 ± 17.45 Urea nitrogen (mmol/L) 5.64 ± 1.40 PT (s) 10.58 ± 0.75 APTT (s) 25.93 ± 2.01 Heart rate (bmp), mean ± SD Preoperative 73.55 ± 9.38 Institution 72.48 ± 8.90 Intraoperative 62.17 ± 7.46 Postoperative 63.97 ± 9.35 MAP (mmHg) Preoperative 105.76 Institution 95.88 Intraoperative 78.18 Postoperative 85.23 BIS, mean ± SD Preoperative 98.03 ± 0.18 Institution 56.17 ± 5.99 Intraoperative 47.55 ± 4.40 Postoperative 62.45 ± 4.02 Oxygen saturation (SPO 2 %) 100% Intraoperative medication, mean ± SD Propofol (mg) 556.21 ± 272.82 Sevoflurane (%) 1.84 ± 0.42 Remifentanil (mg) 1.08 ± 0.62 Sufentanil (µg) 28.28 ± 5.61 Rocuronium (mg) 51.03 ± 11.32 Anesthesia time (min), mean ± SD 92.11 ± 31.93 Surgical time (min), mean ± SD 67.42 ± 31.18 3.2 MRS results 3.2.1 GABA concentration (MEGA-PRESS) There were 30 participants in total. Seven participants did not have follow-up scans and one participant’s data was not able to retrieved due to technical issue, leaving a total of 52 data points, including baseline and follow-up. Further quality checks removed seven data points, yielding 45 data points (17 pairs and 11 unpaired data points) (Fig. 3 ). GABA concentration was significantly reduced at follow-up, comparing to baseline. The paired t-test showed p = 0.0205, t = 2.571, R 2 = 0.2923 significant reduction by 12.83% +/- 6.45%, as showed in Fig. 4 A. 3.2.2 Results of PRESS sequence Similar as 3.2.1 , there were a total of 30 participants. Seven participants did not have follow-up scans and one participant's data was damaged due to technical issue, leaving a total of 52 data, including baseline and follow-up. Same quality check parameters were applied, including global and metabolite-specific criteria as described in the methods part, and sample sizes will be specified below for each metabolite of short TE and long TE PRESS data. Glx (Glutamate and Glutamine) concentrations were significantly increased at follow-up compared to baseline ( p = 0.0091, t = 2.940, n = 47, R 2 = 0.3371, 18 pairs, Fig. 4 B). Further analysis showed a significant increase in glutamate (Glu) ( p = 0.0129, t = 2.744, N = 49, R 2 = 0.2837, 20 pairs, Fig. 4 C). Total Choline concentrations also increased significantly ( p = 0.0489, t = 2.105, N = 49, R 2 = 0.1891, 20 pairs). However, no significant results were found for total NAA (p = 0.2437, N = 49, 20 pairs) and Creatine ( p = 0.4933, N = 49, 20 pairs). To be conservative, Bonferroni correction was conducted to correct the potential multiple comparison error from the five independent tests across different metabolites. After correction, a significant increase in Glx was survived, p -corrected = 0.0455, but not other metabolites. For long echo-time (TE=144ms) sequence, there’s a significantly increased Glx concentration at follow-up compared to baseline ( p = 0.0069, t = 2.996, n = 50, R 2 = 0.2994, 22 pairs) as shown in Fig. 4 D. As the concentration of Gln was zero in all the long echo-time PRESS scan, the results applied to Glutamate (Glu). However, for total NAA ( p = 0.8215, N = 51, 22 pairs), Choline ( p = 0.9773, N = 51, 22 pairs) and Creatine ( p = 0.8828, N = 51, 21 pairs) no significant results. Similarly, after Bonferroni correction, the significant increase of Glx (also for Glu) survived, p -corrected = 0.0345. 4. Discussion Although there are many methods available to detect changes in metabolic pathways in the brain caused by anesthesia and surgery in animal experiments, the options are limited in clinical. The study conducted ¹HMRS scans of the head in elderly patients with oral and maxillofacial surgery one day before and one day after surgery. The results indicated a significant decrease in GABA concentration and significant increases in Glx and Glu concentrations in the prefrontal cortex postoperatively. However, after multiple comparison correction, the change in Glu concentration did not reach statistical significance. This suggests that the observed changes in Glx may be due to the synergistic effect of Glu and glutamine (Gln). We provide new insights into the impact of general anesthesia on brain metabolism in elderly patients. It reveals that anesthesia can influence brain function by modulating the concentrations of key metabolites in the prefrontal cortex and offers a theoretical foundation for investigating the mechanisms by which anesthesia affects the neurotransmitter system. GABA and Glx are the primary inhibitory and excitatory neurotransmitters in the brain, respectively, and are crucial for maintaining the excitability balance of neurons. The prefrontal cortex, as a key region responsible for high-level cognitive functions such as decision-making, working memory, and emotional regulation (Noda et al., 2017 ; Perica et al., 2022 ), is particularly sensitive to changes in GABA and Glx levels, which may significantly affect these functions. Previous studies have indicated that reduced GABA and increased glutamate in the prefrontal cortex may be associated with neuronal damage and cognitive dysfunction (Chen et al., 2017 ; Pereira et al., 2017 ; Yang et al., 2022 ). The decrease in GABA observed in this study aligns with reports from animal model(Won et al., 2024 ; Zhang et al., 2023 ), and also involves changes in Glx and Glu. Although this study observed a trend towards increased Glu concentrations, it did not reach statistical significance, possibly suggesting that the contribution of Glu to changes in Glx is indirect, or that the increase in Glu was insufficient to independently drive significant changes in Glx. In addition, the metabolic alterations observed under acute anesthesia may differ from those in chronic pathological states. A notable innovation of the study is its focus on the direct impact of general anesthesia and surgery on brain metabolism. In contrast, previous MRS studies have primarily concentrated on changes in prefrontal metabolites in pathological conditions such as schizophrenia and neurodegenerative diseases (Chen et al., 2017 ; Pereira et al., 2017 ; Yang et al., 2022 ). The anesthesia/surgery-induced changes in prefrontal metabolites, particularly the significant GABA reduction and Glx increase, may affect cognitive functions by altering the excitatory/inhibitory balance in the brain. A reduction in GABA concentration could lead to weakened local inhibitory networks and relative excitability enhancement (Pereira et al., 2017 ; Yang et al., 2022 ), which is crucial for cognitive control and working memory that depend on the fine-tuning of the excitatory-inhibitory balance (Pereira et al., 2017 ). GABA dysfunction has been considered a potential mechanism for cognitive decline. Meanwhile, the trend in Glx changes (despite the non-significant change in Glu) may also contribute to the regulation of neuronal excitability. However, its specific functional significance under anesthesia and its relationship with cognitive performance may be more complex. Various interventions regulate the GABA/Glx (glutamate) balance to influence neural function. For example, S-ketamine (an NMDA receptor antagonist) exerts antidepressant effects by rapidly affecting the Glu/GABA balance (Li, 2020 ). Transcranial direct current stimulation (tDCS) has been shown to regulate GABA and Glx levels, particularly increasing GABA, which positively impacts cognitive function in patients with mild cognitive impairment (Lengu et al., 2021 ). These findings suggest that targeting the GABA/Glx system with pharmacological or neuromodulatory interventions could provide new strategies for improving anesthesia surgery -related metabolic changes and their potential postoperative cognitive effects. However, we only used single-voxel techniques. Elderly patients may have extensive or heterogeneous brain metabolic changes, and single-voxel MRS cannot provide metabolic maps of the whole brain or key networks. Second, we were unable to establish a correlation between changes in brain metabolism post-anesthesia/ surgery and postoperative neurocognitive dysfunction in our experiments. The primary reason is that patients who can undergo MRS scans before and after surgery must be capable of regaining their voluntary behavior. Additionally, the selected head and face surgeries are typically brief procedures, and the incidence of postoperative cognitive dysfunction in these surgeries is relatively low. In our study, only 3 patients experienced a slight decline in scale scores. Moreover, our study mainly focused on examining the differences before and after the anesthesia surgery, and no further follow-up or MRS at other time points were conducted. By detecting the HMRS at different time points after surgery, we may uncover the connection between alterations in brain metabolism and neurocognitive issues following anesthetic surgery. 5. Conclusion In conclusion, the study emphasizes the significant impact of general anesthesia and surgery on the metabolism of key neurotransmitters in the prefrontal cortex of elderly patients, particularly the marked reduction in GABA and the trend in Glx changes. Although the change in Glu concentration did not reach statistical significance, its potential contribution is crucial to understanding the dynamics of Glx. These metabolic changes are likely associated with cognitive functions, especially attention and working memory. Future research should explore pharmacological interventions and neuromodulation techniques aimed at restoring neurotransmitter homeostasis post-surgery to mitigate potential cognitive impairments. Declarations Funding This research was supported by the National Natural Science Foundation of China (82471280, 82171173), Natural Science Foundation of Shanghai (21dz1200203), and Cross disciplinary Research Fund of Shanghai Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine (JYLJ202304). Ethics approval and consent to participate The clinical investigation was performed in accordance with STROBE guidelines and was registered with clinicaltrials.gov (NCT05555693). Ethics approval was obtained from the Ethics Committee of Shanghai Ninth People’s Hospital (SH9H-2022-T331-2). Consent for publication All patients provided written informed consent. Availability of data and material The datasets used in the present study are available from the first author and corresponding authors on reasonable request. Declaration of Competing Interest The authors declare that they have no conflicts of interest. Consent for publication Not Applicable. 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Zhang, L., Liu, J., Miao, Z., Zhou, R., Wang, H., Li, X., Liu, J., Zhang, J., Yan, J., Xie, Z., Jiang, H., 2025. The Association of Fructose Metabolism With Anesthesia/Surgery-Induced Lactate Production. Anesth Analg 140, 710-722. Zhang, Y., Chu, J.M., Wong, G.T., 2022. Cerebral Glutamate Regulation and Receptor Changes in Perioperative Neuroinflammation and Cognitive Dysfunction. Biomolecules 12. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 May, 2026 Reviewers agreed at journal 21 May, 2026 Reviewers agreed at journal 20 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviewers invited by journal 07 May, 2026 Editor invited by journal 07 May, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 14 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9364712","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":641554974,"identity":"3f2e7956-6a23-4a30-b2a3-003ebd048670","order_by":0,"name":"Haoli Mao","email":"","orcid":"","institution":"Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haoli","middleName":"","lastName":"Mao","suffix":""},{"id":641554975,"identity":"129990e4-6c91-4d38-8236-8de22e341604","order_by":1,"name":"Fengwei Zhang","email":"","orcid":"","institution":"Dongying Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fengwei","middleName":"","lastName":"Zhang","suffix":""},{"id":641554977,"identity":"15559163-bdb9-4aa7-9488-dad5e8ea17c1","order_by":2,"name":"Huan He","email":"","orcid":"","institution":"Shanghai Stomatological Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"He","suffix":""},{"id":641554979,"identity":"202a7a7c-d2f8-4cba-aafd-639c5d673186","order_by":3,"name":"Ren Zhou","email":"","orcid":"","institution":"Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ren","middleName":"","lastName":"Zhou","suffix":""},{"id":641554980,"identity":"bc0ef286-14d7-4c98-ad33-68d6021f211f","order_by":4,"name":"Chao Suo","email":"","orcid":"","institution":"QIMR Berghofer","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Suo","suffix":""},{"id":641554981,"identity":"c25cd57e-f305-428c-874a-ec9074af08b9","order_by":5,"name":"Mengda Jiang","email":"","orcid":"","institution":"Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mengda","middleName":"","lastName":"Jiang","suffix":""},{"id":641554983,"identity":"6fae18e0-469d-493d-9208-a1d7f2883eab","order_by":6,"name":"Lei Zhang","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":641554984,"identity":"2421de4d-05fb-4937-b796-eb76aa5fc244","order_by":7,"name":"Hong Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACxmYQ0QBiHgSTDPzMzIcfkKZFsp0tzYCwVQ1IHIPzPAoS+FQztzM/e/h1h02evOPhNmneNrs848M8DAYMNTbRuB3GZm4seyat2PDAQZCW5GKzw7wHHjAcS8ttwKmFwUxasu1w4sYGsBbmxG2H+RIMGBsO49HC/g1ZS33i5mYeAwn8WnjMJD8CtcxnAGs5nLiBmbCWMmnGtrTEDQwHmy3nnDueOOMwMJAT8PjFsP/4NsmfbTaJ82ccf3jjTVl1Yn//4cMPPtTY4NYClGDmATIMbhxgYOKBCSfgUA4C8iDH/QAx+hsgjFEwCkbBKBgF6AAAZapgsNdpH/kAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-04-09 07:27:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9364712/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9364712/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109440017,"identity":"98d206c2-e011-4ba3-a857-4814b7e1801f","added_by":"auto","created_at":"2026-05-18 07:06:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":483271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVoxel location and segmentation of voxel of interest at left DLPFC. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) The voxel segmentation at individual level (i.e., native space) to correct for the partial volume effect. (\u003cstrong\u003eB\u003c/strong\u003e) the collective voxel heatmap of baseline wave (cold) and follow-up wave (hot) at MNI standard space. The voxel heatmap was defined as over 55% overlapping of all individual voxel of interest after coregistering to standard template according to T1-weighted images.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9364712/v1/89025e17ac140e706451b064.png"},{"id":109799521,"identity":"41e38777-e7cf-4c34-b901-71f27a281837","added_by":"auto","created_at":"2026-05-22 15:30:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":385745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExample of Tarquine report and LCmodel report.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) example of Tarquine report of MEGA-PRESS data, TGABA (total GABA) is the measure of interest in the report. (\u003cstrong\u003eB\u003c/strong\u003e) Examples of LCmodel report for short echo-time (TE=30ms) PRESS scan. (\u003cstrong\u003eC\u003c/strong\u003e) Examples of LCmodel report for long echo-time (TE=144ms) PRESS scan.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9364712/v1/68b55fd6e32288af10a0b7cb.png"},{"id":109760026,"identity":"674427c2-b761-4e8d-ae48-04b75b0973bb","added_by":"auto","created_at":"2026-05-22 07:28:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":389538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e17 pairs raw GABA edited spectrum were illustrated, separating by Baseline and Follow up.\u003c/strong\u003e Shade area indicted the GABA signal at 3.0 ppm.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9364712/v1/d54183ebff95c88ff1230b1d.png"},{"id":109440019,"identity":"758ce829-b438-43a3-a017-2e7acf16642b","added_by":"auto","created_at":"2026-05-18 07:06:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":336132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBrain differential metabolites detected by MRS before and after general anesthesia and surgery in elderly patients.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) A significant decrease in GABA concertation at left DLPFC (\u003cem\u003eP\u003c/em\u003e=0.0205, N=17 pairs, t=2.571). (\u003cstrong\u003eB\u003c/strong\u003e) Short Echo-time (TE=30ms) PRESS sequence detected increase of Glutamate (Glu) concetration at left DLPFC. (\u003cstrong\u003eC\u003c/strong\u003e) Short Echo-time (TE=30ms) PRESS sequence detected increase of the combination of Glutamate and Glutamine (Glx) concertation at left DLPFC. (\u003cstrong\u003eD\u003c/strong\u003e) Long Echo-time (TE=144ms) PRESS sequence detected increase of Glutamate at left DLPFC, which is also Glx due to the absence of Gln in long TE scans. BL = Before surgery; FU = after surgery. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9364712/v1/5e917857dfd2bfdd895b15b1.png"},{"id":109800283,"identity":"8550b1c3-aa98-41ed-86c7-21e91a1b315c","added_by":"auto","created_at":"2026-05-22 15:37:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1699500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9364712/v1/5fad1b52-66e3-4570-930b-807fa6bc3d57.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePostoperative Neurochemical Changes in GABA and Glx by \u003csup\u003e1\u003c/sup\u003eH-Magnetic Resonance Spectroscopy analysis of elderly patients\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe impact of anesthesia and surgery on neural function, including the \u003cem\u003ein vivo\u003c/em\u003e concentration alteration on brain metabolism, has already become a hot research topic (Deng et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Goettel et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) in general population. However, there\u0026rsquo;s a lack of attention and research evidence on aging cohort. Given for the differences of brain metabolites in elderly cohort, and aging related brain degeneration, this cohort is particular vulnerable to the influence of anesthesia and surgery (Brown and Purdon, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It makes it more pressed to understand the metabolic mechanism of anesthesia and surgery for elderly people.\u003c/p\u003e \u003cp\u003eThe impact of anesthesia and surgery on brain function extends beyond the intraoperative loss of consciousness and may also induce postoperative cognitive dysfunction (POCD) by disrupting neurotransmitters and energy metabolism, especially in the elderly populatio. Studies show that the occurrence of postoperative delirium (POD) in elderly patients is associated with elevated cerebral lactate levels, indicating anesthesia surgery leads to activation of glycolysis and metabolic dysfunction (Taylor et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the current research has obvious limitations: animal models inadequately replicate the metabolic characteristics of human aging; invasive methods like cerebrospinal fluid (CSF) analysis have restricted clinical applicability, and conventional MRS lacking sensitivity for certain metabolites. Crucially, there remains a paucity of in-depth research specifically investigating the effects of anesthesia and surgery on cerebral metabolism in elderly patients before and after surgery.\u003c/p\u003e \u003cp\u003eIn recent years, with the continuous development of \u003csup\u003e1\u003c/sup\u003eH-Magnetic Resonance Spectroscopy (\u003csup\u003e1\u003c/sup\u003eHMRS) technology, its potential applications in the field of anesthesia have gradually attracted attention. MRS has significant advantages such as being non-invasive and highly repeatable. It can directly detect the concentration of various brain metabolites, including N-acetylaspartic acid (NAA), Creatine (Cr), glutamate (Glu), glutamine (Gln), γ- Aminobutyric acid (GABA) and etc. (Henning, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The concentration levels of these metabolites in the brain are closely related to neuronal activity and various brain functions. They play a key role in maintaining neurotransmitter balance, energy metabolism, and cellular structural stability (Tomiyasu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA clinical study based on 1H-MRS data shows that in elderly patients, the ratios of hippocampal Glu/Cr and Glx/Cr (glutamate\u0026thinsp;+\u0026thinsp;glutamine over Creatine increase after surgery, while the NAA/Cr ratio decreases (Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These metabolic changes correlate with postoperative sleep disturbance, highlighting the importance and application prospects of MRS in non-invasive monitoring of brain metabolic changes. Thus, it is of great significance to deeply understand the impact of anesthesia and surgery on brain function in the elderly cohort. Filling these research gaps will not only enhance our understanding of the mechanism of anesthesia-induced neurotoxicity, but also facilitate the optimization of individualized anesthesia strategies for the elderly, ultimately alleviating the significant public health burden caused by perioperative neurocognitive disorders. However, due to the limitation of single voxel MRS, other brain region especially those heavily involved in cognitive function and aging, e.g., frontal area, need to be investigate to complete the full theory behind.\u003c/p\u003e \u003cp\u003eGABA, as the main inhibitory neurotransmitter in the brain, plays a critical role in regulating the excitability of neurons and is indispensable for maintaining the stable state of the nervous system. Glutamate is the main excitatory neurotransmitter, which can facilitate the signal transmission between neurons and is crucial for cognitive functions such as learning and memory. Glutamine, as a storage form and precursor of glutamate, is vital in the metabolic cycle between glutamatergic neurons and astrocytes, ensuring glutamate homeostasis. Many studies show that GABA and glutamate are important for cognitive function (Koh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, in AD patients, GABA and Glx levels are often significantly reduced, which may contribute to the early diagnosis of AD (Carello-Collar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, conducting \u003csup\u003e1\u003c/sup\u003eH-MRS studies on elderly surgical populations is critically urgent. By monitoring of the metabolites such as GABA and Glx on the day before and the day after surgery, we can understand the impact of anesthesia and surgery on brain, focusing on the frontal area, then analyze the potential links between metabolic alterations and POCD. This not only helps improve anesthesia management for the elderly and reduces the potential negative impact of anesthesia surgeries on the brain but also presents a new research direction for slowing down or improving aging - related neurodegenerative diseases.\u003c/p\u003e \u003cp\u003eAnesthesia and surgery are inseparable, sharing an indivisible connection in clinical practice. Therefore, the study focuses on elderly patients undergoing oral and maxillofacial surgery. We detected dynamic changes in cerebral metabolites by MRS before surgery (one day prior) and on the first day after surgery, in order to explore the potential mechanisms by which general anesthesia influences brain metabolism. This study aims to provide a theoretical basis for optimizing anesthesia protocols and preventing postoperative cognitive dysfunction in elderly patients, and offer new perspectives and research ideas for the development of geriatric anesthesia medicine.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis study was conducted at Shanghai Ninth People\u0026rsquo;s Hospital at Shanghai Jiao Tong University School of Medicine in Shanghai, China, in 2021\u0026ndash;2022. After receiving approval from the Ethics Committee of Shanghai Ninth People\u0026rsquo;s Hospital (SH9H-2022-T133-2). All procedures involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. Written informed consent was obtained from all individual participants included in the study. This study is a prospective observational study, involving patients who underwent oral and maxillofacial surgery at the Ninth People's Hospital of Shanghai from July 2022 to December 2022. Patients were selected to undergo Magnetic Resonance Imaging (MRI) scans including single voxel Magnetic Resonance Spectroscopy (MRS) scans one day before surgery, and then performed post-surgery MRS scans around one day after surgery. Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects\u003c/h2\u003e \u003cp\u003eElderly patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years) who undergo head, neck, maxillofacial surgery under general anesthesia are the target participant group with inclusion criteria detailed as below: ASA level I-III; No history of allergy to anesthetic drugs; No severe liver and kidney dysfunction; No severe anemia; No history of cardiovascular or cerebrovascular diseases or related surgeries; No neurological or psychiatric disorders; No history of long-term continuous use of sedatives or antidepressants; No alcohol dependence or drug abuse: No hearing or speech impairment. The exclusion criteria include left-handed; Those who use immunosuppressive agents or immunomodulatory therapy; Known chemotherapy drugs that affect cognitive function; Individuals who have experienced severe head injuries before and during testing; Those who do not cooperate in participating in cognitive function tests during the perioperative period; Intraoperative blood loss\u0026thinsp;\u0026gt;\u0026thinsp;1000ml; Intraoperative mean arterial pressure\u0026lt;60mmHg; Perioperative rescue history; Patients with serious complications such as postoperative cerebral embolism, cerebral hemorrhage, and pulmonary infection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Imaging protocol\u003c/h2\u003e \u003cp\u003eThe subjects underwent two baseline magnetic resonance spectroscopy scans of patients before surgery and the day after surgery (with an average interval of 24 hours) in shanghai 9th hospital. A 3T Philip (Ingenia CX, Philips Medical Systems) magnetic resonance imaging system with a 32-channel head coil housed at shanghai 9th hospital (Shanghai, China) was used for collecting MRS and T1-weighted structural imaging data. T1-weighted images were acquired for localisation of the MRS voxel at left Dorsal Lateral Prefrontal cortex (DLPFC) with these detailed parameters: isotropic 1 mm\u003csup\u003e3\u003c/sup\u003e voxel, 172 sagittal slices, voxel resolution\u0026thinsp;=\u0026thinsp;1.0 x 1.0 x 1.0 mm\u003csup\u003e3\u003c/sup\u003e, TR\u0026thinsp;=\u0026thinsp;1900ms, TE\u0026thinsp;=\u0026thinsp;2.52ms, flip angle\u0026thinsp;=\u0026thinsp;9\u0026deg;, field of view 256 x 256 mm, acquisition time\u0026thinsp;=\u0026thinsp;3 minutes). MRS voxel of interest (VOI) size is 40 \u0026times; 20 \u0026times; 20 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), covering the left dorsolateral prefrontal cortex (DLPFC), the position and size of each voxel are carefully adjusted to ensure accurate data collection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOnce the VOI was chosen, three single-voxel MRS scanning sequences were used to assess the metabolite concentration of the same VOI at the same location. First, GABA concentration was measured using a Mescher-Garwood point-resolved spectral sequence (MEGA-PRESS), with the parameters as below: TE= 68ms, TR\u0026thinsp;=\u0026thinsp;2000ms, edit frequency\u0026thinsp;=\u0026thinsp;1.95 ppm, average\u0026thinsp;=\u0026thinsp;32, each average including one edit-on (at 1.95 ppm) and one edit-off measurement, scanning time\u0026thinsp;=\u0026thinsp;~\u0026thinsp;5 min. Then, a short-echo PRESS sequence (TE\u0026thinsp;=\u0026thinsp;30, TR\u0026thinsp;=\u0026thinsp;2000, Ave\u0026thinsp;=\u0026thinsp;96, ~\u0026thinsp;4 min) followed by a long-echo PRESS sequence (TE\u0026thinsp;=\u0026thinsp;144, TR\u0026thinsp;=\u0026thinsp;2000, Ave\u0026thinsp;=\u0026thinsp;96, ~\u0026thinsp;4 min) were conducted to quantify the five major metabolites such as N-acetylaspartate (NAA), Creatine (Crn), Choline, Glutamate/Glutamine (Glx) and Myo-Inositol (MI) at the same location.\u003c/p\u003e \u003cp\u003eWhile performing the MEGA-PRESS, short-echo PRESS and long-echo PRESS sequences, the corresponding water signals (water unsuppressed spectra) were collected at the same location as reference data for the subsequent quantification process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MRS Data Processing\u003c/h2\u003e \u003cp\u003e \u003cem\u003eMEGA-PRESS.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFor GABA concentration, TARQUIN (Version 4.3.10, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tarquin.sourceforge.net/\u003c/span\u003e\u003cspan address=\"https://tarquin.sourceforge.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was applied with standard MEGA-PRESS GABA setup for data preprocessing and quality checks, including automatic phase correction, automatic reference, and eddy current correction. The fitting was also performed using TARQUIN, using the default option of MEGA-PRESS GABA basis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Exclude data that did not meet quality inspection standards (Cramer-R\u0026agrave;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003e lower bounds based Standard Deviation (i.e., S.D.)\u0026thinsp;\u0026lt;\u0026thinsp;40%, SNR (signal to noise ratio)\u0026thinsp;\u0026gt;\u0026thinsp;10, lined with \u0026lt;\u0026thinsp;0.1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePartial volume corrections (e.g., voxel splitting) were performed to prevent quantitative data inflation from gray and white matter variance(Ganji et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Specifically, the partial volume effect of the GABA concentration was approximated and corrected by considering the gray and white matter volume ratio of the voxel of interest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) (Kirkovski et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The corrected concentrations were transformed using the following equation, where GMV, WMV and CSF denote the gray matter, white matter and CSF volumes.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{GABA}_{corrected}={GABA}_{RAW}\u0026divide;\\frac{GMV}{GMV+WMV+CSFV}\\times\\:100\\%$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003ePRESS.\u003c/em\u003e Both short TE (30 ms) PRESS and long TE (144 ms) PRESS data were analyzed using the LCModel toolbox (Version 6.3-1R; Provencher, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; 2001)(Provencher, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), a widely used automated program for 1H-MRS analysis. Default spectral preprocessing, including baseline correction, phase correction, and eddy current correction, was performed in LCModel. The standard base-sets were used for the corresponding MRS data with matched echo times (TE) provided by LCmodel for the further metablites fitting. Estimated quality check parameters were set at SNR\u0026thinsp;\u0026gt;\u0026thinsp;10, FWHM (line with)\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and Cramer-R\u0026agrave;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003e lower bounds based Standard Deviation (i.e., S.D.)\u0026thinsp;\u0026gt;\u0026thinsp;10% for NAA, Crn, choline, MI and Glx (S.D. \u0026gt; 30% for other metabolites).\u003c/p\u003e \u003cp\u003eSimilar as above, partial volume corrections (e.g., voxel splitting) were performed to prevent quantitative data inflation from gray and white matter variance. Specifically, the partial volume effects of the metabolite concentration were approximated and corrected by considering the gray and white matter volume ratio of the voxel of interest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) (Kirkovski et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The corrected concentrations were transformed using the following equation, where GMV, WMV and CSF denote the gray matter, white matter and CSF volumes.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{Concentration}_{corrected}={Concentration}_{LCModel}\u0026divide;\\frac{GMV+WMV}{GMV+WMV+CSFV}\\times\\:100\\%$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis methods\u003c/h2\u003e \u003cp\u003eThe data analysis was conducted using the IBM Social Science Statistics package (SPSS; Version 22, Armonk, U.S.A, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibm.com/products/spss-statistics\u003c/span\u003e\u003cspan address=\"https://www.ibm.com/products/spss-statistics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Paired sample \u003cem\u003et\u003c/em\u003e-test is used to determine whether there is a significant increase or decrease between baseline and follow-up. The effect sizes were reported for significant findings with R-squared (R\u003csup\u003e2\u003c/sup\u003e). To correct the false positive effect of perform the independent tests on five different major metabolites, Bonferroni correction was applied. And p-value smaller than 0.05 after Bonferroni correction was considered significant for PRESS data.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline clinical and demographic information\u003c/h2\u003e \u003cp\u003eThe study initially included 30 patients scheduled for oral and maxillofacial surgery. One patient's anesthesia method was changed to local anesthesia and was therefore excluded. Among the 29 included patients, the surgical duration was (67.42\u0026thinsp;\u0026plusmn;\u0026thinsp;31.18) minutes, and the anesthesia duration was (92.11\u0026thinsp;\u0026plusmn;\u0026thinsp;31.93) minutes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 30 patients underwent MRS scans one day before surgery. However, 7 patients declined the postoperative MRS follow-up (including 1 patient's local anesthesia). Consequently, a total of 23 patients completed both the preoperative and postoperative \u003csup\u003e1\u003c/sup\u003eHMRS image. The time between the postoperative scan and the end of the surgery was (1414.87\u0026thinsp;\u0026plusmn;\u0026thinsp;232.23) minutes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline of oral statistics patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProject name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender (number, percent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14, 48.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15, 51.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAcademic qualifications (number, percentage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliteracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 6.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8, 27.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8, 27.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6, 20.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5, 17.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eASA classification (number, percentage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 3.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28, 96.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnderlying diseases (number, percentage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22, 75.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7, 24.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eLaboratory indicators, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHemoglobin (g/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.89\u0026thinsp;\u0026plusmn;\u0026thinsp;17.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed blood cell (\u0026times;10\u003csup\u003e12\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite blood cell (\u0026times;10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatelet (\u0026times;10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209.33\u0026thinsp;\u0026plusmn;\u0026thinsp;55.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal albumin (g/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.78\u0026thinsp;\u0026plusmn;\u0026thinsp;17.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrea nitrogen (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPTT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHeart rate (bmp),\u003c/p\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreoperative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.48\u0026thinsp;\u0026plusmn;\u0026thinsp;8.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntraoperative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostoperative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.97\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003cp\u003e(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreoperative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntraoperative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostoperative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBIS, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreoperative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntraoperative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostoperative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOxygen saturation (SPO\u003csub\u003e2\u003c/sub\u003e%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eIntraoperative medication, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePropofol (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e556.21\u0026thinsp;\u0026plusmn;\u0026thinsp;272.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevoflurane (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemifentanil (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSufentanil (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.28\u0026thinsp;\u0026plusmn;\u0026thinsp;5.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRocuronium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.03\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAnesthesia time (min), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.11\u0026thinsp;\u0026plusmn;\u0026thinsp;31.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSurgical time (min), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.42\u0026thinsp;\u0026plusmn;\u0026thinsp;31.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 MRS results\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 GABA concentration (MEGA-PRESS)\u003c/h2\u003e \u003cp\u003eThere were 30 participants in total. Seven participants did not have follow-up scans and one participant\u0026rsquo;s data was not able to retrieved due to technical issue, leaving a total of 52 data points, including baseline and follow-up. Further quality checks removed seven data points, yielding 45 data points (17 pairs and 11 unpaired data points) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGABA concentration was significantly reduced at follow-up, comparing to baseline. The paired t-test showed \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0205, t\u0026thinsp;=\u0026thinsp;2.571, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2923 significant reduction by 12.83% +/- 6.45%, as showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Results of PRESS sequence\u003c/h2\u003e \u003cp\u003eSimilar as \u003cb\u003e3.2.1\u003c/b\u003e, there were a total of 30 participants. Seven participants did not have follow-up scans and one participant's data was damaged due to technical issue, leaving a total of 52 data, including baseline and follow-up. Same quality check parameters were applied, including global and metabolite-specific criteria as described in the methods part, and sample sizes will be specified below for each metabolite of short TE and long TE PRESS data.\u003c/p\u003e \u003cp\u003eGlx (Glutamate and Glutamine) concentrations were significantly increased at follow-up compared to baseline (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0091, t\u0026thinsp;=\u0026thinsp;2.940, n\u0026thinsp;=\u0026thinsp;47, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.3371, 18 pairs, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Further analysis showed a significant increase in glutamate (Glu) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0129, t\u0026thinsp;=\u0026thinsp;2.744, N\u0026thinsp;=\u0026thinsp;49, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2837, 20 pairs, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Total Choline concentrations also increased significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0489, t\u0026thinsp;=\u0026thinsp;2.105, N\u0026thinsp;=\u0026thinsp;49, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.1891, 20 pairs). However, no significant results were found for total NAA (p\u0026thinsp;=\u0026thinsp;0.2437, N\u0026thinsp;=\u0026thinsp;49, 20 pairs) and Creatine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4933, N\u0026thinsp;=\u0026thinsp;49, 20 pairs). To be conservative, Bonferroni correction was conducted to correct the potential multiple comparison error from the five independent tests across different metabolites. After correction, a significant increase in Glx was survived, \u003cem\u003ep\u003c/em\u003e-corrected\u0026thinsp;=\u0026thinsp;0.0455, but not other metabolites.\u003c/p\u003e \u003cp\u003eFor long echo-time (TE=144ms) sequence, there\u0026rsquo;s a significantly increased Glx concentration at follow-up compared to baseline (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0069, t\u0026thinsp;=\u0026thinsp;2.996, n\u0026thinsp;=\u0026thinsp;50, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2994, 22 pairs) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD. As the concentration of Gln was zero in all the long echo-time PRESS scan, the results applied to Glutamate (Glu). However, for total NAA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8215, N\u0026thinsp;=\u0026thinsp;51, 22 pairs), Choline (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9773, N\u0026thinsp;=\u0026thinsp;51, 22 pairs) and Creatine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8828, N\u0026thinsp;=\u0026thinsp;51, 21 pairs) no significant results. Similarly, after Bonferroni correction, the significant increase of Glx (also for Glu) survived, \u003cem\u003ep\u003c/em\u003e-corrected\u0026thinsp;=\u0026thinsp;0.0345.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAlthough there are many methods available to detect changes in metabolic pathways in the brain caused by anesthesia and surgery in animal experiments, the options are limited in clinical. The study conducted \u0026sup1;HMRS scans of the head in elderly patients with oral and maxillofacial surgery one day before and one day after surgery. The results indicated a significant decrease in GABA concentration and significant increases in Glx and Glu concentrations in the prefrontal cortex postoperatively. However, after multiple comparison correction, the change in Glu concentration did not reach statistical significance. This suggests that the observed changes in Glx may be due to the synergistic effect of Glu and glutamine (Gln). We provide new insights into the impact of general anesthesia on brain metabolism in elderly patients. It reveals that anesthesia can influence brain function by modulating the concentrations of key metabolites in the prefrontal cortex and offers a theoretical foundation for investigating the mechanisms by which anesthesia affects the neurotransmitter system.\u003c/p\u003e \u003cp\u003eGABA and Glx are the primary inhibitory and excitatory neurotransmitters in the brain, respectively, and are crucial for maintaining the excitability balance of neurons. The prefrontal cortex, as a key region responsible for high-level cognitive functions such as decision-making, working memory, and emotional regulation (Noda et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Perica et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), is particularly sensitive to changes in GABA and Glx levels, which may significantly affect these functions. Previous studies have indicated that reduced GABA and increased glutamate in the prefrontal cortex may be associated with neuronal damage and cognitive dysfunction (Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The decrease in GABA observed in this study aligns with reports from animal model(Won et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and also involves changes in Glx and Glu. Although this study observed a trend towards increased Glu concentrations, it did not reach statistical significance, possibly suggesting that the contribution of Glu to changes in Glx is indirect, or that the increase in Glu was insufficient to independently drive significant changes in Glx. In addition, the metabolic alterations observed under acute anesthesia may differ from those in chronic pathological states. A notable innovation of the study is its focus on the direct impact of general anesthesia and surgery on brain metabolism. In contrast, previous MRS studies have primarily concentrated on changes in prefrontal metabolites in pathological conditions such as schizophrenia and neurodegenerative diseases (Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe anesthesia/surgery-induced changes in prefrontal metabolites, particularly the significant GABA reduction and Glx increase, may affect cognitive functions by altering the excitatory/inhibitory balance in the brain. A reduction in GABA concentration could lead to weakened local inhibitory networks and relative excitability enhancement (Pereira et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which is crucial for cognitive control and working memory that depend on the fine-tuning of the excitatory-inhibitory balance (Pereira et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). GABA dysfunction has been considered a potential mechanism for cognitive decline. Meanwhile, the trend in Glx changes (despite the non-significant change in Glu) may also contribute to the regulation of neuronal excitability. However, its specific functional significance under anesthesia and its relationship with cognitive performance may be more complex.\u003c/p\u003e \u003cp\u003eVarious interventions regulate the GABA/Glx (glutamate) balance to influence neural function. For example, S-ketamine (an NMDA receptor antagonist) exerts antidepressant effects by rapidly affecting the Glu/GABA balance (Li, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Transcranial direct current stimulation (tDCS) has been shown to regulate GABA and Glx levels, particularly increasing GABA, which positively impacts cognitive function in patients with mild cognitive impairment (Lengu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings suggest that targeting the GABA/Glx system with pharmacological or neuromodulatory interventions could provide new strategies for improving anesthesia surgery -related metabolic changes and their potential postoperative cognitive effects.\u003c/p\u003e \u003cp\u003eHowever, we only used single-voxel techniques. Elderly patients may have extensive or heterogeneous brain metabolic changes, and single-voxel MRS cannot provide metabolic maps of the whole brain or key networks. Second, we were unable to establish a correlation between changes in brain metabolism post-anesthesia/ surgery and postoperative neurocognitive dysfunction in our experiments. The primary reason is that patients who can undergo MRS scans before and after surgery must be capable of regaining their voluntary behavior. Additionally, the selected head and face surgeries are typically brief procedures, and the incidence of postoperative cognitive dysfunction in these surgeries is relatively low. In our study, only 3 patients experienced a slight decline in scale scores. Moreover, our study mainly focused on examining the differences before and after the anesthesia surgery, and no further follow-up or MRS at other time points were conducted. By detecting the HMRS at different time points after surgery, we may uncover the connection between alterations in brain metabolism and neurocognitive issues following anesthetic surgery.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the study emphasizes the significant impact of general anesthesia and surgery on the metabolism of key neurotransmitters in the prefrontal cortex of elderly patients, particularly the marked reduction in GABA and the trend in Glx changes. Although the change in Glu concentration did not reach statistical significance, its potential contribution is crucial to understanding the dynamics of Glx. These metabolic changes are likely associated with cognitive functions, especially attention and working memory. Future research should explore pharmacological interventions and neuromodulation techniques aimed at restoring neurotransmitter homeostasis post-surgery to mitigate potential cognitive impairments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (82471280, 82171173), Natural Science Foundation of Shanghai (21dz1200203), and Cross disciplinary Research Fund of Shanghai Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine (JYLJ202304).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical investigation was performed in accordance with STROBE guidelines and was registered with clinicaltrials.gov (NCT05555693). Ethics approval was obtained from the Ethics Committee of Shanghai Ninth People’s Hospital (SH9H-2022-T331-2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in the present study are available from the first author and corresponding authors on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHL M: Patient recruitment, data collection, writing – original draft. FW Z: patient recruitment, data collection; HH: Patient recruitment, data collection. RZ: Software, validation; CS: Software, Data curation. MD J: Software, Data curation. LZ: Conceptualization, investigation, writing – reviewing and editing, funding acquisition, Supervision. HJ: Conceptualization, writing – reviewing and editing, methodology, funding acquisition, Supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBrown, E.N., Purdon, P.L., 2013. The aging brain and anesthesia. Curr Opin Anaesthesiol 26, 414-419.\u003c/li\u003e\n \u003cli\u003eCarello-Collar, G., Bellaver, B., Ferreira, P.C.L., Ferrari-Souza, J.P., Ramos, V.G., Therriault, J., Tissot, C., De Bastiani, M.A., Soares, C., Pascoal, T.A., Rosa-Neto, P., Souza, D.O., Zimmer, E.R., 2023. The GABAergic system in Alzheimer\u0026apos;s disease: a systematic review with meta-analysis. 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The Association of Fructose Metabolism With Anesthesia/Surgery-Induced Lactate Production. Anesth Analg 140, 710-722.\u003c/li\u003e\n \u003cli\u003eZhang, Y., Chu, J.M., Wong, G.T., 2022. Cerebral Glutamate Regulation and Receptor Changes in Perioperative Neuroinflammation and Cognitive Dysfunction. Biomolecules 12.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"anesthesia, elderly, Magnetic Resonance Spectroscopy, metabolite, prefrontal cortex","lastPublishedDoi":"10.21203/rs.3.rs-9364712/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9364712/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe underlying mechanisms by which general anesthesia and surgery affect brain function remain unclear, particularly in the elderly. Investigating their impact on cerebral metabolism may be key to understanding these processes. At present, research on brain metabolism mainly relies on cerebrospinal fluid and animal models. Magnetic Resonance Spectroscopy (MRS) can explore changes in metabolic products in specific regions of the brain, providing a new non-invasive \u003cem\u003ein vivo\u003c/em\u003e detection method for studying the effects of general anesthesia and surgery on brain metabolism. We employed single-voxel brain \u003csup\u003e1\u003c/sup\u003eH-MRS for the longitudinal study. 30 patients with oral and maxillofacial surgery were randomly selected and evaluated with a cognitive function scale before surgery to ensure that they have no neurocognitive dysfunction. Head MRS scans were performed on the elderly patients one day before and one day after surgery. A total of 23 patients were finally included. The detection sequences included MEGA-PRESS optimsed for γ- Aminobutyric acid detection, and short echo-time (TE = 30 ms) as well as long echo-time (TE = 144 ms) PRESS sequences at left prefrontal area. MRS data were analyzed and compared using LCModel and TARQUIN. Paired t-test was conducted to detecting metabolites’ change before and after the surgery. We found a significant decrease of γ- Aminobutyric acid concentration using MEGA-PRESS sequence, and a significant increase of Glutamate/Glutamine concentration detected by both short TE and long TE PRESS sequences. In conclusion, the study confirmed the presence of postoperative neurochemical changes characterized by decreased GABA and increased Glx in elderly patients. These metabolic changes may be a potential mechanism underlying postoperative cognitive dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOne sentence summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eHMRS and cerebral metabolite in elderly after surgery\u003c/p\u003e","manuscriptTitle":"Postoperative Neurochemical Changes in GABA and Glx by 1H-Magnetic Resonance Spectroscopy analysis of elderly patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 07:06:12","doi":"10.21203/rs.3.rs-9364712/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-22T10:46:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293694526633179136660995570618877022903","date":"2026-05-21T09:19:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207398052782411420581551012296852879158","date":"2026-05-20T16:21:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29163759259600779596599181170284080436","date":"2026-05-14T08:40:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T14:38:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-07T12:31:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T06:10:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T02:38:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2026-04-15T02:34:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb10988a-d7e3-42b8-b6ac-fd2918bff5a4","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-22T10:46:58+00:00","index":38,"fulltext":""},{"type":"reviewerAgreed","content":"293694526633179136660995570618877022903","date":"2026-05-21T09:19:09+00:00","index":36,"fulltext":""},{"type":"reviewerAgreed","content":"207398052782411420581551012296852879158","date":"2026-05-20T16:21:02+00:00","index":35,"fulltext":""},{"type":"reviewerAgreed","content":"29163759259600779596599181170284080436","date":"2026-05-14T08:40:37+00:00","index":26,"fulltext":""},{"type":"reviewersInvited","content":"11","date":"2026-05-07T14:38:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-07T12:31:41+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T07:06:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 07:06:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9364712","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9364712","identity":"rs-9364712","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}