Characteristics of auditory event-related potential and prediction of IDH1 mutation in patients with insular glioma
Original Article

Characteristics of auditory event-related potential and prediction of IDH1 mutation in patients with insular glioma

Chuanhao Zhang1, Xinxin Wang2, Bowen Xue1, Xinyu Song1, Qifeng He1, Xinlong Liu1, Zhenghai Deng1,3, Zonggang Hou1,3, Ruquan Han2, Jian Xie1,3

1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; 2Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; 3China National Clinical Research Center for Neurological Diseases, Beijing, China

Contributions: (I) Conception and design: J Xie, R Han, Z Hou; (II) Administrative support: J Xie; (III) Provision of study materials or patients: J Xie, R Han, Z Hou; (IV) Collection and assembly of data: C Zhang, X Wang; (V) Data analysis and interpretation: C Zhang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Jian Xie, MD. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 of South 4th Ring Road, Fengtai District, Beijing 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, China. Email: xiejian0630@126.com; Ruquan Han, MD. Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, No. 119 of South 4th Ring Road, Fengtai District, Beijing 100070, China. Email: ruquan.han@ccmu.edu.cn; Zonggang Hou, MD. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 of South 4th Ring Road, Fengtai District, Beijing 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, China. Email: houzg2006@163.com.

Background: Gliomas are the most prevalent primary brain tumors, exhibit highly infiltrative characteristics, and pose significant challenges to patient survival and quality of life. Isocitrate dehydrogenase (IDH) mutation status, a crucial prognostic indicator and classification criterion, can be used to determine the survival outcomes of patients glioma. The insula is a prime site for glioma formation and presents surgical challenges due to its deep location and complex arterial network. The purpose of this study was to examine the differences in auditory event-related potential (AERP) parameters between patients with IDH1-wildtype insular glioma (IDH1-wt) and those with the IDH1-mutant type (IDH1-mu) and to determine whether AERP parameters are effective in predicting the IDH1 mutation status of patients with insular glioma.

Methods: This study included 33 patients with primary insular glioma. The patients were categorized into IDH1-wt and IDH1-mu groups based on their IDH1 examination results. Differences in the AERP parameters, including mismatch negativity (MMN) and P300, were analyzed between the two groups.

Results: Compared to that of the IDH1-mu group, the average amplitude of the MMN componentwas significantly higher in IDH1-wt group when evoked by a novel stimulus at the Fz electrode (−8.91±3.08 vs. −4.66±3.29 µV; P=0.012) and the Cz electrode (−10.11±4.86 vs. −5.6±3.65 µV; P=0.038). The average latency of the P300 componentevoked by a deviant stimulus at Fz was significantly longer in the IDH1-wt group than in the IDH1-mu group (414.71±63.07 vs. 356.58±55.28 ms; P=0.023). In multiple logistic regression analysis,the average amplitude of the MMN componentevoked by a novel stimulus at Fz [odds ratio (OR) =1.919; area under the curve (AUC) =0.813; P=0.043] and the average latency of the P300 componentevoked by a deviant stimulus at Fz (OR =0.976; AUC =0.783; P=0.040) were independent predictors of IDH1 mutation status. The combination of these factors improved the accuracy (AUC =0.912).

Conclusions: TheIDH1-wt group showed higher amplitude of MMN evoked by novel stimulus at Fz and Cz and longer latency of the P300 component evoked by a deviant stimulus at Fz. The combination of AERP parameters yielded a more effective means to predict IDH1 mutation status in patients with insular glioma, which may provide guidance for the surgical intervention of this disease.

Keywords: Auditory event-related potentials (AERPs); isocitrate dehydrogenase (IDH); insular glioma; mismatch negativity (MMN); P300

Submitted Mar 12, 2025. Accepted for publication Oct 31, 2025. Published online Dec 31, 2025.

doi: 10.21037/qims-2025-629

Introduction

Glioma, the most prevalent primary brain tumor, is characterized by a high degree of infiltration (1). The World Health Organization (WHO) 2021 classification of brain tumors underscores the pivotal role of molecular characterization in subclassifying gliomas (2). Notably, isocitrate dehydrogenase (IDH) mutation status has emerged as a crucial prognostic indicator and classification criterion (3). Specifically, patients with IDH1-wildtype (IDH1-wt) glioma generally face poorer prognosis and shorter survival, whereas those with IDH1-mutant (IDH1-mu) glioma typically exhibit longer survival. Specifically, the median overall survival of patients with IDH1-mu glioma is 36 months, while those with wild-type glioma is 13.5 months (4,5). The 2016 WHO classification of tumors of the central nervous system (CNS) integrated IDH mutation status into the categorization of diffuse infiltrative gliomas (3,6). The WHO 2021 classification leverages IDH mutation status for more precise subclassification, delineating a family of tumors spanning from low-grade to advanced stages (2,7).

The insula is recognized as a prime site for glioma formation, constituting 25% of IDH1-wt low-grade gliomas and 10.8% of glioblastoma in the supratentorial region (8). Nonetheless, the surgical difficulty of insular glioma is extremely high, as its deep-set location within the Sylvian fissure, shielded by the M2 segment of the middle cerebral artery, poses a surgical challenge, and tumor resection can solely be executed within the confines of this intricate arterial network (1). Furthermore, insular gliomas frequently involve the lenticulostriate arteries, necessitating meticulous identification and preservation during surgical intervention. Given these considerations, surgical professionals need to balance maximal tumor resection with preservation of functionality during insular glioma surgery in order to enhance the patient’s survival duration and quality of life (9). Consequently, for cases in which a tumor adjoins critical blood vessels, leaving a residual tumor may be a viable option if a favorable prognosis is anticipated based on the IDH1 status.

Event-related potentials (ERPs) are bioelectrical responses detected subsequent to the administration of a specific stimulus to the nervous system. These responses exhibit a relatively fixed time interval and a specific phase with the stimulus, thereby reflecting the higher cortical brain’s response to a particular stimulus (10). Among the ERPs elicited by various sensory stimuli, auditory ERPs (AERPs) have been extensively used in studies assessing brain information-processing capabilities (11). Mismatch negativity (MMN) and the P300 are the most prevalent components of AERPs. MMN represents an ERP component that mirrors changes in auditory signals at the preattentional level of the brain. During experiments, participants are not required to actively recognize deviations or novel stimuli, indicating their automatic recognition process of stimuli and, consequently, the brain’s automatic information-processing capability (12). The occurrence of MMN is independent of the participants’ attention to the sound, manifesting even in the absence of focused attention. Therefore, as an objective, noninvasive, and reproducible electrophysiological indicator, MMN objectively reflects the automatic processing of auditory information in the brain and serves as a proxy for auditory memory integrity (13). Research has demonstrated that the integrity of the glutamatergic system significantly impacts this component of MMN (14). The interaction between tumor cells and neuronal circuits, specifically synaptic neuron–tumor communication, has emerged as a pivotal determinant in tumor progression and invasion. Synaptic inputs onto adult glioblastoma cells have been identified primarily as local glutamatergic projections (15). Conversely, P300 is a positive potential that peaks approximately 300 milliseconds after stimulus onset and is associated with various conscious activities, including attention allocation, sustained attention, updating of working memory, and information classification (16-20). AERPs have been extensively employed in research on cognition and brain function, with numerous reports detailing their application in patients with traumatic brain injury, stroke, or neurological and psychiatric disorders (16,21,22). However, their use in brain tumors, particularly gliomas, has not been frequently reported. Previous studies have indicated that patients with IDH1-wt glioma exhibit greater preoperative neurocognitive impairment as compared to those with IDH1-mu glioma (23). IDH1-wt gliomas have stronger invasiveness than do IDH1-mu gliomas, which may lead to stronger interactions between tumor cells and neurons, resulting in enhanced activity of the glutamatergic system and influence on the MMN component. The currently used methods for detecting IDH1 mutation status primarily involve immunohistochemistry, Sanger sequencing, and next-generation sequencing (24,25). Although these methods have high accuracy, they have certain drawbacks such as invasiveness (involving surgical risks associated with obtaining tissue samples), high cost, and substantial requirements for technical equipment and personnel. Moreover, all of these methods are conducted postsurgery and so are unable to positively influence the surgical resection scheme. Once the prediction model based on AERP parameters is established and validated, it can quickly provide clinicians with information regarding the tumor molecular characteristics before surgery, aiding in the development of personalized treatment plans. Consequently, our study aimed to investigate the differences in AERP parameters between patients with IDH1-mu insular glioma and those with the IDH1-wt type and to identify those parameters that can predict IDH1 mutation status, inform prognosis and postoperative planning for patients, and influence the intraoperative strategy to some extent. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-629/rc).

Methods

Study population

In this study, a total of 33 patients with insular glioma were recruited from Beijing Tiantan Hospital. All the patients were at their first diagnosis of unilateral insular glioma, had not received any adjuvant oncological therapies, and did not have additional neurological or psychiatric disorders. Patients’ clinical and demographic data were retrieved from the medical records of Tiantan Hospital. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by Beijing Tiantan Hospital, Capital Medical University (No. KY2020–146-02). Informed consent was obtained from all participants.

The inclusion criteria for patients were as follows: aged 18–60 years, a first diagnosis of insular glioma (WHO grade 2–4) with the main body of the lesion located in the insula, unilateral glioma, known IDH1 mutation status, a mother tongue of Chinese, and voluntarily participation and the ability to follow the study schedule and testing procedures. Due to patients experiencing epileptic seizures or their interval, certain components of ERP (such as P300) may not appear normally or exhibit abnormal waveforms, making it difficult to accurately extract ERP waveforms; therefore, participants were also required to have no seizures in the month prior to ERP measurement.

Meanwhile, the exclusion criteria were as follows: additional neurological or psychiatric disorders, infectious diseases, hypertension, diabetes, or other serious systemic diseases; administration of adjuvant oncological therapies; hearing impairment; long-term smoking, drinking, or psychotropic drug intake; and left-handedness. According to the results of the postoperative pathological examination, all patients were assigned to two groups: IDH1-mu or IDH1-wt.

Data acquisition

Electroencephalogram (EEG) signals were recorded with 64-channel scalp electrodes (BrainAmp, Brain Products GmbH, Gilching, Germany), with a silver-silver chloride cap. The online sampling rate was 1,000 Hz, and the resistance was controlled below 20 kΩ for each electrode (most were below 10 K Ω). CPz was set as the reference electrode during recording. All patients underwent preoperative resting-state magnetic resonance imaging (MRI) assessments with a 3T MAGNETOM Prisma scanner (Siemens Healthineers, Erlangen, Germany). The acquired MRI datasets were processed with MRIcron software (https://www.mricro.com/) for manual tumor segmentation and volume quantification.

Stimuli and procedures

AERP measurement was performed with auditory stimulus events based on the Oddball paradigm, with the following three types of sound stimuli: standard stimuli (Std) (500-Hz pure tone, lasting for 100 ms, with a probability of occurrence of 70%), deviant stimuli (Dev) (1,000-Hz pure tone, lasting for 100 ms, with a probability of occurrence of 15%), and novel stimuli (Nov) (a computer-generated pronunciation of a patient’s name, with a probability of occurrence of 15%). These three stimuli were used a total of 400 times, with an interval of 800 to 1,200 ms and a duration of about 8 minutes. The above stimulus information was presented in a pseudorandom manner with E-Prime 3.0 software (Psychology Software Tools, Pittsburgh, PA, USA). The deviant and novel stimuli were preceded by a minimum of two consecutive standard tones. The auditory stimuli were presented via a pair of CX 80S earphones (Sennheiser, Wedemark, Germany) with the volume adjusted to a comfortable level for the participants, ranging between 60 and 80 dB.

EEG signal processing and analysis

Raw EEG data were preprocessed with the EEGLAB toolkit based on the MATLAB environment (MathWorks, Natick, MA, USA). Continuous EEG data were band-pass filtered at 1–30 Hz. Each trial was segmented with a time window of 1,500 ms (−500 to 1,000 ms), and baseline correction was performed with the prestimulus interval. Following this, visual inspection was conducted on the segmented data, and independent component analysis was used to correct the samples contaminated by eye blinking or body movement, with the average value of bilateral mastoid serving as the reference.

Each trial of each participant was overlayed and averaged according to different stimulus types to obtain the average AERP waveform between trials. Subsequently, the average AERP waveforms of all participants were averaged again to obtain group-level AERP waveforms, and the scalp topography distribution was calculated. MMN was defined as the negative component generated in the differential wave of Dev minus Std or Nov minus Std within 150–250 ms after stimulation. P300 was defined as the positive component generated in the differential wave within 300–500 ms after stimulation. MMN and P300 were both measured on the Fz and Cz electrodes. The average wave amplitude and peak latencies of the MMN and P300 components were calculated separately within the abovementioned time window and electrode settings.

Statistical analysis

The Kolmogorov-Smirnov test was performed to confirm the Gaussian distribution of all continuous variables, and then Mann-Whitney test was applied to compare the ERP parameters between the IDH1-mu and IDH1-wt groups. Pearson correlation analysis and univariate analysis for the ERP parameters and IDH1 mutation status were performed. A P value <0.05 was considered statistically significant in the Pearson correlation analysis and univariate analysis, and statistically significant results were analyzed via multiple logistic stepwise regression analysis. For statistically significant ERP parameters, a receiver operating characteristic (ROC) curve analysis was also used to evaluate the diagnostic efficiency individually and jointly, from which the area under the curve (AUC) and cutoff value were also acquired. Finally, the Hosmer-Lemeshow (HL) test was conducted to evaluate the adequacy of the predictions, in which statistical nonsignificance (P>0.05) indicated a goodness of fit in the prediction.

Results

Characteristics and demographics

A total of 33 patients were recruited for this study and subsequently categorized into an IDH1-wt group (n=7) and an IDH1-mu group (n=26) based on the results of postoperative molecular pathological examinations. To eliminate the influence of other factors that could have affected ERP parameters, we used the Mann-Whitney test to compare the differences in gender, age, body mass index (BMI), education level, and tumor volume between the IDH1-wt and IDH1-mu groups and found no significant differences (all P values >0.05). The clinical characteristics and demographic features of the patients are summarized in Table 1.

Table 1

Clinical characteristics of patients in the IDH1-wt and IDH1-mu groups

Groups Males, n Age (years) BMI (kg/m2) Education level (year) Tumor volume (cm3)
IDH1-wt 5 46 25.98 13.57 99.95
IDH1-mu 15 41.12 24.1 13.69 88.63
P 0.516 0.216 0.152 0.947 0.628

BMI, body mass index; IDH, isocitrate dehydrogenase; IDH1-mu, IDH1-mutant insular glioma; IDH1-wt, IDH1-wildtype insular glioma.

Univariate analysis

The obtained averages of the MMN and P300 waveforms at the Fz and Cz points from the IDH1-wt and IDH1-mu groups after different stimuli are presented in Figure 1, while the averages of the MMN and P300 topographic map across all patients are presented in Figure 2. Table 2 presents the comparison of MMN and P300 amplitude and latency between the IDH1-wt and IDH1-mu groups. We used the Mann-Whitney test for univariate analysis, and the results indicated that at the Fz and Cz points, the average MMN amplitude induced by novel stimuli was significantly higher in the IDH1-wt group than in the IDH1-mu group (P=0.012 and P=0.038, respectively); moreover, the P300 latency induced by Dev at the Fz point was longer in the IDH1-wt group than in the IDH1-mu group (P=0.023) (Table 2 and Figure 1E-1G).

Figure 1 The average MMN and P300 waveforms of patients with glioma and different IDH1 mutation statuses after distinct stimulus types were applied. (A) The average MMN and P300 waveforms at the Fz point in patients with IDH1-wt insular glioma after different stimuli. (B) The average MMN and P300 waveforms at the Fz point in patients with IDH1-mu insular glioma after different stimuli. (C) The average MMN and P300 waveforms at the Cz point in patients with IDH1-wt insular glioma after different stimuli. (D) The average MMN and P300 waveforms at the Cz point in patients with IDH1-mu insular glioma after different stimuli. (E) Comparison of the MMN amplitude induced by novel stimuli at the Fz point between the IDH1-wt and IDH1-mu group. (F) Comparison of the MMN amplitude induced by novel stimuli at the Cz point between the IDH1-wt and IDH1-mu groups. (G) Comparison of the P300 latency induced by deviant stimuli at the Fz point between the IDH1-wt and IDH1-mu groups. *, P<0.05; **, P<0.01. Dev, deviant stimulus; IDH, isocitrate dehydrogenase; MMN, mismatch negativity; mu, mutant; Nov, novel stimulus; Std, standard stimulus; wt, wild type.
Figure 2 The average MMN and P300 topographic map of patients with glioma and different IDH1 mutation statuses after distinct stimulus types were applied. (A) The average MMN topographic map in patients with IDH1-wt insular glioma after deviant stimuli. (B) The average MMN topographic map in patients with IDH1-wt insular glioma after novel stimuli. (C) The average MMN topographic map in patients with IDH1-mu insular glioma after deviant stimuli. (D) The average MMN topographic map in patients with IDH1-mu insular glioma after novel stimuli. (E) The average P300 topographic map in patients with IDH1-wt insular glioma after deviant stimuli. (F) The average P300 topographic map from in patients with IDH1-wt insular glioma after novel stimuli. (G) The average P300 topographic map in patients with IDH1-mu insular glioma after deviant stimuli. (H) The average P300 topographic map in patients with IDH1-mu insular glioma after novel stimuli. Dev, deviant stimulus; IDH, isocitrate dehydrogenase; Nov, novel stimulus; MMN, mismatch negativity; mu, mutant; wt, wild type.

Table 2

The comparison of MMN and P300 amplitude and latency between the IDH1-wt and IDH1-mu groups

Variable IDH1-wt IDH1-mu P value
FzMMNA-DEV 0.56±2.77 −0.69±1.83 0.291
FzMMNA-NOV −8.91±3.08 −4.66±3.29 0.012*
FzMMNL-DEV 205.71±24.96 185.88±27.04 0.103
FzMMNL-NOV 191.57±37.12 199.58±35.29 0.724
CzMMNA-DEV 0.44±2.99 −0.03±1.59 0.692
CzMMNA-NOV −10.11±4.86 −5.6±3.65 0.038*
CzMMNL-DEV 201±28.72 195.19±32.57 0.792
CzMMNL-NOV 198±43.82 201.62±37.46 0.808
FzP300A-DEV 1.29±2.39 0.44±1.45 0.378
FzP300A-NOV 3.96±2.94 3.47±3.10 0.509
FzP300L-DEV 414.71±63.07 356.58±55.28 0.023*
FzP300L-NOV 393.14±61.07 379.19±59.18 0.612
CzP300A-DEV 1.33±2.20 0.38±1.67 0.481
CzP300A-NOV 3.95±3.01 3.27±3.51 0.597
CzP300L-DEV 396.43±57.79 361.73±51.25 0.152
CzP300L-NOV 363±37.39 372.54±44.94 0.454

*, P<0.05. CzMMNA-DEV, mismatch negativity amplitude induced by deviant stimuli at the Cz point; CzMMNA-NOV, mismatch negativity amplitude induced by novel stimuli at the Cz point; CzMMNL-DEV, mismatch negativity latency induced by deviant stimuli at the Cz point; CzMMNL-NOV, mismatch negativity latency induced by novel stimuli at the Cz point; CzP300A-DEV, P300 amplitude induced by deviant stimuli at the Cz point; CzP300A-NOV, P300 amplitude induced by novel stimuli at the Cz point; CzP300L-DEV, P300 latency induced by deviant stimuli at the Cz point; CzP300L-NOV, P300 latency induced by novel stimuli at the Cz point; FzMMNA-DEV, mismatch negativity amplitude induced by deviant stimuli at the Fz point; FzMMNA-NOV, mismatch negativity amplitude induced by novel stimuli at the Fz point; FzMMNL-DEV, mismatch negativity latency induced by deviant stimuli at the Fz point; FzMMNL-NOV, MMN latency induced by novel stimuli at the Fz point; FzP300A-DEV, P300 amplitude induced by deviant stimuli at the Fz point; FzP300A-NOV, P300 amplitude induced by novel stimuli at the Fz point; FzP300L-DEV, P300 latency induced by deviant stimuli at the Fz point; FzP300L-NOV, P300 latency induced by novel stimuli at the Fz point; IDH, isocitrate dehydrogenase; IDH1-mu, IDH1-mutant insular glioma; IDH1-wt, IDH1-wildtype insular glioma; MMN, mismatch negativity.

Multiple logistic regression analysis

The statistically significant factors in the univariate analysis, including the amplitude of MMN from a Nov at the Fz (FzMMNA-NOV), the amplitude of MMN from Nov at the Cz (CzMMNA-NOV), and the latency of the P300 from Dev at the Fz (FzP300L-DEV) were analyzed via multiple logistic stepwise regression analysis. The results revealed that only FzMMNA-NOV and FzP300L-DEV were independent predictors of the IDH1 mutation status [FzMMNA-NOV: odds ratio (OR) =1.919, P=0.043; FzP300L-DEV: OR =0.976, P=0.040] (Table 3).

Table 3

Multiple logistic regression of factors associated with IDH1 mutation status

Factor SE OR (95% CI) P
FzMMNA-NOV 0.322 1.919 (1.020–3.609) 0.043
FzP300L-DEV 0.012 0.976 (0.953–0.999) 0.04

CI, confidence interval; FzMMNA-NOV, mismatch negativity amplitude induced by novel stimuli at the Fz point; FzP300L-DEV, P300 latency induced by deviant stimuli at the Fz point; IDH, isocitrate dehydrogenase; OR, odds ratio; SE, standard error.

Accuracy of FzMMNA-NOV and FzP300L-DEV in predicting IDH1 mutation status

The FzMMNA-NOV and FzP300L-DEV were found to be capable of predicting IDH1 mutation status. A higher average MMN amplitude and a longer P300 latency were associated with IDH1-wt. The AUCs of FzMMNA-NOV and FzP300L-DEV were 0.813 [95% confidence interval (CI): 0.658–0.968; P=0.012] and 0.783 (95% CI: 0.616–0.949, P=0.023), respectively. The cutoff value of FzMMNA-NOV as calculated by the Youden index was −6.24 µV, and its sensitivity and specificity in predicting IDH1 mutation status were 73.1% and 85.7%, respectively. The cutoff value of the FzP300L-DEV calculated by the Youden index was 341.5 ms, and its sensitivity and specificity in predicting IDH1 mutation status were 61.5% and 100%, respectively. Combining these parameters improved the prognostic prediction accuracy, with an AUC of 0.912 (95% CI: 0.802–1.00; P<0.001) (Figure 3). The adequacy of these predictions was evaluated by the HL test, in which a statistical nonsignificance (P>0.05) implies a goodness of fit in the prediction. Our prediction model had a chi-squared value of 6.029 and a P value of 0.644, indicating that there was no statistically significant difference between the predicted values and the actual observed values. The predicted model demonstrated good calibration ability.

Figure 3 The ROC curve based on the FzMMNA-NOV, FzP300L-DEV, and their combination for predicting the IDH1 mutation status of patients with insular glioma. FzMMNA-NOV, mismatch negativity amplitude induced by novel stimuli at the Fz point; FzP300L-DEV, P300 latency induced by deviant stimuli at the Fz point; IDH, isocitrate dehydrogenase; ROC, receiver operating characteristic.

Discussion

In this study, we examined and compared the characteristics of AERPs, including the MMN and P300, in patients with insular glioma with different IDH1 mutation statuses. We found that the IDH1-wt group had a significantly higher average MMN amplitude induced by Nov at the Fz (P=0.012) and Cz (P=0.038) points than did the IDH1-mu group. The P300 latency induced by Dev of the IDH1-wt group at the Fz point was significantly longer than that of the IDH1-mu group (P=0.023). From the averaged MMN and P300 topographic map across all patients, we could discern that compared to IDH1-mu patients, IDH1-wt patients had a larger potential range and intensity of power in the MMN topographic map after Nov and in the P300 topographic map after Dev. The MMN mainly activates the frontal and temporal lobes, while P300 mainly activates the parietal lobe, which is consistent with the conclusions derived from the ERP waveform. For multiple logistic regression analysis, the average MMN amplitude induced by Nov at Fz and the P300 latency induced by Dev at Fz were independent predictors of IDH1 mutation status. This indicates that AERPs parameters have considerable potential as noninvasive predictive markers. Similar to the biomarkers in a previous study (26), our AERP-based markers could provide valuable information for preoperative planning and personalized treatment strategies for patients with insular glioma.

AERPs serve as a metric for assessing the spatial information-processing rate within extensive neural networks, making them particularly useful in the evaluation of cognitive impairment, especially in patients with neurodegenerative and neuropsychiatric disorders accompanied by cognitive deficiencies (27). The oddball paradigm is the most frequently used framework for analyzing cognitive processes and detecting alterations in brain activity (28). In this paradigm, infrequent stimuli are differentiated from regular ones, eliciting corresponding neural response components, with MMN and P300 being the most extensively studied AERP components. MMN typically signifies the automatic process of deviance detection, mirroring the brain’s innate ability to recognize irregular inputs (12). Conversely, P300 is the most prominent late-latency AERP component, frequently analyzed in cognitive research and associated is with a range of cognitive processes, including attention allocation, updating of working memory, and perceptual discrimination (17).

To our knowledge, this is the first study to use ERP parameters to predict IDH1 mutation status in patients with insular glioma. Past research has employed ERPs to identify MMN and P300 features in patients with various neurological and psychiatric disorders. For Parkinson disease, Ebmeier et al. reported increased MMN latencies, while Tsuchiya et al. observed reduced P300 amplitudes in the frontal electrodes (29,30). For multiple sclerosis, Jung et al. noted smaller amplitudes (AUC) and prolonged latencies for both MMN and P300 in a multiple sclerosis group as compared to a control group (31). For amyotrophic lateral sclerosis, Raggi et al. found decreased amplitudes and longer latencies in both MMN and P300 elicited in patients when Nov were subtracted from Std (32). For Huntington disease, Beste et al. reported that a Huntington disease group with motor symptoms exhibited MMN with greater amplitude and shorter latency than did a control group (33). In patients with traumatic brain injury and mild head injury, Kaipio et al. observed increased amplitudes in the MMN and the late portion of the P300, interpreted as hyperreactivity in involuntary attention mechanisms and abnormal distractibility (34). Among patients with schizophrenia, Takahashi et al. reported smaller amplitudes of MMN and P300 (35). For depression, Takei et al. found there to be smaller MMN amplitudes in individuals with depression as compared to controls, while Chen et al. reported lower P300 amplitudes and longer latencies at the frontocentral electrodes as compared to controls (36,37).

The insula is a crucial component of the core network, is considered essential for sustaining activity during continuous cognitive and behavioral tasks, and is likely involved in target detection tasks (38). Previous studies have definitively demonstrated the role of the insula in P300 generation during an oddball paradigm (39). Our study revealed that patients with IDH1-wt insular glioma exhibited longer P300 latencies than did those with IDH1-mu insular glioma. This finding suggests that IDH1-wt insular gliomas exert a more significant destructive impact on cognitive function and core networks, resulting in prolonged P300 latencies. MMN reflects the brain’s automatic detection capability for irregular input, with its amplitude indicating the brain’s automatic response to external information (12). Research has shown that the integrity of the glutamatergic system, particularly the N-methyl-D-aspartate (NMDA) receptors, significantly influences this MMN component (14). Rowland et al. reported that reduced MMN amplitude is significantly correlated with lower levels of glutamate and gamma-aminobutyric acid in the frontal lobe of patients with psychiatric disorders (40). Furthermore, Schall et al. found that moderate treatment with ketamine, an NMDA receptor antagonist, can cause cognitive impairment similar to schizophrenia in birds, rhesus monkeys, and healthy humans, accompanied by reduced MMN (41). The interaction between tumor cells and neuronal circuits, especially synaptic neuron-tumor communication, has emerged as a crucial factor in tumor progression and invasion (42-45). Synaptic inputs to adult glioblastoma cells have been identified as local glutamatergic projections (15). It is possible that glioma invasion promotes the accumulation and interaction of glutamate within synapses, thereby enhancing MMN production. Our study revealed that patients with IDH1-wt insular glioma exhibited higher amplitudes of MMN induced by novel stimuli at the Fz and Cz electrode sites as compared to those with IDH1-mu insular glioma. This finding can be explained through the following perspectives: first, patients with IDH1-wt insular glioma demonstrate hyperreactivity in involuntary attention mechanisms and exhibit higher vigilance. Second, the higher invasiveness of IDH1-wt insular glioma results in more frequent communication between glioma and neural synapses, leading to the accumulation of glutamate and contributing to the increased MMN amplitude. Finally, we observed that the MMN amplitude evoked by the Dev was not prominent and was lower than that evoked by the Nov (the participant’s name). This may be due to the brain’s distinct processing patterns for pure tone and semantic stimuli, in which familiar semantic information attracts more attention and cognitive resources.

In addition, previous studies have shown that tumor treating fields (TTF) can affect the electrical activity of tumor cells and the surrounding brain tissue (46). Our research on AERPs, which reflect the neural processing in the brain, may offer a novel perspective on understanding the mechanisms underlying TTF. For instance, the differences in AERP parameters between IDH1-wt and IDH1-mu insular glioma could imply variations in the neural environment and responsiveness to TTF. This could potentially guide the optimization of TTF parameters for different subtypes of insular glioblastoma.

Certain limitations to this study should be addressed. First, as mentioned above, due to the high proportion of low-grade tumors in insular glioma, the proportion of patients with IDH-wt glioma was low in our study, and future research should include a larger sample of patients with insular glioma. Second, we focused solely on insular gliomas, and thus the AERP information of patients with glioma in other locations is lacking. Therefore, whether this predicted model and the differences in AERP parameters are generalizable to gliomas in other brain areas remains to be determined. To this end, we will include patients with glioma at other sites in subsequent research.

Conclusions

Overall, the MMN amplitude at Fz after Nov and the P300 latency at Fz after Dev may be reliable indicators for predicting the IDH1 mutation status of patients with insular glioma. Moreover, their combination may provide even greater value. Consequently, our findings can aid clinical staff in predicting IDH1 mutation status, inform prognosis and postoperative planning for insular glioma patients, and improve the intraoperative strategy to some extent.

Acknowledgments

We thank all the patients and staff involved in the project.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-629/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-629/dss

Funding: This study was supported by the National Natural Science Foundation of China (No. 82172028).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-629/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by Beijing Tiantan Hospital, Capital Medical University (No. KY2020–146-02) and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Cite this article as: Zhang C, Wang X, Xue B, Song X, He Q, Liu X, Deng Z, Hou Z, Han R, Xie J. Characteristics of auditory event-related potential and prediction of IDH1 mutation in patients with insular glioma. Quant Imaging Med Surg 2026;16(1):28. doi: 10.21037/qims-2025-629

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