Search Results (27)

Intraoperative neurophysiological monitoring (IONM) is widely used during brainstem surgery to reduce the risk of neurological injury, although its prognostic value in adult brainstem glioma (BSG) surgery remains unclear. We retrospectively analyzed 22 consecutive adults who underwent surgery for BSGs with multimodal IONM between 2010 and 2023. Monitoring included somatosensory evoked potentials (SSEPs), motor evoked potentials (MEPs), brainstem auditory evoked potentials (BAEPs), corticobulbar MEPs (CoMEPs), and cranial nerves electromyography. The primary endpoint was the association between intraoperative neurophysiological changes and postoperative neurological functional status at hospital discharge, assessed with the modified McCormick Scale (mMCS). Secondary endpoints included postoperative complications and length of hospital stay. SSEP warning criteria were met in 10 patients (45.5%), while 10 patients (45.5%) experienced a ≥50% reduction in MEP amplitude in at least one monitored muscle. Despite these findings, neither SSEP nor MEP amplitude changes were significantly associated with postoperative neurological status. Transcranial MEP stimulation thresholds increased significantly during surgery (p = 0.007), and threshold elevation was associated with postoperative complications (Kendall’s τ = 0.498, p = 0.007). BAEP recordings remained stable throughout all procedures. Conventional amplitude-based IONM changes were not associated with early neurological outcome after adult BSG resection. In contrast, increases in MEP stimulation threshold may represent a sensitive indicator of postoperative complications. Further prospective studies are warranted. Full article

The educational pathway for expertise in intraoperative neurophysiological monitoring (IONM) is complex and lengthy, requiring a solid foundation in neuroscience, neurophysiology, and neuroanatomy. It also demands direct familiarity with a broad range of neurosurgical scenarios, including supratentorial, infratentorial, and spinal procedures, gained through exposure to at least ten distinct surgical approaches. Intraoperative neurophysiology must be tailored to each patient’s preoperative assessments. It relies on a variety of methods to collect, analyze, and report neurophysiological signals that are relevant to the surgical procedure. Despite its importance, there remains a substantial shortage of training tools designed to support realistic practice and skill development. To address this gap, we developed a comprehensive framework (ION-Sim) that integrates all laboratory testing modalities and adapts them to the operating room environment. ION_sim supports the simulation and analysis of spontaneous EEG and EMG activity, a wide range of evoked potentials, and intraoperative stimulus–response testing protocols. The framework provides a unified environment for practicing, testing, and validating the core neurophysiological procedures employed during neurosurgical interventions. In addition, it incorporates a robust data-management architecture, maintaining a database with system setups, user profiles, educational performance metrics, and automatically generating reports. This structure enables the longitudinal tracking of objective skill acquisition and facilitates standardized assessments of trainee progress. ION_Sim is distributed both as a ready-to-use application, suitable for direct integration into teaching and training programs, and as a modular scientific library. Through its dedicated APIs, users can design customized configurations, create novel simulation scenarios, and extend the platform to support additional research or educational objectives. It is available upon request for educational purposes and is open-source and released under the GNU General Public License, ensuring transparency, reproducibility, and long-term accessibility for the scientific and clinical communities. Full article

Aim: To observe the correlations between electroencephalography (EEG) signals and clinical outcomes in patients with traumatic brain injury (TBI). Methods: A total of 174 patients diagnosed with TBI at Xiangya Hospital during January 2017 and June 2024 were included in this study. Quantitative EEG parameters, including spectral entropy (SE), alpha variability (RAV), and relative spectral energy (RBP), along with somatosensory evoked potential (SSEP) recordings (N20 amplitude) were assessed within 7–14 days after the disease onset. Patients were divided into a good-prognosis group and a poor-prognosis group based on the Glasgow Outcome Scale (GOS) scores at six months after discharge. Results: Significant correlations were found between the initial Synek EEG grading and 6-month GOS score (ρ = −0.709, p < 0.001). Compared with patients in the poor-prognosis group, significantly higher N20 amplitudes (p < 0.001), higher SE (p = 0.049), higher RAV (p = 0.009), and lower relative beta energy (p < 0.05) were found in TBI patients with good prognosis. Among these parameters, N20 amplitude demonstrated the best predictive performance. The N20 amplitude threshold of >1.975 μV predicted a good outcome with a sensitivity of 93.3% and a specificity of 94.1%. Conclusions: These findings may provide a reliable and sensitivity method to evaluate and predict the prognosis of TBI patients, which has important clinical management significance. Full article

Background/Objectives: Monoclonal antibodies targeting the calcitonin gene-related peptide (CGRP) pathway are effective drugs for migraine prevention. The worsening of symptoms after treatment discontinuation has raised the question of whether these agents are associated with sustained central neurophysiological adaptation. This study investigated treatment-associated changes in sensory processing and cortical network efficiency during preventive treatment with CGRP monoclonal antibodies (mAbs). Methods: Twenty-two patients with episodic migraine (21 female, 46.2 ± 13.8 years) and 22 age- and sex-matched healthy controls underwent visual and somatosensory evoked-potential (VEP, SSEP) assessments and quantitative electroencephalography (qEEG). Patients were investigated before treatment initiation (V0) and after 3 months of CGRP mAb treatment (V3). Healthy controls were assessed once. Results: The lack of habituation of VEPs at V0 shifted toward habituation at V3 following treatment with CGRP mAbs (Δslope: −0.37 ± 0.83, p = 0.03). VEP habituation at V3 no longer differed significantly from controls. SSEP amplitudes remained stable and did not differ significantly between groups across the study interval. Exploratory qEEG parameters indicated a less efficient cortical network organization at V0 that was no longer significantly different from controls at V3. Conclusions: Three months of CGRP mAb treatment was associated with a partial normalization of selected neurophysiological parameters, particularly VEP habituation and exploratory qEEG network measures. Given the study design and small sample size, these findings indicate adaptive changes in multi-domain processing, yet these should not be overinterpreted as proof of disease modification. Full article

Introduction: Idiopathic adolescent scoliosis (IAS) is commonly managed non-surgically; however, patients with a Cobb angle >45° before skeletal maturity often require posterior spinal fusion. Because this procedure carries a risk of neurological complications, intraoperative neurophysiological monitoring (IONM) is essential for early detection of spinal cord compromise. Case report: We present a 13-year-old girl with rapidly progressing scoliosis (Cobb angle 78°) who developed intraoperative changes in motor evoked potentials (MEPs) during posterior fusion from L4 to Th2. Total intravenous anesthesia without muscle relaxants was used, and standard multimodal IONM with somatosensory evoked potentials (SSEPs), MEPs, and spontaneous/triggered electromyography was applied. After induction of general anesthesia and surgical exposure, pedicle preparation at Th8–Th9 was followed by increased bleeding from the vertebral bodies and an abrupt loss of MEPs in both lower limbs, most prominently in the tibialis anterior muscles, whilst SSEPs remained unchanged. Intraoperative radiography confirmed correct screw placement, and anesthetic variables were reassessed with no reversible cause identified. Because MEPs remained absent, a wake-up test was performed and demonstrated intact voluntary movement, allowing the surgery to continue. By the end of the procedure, MEPs recovered fully on the left side and partially on the right. The patient awoke without any postoperative motor deficit. Conclusion: It is well known that motor responses can show variability during surgery, including a gradual decrease due to prolonged anesthesia. After excluding anesthetic and mechanical factors, one of the hypothetical explanations for the transient MEP loss was temporary venous congestion and retrograde flow within the intravertebral and epidural/intraspinal venous networks, resulting in reversible spinal cord drainage impairment. Another hypothetical possibility was transient vasospasm from surgical manipulation without direct neural or vascular injury. This case highlights the critical role of continuous multimodal neuromonitoring in detecting reversible spinal cord dysfunction and guiding safe decision-making during complex scoliosis surgery. Full article

Background/Objectives: Intraoperative neurophysiological monitoring (IONM) is used to detect and prevent neurological injury during extramedullary spinal cord tumor (EMSCT) resection, but its diagnostic accuracy lacks systematic validation with recent evidence. This meta-analysis evaluates the performance of somatosensory evoked potentials (SSEPs), transcranial motor evoked potentials (TcMEPs), and multimodal (SSEP + TcMEP) IONM in predicting deficits during EMSCT resections. Methods: Following PRISMA-DTA guidelines, we searched MEDLINE, PubMed, and Ovid (inception to April 2025) for studies on IONM in EMSCT surgeries (PROSPERO: CRD420251047345). Pooled sensitivity, specificity, and reversibility metrics were calculated using bivariate models, with quality assessed via QUADAS-2. Z-test and Bayesian meta-analysis were used for comparisons. Results: Across 20 studies (2672 patients), multimodal IONM showed a log DOR of 4.310 (95% CI: 3.581–5.039) and an AUC of 94.2%, TcMEP monitoring showed a log DOR of 4.367 (95% CI: 3.765–5.127) and an AUC of 92%, while SSEP monitoring showed a log DOR of 3.463 (95% CI: 2.702–4.224) and an AUC of 82%. All modalities demonstrated high specificity (>95%), indicating low false-positive rates. Bayesian analysis revealed >90% probability that TcMEP-based approaches were superior to SSEPs. Reversible TcMEP changes were associated with an 11% (95% CI: 4–24%) postoperative deficit rate, compared to 35% (95% CI: 12–67%) for SSEPs. Conclusions: These findings caution against relying solely on SSEPs and support the use of multimodal IONM strategies, which enhance early detection of impending neurological injury, enable timely surgical interventions, and help prevent permanent neurological damage in EMSCT resections. Although TcMEP and multimodal monitoring showed similar diagnostic accuracy, we continue to recommend multimodal approaches as the current standard of care, pending prospective studies to determine if TcMEP alone can reliably replace multimodal monitoring. Full article

Evoked potentials (EPs), including somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs), are used to assess neural conduction in spinal cord injury (SCI) and multiple sclerosis (MS), conditions marked by demyelination, inflammation, and axonal damage. Machine learning (ML), using data-driven algorithms, enhances EPs’ prognostic utility, but evidence synthesis is limited. This meta-analysis evaluated the predictive performance of EP-based ML models for SCI recovery (ASIA scale) and MS progression (EDSS) using a random-effects model. Five studies (n = 583) were included, extracting accuracy and area under the curve (AUC). Pooled results showed high predictive accuracy of 77.7% (95% CI, 75.1–80.3%; I² = 57%) and AUC 0.82 (95% CI, 0.79–0.85; I² = 55%). Stratified analyses by disease type (SCI vs. MS) or injury severity were not feasible due to the limited number of studies (n = 5). Sensitivity analysis excluding a rat model (N = 551) showed stable results (accuracy 76.9%; AUC 0.81). SSEP latency and MEP time series were key predictors, with amplitude critical in SCI and multimodal approaches enhancing performance. Moderate heterogeneity (I² = 55–57%) and limited studies constrain generalizability. This meta-analysis highlights EPs’ prognostic potential in ML-driven precision neurology, advocating for further human studies to validate multimodal approaches. Full article

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship between variations in somatosensory evoked potentials (SSEPs) and ASIA scores, especially in the early stages of SCI. Machine learning’s (ML’s) increasing importance in medicine is driven by the growing availability of health data and improved algorithms. It enables the creation of predictive models for disease diagnosis, progression prediction, personalized treatment, and improved healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, and facilitate personalized medicine. The meticulous analysis of medical data is crucial for timely disease identification, leading to effective symptom management and appropriate treatment. This study applies artificial intelligence to identify predictors of SCI progression, as measured by the disability index, ASIA impairment scale (AIS), and final motor recovery. We aim to clarify the prognostic role of electrophysiological testing (SSEPs, MEPs, and nerve conduction studies (NCSs)) in SCI. We analyzed data from a medical database of 123 records. We developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees and neural network approaches, to predict SCI recovery. Our evaluation showed SEP accuracies of 90% for motor recovery prediction and 80% for AIS scale determination, comparable to full electrophysiology evaluation accuracies of 93% and 89%, respectively, and generally superior results compared to MEP and NCS results. EPs emerged as the best predictors, comparable to a comprehensive electrophysiology assessment, significantly improving accuracy compared to clinical findings alone. An electrophysiological assessment, when available, increased overall accuracy for final motor recovery prediction to 93% (from a maximum of 75%) and, for ASIA score determination, to 89% (from a maximum of 66%). Further validation is needed with a larger dataset. Future research should validate that sensory electrophysiology assessment is a less expensive, portable, and simpler alternative to other prognostic tests and more effective than clinical assessments, like the AIS, biomarker for SCI, and personalized rehabilitation planning. Full article

Background: In this study, we used intraoperative neurophysiological monitoring (IONM) during anterior cervical discectomy and fusion (ACDF). Rather than emphasizing its use for safety purposes, our goal was to evaluate how neurophysiological parameters change during surgery and their correlation with clinical findings. Methods: This study included 30 patients who underwent ACDF. Detailed neurological examination was performed together with manual muscle testing (MMT), the Numeric Pain Rating Scale (NPRS), and the Neck Disability Index (NDI) questionnaire. During surgery, somatosensory-evoked potentials (SSEPs), motor-evoked potentials (MEPs), and spontaneous electromyography were registered. Results: There were statistically significant difference in the latency and amplitude of SSEPs of the right median nerve. Regarding the left median nerve, there was a statistically significant difference in amplitude, but not in latency. Differences were also observed in the amplitudes of right and left tibial nerve SSEPs, though no significant differences were found in their latencies. No statistically significant difference was found in the threshold values required to elicit MEPs between the beginning and end of the surgery. Additionally, we found a statistically significant positive correlation between the latency of the left and right median nerve and the left tibial nerve with somatosensory impairment. There was also a significant negative correlation between the amplitude of both tibial nerves and somatosensory impairment, and their latency showed a significant negative correlation with pain level before surgery. We found statistically significant decreases in NDI and pain level values one month after surgery. Conclusions: The results show significant changes in SSEPs and a correlation between clinical and neurophysiological findings and emphasize the importance of using MEPs to assess the condition of the motor system. Additionally, there was a general improvement in the patients’ condition, as assessed by NDI and pain scores. This study identifies critical surgical phases to consider in the absence of real-time neuromonitoring feedback and emphasizes that clinical observations may not fully reflect the condition of neurological structures in patients with myelopathy, which is crucial when deciding on timely surgery. Full article

Objective: Due to the absence of studies supporting the role of intraoperative neurophysiological monitoring (IONM) in intradural spinal tumors, this study evaluates the clinical outcome after these surgeries in relation to the use of the advanced intraoperative neurophysiological techniques. Methods: This is an observational, descriptive and retrospective study of two cohort groups in relation to the presence or absence of IONM during the intervention and the subsequent evaluation of the clinical and functional results in the short and medium terms. Ninety-six patients with extra- or intramedullary intradural spinal tumors operated on by the neurosurgery team of our center completed the current study. Results: We observed improvements in the Prolo, Brice and McKissock and McCormick scales scores in the monitored patients. These results examine the usefulness of IONM to preserve neurological functions and, therefore, its impact on quality of life. The rate of neurological deficits in the unmonitored patients was 14.5%, whereas it was 8.3% of the patients whose treatment included IONM. Conclusions: It is important to emphasize the importance of implementing IONM for early recognition of possible neurological damage, the improvement of postoperative functional outcomes, and for decreasing the rate of neurological complications. Significance: This study provides reliable results on the importance of IONM in intradural spinal tumor surgeries. Full article

Background/Objectives: Chronic migraine with medication-overuse headache (CM-MOH) is neurophysiologically characterized by increased cortical excitability with sensitization at both the thalamocortical and the cortical levels. It is unclear whether the increased cortical excitability could be reverted by medication withdrawal (i.e., brain state) or whether it is a brain trait of individuals predisposed to medication overuse. In this study, we aim to investigate whether withdrawal from overused drugs can influence and restore these neurophysiological abnormalities. Methods: Somatosensory evoked potentials (SSEPs) were elicited by electrical stimulation of the median nerve (M), the ulnar nerve (U), and the simultaneous stimulation of both nerves (MU) in 14 patients with CM-MOH before (T₀) and after (T₁) a three-week withdrawal protocol and, for comparison, in 14 healthy volunteers (HVs) of a comparable age distribution. We measured the level of thalamocortical (pre-HFO) and cortical activation (post-HFO) by analyzing the high-frequency oscillations (HFOs) embedded in parietal N20 median SSEPs. Furthermore, we calculated the habituation and the degree of cortical lateral inhibition (dLI) of N20-P25 low-frequency SSEPs. Results: After the three-week withdrawal protocol (T₁), we observed a normalization of the baseline habituation deficit (T₀: +0.10 ± 0.54; T₁: −0.53 ± 0.8; p = 0.040) and a reduction in the amplitude for both pre-HFO (p < 0.009) and post-HFO (p = 0.042), with values comparable to those of the HVs. However, no effects were observed on the dLI (p = 0.141). Conclusions: Our findings showed that withdrawal from overused drugs could affect the increased excitability of the non-painful somatosensory system in patients with CM-MOH, reducing the level of sensitization at both the thalamocortical and the cortical levels. Full article

Chronic immune sensory polyradiculopathy (CISP) is a rare inflammatory immune disorder affecting the nervous system, primarily targeting the proximal sensory nerve roots. The condition was first described by Sinreich in 2004. We conducted a systematic review of CISP cases published on PubMed to identify common clinical presentations, along with neurophysiological, radiological, cerebrospinal fluid (CSF), and other findings. Our review included a total of 22 patients from 8 articles. Many patients presented with gait difficulties and sensory ataxia and were found to have normal nerve conduction studies (NCS) and electromyography (EMG) but exhibited characteristic abnormalities in somatosensory evoked potentials (SSEP), elevated CSF protein levels, thickened nerve roots on contrast-enhanced lumbar spine MRIs, and histological changes on nerve root biopsies. Clinical improvement was observed following treatment with steroids and/or intravenous immunoglobulin (IVIG). The study concluded that while CISP is rare, it is an important clinical entity to consider, as accurate diagnosis and appropriate treatment can lead to significant improvements in neurological symptoms and disabilities. Full article

Surgery for spinal cord tumors poses a significant challenge due to the inherent risk of neurological deterioration. Despite being performed at numerous centers, there is an ongoing debate regarding the efficacy of pre- and intraoperative neurophysiological investigations in detecting and preventing neurological lesions. This study begins by providing a comprehensive review of the neurophysiological techniques commonly employed in this context. Subsequently, we present findings from a cohort of 67 patients who underwent surgery for intradural tumors. These patients underwent preoperative and intraoperative multimodal somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs), with clinical evaluation conducted three months postoperatively. The study aimed to evaluate the neurophysiological, clinical, and radiological factors associated with neurological outcomes. In univariate analysis, preoperative and intraoperative potential alterations, tumor size, and ependymoma-type histology were linked to the risk of worsening neurological condition. In multivariate analysis, only preoperative and intraoperative neurophysiological abnormalities remained significantly associated with such neurological deterioration. Interestingly, transient alterations in intraoperative MEPs and SSEPs did not pose a risk of neurological deterioration. The machine learning model we utilized demonstrated the possibility of predicting clinical outcome, achieving 84% accuracy. Full article

Objective: The study aimed at evaluating the efficacy and the ability of D-wave monitoring combined with somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs) to predict functional outcomes in intramedullary spinal cord tumor (IMSCT) surgery. Methods: Between December 2011 and December 2020, all patients harboring IMSCT who underwent surgery at our institution were prospectively collected in a surgical spinal registry and retrospectively analyzed. Patient charts and surgical and histological reports were analyzed. The multimodal IONM included SSEPs, MEPs, and—whenever possible—D-waves. All patients were evaluated using the modified McCormick and Frankel grade at admission and 3, 6, and 12 months of follow-up. Results: Sixty-four patients were enrolled in the study. SSEP and MEP monitoring was performed in all patients. The D-wave was not recordable in seven patients (11%). Significant IONM changes (at least one evoked potential modality) were registered in 26 (41%) of the 64 patients. In five cases (8%) where the SSEPs and MEPs lost and the D-wave permanently dropped by about 50%, patients experienced a permanent deterioration of their neurological status. Multimodal IONM (SSEP, MEP, and D-wave neuromonitoring) significantly predicted postoperative deficits (p = 0.0001), with a sensitivity of 100.00% and a specificity of 95.65%. However, D-waves demonstrated significantly higher sensitivity (100%) than MEPs (62.5%) and SSEPs (71.42%) alone. These tests’ specificities were 85.10%, 13.89%, and 17.39%, respectively. Comparing the area under ROC curves (AUCs) of these evoked potentials in 53 patients (where all three modalities of IONM were registered) using the pairwise t-test, D-wave monitoring appeared to have higher accuracy and ability to predict postoperative deficits with strong statistical significance compared with MEP and SSEP alone (0.992 vs. 0.798 vs. 0.542; p = 0.018 and p < 0.001). Conclusion: The use of multimodal IONM showed a statistically significant greater ability to predict postoperative deficits compared with SSEP, MEP, and D-wave monitoring alone. D-wave recording significantly increased the accuracy and clinical value of neurophysiological monitoring in IMSCT tumor resection. Full article

In this study, a small sample of patients’ neuromonitoring data was analyzed using machine learning (ML) tools to provide proof of concept for quantifying complex signals. Intraoperative neurophysiological monitoring (IONM) is a valuable asset for monitoring the neurological status of a patient during spine surgery. Notably, this technology, when operated by neurophysiologists and surgeons familiar with proper alarm criteria, is capable of detecting neurological deficits. However, non-surgical factors, such as volatile anesthetics like sevoflurane, can negatively influence robust IONM signal generation. While sevoflurane has been shown to affect the latency and amplitude of somatosensory evoked potential (SSEP), a more complex and nuanced analysis of the SSEP waveform has not been performed. In this study, signal processing and machine learning techniques were used to more intricately characterize and predict SSEP waveform changes as a function of varying end-tidal sevoflurane concentration. With data from ten patients who underwent spinal procedures, features describing the SSEP waveforms were generated using principal component analysis (PCA), phase space curves (PSC), and time-frequency analysis (TFA). A minimum redundancy maximum relevance (MRMR) feature selection technique was then used to identify the most important SSEP features associated with changing sevoflurane concentrations. Once the features carrying the maximum amount of information about the majority of signal waveform variability were identified, ML models were used to predict future changes in SSEP waveforms. Linear regression, regression trees, support vector machines, and neural network ML models were then selected for testing. Using SSEP data from eight patients, the models were trained using a range of features selected during MRMR calculations. During the training phase of model development, the highest performing models were identified as support vector machines and regression trees. After identifying the highest performing models for each nerve group, we tested these models using the remaining two patients’ data. We compared the models’ performance metrics using the root mean square error values (RMSEs). The feasibility of the methodology described provides a general framework for the applications of machine learning strategies to further delineate the effects of surgical and non-surgical factors affecting IONM signals. Full article