Rapid Passive Gamma Mapping as an Adjunct to Electrical Stimulation Mapping for Functional Localization in Resection of Primary Brain Neoplasms.

OBJECTIVE: We examined the utility of passive high gamma mapping (HGM) as an adjunct to conventional awake brain mapping during glioma resection. We compared functional and survival outcomes before and after implementing intraoperative HGM. METHODS: This was a retrospective cohort study of 75 patients who underwent a first-time, awake craniotomy for glioma resection. Patients were stratified by whether their operation occurred before or after the implementation of a U.S. Food and Drug Administration-approved high-gamma mapping tool in July 2017. RESULTS: The preimplementation and postimplementation cohorts included 28 and 47 patients, respectively. Median intraoperative time (261 vs. 261 minutes, P = 0.250) and extent of resection (97.14% vs. 98.19%, P = 0.481) were comparable between cohorts. Median Karnofsky performance status at initial follow-up was similar between cohorts (P = 0.650). Multivariable Cox regression models demonstrated an adjusted hazard ratio for overall survival of 0.10 (95% confidence interval: 0.02-0.43, P = 0.002) for the postimplementation cohort relative to the preimplementation cohort. Progression-free survival adjusted for insular involvement showed an adjusted hazard ratio of 1.00 (95% confidence interval: 0.49-2.06, P = 0.999) following HGM implementation. Falling short of statistical significance, prevalence of intraoperative seizures and/or afterdischarges decreased after HGM implementation as well (12.7% vs. 25%, P = 0.150). CONCLUSIONS: Our results tentatively indicate that passive HGM is a safe and potentially useful adjunct to electrical stimulation mapping for awake cortical mapping, conferring at least comparable functional and survival outcomes with a nonsignificant lower rate of intraoperative epileptiform events. Considering the limitations of our study design and patient cohort, further investigation is needed to better identify optimal use cases for HGM.

Graph neural networks in EEG spike detection.

OBJECTIVE: This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital. METHODS: Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN. Attention mechanism is applied on a complete graph to let the neural network select its important edges, hence bypassing the extraction of features, a model we refer to as CA-GNN. RESULTS: On the Baptist Hospital dataset, the results were as follows: Vanilla Self-Attention →0.9029±0.0431, Hierarchical Attention →0.8546±0.0587, Vanilla Visual Geometry Group (VGG) →0.92±0.0618, Satelight →0.9219±0.046, FC-GNN →0.9731±0.0187, and CA-GNN →0.9788±0.0125. In the same order, the results on the Temple University Hospital dataset are 0.9692, 0.9113, 0.97, 0.9575, 0.963, and 0.9879. CONCLUSION: Based on the good results they yield, GNNs prove to have a strong potential in detecting epileptogenic activity. SIGNIFICANCE: This study opens the door for the discovery of the powerful role played by GNNs in capturing IEDs, which is an essential step for identifying the epileptogenic networks of the affected brain and hence improving the prospects for more accurate 3D source localization.

Dynamics and Distant Effects of Frontal/Temporal Epileptogenic Focus Using Functional Connectivity Maps.

OBJECTIVE: Connectivity patterns of interictal epileptiform discharges are all subtle indicators of where the three-dimensional (3D) source of a seizure could be located. These specific patterns are explored in the recorded electroencephalogram (EEG) signals of 20 individuals diagnosed with focal epilepsy to assess how their functional brain maps could be affected by the 3D onset of a seizure. METHODS: Functional connectivity maps, estimated by phase synchrony among EEG electrodes, were obtained by applying a data-driven recurrence-based method. This is augmented through a novel approach for selecting optimal parameters that produce connectivity matrices that are deemed significant for assessing epileptiform activity in context to the 3D source localization of seizure onset. These functional connectivity matrices were evaluated in different brain areas to gauge the regional effects of the 3D epileptic source. RESULTS: Empirical evaluations indicate high synchronization in the temporal and frontal areas of the effected epileptic hemisphere, whereas strong links connect the irritated area to frontal and temporal lobes of the opposite hemisphere. CONCLUSION: Epileptic activity originating in the temporal or frontal areas is seen to affect these areas in both hemispheres. SIGNIFICANCE: The results obtained express the dynamics of focal epilepsy in context to both the epileptogenic zone and the affected distant areas of the brain.

Intraoperative Neurophysiologic Monitoring for Adult Patients Undergoing Posterior Spinal Fusion.

BACKGROUND: Somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs) are frequently used to monitor neurologic function during spinal deformity surgery. The sensitivity and specificity of intraoperative neurophysiologic monitoring (IONM) in patients undergoing posterior spinal fusion (PSF) is debatable. METHODS: A retrospective review of all patients undergoing PSF with IONM from October 2008 to December 2012 was performed. Factors including sex, operative time, and spinal levels of posterior fusion were analyzed as risk factors for intraoperative alerts. RESULTS: A total of 784 consecutive patients who underwent PSF with IONM without any baseline deficits were analyzed. Patients included 45% men (n = 356) and 55% women (n = 428), with a mean age of 56 years. The mean procedure time was 7 hours. Intraoperative alerts were noted for 3.3% (n = 26) of patients. In this cohort, the average number of levels involved per procedure was approximately 7, ranging from 1 to 16 levels. Of all the spinal levels, the cervicothoracic region had the highest incidence of intraoperative alerts (6 of 97 cervicothoracic cases, P = 0.06). Among these patients, age (P = 0.32), sex (P = 0.66), and procedure time (P = 0.63) were not predictive factors. Four out of 26 (15%) patients had neurologic deficits despite surgeon intervention after neuromonitoring alerts. CONCLUSIONS: SSEP and MEP changes occurred in 3.3% of patients undergoing PSF, with the highest incidence at the cervicothoracic level. Twenty-three out of 26 patients with intraoperative neuromonitoring changes had improvements in IONM signals after interventions during surgery. Further studies using larger patient numbers may be useful in establishing the utility of neuromonitoring in PSF.

Intraoperative neurophysiological monitoring in anterior lumbar interbody fusion surgery.

PURPOSE: Somatosensory evoked potential (SSEP) and motor evoked potentials (MEP) are frequently fused to monitor neurological function during spinal deformity surgery. However, there are few studies regarding the utilization of intraoperative neuromonitoring during anterior lumbar interbody fusion (ALIF). This study presents the authors' experience with intraoperative neuromonitoring in ALIF. METHODS: A retrospective review of all patients undergoing ALIF with intraoperative neuromonitoring from November 2008 to July 2013 was performed. Factors including gender, operative time, blood loss, and number and levels of interbody fusions were analyzed as risk factors for interoperational alerts. RESULTS: A total of 189 consecutive patients who underwent ALIFs were studied. All 189 patients had SSEP, and 131 patients had MEP as part of the intraoperative neuromonitoring in addition. The remaining 58 patients did not have MEP due to neuromuscular blockade requested by the exposure surgeon. There were no isolated intraoperative MEP changes. A total of 15 (7.9%) patients experienced intraoperative alerts. Thirteen (6.8%) of them were in SSEP. Two (1.1%) had MEP and SSEP changes together. None of these patients had new neurologic deficits postoperatively because of the surgeon's responses to the intraoperative alert. Increased risk of SSEP changes was seen in patients undergoing fusion of both L4/5 and L5/S1 (P = 0.024) and longer surgical duration (P = 0.036). No correlation was found between age and positive SSEP changes (P > 0.05). CONCLUSIONS: Somatosensory evoked potential changes occur relatively, frequently, and intraoperatively during ALIF. No patients with positive intraoperative SSEP changes demonstrated new postoperational deficits. Concurrent fusion of both the L4/5 and L5/S1 levels was significant risk factors for SSEP changes leading to intraoperative alerts. Operative duration and increased blood loss during surgery trended toward but did not reach statistical significance.

Quasi-stationarity of EEG for intraoperative monitoring during spinal surgeries.

We present a study and application of quasi-stationarity of electroencephalogram for intraoperative neurophysiological monitoring (IONM) and an application of Chebyshev time windowing for preconditioning SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarity of EEG on 12 preconditioned trials. This method is shown empirically to be more clinically viable than present day approaches. In all twelve cases, the algorithm takes 4 sec to extract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provide a much improved and effective neurophysiological monitoring process.

Peak detection of somatosensory evoked potentials using an integrated principal component analysis-walsh method.

Clinical application of somatosensory evoked potentials (SSEP) in intraoperative neurophysiological monitoring still requires anywhere between 200 to 500 trials, which is excessive and introduces a delay during surgery. In this study, the analysis was performed on the data recorded in 20 patients undergoing surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp. The first 10 trials were analyzed using an eigen decomposition technique, and a signal extraction algorithm eliminated the common components of the signals not contributing to the SSEP. A unique Walsh transform operation was then used to identify the position of the SSEP event within the clinical requirements of 10% time in latency deviation and 50% peak-to-peak amplitude deviation using only 10 trials. The algorithm also shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials.

An effective intra-operative neurophysiological monitoring scheme for aneurysm clipping and spinal fusion surgeries.

Somatosensory-evoked potentials (SSEPs) have been widely used for intra-operative neurophysiological monitoring (IONM). Currently at least 200-300 trials are required to generate a readable SSEP signal. This study introduces a novel approach that yields accurate detection results of the SSEP signal yet with a significantly reduced number of trials, resulting in an effectual monitoring process. The analysis was performed on data recorded in seven patients undergoing surgery, where the posterior tibial nerve was stimulated and the SSEP response was recorded from scalp electroencephalography using two bipolar electrodes, C(3)-C(4) and C(Z)-F(Z). The proposed approach employs an innovative, simple yet effective algorithm based on a patient-specific Gaussian template to detect the SSEP using only 30 trials. The time latencies of the P37 and N45 peaks are detected along with the peak-to-peak amplitudes. The time latencies are detected with a mean accuracy greater than 95%. Also, the P37 and N45 peak latencies and the peak-to-peak amplitude were found to be consistent throughout the surgical procedure within the 10% and 50% acceptable clinical limits, respectively. The results obtained support the assertion that the algorithm is capable of detecting SSEPs with high accuracy and consistency throughout the entire surgical procedure using only 30 trials.

Intraoperative neurophysiological monitoring of somatosensory evoked potentials during hip arthroscopy surgery.

Arthroscopic hip surgery is used to treat many of the causes of hip pain, hip instability, and hip disorders. Hip pain and instability are often caused by injuries to the acetabular labrum. Repairing labral tears, suturing, and debridement involve stabilizing the hip and placing the operative side leg in traction (Phillipon 2006, Phillipon and Schenker 2006) to allow for instrument clearance and to avoid iatrogenic injury to the chondral surfaces. This places the sciatic nerve in a stretched position and may cause temporary or permanent nerve injury. Transient neuropraxia is the most common injury occurring in 5% of the patients undergoing arthroscopic hip surgery (McCarthy and Lee 2006). 35 patients; 24 women and 11 men, (a total of 36 surgeries) were monitored with intraoperative neurophysiological monitoring using somatosensory evoked potentials (SSEPs) during hip arthroscopy for labral repair and femoral head osteoplasty. They ranged in age from 15 to 59 years; mean age: 39.81 years. During surgery 19 (54%) patients experienced significant SSEP waveform changes. Time from placement of traction to loss of signals in those patients experiencing SSEP changes ranged from 7 minutes to 46 minutes. Recovery of SSEP signals ranged from 2 minutes to over 15 minutes when the traction of the leg was released. Surgeries ranged from 2 to 4 hours; mean: 2.78 hours. These findings show that neuromonitoring during hip arthroscopic labral repair and debridement procedures might be useful to prevent temporary and permanent neural tissue injuries.