Intraoperative direct electrocortical stimulation for glioma surgery

see also Awake surgery for glioma.

see also Resting-state functional magnetic resonance for glioma surgery.


Stimulation-induced seizures (SISs) are rare but serious events during electrocortical stimulation (ECS) mapping. SISs are most common when mapping the frontal lobe. Greater stimulation current is not associated with the identification of more cortical functional sites during glioma surgery 1).


Glioma surgery represents a significant advance with respect to improving resection rates using new surgical techniques, including intraoperative functional mappingmonitoring, and imaging. Functional mapping under awake craniotomy can be used to detect individual eloquent tissues of speech and/or motor functions in order to prevent unexpected deficits and promote extensive resection. In addition, monitoring the patient’s neurological findings during resection is also very useful for maximizing the removal rate and minimizing deficits by alarming that the touched area is close to eloquent regions and fibers. Assessing several types of evoked potentials, including motor evoked potentials (MEPs), sensory evoked potentials (SEPs), and visual evoked potentials (VEPs), is also helpful for performing surgical monitoring in patients under general anesthesia (GA) 2).


The greater extent of resection (EOR) of low-grade gliomas is associated with improved survival. Proximity to eloquent cortical regions often limits resectability and elevates the risk of surgery-related deficits. Therefore, functional localization of eloquent cortex or subcortical fiber tracts can enhance the EOR and functional outcomeImaging techniques such as functional MRI and diffusion tensor imaging fiber tracking, and neurophysiological methods like navigated transcranial magnetic stimulation and magnetoencephalography, make it possible to identify eloquent areas prior to resective surgery and to tailor indication and surgical approach but also to assess the surgical risk. Intraoperative monitoring with direct cortical stimulation and subcortical stimulation enables surgeons to preserve essential functional tissue during surgery. Through tailored, pre-and intraoperative mapping and monitoring the EOR can be maximized, with reduced rates of surgery-related deficits 3).


As the most accurate and reliable method of brain functional area positioning, Intraoperative direct electrocortical stimulation is able to determine in real-time the parts of the brain necessary for such functions as movementsensationlanguage, and even memory. A meta-analysis suggested that it could also improve the degree of resection of glioma while reducing the incidence of permanent neurological dysfunction 4).


Findings suggest that surgeons using Intraoperative direct electrocortical stimulation and awake craniotomy during their resections of high-grade glioma in eloquent areas experienced better surgical outcomes: a significantly longer overall postoperative survival, a lower rate of postoperative complications, and a higher percentage of GTR 5).


Resting-state functional magnetic resonance imaging likely reflects similar neural information as detected with intraoperative direct electrocortical stimulation (DES), but in its current form does not reach the spatial resolution of DES. 6).


1)

Muster RH, Young JS, Woo PYM, Morshed RA, Warrier G, Kakaizada S, Molinaro AM, Berger MS, Hervey-Jumper SL. The Relationship Between Stimulation Current and Functional Site Localization During Brain Mapping. Neurosurgery. 2021 May 13;88(6):1043-1050. doi: 10.1093/neuros/nyaa364. PMID: 33289525; PMCID: PMC8117445.
2)

Saito T, Muragaki Y, Maruyama T, Tamura M, Nitta M, Okada Y. Intraoperative Functional Mapping and Monitoring during Glioma Surgery. Neurol Med Chir (Tokyo). 2015;55 Suppl 1:1-13. PMID: 26236798.
3)

Ottenhausen M, Krieg SM, Meyer B, Ringel F. Functional preoperative and intraoperative mapping and monitoring: increasing safety and efficacy in glioma surgery. Neurosurg Focus. 2015 Jan;38(1):E3. doi: 10.3171/2014.10.FOCUS14611. PMID: 25552283.
4)

De Witt Hamer PC, Robles SG, Zwinderman AH, Duffau H, Berger MS. Impact of intraoperative stimulation brain mapping on glioma surgery outcome: a meta-analysis. J Clin Oncol. 2012;30:2559–2565. doi: 10.1200/JCO.2011.38.4818.
5)

Gerritsen JKW, Arends L, Klimek M, Dirven CMF, Vincent AJE. Impact of intraoperative stimulation mapping on high-grade glioma surgery outcome: a meta-analysis. Acta Neurochir (Wien). 2019 Jan;161(1):99-107. doi: 10.1007/s00701-018-3732-4. Epub 2018 Nov 21. PMID: 30465276; PMCID: PMC6331492.
6)

van Lieshout J, Debaene W, Rapp M, Noordmans HJ, Rutten GJ. fMRI Resting-State Connectivity between Language and Nonlanguage Areas as Defined by Intraoperative Electrocortical Stimulation in Low-Grade Glioma Patients. J Neurol Surg A Cent Eur Neurosurg. 2021 Feb 22. doi: 10.1055/s-0040-1721757. Epub ahead of print. PMID: 33618418.

Intraoperative microelectrode recording

Intraoperative microelectrode recording

Microelectrode recording (MER) is used to confirm targeting accuracy during deep brain stimulation (DBS) surgery.


While the efficacy of deep brain stimulation (DBS) to treat various neurological disorders is undisputed, the surgical methods differ widely and the importance of intraoperative microelectrode recording (MER) or macrostimulation (MS) remains controversially debated 1).


At many centers around the world that treat movement disorders, the gold standard for optimally targeting the sensorimotor area of the STN currently relies on microelectrode recording (MER) of single and multi-neuron activity traversing the planned surgical trajectories.

see HaGuide Tool.

When extracellular microelectrodes (tens of microns in diameter) are placed within the brain, they record the extracellular electric field generated by multiple nearby spiking neurons. This is the basis of the microelectrode recording technique used daily by many functional neurosurgeons, and is core to the development of various brain computer interfaces.


The functional regions clustering through microelectrode recording (MER) is a critical step in deep brain stimulation (DBS) surgery. The localization of the optimal target highly relies on the neurosurgeon’s empirical assessment of the neurophysiological signal. This work presents an unsupervised clustering algorithm to get the optimal cluster result of the functional regions along the electrode trajectory.

The dataset consists of the MERs obtained from the routine bilateral DBS for PD patients. Several features have been extracted from MER and divided into groups based on the type of neurophysiological signal. We selected single feature groups rather than all features as the input samples of each division of the divisive hierarchical clustering (DHC) algorithm. And the optimal cluster result has been achieved through a feature group combination selection (FGS) method based on genetic algorithm (GA). To measure the performance of this method, we compared the accuracy and validation indexes of three methods, including DHC only, DHC with GA-based FGS and DHC with GA-based feature selection (FS) in other studies, on the universal and DBS datasets.

Results show that the DHC with GA-based FGS achieved the optimal cluster result compared with other methods. The three borders of the STN can be identified from the cluster result. The dorsoventral sizes of the STN and dorsal STN are 4.45 mm and 2.02 mm. In addition, the features extracted from the multiunit activity, background unit activity and local field potential are found to be the most representative feature groups to identify the dorsal, d-v and ventral borders of the STN, respectively.

The clustering algorithm showed a reliable performance of the automatic identification of functional regions in DBS. The findings can provide valuable assistance for both neurosurgeons and stereotactic surgical robots in DBS surgery 2).


It is unclear which magnetic resonance imaging (MRI) sequence most accurately corresponds with the electrophysiological subthalamic nucleus (STN) obtained during microelectrode recording (MER, MER-STN). CT/MRI fusion allows for comparison between MER-STN and the STN visualized on preoperative MRI (MRI-STN).

Kochanski et al. describe a technique using intraoperative computed tomography (CT) extrapolation (iCTE) to predetermine and adjust the trajectory of the guide tube to improve microelectrode targeting accuracy. They hypothesized that this technique would decrease the number of MER tracks and operative time, while increasing the recorded length of the subthalamic nucleus (STN) 3).

Case series

Krauss et al. included 101 patients who underwent awake bilateral implantation of electrodes in the subthalamic nucleus with microelectrode recording (MER) and macrostimulation (MS) for Parkinson’s disease from 2009 to 2017 in a retrospective observational study. They analyzed intraoperative motor outcomes between anatomically planned stimulation point (PSP) and definite stimulation point (DSP), lead adjustments, and Unified Parkinson’s Disease Rating Scale Item III (UPDRS-III), levodopa equivalent daily dose (LEDD), and adverse events (AE) after 6 months.

They adjusted 65/202 leads in 47/101 patients. In adjusted leads, MS results improved significantly when comparing PSP and DSP (p < 0.001), resulting in a number needed to treat of 9.6. After DBS, UPDRS-III and LEDD improved significantly after 6 months in adjusted and nonadjusted patients (p < 0.001). In 87% of leads, the active contact at 6 months still covered the optimal stimulation point during surgery. In total, 15 AE occurred.

MER and MS have a relevant impact on the intraoperative decision of final lead placement and prevent a substantial rate of poor stimulation outcome. The optimal stimulation points during surgery and chronic stimulation strongly overlap. Follow-up UPDRS-III results, LEDD reductions, and DBS-related AE correspond well to previously published data 4).


The precision and accuracy of direct targeting with quantitative susceptibility mapping (QSM) was examined in a total of 25 Parkinson’s disease patients between 2013 and 2015 at the Department of Neurosurgery, Mount Sinai Health System, New York. QSM was utilized as the primary magnetic resonance imaging (MRI) method to perform direct STN targeting on a stereotactic planning station utilizing computed tomography/MR fusion. Intraoperative microelectrode recordings (MER) were obtained to confirm appropriate trajectory through the sensorimotor STN.

Estimations of STN thickness between the MER and QSM methods appeared to be correlated. Mean STN thickness was 5.3 mm. Kinesthetic responsive cells were found in > 90% of electrode runs. The mean radial error (±SEM) was 0.54 ± 0.1 mm. Satisfactory clinical response as determined by Unified Parkinson’s Disease Rating Scale (UPDRS III) was seen at 12 mo after surgery.

Direct targeting of the sensorimotor STN using QSM demonstrates MER correlation and can be safely used for deep brain stimulation lead placement with satisfactory clinical response. These results imply that targeting based on QSM signaling alone is sufficient to obtain reliable and reproducible outcomes in the absence of physiological recordings 5).

In their analysis, Rasouli et al accept that the raw measurements they derived by the 2 methods (microelectrode recording [MER] vs quantitaive susceptibility mapping [QSM]) do not exhibit a high degree of correlation. They offer several reasons for this (differences in resolution, standard deviations, and narrow range of measurements), thereby justifying the use of normalized data and the Bland–Altman analysis. In contrast to the Bland–Altman analysis, which suggests agreement, the intra-correlation coefficient (ICC) = 0.12 implies that there is high variability between QSM and MER measurements within an individual (ie, they are not in good agreement). More useful in our view would be to see how well the actual measurements made with the 2 methods agree on a case by case basis. How often do the measurements agree within 0.1, 0.5, 1, 2 mm, etc.? Such valuable information would allow the readers to decide for themselves whether a measured subthalamic nucleus (STN) span on QSM is a legitimate proxy for the gold standard of measuring the STN with MER and we urge the authors to publish this data in a subsequent letter 6).

References

1) , 4)

Krauss P, Oertel MF, Baumann-Vogel H, Imbach L, Baumann CR, Sarnthein J, Regli L, Stieglitz LH. Intraoperative Neurophysiologic Assessment in Deep Brain Stimulation Surgery and its Impact on Lead Placement. J Neurol Surg A Cent Eur Neurosurg. 2020 Oct 13. doi: 10.1055/s-0040-1716329. Epub ahead of print. PMID: 33049794.
2)

Cao L, Jie L, Zhou Y, Liu Y, Liu H. Automatic feature group combination selection method based on GA for the functional regions clustering in DBS. Comput Methods Programs Biomed. 2019 Sep 23;183:105091. doi: 10.1016/j.cmpb.2019.105091. [Epub ahead of print] PubMed PMID: 31590098.
3)

Kochanski RB, Bus S, Pal G, Metman LV, Sani S. Optimization of Microelectrode Recording in Deep Brain Stimulation Surgery Using Intraoperative Computed Tomography. World Neurosurg. 2017 Jul;103:168-173. doi: 10.1016/j.wneu.2017.04.003. Epub 2017 Apr 10. PubMed PMID: 28408262.
5)

Rasouli J, Ramdhani R, Panov FE, Dimov A, Zhang Y, Cho C, Wang Y, Kopell BH. Utilization of Quantitative Susceptibility Mapping for Direct Targeting of the Subthalamic Nucleus During Deep Brain Stimulation Surgery. Oper Neurosurg (Hagerstown). 2018 Apr 1;14(4):412-419. doi: 10.1093/ons/opx131. PubMed PMID: 28531270.
6)

Alterman RL, Fleishman A, Ngo L. In Reply: Commentary: Utilization of Quantitative Susceptibility Mapping for Direct Targeting of the Subthalamic Nucleus During Deep Brain Stimulation Surgery. Oper Neurosurg (Hagerstown). 2018 Jul 13. doi: 10.1093/ons/opy139. [Epub ahead of print] PubMed PMID: 30011048.

Intraoperative Ultrasound for Spine Surgery

Intraoperative Ultrasound for Spine Surgery

Accurate and efficient registration of pre-operative computed tomography or magnetic resonance images with iUS images are key elements in the success of iUS-based spine navigation. While widely investigated in research, iUS-based spine navigation has not yet been established in the clinic. This is due to several factors including the lack of a standard methodology for the assessment of accuracy, robustness, reliability, and usability of the registration method. To address these issues, Gueziri et al. presented a systematic review of the state-of-the-art techniques for iUS-guided registration in spinal image guided surgery (IGS). The review follows a new taxonomy based on the four steps involved in the surgical workflow that include pre-processing, registration initialization, estimation of the required patient to image transformation, and a visualization process. They provided a detailed analysis of the measurements in terms of accuracy, robustness, reliability, and usability that need to be met during the evaluation of a spinal IGS framework. Although this review is focused on spinal navigation, they expect similar evaluation criteria to be relevant for other IGS applications 1).


Intraoperative ultrasound (iUS) has been applied in spinal surgery for all kinds of diseases 2) 3) ranging from trauma, 4) degenerative diseases, 5) 6) developmental malformations, 7) vascular diseases, 8). to imaging in spinal tumor surgery

Intraoperative Ultrasound for spinal tumor surgery

Intraoperative Ultrasound for spinal tumor surgery

Syringomyelia

Intraoperative ultrasound is often helpful for:

a) localizing the cyst

b) assessing for septations (to avoid shunting only part of cyst)

Controversial,for intramedullary spinal cord tumors 9) favored by some experts. Astrocytomas are usually iso-echoic with the spinal cord, whereas ependymomas are usually hyperechoic.

Transpedicular thoracic discectomy

Intraoperative ultrasound is a simple yet valuable tool for real-time imaging during transpedicular thoracic discectomy. Visualization provided by intraoperative US increases the safety profile of posterior approaches and may make thoracotomy unnecessary in a selected group of patients, especially when a patient has existing pulmonary disease or is otherwise not medically fit for the transthoracic approach 10) 11).

References

1)

Gueziri HE, Santaguida C, Collins DL. The state-of-the-art in ultrasound-guided spine interventions [published online ahead of print, 2020 Jun 26]. Med Image Anal. 2020;65:101769. doi:10.1016/j.media.2020.101769
2)

Ganau M, Syrmos N, Martin AR, Jiang F, Fehlings MG. Intraoperative ultrasound in spine surgery: history, current applications, future developments. Quant Imaging Med Surg. 2018;8: 261-267.
3)

Vasudeva VS, Abd-El-Barr M, Pompeu YA, Karhade A, Groff MW, Lu Y. Use of intraoperative ultrasound during spinal surgery. Glob Spine J. 2017;7:648-656.
4)

Meinig H, Doffert J, Linz N, Konerding MA, Gercek E, Pitzen T. Sensitivity and specificity of ultrasound in spinal trauma in 29 consecutive patients. Eur Spine J. 2015;24:864-870.
5)

Nishimura Y, Thani NB, Tochigi S, Ahn H, Ginsberg HJ. Thoracic discectomy by posterior pedicle-sparing, transfacet approach with realtime intraoperative ultrasonography: clinical article. J Neurosurg Spine. 2014;21:568-576.
6)

Goodkin R, Haynor DR, Kliot M. Intraoperative ultrasound for monitoring anterior cervical vertebrectomy. Technical note. J Neurosurg. 1996;84: 702-704.
7)

. Cui LG, Jiang L, Zhang HB, et al. Monitoring of cerebrospinal fluid flow by intraoperative ultrasound in patients with Chiari I malformation. Clin Neurol Neurosurg. 2011;113:173-176.
8)

Prada F, Del Bene M, Farago G, DiMeco F. Spinal dural arteriovenous fistula: is there a role for intraoperative contrast-enhanced ultrasound? World Neurosurg. 2017;100:712.e15-712.e18.
9)

Albright AL. Pediatric Intramedullary Spinal Cord Tumors. Childs Nerv Syst. 1999; 15:436–437
10)

Tan LA, Lopes DK, Fontes RB. Ultrasound-guided posterolateral approach for midline calcified thoracic disc herniation. J Korean Neurosurg Soc. 2014 Jun;55(6):383-6. doi: 10.3340/jkns.2014.55.6.383. Epub 2014 Jun 30. PubMed PMID:25237439.
11)

Nishimura Y, Thani NB, Tochigi S, Ahn H, Ginsberg HJ. Thoracic discectomy by posterior pedicle-sparing, transfacet approach with real-time intraoperative ultrasonography. J Neurosurg Spine. 2014 Jul 18:1-9. [Epub ahead of print] PubMed PMID: 25036220.
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