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.
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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).