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  • Author or Editor: Francesco Sammartino x
  • By Author: King, Nicolas Kon Kam x
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Nicolas Kon Kam King, Vibhor Krishna, Diellor Basha, Gavin Elias, Francesco Sammartino, Mojgan Hodaie, Andres M. Lozano and William D. Hutchison

OBJECTIVE

The ventral intermediate nucleus (VIM) of the thalamus is not visible on structural MRI. Therefore, direct VIM targeting methods for stereotactic tremor surgery are desirable. The authors previously described a direct targeting method for visualizing the VIM and its structural connectivity using deterministic tractography. In this combined electrophysiology and imaging study, the authors investigated the electrophysiology within this tractography-defined VIM (T-VIM).

METHODS

Thalamic neurons were classified based on their relative location to the T-VIM: dorsal, within, and ventral to the T-VIM. The authors identified the movement-responsive cells (kinesthetic and tremor cells), performed spike analysis (firing rate and burst index), and local field potential analysis (area under the curve for 13–30 Hz). Tremor efficacy in response to microstimulation along the electrode trajectory was also assessed in relation to the T-VIM.

RESULTS

Seventy-three cells from a total of 9 microelectrode tracks were included for this analysis. Movement-responsive cells (20 kinesthetic cells and 26 tremor cells) were identified throughout the electrode trajectories. The mean firing rate and burst index of cells (n = 27) within the T-VIM are 18.8 ± 9.8 Hz and 4.5 ± 5.4, respectively. Significant local field potential beta power was identified within the T-VIM (area under the curve for 13–30 Hz = 6.6 ± 7.7) with a trend toward higher beta power in the dorsal T-VIM. The most significant reduction in tremor was also observed in the dorsal T-VIM.

CONCLUSIONS

The electrophysiological findings within the VIM thalamus defined by tractography, or T-VIM, correspond with the known microelectrode recording characteristics of the VIM in patients with tremor.

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Rosa Q. So, Vibhor Krishna, Nicolas Kon Kam King, Huijuan Yang, Zhuo Zhang, Francesco Sammartino, Andres M. Lozano, Richard A. Wennberg and Cuntai Guan

OBJECTIVE

The authors explored the feasibility of seizure detection and prediction using signals recorded from the anterior thalamic nucleus, a major target for deep brain stimulation (DBS) in the treatment of epilepsy.

METHODS

Using data from 5 patients (13 seizures in total), the authors performed a feasibility study and analyzed the performance of a seizure prediction and detection algorithm applied to simultaneously acquired scalp and thalamic electroencephalography (EEG). The thalamic signal was obtained from DBS electrodes. The applied algorithm used the similarity index as a nonlinear measure for seizure identification, with patient-specific channel and threshold selection. Receiver operating characteristic (ROC) curves were calculated using data from all patients and channels to compare the performance between DBS and EEG recordings.

RESULTS

Thalamic DBS recordings were associated with a mean prediction rate of 84%, detection rate of 97%, and false-alarm rate of 0.79/hr. In comparison, scalp EEG recordings were associated with a mean prediction rate of 71%, detection rate of 100%, and false-alarm rate of 1.01/hr. From the ROC curves, when considering all channels, DBS outperformed EEG for both detection and prediction of seizures.

CONCLUSIONS

This is the first study to compare automated seizure detection and prediction from simultaneous thalamic and scalp EEG recordings. The authors have demonstrated that signals recorded from DBS leads are more robust than EEG recordings and can be used to predict and detect seizures. These results indicate feasibility for future designs of closed-loop anterior nucleus DBS systems for the treatment of epilepsy.