Epilepsy surgery and intracranial monitoring have a long history in the Kingdom of Saudi Arabia, spanning over 30 years. Stereo-EEG however, is a more recent offering. In this short communication, we discuss how Stereo-EEG has grown in the context of the Kingdom's healthcare model and the Vision 2030 model. We discuss the various positives of this technique and methodology as well as the various challenges that the hospitals offering Stereo-EEG have faced.
To explore the factors associated with poor prognosis in critically ill patients with Electroencephalogram (EEG) patterns exhibiting stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs), and to construct a prognostic prediction model.
This study included a total of 53 critically ill patients with EEG patterns exhibiting SIRPIDs who were admitted to the First Affiliated Hospital of Chongqing Medical University from May 2023 to March 2024. Patients were divided into two groups based on their Modified Rankin Scale (mRS) scores at discharge: good prognosis group (0–3 points) and poor prognosis group (4–6 points). Retrospective analyses were performed on the clinical and EEG parameters of patients in both groups. Logistic regression analysis was applied to identify the risk factors related to poor prognosis in critically ill patients with EEG patterns exhibiting SIRPIDs; a risk prediction model for poor prognosis was constructed, along with an individualized predictive nomogram model, and the predictive performance and consistency of the model were evaluated.
Multivariate logistic regression analysis revealed that APACHE II score (OR=1.217, 95 %CI=1.030∼1.438), slow frequency bands or no obvious brain electrical activity (OR=8.720, 95 %CI=1.220∼62.313), and no sleep waveforms (OR=9.813, 95 %CI=1.371∼70.223) were independent risk factors for poor prognosis in patients. A regression model established based on multivariate logistic regression analysis had an area under the curve of 0.902. The model's accuracy was 90.60 %, with a sensitivity of 92.86 % and a specificity of 89.70 %. The nomogram model, after internal validation, showed a concordance index of 0.904.
A high APACHE II score, EEG patterns with slow frequency bands or no obvious brain electrical activity, and no sleep waveforms were independent risk factors for poor prognosis in patients with SIRPIDs. The nomogram model constructed based on these factors had a favorably high level of accuracy in predicting the risk of poor prognosis and held certain reference and application value for clinical neurofunctional assessment and prognostic determination.
Explore how anatomical measurements and field modeling can be leveraged to improve investigations of transcranial magnetic stimulation (TMS) effects on both motor and non-motor TMS targets.
TMS motor effects (targeting the primary motor cortex [M1]) were evaluated using the resting motor threshold (rMT), while TMS non-motor effects (targeting the superior temporal gyrus [STG]) were assessed using a pain memory task. Anatomical measurements included scalp-cortex distance (SCD) and cortical thickness (CT), whereas field modeling encompassed the magnitude of the electric field (E) induced by TMS.
Anatomical measurements and field modeling values differed significantly between M1 and STG. For TMS motor effects, rMT was correlated with SCD, CT, and E values at M1 (p < 0.05). No correlations were found between these metrics for the STG and TMS non-motor effects (pain memory; all p-values > 0.05).
Although anatomical measurements and field modeling are closely related to TMS motor effects, their relationship to non-motor effects – such as pain memory – appear to be much more tenuous and complex, highlighting the need for further advancement in our use of TMS and virtual lesion paradigms.