Journal of neurosurgery
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Journal of neurosurgery · Mar 2024
Topographical anatomy of the subthalamic region with special interest in the human medial forebrain bundle.
The medial forebrain bundle (MFB) is a novel promising deep brain stimulation (DBS) target in severe affective disorders that courses through the subthalamic region according to tractography studies. Its potential therapeutic role arose in connection with the development of hypomania during stimulation of the subthalamic nucleus (STN) in Parkinson's disease, offering an alternative explanation for the occurrence of this side effect. However, until now its course exclusively described by tractography had not yet been confirmed by any anatomical method. The aim of this study was to fill this gap as well as to provide a detailed description of the fiber tracts surrounding the STN to facilitate a better understanding of the background of side effects occurring during STN DBS. ⋯ According to this study's findings, the streamlines of the MFB described by tractography arise from the limitations of the diffusion-weighted MRI fiber tracking method and actually correspond to subthalamic fiber bundles, especially the ansa lenticularis and lenticular fasciculus, which erroneously continue in the anterior limb of the internal capsule, toward the prefrontal cortex.
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Journal of neurosurgery · Mar 2024
Clinical evaluation of a stereotactic system for single-stage deep brain stimulation surgery under general anesthesia: technical note.
Conventional frame-based stereotactic systems have circumferential base frames, often necessitating deep brain stimulation (DBS) surgery in two stages: intracranial electrode insertion followed by surgical re-preparation and pulse generator implantation. Some patients do not tolerate awake surgery, underscoring the need for a safe alternative for asleep DBS surgery. A frame-based stereotactic system with a skull-mounted "key" in lieu of a circumferential base frame received US FDA clearance. The authors describe the system's application for single-stage, asleep DBS surgery in 8 patients at their institution and review its workflow and technical considerations. ⋯ The stereotactic system facilitated safe and effective asleep, single-stage DBS surgery, maintaining traditional lead accuracy standards.
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Journal of neurosurgery · Mar 2024
Performance of the IMPACT and CRASH prognostic models for traumatic brain injury in a contemporary multicenter cohort: a TRACK-TBI study.
The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization After Significant Head Injury (CRASH) prognostic models for mortality and outcome after traumatic brain injury (TBI) were developed using data from 1984 to 2004. This study examined IMPACT and CRASH model performances in a contemporary cohort of US patients. ⋯ The IMPACT and CRASH models adequately discriminated mortality and unfavorable outcome. Observed overestimations of mortality and unfavorable outcome underscore the need to update prognostic models to incorporate contemporary changes in TBI management and case-mix. Investigations to elucidate the relationships between increased survival, outcome, treatment intensity, and site-specific practices will be relevant to improve models in specific TBI subpopulations (e.g., older adults), which may benefit from the inclusion of blood-based biomarkers, neuroimaging features, and treatment data.
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Journal of neurosurgery · Mar 2024
A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data.
In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP). The authors aimed to develop an ML-based algorithm that automatically classifies normal signals, artifacts, and drainages in high-resolution ICP monitoring data from EVDs, making the data suitable for real-time artifact removal and for future ML applications. ⋯ Here, the authors developed a well-performing supervised model with external validation that can detect normal signals, artifacts, and drainages in ICP signals from patients in neurocritical care units. For future analyses, this is a powerful tool to discard artifacts or to detect drainage events in ICP monitoring signals.