Neurosurgical management of patients with Ehlers-Danlos syndrome: A descriptive case series.

Introduction: Ehlers-Danlos syndrome (EDS) is a connective tissue disorder that has been linked to several neurological problems including Chiari malformations, atlantoaxial instability (AAI), craniocervical instability (CCI), and tethered cord syndrome. However, neurosurgical management strategies for this unique population have not been well-explored to date. The purpose of this study is to explore cases of EDS patients who required neurosurgical intervention to better characterize the neurological conditions they face and to better understand how neurosurgeons should approach the management of these patients. Methods: A retrospective review was done on all patients with a diagnosis of EDS who underwent a neurosurgical operation with the senior author (FAS) between January 2014 and December 2020. Demographic, clinical, operative, and outcome data were collected, with additional radiographic data collected on patients chosen as case illustrations. Results: Sixty-seven patients were identified who met the criteria for this study. The patients experienced a wide array of preoperative diagnoses, with Chiari malformation, AAI, CCI, and tethered cord syndrome representing the majority. The patients underwent a heterogeneous group of operations with the majority including a combination of the following procedures- suboccipital craniectomy, occipitocervical fusion, cervical fusion, odontoidectomy, and tethered cord release. The vast majority of patients experienced subjective symptomatic relief from their series of procedures. Conclusions: EDS patients are prone to instability, especially in the occipital-cervical region, which may predispose these patients to require a higher rate of revision procedures and may require modifications in neurosurgical management that should be further explored.

Craniocervical Stabilization After Failed Chiari Decompression: A Case Series of a Population with High Prevalence of Ehlers-Danlos Syndrome.

BACKGROUND: In the treatment of Chiari malformation type I (CM-I), posterior fossa decompression is achieved via suboccipital craniectomy (SOC); however, some patients continue to experience symptoms after treatment, which may be due to craniocervical instability (CCI). The purposes of this study were to analyze data from patients who required an occipitocervical fusion (OCF) for the management of CCI after having previously undergone SOC for CM-I to determine if OCF is a safe and effective option and to determine any identifiable risk factors for CCI in these patients. METHODS: A retrospective review was done on all patients who underwent an OCF performed by the senior author between November 2013 and June 2020 after having previously undergone SOC for CM-I. Demographic, radiographic, perioperative, and outcome data were collected and clivoaxial angles (CXAs) were measured pre- and postoperatively. RESULTS: Fifteen patients were identified who developed symptomatic CCI after previously undergoing a suboccipital craniectomy for the treatment of CM-I. All 15 patients were treated by OCF with good outcome. Of these, 12 patients had a known diagnosis of Ehlers-Danlos syndrome (EDS). Overall, the CXAs of these patients were found to be corrected to a more anatomical alignment. CONCLUSIONS: Symptomatic CCI should be recognized as a delayed postoperative complication in the surgical treatment of CM-I, with an underlying connective tissue hypermobility disorder such as EDS serving as a potential risk factor its development. CCI can be managed with OCF as a safe and effective treatment option for this patient population.

Robot-assisted and augmented reality-assisted spinal instrumentation: a systematic review and meta-analysis of screw accuracy and outcomes over the last decade.

OBJECTIVE: The use of technology-enhanced methods in spine surgery has increased immensely over the past decade. Here, the authors present the largest systematic review and meta-analysis to date that specifically addresses patient-centered outcomes, including the risk of inaccurate screw placement and perioperative outcomes in spinal surgeries using robotic instrumentation and/or augmented reality surgical navigation (ARSN). METHODS: A systematic review of the literature in the PubMed, EMBASE, Web of Science, and Cochrane Library databases spanning the last decade (January 2011-November 2021) was performed to present all clinical studies comparing robot-assisted instrumentation and ARSN with conventional instrumentation techniques in lumbar spine surgery. The authors compared these two technologies as they relate to screw accuracy, estimated blood loss (EBL), intraoperative time, length of stay (LOS), perioperative complications, radiation dose and time, and the rate of reoperation. RESULTS: A total of 64 studies were analyzed that included 11,113 patients receiving 20,547 screws. Robot-assisted instrumentation was associated with less risk of inaccurate screw placement (p < 0.0001) regardless of control arm approach (freehand, fluoroscopy guided, or navigation guided), fewer reoperations (p < 0.0001), fewer perioperative complications (p < 0.0001), lower EBL (p = 0.0005), decreased LOS (p < 0.0001), and increased intraoperative time (p = 0.0003). ARSN was associated with decreased radiation exposure compared with robotic instrumentation (p = 0.0091) and fluoroscopy-guided (p < 0.0001) techniques. CONCLUSIONS: Altogether, the pooled data suggest that technology-enhanced thoracolumbar instrumentation is advantageous for both patients and surgeons. As the technology progresses and indications expand, it remains essential to continue investigations of both robotic instrumentation and ARSN to validate meaningful benefit over conventional instrumentation techniques in spine surgery.

Non-invasive in situ Visualization of the Murine Cranial Vasculature.

Understanding physiologic and pathologic central nervous system function depends on our ability to map the entire in situ cranial vasculature and neurovascular interfaces. To accomplish this, we developed a non-invasive workflow to visualize murine cranial vasculature via polymer casting of vessels, iterative sample processing and micro-computed tomography, and automatic deformable image registration, feature extraction, and visualization. This methodology is applicable to any tissue and allows rapid exploration of normal and altered pathologic states.

Leveraging machine learning to characterize the role of socio-economic determinants on physical health and well-being among veterans.

INTRODUCTION: We investigate the contribution of demographic, socio-economic, and geographic characteristics as determinants of physical health and well-being to guide public health policies and preventative behavior interventions (e.g., countering coronavirus). METHODS: We use machine learning to build predictive models of overall well-being and physical health among veterans as a function of these three sets of characteristics. We link Gallup's U.S. Daily Poll between 2014 and 2017 over a range of demographic and socio-economic characteristics with zipcode characteristics from the Census Bureau to build predictive models of overall and physical well-being. RESULTS: Although the predictive models of overall well-being have weak performance, our classification of low levels of physical well-being performed better. Gradient boosting delivered the best results (80.2% precision, 82.4% recall, and 80.4% AUROC) with perceptions of purpose in the workplace and financial anxiety as the most predictive features. Our results suggest that additional measures of socio-economic characteristics are required to better predict physical well-being, particularly among vulnerable groups, like veterans. CONCLUSION: Socio-economic characteristics explain large differences in physical and overall well-being. Effective predictive models that incorporate socio-economic data will provide opportunities to create real-time and personalized feedback to help individuals improve their quality of life.

Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features.

Purpose: Tinnitus is a common but obscure auditory disease to be studied. This study will determine whether the connectivity features in electroencephalography (EEG) signals can be used as the biomarkers for an efficient and fast diagnosis method for chronic tinnitus. Methods: In this study, the resting-state EEG signals of tinnitus patients with different tinnitus locations were recorded. Four connectivity features [including the Phase-locking value (PLV), Phase lag index (PLI), Pearson correlation coefficient (PCC), and Transfer entropy (TE)] and two time-frequency domain features in the EEG signals were extracted, and four machine learning algorithms, included two support vector machine models (SVM), a multi-layer perception network (MLP) and a convolutional neural network (CNN), were used based on the selected features to classify different possible tinnitus sources. Results: Classification accuracy was highest when the SVM algorithm or the MLP algorithm was applied to the PCC feature sets, achieving final average classification accuracies of 99.42 or 99.1%, respectively. And based on the PLV feature, the classification result was also particularly good. And MLP ran the fastest, with an average computing time of only 4.2 s, which was more suitable than other methods when a real-time diagnosis was required. Conclusion: Connectivity features of the resting-state EEG signals could characterize the differentiation of tinnitus location. The connectivity features (PCC and PLV) were more suitable as the biomarkers for the objective diagnosing of tinnitus. And the results were helpful for clinicians in the initial diagnosis of tinnitus.

Real-Time Evaluation of Anterior Choroidal Artery Patency During Aneurysm Clipping.

Inadvertent occlusion of the anterior choroidal artery during aneurysm clipping can cause a disabling stroke in minutes. We evaluate the clinical utility of direct cortical motor evoked potential (MEP) monitoring during aneurysm clipping, as a real-time assessment of arterial patency, prior to performing indocyanine green videoangiography.   Direct cortical MEPs were recorded in seven patients undergoing surgery for aneurysms that involved or abutted the anterior choroidal artery. The aneurysms clipped in those seven patients included four anterior choroidal artery aneurysms and six posterior communicating artery aneurysms. Serial MEP recordings were performed during the intradural dissection, aneurysm exposure, and clip placement. A significant change in MEPs after clip placement would prompt immediate inspection and removal or repositioning of the clip. If the clip placement appeared satisfactory and MEP recordings were stable, then an intraoperative indocyanine green videoangiogram was performed to confirm obliteration of the aneurysm and patency of the arteries.  Seven patients underwent successful clipping of anterior choroidal artery aneurysms and posterior communicating artery aneurysms using direct cortical MEP monitoring, with good clinical and radiographic outcomes. In six patients, no changes in MEP amplitudes were observed following permanent clip placement. In one patient, a profound decrease in MEP amplitude occurred 220 seconds after placement of a permanent clip on a large posterior communicating aneurysm. An inspection revealed that the anterior choroidal artery was kinked. The clip was immediately removed, and the MEP signals returned to baseline shortly thereafter. A clip was then optimally placed, and the patient awoke without neurologic deficit.  Direct cortical MEPs are a useful adjunct to standard electrophysiologic monitoring in aneurysm surgery, particularly when the anterior choroidal artery or lenticulostriate arteries are at risk. When these arteries are occluded, infarction may occur before the occlusion is detected by indocyanine green videoangiography or intraoperative angiography. The use of MEPs allows real-time detection of ischemia to subcortical motor pathways.