Posterior resection of sacral osteosarcoma utilizing cement-infused chest tube interbody reconstruction and lumbopelvic fusion

Background: Primary osteosarcoma (OS) of the spine is very rare. En bloc resection of spinal OS is challenging due to anatomical constraints. Surgical planning must balance the benefits of en bloc resection with its potential risks of causing a significant neurological deficit. In this case, we successfully performed a posterior-only approach for decompression with S1 reconstruction via a cement-infused chest tube interbody device, along with a navigated L4 to pelvis fusion. Case Description: A 49-year-old female presented with a primary sacral OS. Computed tomography (CT) and magnetic resonance (MR) imaging revealed an S1 lytic vertebral body lesion with severe stenosis and progressive L5 on S1 anterior subluxation. Surgical decompression with tumor resection and S1 corpectomy with S1 reconstruction via a cement-infused 32-French chest tube interbody device accompanied by L4 -pelvis fusion utilizing S2-alar-iliac screws was completed. 6 months postoperatively, the patient continues to have significant pain relief and the instrumentation remains intact. Conclusion: A 49-year-old female with an S1 OS successfully underwent a posterior-only approach that included an S1 corpectomy with anterior column reconstruction via a cement-infused chest tube interbody plus a navigated L4 to pelvis fusion.

A Primer on Design and Data Analysis for Cognitive Pupillometry

This chapter presents an accessible overview of methodological considerations, open questions, and solutions to common problems encountered conducting a valid and reliable cognitive pupillometry study. Topics include historical evolution of pupillary measurement techniques, parameterization of the human task-evoked (cognitive) pupil response, individual differences, and idiosyncratic anatomical constraints imposed by the human eye.

Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy

Accurate localization and analyses of functional liver segments are crucial in devising various surgical procedures, including hepatectomy. To this end, they require the extraction of a liver from computed tomography, and then the identification of resection correspondence between individuals. The first part is usually impeded by inherent deficiencies, as present in medical images, and vast anatomical variations across subjects. While the model-based approach is found viable to tackle both issues, it is often undermined by an inadequate number of labeled samples, to capture all plausible variations. To address segmentation problems by balancing between accuracy, resource consumption, and data availability, this paper presents an efficient method for liver segmentation based on a graph-cut algorithm. One of its main novelties is the incorporation of a feature preserving a metric for boundary separation. Intuitive anatomical constraints are imposed to ensure valid extraction. The second part involves the symmetric conformal parameterization of the extracted liver surface onto a genus-0 domain. Provided with a few landmarks specified on two livers, we demonstrated that, by using a modified Beltrami differential, not only could they be non-rigidly registered, but also the hepatectomy on one liver could be envisioned on another. The merits of the proposed scheme were elucidated by both visual and numerical assessments on a standard MICCAI SLIVER07 dataset.

A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model

Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. Theory-driven efforts will be required to tease apart the functional logic of cortical circuits from the vast amounts of experimental data on cortical connectivity and physiology. Although the theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation, making precise and falsifiable biological mappings need models that tackle the challenge of real world tasks. Based on a recent generative model, Recursive Cortical Networks, that demonstrated excellent performance on visual task benchmarks, we derive a family of anatomically instantiated and functional cortical circuit models. Efficient inference and generalization guided the representational choices in the original computational model. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints. The derived model suggests precise functional roles for the feed-forward, feedback, and lateral connections observed in different laminae and columns, assigns a computational role for the path through the thalamus, predicts the interactions between blobs and inter-blobs, and offers an algorithmic explanation for the innate inter-laminar connectivity between clonal neurons within a cortical column. The model also explains several visual phenomena, including the subjective contour effect, and neon-color spreading effect, with circuit-level precision. Our work paves a new path forward in understanding the logic of cortical and thalamic circuits.

Magnetoencephalography Source Estimation

This chapter describes the source estimation approaches to magnetoencephalography (MEG) analysis. Both MEG and electroencephalography (EEG) are measures of ongoing neuronal activity, and are ultimately generated by the same sources: postsynaptic currents in groups of neurons which have a geometrical arrangement favoring currents with a uniform direction across nearby neurons. From the outset, the overarching theme of MEG analysis methods has been the desire to transform the signals measured by the MEG sensors outside the head into estimates of source activity. This problem is challenging because of the ill-posed nature of the electromagnetic inverse problem. However, thanks to being able to capitalize on appropriate physiological and anatomical constraints, several reliable and widely used source estimation methods have emerged. The chapter then identifies the forward modeling approaches needed to relate the signals in the source and sensor spaces, and characterizes two popular approaches to source estimation: the parametric dipole model and distributed source estimates. Until 50 years ago, electroencephalography (EEG) was the only noninvasive technique capable of directly measuring neuronal activity with a millisecond time resolution. However, with the birth of magnetoencephalography (MEG), functional brain activity can now be resolved with this time resolution at a new level of spatial detail. The use of MEG in practical studies began with the first real-time measurements in the beginning of 1970s. During the following decade, multichannel MEG systems were developed in parallel with both investigations of normal brain activity and clinical studies, especially in epileptic patients. The first whole-head MEG system with more than 100 channels was introduced in 1992. Up to now, such instruments have been delivered to researchers and clinicians worldwide. The overarching theme of MEG analysis methods has been from the outset the desire to transform the signals measured by the MEG sensors outside the head into estimates of source activity. This problem is challenging because of the ill-posed nature of the electromagnetic inverse problem. However, thanks to being able to capitalize on appropriate physiological and anatomical constraints, several reliable and widely used source estimation methods have emerged. This chapter starts by describing the overall characteristics of MEG, followed a general description of the source estimation problem. The chapter then discusses the forward modeling approaches needed to relate the signals in the source and sensor spaces, and finally characterizes two popular approaches to source estimation: the parametric dipole model and distributed source estimates.