Integrating Data Directly into Publications with Augmented Reality and Web-Based Technologies – Schol-AR

EEG electrode localization with 3D iPhone scanning using point-cloud electrode selection (PC-ES)

Objective.Electroencephalography source imaging (ESI) is a valuable tool in clinical evaluation for epilepsy patients but is underutilized in part due to sensitivity to anatomical modeling errors. Accurate localization of scalp electrodes is instrumental to ESI, but existing localization devices are expensive and not portable. As a result, electrode localization challenges further impede access to ESI, particularly in inpatient and intensive care settings.Approach.To address this challenge, we present a portable and affordable electrode digitization method using the 3D scanning feature in modern iPhone models. This technique combines iPhone scanning with semi-automated image processing using point-cloud electrode selection (PC-ES), a custom MATLAB desktop application. We compare iPhone electrode localization to state-of-the-art photogrammetry technology in a human study with over 6000 electrodes labeled using each method. We also characterize the performance of PC-ES with respect to head location and examine the relative impact of different algorithm parameters.Main Results.The median electrode position variation across reviewers was 1.50 mm for PC-ES scanning and 0.53 mm for photogrammetry, and the average median distance between PC-ES and photogrammetry electrodes was 3.4 mm. These metrics demonstrate comparable performance of iPhone/PC-ES scanning to currently available technology and sufficient accuracy for ESI.Significance.Low cost, portable electrode localization using iPhone scanning removes barriers to ESI in inpatient, outpatient, and remote care settings. While PC-ES has current limitations in user bias and processing time, we anticipate these will improve with software automation techniques as well as future developments in iPhone 3D scanning technology.

Metaverse Wearables for Immersive Digital Healthcare: A Review

The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.

Further refining the boundaries of the hippocampus CA2 with gene expression and connectivity: Potential subregions and heterogeneous cell types

Over the last two decades, the definition of hippocampal area CA2 has evolved from Lorente de Nó's original Golgi-based morphological description with the discovery of specific CA2 gene expression markers. Exploiting the specificity of these molecules has allowed for the genetic dissection of CA2 structure and function in transgenic mice. With this change in criteria, the anatomical boundaries of the CA2 have expanded across the hippocampal axis but the CA2's full rostrocaudal extent is not consistently delineated across atlases. The Hippocampus Gene Expression Atlas (HGEA) provides a comprehensive map of 20 gene expression domains across the entire mouse hippocampus including the CA2. In this commentary, I will review the consensus gene expression patterns that demarcate the expanded CA2 boundaries in the HGEA. Using DropViz single-cell transcriptomics and Mouse Connectome Project connectomics data, I will then suggest potential differences in CA2 cell type heterogeneity and connectivity that may identify and characterize further CA2 subregions.