Mapping functional gradients of the striatal circuit using simultaneous microelectric stimulation and ultrahigh-field fMRI in non-human primates.

Advances in functional magnetic resonance imaging (fMRI) have significantly enhanced our understanding of the striatal system of both humans and non-human primates (NHP) over the last few decades. However, its circuit-level functional anatomy remains poorly understood, partly because in-vivo fMRI cannot directly perturb a brain system and map its casual input-output relationship. Also, routine 3T fMRI has an insufficient spatial resolution. We performed electrical microstimulation (EM) of the striatum in lightly-anesthetized NHPs while simultaneously mapping whole-brain activation, using contrast-enhanced fMRI at ultra-high-field 7T. By stimulating multiple positions along the striatum's main (dorsal-to-ventral) axis, we revealed its complex functional circuit concerning mutually connected subsystems in both cortical and subcortical areas. Indeed, within the striatum, there were distinct brain activation patterns across different stimulation sites. Specifically, dorsal stimulation revealed a medial-to-lateral elongated shape of activation in upper caudate and putamen areas, whereas ventral stimulation evoked areas confined to the medial and lower caudate. Such dorsoventral gradients also appeared in neocortical and thalamic activations, indicating consistent embedding profiles of the striatal system across the whole brain. These findings reflect different forms of within-circuit and inter-regional neuronal connectivity between the dorsal and ventromedial striatum. These patterns both shared and contrasted with previous anatomical tract-tracing and in-vivo resting-state fMRI studies. Our approach of combining microstimulation and whole-brain fMRI mapping in NHPs provides a unique opportunity to integrate our understanding of a targeted brain area's meso- and macro-scale functional systems.

Application of augmented reality using automatic markerless registration for facial plastic and reconstructive surgery.

This study aimed to present a novel markerless augmented reality (AR) system using automatic registration based on machine-learning algorithms that visualize the facial region and provide an intraoperative guide for facial plastic and reconstructive surgeries. This study prospectively enrolled 20 patients scheduled for facial plastic and reconstructive surgeries. The AR system visualizes computed tomographic data in three-dimensional (3D) space by aligning with the point clouds captured by a 3D camera. Point cloud registration consists of two stages: the preliminary registration gives an initial estimate of the transformation using landmark detection, followed by the precise registration using Iterative Closest Point algorithms. Computed Tomography (CT) data can be visualized as two-dimensional slice images or 3D images by the AR system. The AR registration error was defined as the cloud-to-cloud distance between the surface data obtained from the CT and 3D camera. The error was calculated in each facial territory, including the upper, middle, and lower face, while patients were awake and orally intubated, respectively. The mean registration errors were 1.490 ± 0.384 mm and 1.948 ± 0.638 mm while patients were awake and orally intubated, respectively. There was a significant difference in the errors in the lower face between patients while they were awake (1.502 ± 0.480 mm) and orally intubated (2.325 ± 0.971 mm) when stratified by facial territories (p = 0.006). The markerless AR can accurately visualize the facial region with a mean overall registration error of 1-2 mm, with a slight increase in the lower face due to errors arising from tube intubation.

The evolution of social health research topics: A data-driven analysis.

The realm of social health has not yet been properly established in terms of fixed definitions, concepts, and research areas. This study attempts to define social health using macro and micro perspectives and explores trends in social health research by mapping their topics and fields. We used Latent Dirichlet allocation (LDA) topic modeling, which allows the extraction of key terms and topics derived from a large volume of literature. We traced the evolution of research topics from past (the literature that "present" articles cited), present (existing journal articles on social health), to future (the literature which cited the articles) studies based on connections between citations. The datasets were collected by the query terms "social health" in the Scopus database, including title, abstract, and keywords of journal articles. We collected a total of 443 articles from recent social health literature, 6588 articles from past literature that the recent articles on social health cited, and 2680 articles from future literature in which recent social health articles were cited. We defined social health as positive interaction that increases individual engagement in social life at the micro level, and the high degree of social integration that deals with collective problems in society at the macro level. The results of LDA showed that social health research has developed into seven fields: Health Care Delivery; Vulnerable Groups; Measurement; Health Inequality; Social Network and Empowerment; Clinical/Physical Health; and Mental/Behavioral Health. Based on citation relationships, topics grounded in an individual/micro perspective have grown increasingly specialized and productive, while topics grounded in a social/macro perspective have stagnated or was underexplored. Our findings imply that social health studies should follow a more interdisciplinary approach to integrate current health models of individual-centered treatments with social science concerns on building collective capacity for social well-being.