The links between physical activity and prosocial behavior: an fNIRS hyperscanning study.

The prevalence of physically inactive lifestyles in modern society raises concerns about the potential association with poor brain health, particularly in the lateral prefrontal cortex, which is crucial for human prosocial behavior. Here, we explored the relationship between physical activity and prosocial behavior, focusing on potential neural markers, including intra-brain functional connectivity and inter-brain synchrony in the lateral prefrontal cortex. Forty participants, each paired with a stranger, completed two experimental conditions in a randomized order: (i) face-to-face and (ii) face stimulus (eye-to-eye contact with a face stimulus of a fictitious person displayed on the screen). Following each condition, participants played economic games with either their partner or an assumed person displayed on the screen. Neural activity in the lateral prefrontal cortex was recorded by functional near-infrared spectroscopy hyperscanning. Sparse multiset canonical correlation analysis showed that a physically inactive lifestyle was covaried with poorer reciprocity, greater trust, shorter decision-making time, and weaker intra-brain connectivity in the dorsal lateral prefrontal cortex and poorer inter-brain synchrony in the ventral lateral prefrontal cortex. These associations were observed exclusively in the face-to-face condition. Our findings suggest that a physically inactive lifestyle may alter human prosocial behavior by impairing adaptable prosocial decision-making in response to social factors through altered intra-brain functional connectivity and inter-brain synchrony.

Hippocampal volume mediates the relationship of parental rejection in childhood with social cognition in healthy adults.

Childhood abuse reduces hippocampal and amygdala volumes and impairs social cognition, including the ability to recognize facial expressions. However, these associations have been studied primarily in individuals with a history of severe abuse and psychiatric symptoms; researchers have not determined whether these associations can also be observed in healthy adults. In the present study, we analyzed data from 400 healthy adults (208 men and 192 women) at Tamagawa University. Parental rejection reflecting childhood abuse was assessed using the short form of Egna Minnen Beträffande Uppfostran, while social cognition was assessed using the "Fake Smile Detection Task." Hippocampal and amygdala volumes were extracted from T1-weighted magnetic resonance imaging data using FreeSurfer. We found that greater parental rejection resulted in smaller hippocampal and amygdala volumes and poorer performance in the Fake Smile Detection Task. Structural equation modeling analysis supported the model that hippocampal volume mediates maternal rejection effect on performance on the Fake Smile Detection Task, with involvement of the amygdala. These findings are in line with the structural and functional connectivity found between the hippocampus and amygdala and their joint involvement in social cognition. Therefore, parental rejection may affect hippocampal and amygdala volumes and social cognitive function even in symptom-free adults.

Association between arginine vasopressin receptor 1A (AVPR1A) polymorphism and inequity aversion.

Although numerous studies have focused on brain functions related to inequity aversion, few have examined its genetic basis. Here, we show the association between estimated inequity aversion and polymorphisms in three genes associated with human sociality. Non-student adult participants took part in five economic game experiments on different days. Disadvantageous inequity aversion (DIA) and advantageous inequity aversion (AIA) were calculated from behavioural responses using Bayesian estimation. We investigated the association between genetic polymorphisms in the oxytocin receptor (OXTR rs53576), arginine vasopressin receptor 1A (AVPR1A RS3) and opioid receptor mu 1 (OPRM1 rs1799971) and inequity aversion. Regarding AVPR1A RS3, participants with the SS genotype had higher AIA than those with the SL or LL genotypes, but no association was found for DIA. Moreover, we observed no aversion associations for OXTR rs53576 or OPRM1 rs1799971. The results suggest that AVPR1A plays an important role in aversion when one's own gain is greater than that of others. Our findings may provide a solid theoretical basis for future studies on the relationship between genetic polymorphisms and inequity aversion.

Right dorsolateral prefrontal cortex regulates default prosociality preference.

The dorsolateral prefrontal cortex has been shown to be associated with prosocial behavior. However, the direction of this relationship remains controversial. To resolve inconsistencies in the existing literature, we introduced the concept of default prosociality preference and hypothesized that this preference moderates the relationship between gray matter volume in the dorsolateral prefrontal cortex and prosocial behavior. This study analyzed the data of 168 participants obtained from voxel-based morphometry, 4 types of economic games, and 3 different measures of social value orientation that represent default prosociality preference. Here we show that, in individuals who were consistently classified as proself on the 3 social value orientation measures, gray matter volume in the right dorsolateral prefrontal cortex was positively associated with prosocial behavior. However, in individuals who were consistently classified as prosocial, the direction of this association was vice versa. These results indicate that the right dorsolateral prefrontal cortex regulates default prosociality preference.

Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase.

Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrades the quality of CT-based diagnosis. Traditional approaches such as high-resolution computed tomography (HRCT) and linear interpolation can solve this problem. However, HRCT suffers from dose increase, and linear interpolation causes artifacts. In this study, we propose a deep-learning-based approach to reconstruct densely sliced CT from sparsely sliced CT data without any dose increase. The proposed method reconstructs CT images from neighboring slices using a U-net architecture. To prevent multiple reconstructed slices from influencing one another, we propose a parallel architecture in which multiple U-net architectures work independently. Moreover, for a specific organ (i.e., the liver), we propose a range-clip technique to improve reconstruction quality, which enhances the learning of CT values within this organ by enlarging the range of the training data. CT data from 130 patients were collected, with 80% used for training and the remaining 20% used for testing. Experiments showed that our parallel U-net architecture reduced the mean absolute error of CT values in the reconstructed slices by 22.05%, and also reduced the incidence of artifacts around the boundaries of target organs, compared with linear interpolation. Further improvements of 15.12%, 11.04%, 10.94%, and 10.63% were achieved for the liver, left kidney, right kidney, and stomach, respectively, using the proposed range-clip algorithm. Also, we compared the proposed architecture with original U-net method, and the experimental results demonstrated the superiority of our approach.

Evolutionary loss of complexity in human vocal anatomy as an adaptation for speech.

Human speech production obeys the same acoustic principles as vocal production in other animals but has distinctive features: A stable vocal source is filtered by rapidly changing formant frequencies. To understand speech evolution, we examined a wide range of primates, combining observations of phonation with mathematical modeling. We found that source stability relies upon simplifications in laryngeal anatomy, specifically the loss of air sacs and vocal membranes. We conclude that the evolutionary loss of vocal membranes allows human speech to mostly avoid the spontaneous nonlinear phenomena and acoustic chaos common in other primate vocalizations. This loss allows our larynx to produce stable, harmonic-rich phonation, ideally highlighting formant changes that convey most phonetic information. Paradoxically, the increased complexity of human spoken language thus followed simplification of our laryngeal anatomy.

Association between salivary oxytocin levels and the amygdala and hippocampal volumes.

Salivary oxytocin levels have been widely measured and studied in relation to social behavior because of procedural simplicity and noninvasiveness. Although the relationship between oxytocin levels in the blood and the hippocampus and amygdala is now becoming clear with reliable blood oxytocin studies, few studies have examined the relationship between salivary oxytocin and the brain function and structure. This study aimed to investigate whether the salivary oxytocin level is associated with the volume of the amygdala and hippocampus in 178 adults (92 women and 86 men) in their third to seventh decade of life. We performed volumetric analysis of the amygdala and hippocampus using FreeSurfer and measured salivary oxytocin levels using enzyme-linked immunosorbent assay. The results showed contradictory effects of the salivary oxytocin level on the amygdala volume by sex and no significant effect on the hippocampal volume. Specifically, men showed a positive correlation between the salivary oxytocin level and amygdala volume, whereas women showed a negative correlation between the salivary oxytocin level and amygdala volume. The present study's finding of sex differences in the association between salivary oxytocin and brain structure supports previous findings that there are sex differences in the oxytocin system.

Association between relational mobility, brain structure, and prosociality in adolescents.

Trust plays a vital role in human society. Previous studies have suggested that trust comprises general trust and caution. General trust is a belief that others, in general, are trustworthy, and caution is a belief in the importance of vigilance in dealing with others. Adolescence is a critical period for establishing these psychological traits. It is a period of physical and mental development, and the social environment during this period influences adolescents' psychology, including their brain structures. In this study, we focus on relational mobility as a socio-environmental factor that influences the development of adolescents' psychology and the brain. Relational mobility refers to the degree of freedom to choose and replace social relationships and consists of two subfactors (the degree of freedom to choose and replace social relationships and the number of opportunities to meet new people). Accordingly, we analyzed each subfactor separately. Results showed that the degree of freedom to choose and replace social relationships was only negatively associated with caution and left posterior superior temporal gyrus (pSTG) volume in adolescents. Furthermore, the effect of the freedom to choose and replace social relationships on caution was significantly relevant to the left pSTG volume. In contrast, the degree of opportunities to meet new people was associated with neither general trust nor caution, whereas it was positively associated with the right supramarginal gyrus volume. This study suggests that the social environment during adolescence influences brain structures related to prosociality.

Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs.

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy.

Association of Cardiovascular Risk Markers and Fitness with Task-Related Neural Activity during Animacy Perception.

PURPOSE: Numerous studies have demonstrated the association between cardiovascular risk markers and fitness, and broad aspects of cognition; however, the possible association of cardiovascular risk markers and fitness with social cognition, which plays a significant role in the development and maintenance of social relationships, has largely been ignored. Herein, we investigated the relationship of cardiovascular risk markers and fitness with task-related neural activity during animacy perception. METHODS: We analyzed data from the Human Connectome Project derived from 1027 adults age 22-37 yr. Canonical correlation analysis (CCA) was conducted to evaluate the association between participants' body mass index, systolic and diastolic blood pressure, submaximal endurance, gait speed, hand dexterity, and muscular strength with task-related neural activity during animacy perception. RESULTS: We observed a single significant CCA mode. Body mass index and blood pressure demonstrated negative cross-loadings with task-related neural activity in the temporoparietal, superior and anterior temporal, posterior cingulate, and inferior frontal regions, whereas submaximal endurance, hand dexterity, and muscular strength demonstrated positive cross-loadings. The observed CCA variates did not seem highly heritable, as the absolute differences in CCA variates in monozygotic twins, dizygotic twins, and nontwin siblings were not statistically different. Furthermore, the cardiovascular risk markers and fitness CCA variates were positively associated with animacy perception and emotion recognition accuracy, which was mediated by the task-related neural activity. CONCLUSIONS: The present findings can provide new insights into the role of markers for cardiovascular health and fitness, specifically their association with social cognition and the underlying neural basis. The intervention for cardiovascular risk and fitness could be a potentially cost-effective method of targeting social cognition.

Creation of a video library for education and virtual simulation of anatomical lung resection.

OBJECTIVES: Recently, preoperative and intraoperative simulation using three-dimensional computed tomography (CT) has attracted much attention in thoracic surgery. However, because conventional three-dimensional CT only shows static images, dynamic simulation is required for a more precise operation. We previously reported on a resection process map for pulmonary resection, which we developed to generate virtual dynamic images from preoperative patient-specific CT scans. The goal of this study was to evaluate the feasibility of the clinical use of the resection process map for anatomical lung resection. METHODS: This study included 5 lobectomies for different lobes and 4 representative segmentectomies. Dissection of the pulmonary arteries, veins and bronchi were considered key parts of each procedure. To assess the description of images obtained from the resection process map, relevant clips from the actual surgical videos were collected, retrospectively replicated and superimposed on the resection process map to explain the procedures. RESULTS: In all surgical procedures, the resection process map successfully and semiautomatically generated a virtual dynamic image from the patient-specific CT data. Moreover, superimposition of the virtual images on the selected clips from the surgical videos showed no major differences. CONCLUSIONS: The resection process map could generate virtual images that corresponded to the actual surgical videos and has the potential for clinical use as preoperative and intraoperative simulation.

Improvement of Image Quality of Cone-beam CT Images by Three-dimensional Generative Adversarial Network.

Artifacts and defects in Cone-beam Computed Tomography (CBCT) images are a problem in radiotherapy and surgical procedures. Unsupervised learning-based image translation techniques have been studied to improve the image quality of head and neck CBCT images, but there have been few studies on improving the image quality of abdominal CBCT images, which are strongly affected by organ deformation due to posture and breathing. In this study, we propose a method for improving the image quality of abdominal CBCT images by translating the numerical values to the values of corresponding paired CT images using an unsupervised CycleGAN framework. This method preserves anatomical structure through adversarial learning that translates voxel values according to corresponding regions between CBCT and CT images of the same case. The image translation model was trained on 68 CT-CBCT datasets and then applied to 8 test datasets, and the effectiveness of the proposed method for improving the image quality of CBCT images was confirmed.

Shape Reconstruction for Abdominal Organs based on a Graph Convolutional Network.

Computed tomography and magnetic resonance imaging produce high-resolution images; however, during surgery or radiotherapy, only low-resolution cone-beam CT and low-dimensional X-ray images can be obtained. Furthermore, because the duodenum and stomach are filled with air, even in high-resolution CT images, it is hard to accurately segment their contours. In this paper, we propose a method that is based on a graph convolutional network (GCN) to reconstruct organs that are hard to detect in medical images. The method uses surrounding detectable-organ features to determine the shape and location of the target organ and learns mesh deformation parameters, which are applied to a target organ template. The role of the template is to establish an initial topological structure for the target organ. We conducted experiments with both single and multiple organ meshes to verify the performance of our proposed method.

Deformation analysis of surface and bronchial structures in intraoperative pneumothorax using deformable mesh registration.

The positions of nodules can change because of intraoperative lung deflation, and the modeling of pneumothorax-associated deformation remains a challenging issue for intraoperative tumor localization. In this study, we introduce spatial and geometric analysis methods for inflated/deflated lungs and discuss heterogeneity in pneumothorax-associated lung deformation. Contrast-enhanced CT images simulating intraoperative conditions were acquired from live Beagle dogs. The images contain the overall shape of the lungs, including all lobes and internal bronchial structures, and were analyzed to provide a statistical deformation model that could be used as prior knowledge to predict pneumothorax. To address the difficulties of mapping pneumothorax CT images with topological changes and CT intensity shifts, we designed deformable mesh registration techniques for mixed data structures including the lobe surfaces and the bronchial centerlines. Three global-to-local registration steps were performed under the constraint that the deformation was spatially continuous and smooth, while matching visible bronchial tree structures as much as possible. The developed framework achieved stable registration with a Hausdorff distance of less than 1 mm and a target registration error of less than 5 mm, and visualized deformation fields that demonstrate per-lobe contractions and rotations with high variability between subjects. The deformation analysis results show that the strain of lung parenchyma was 35% higher than that of bronchi, and that deformation in the deflated lung is heterogeneous.

Childhood exercise predicts response inhibition in later life via changes in brain connectivity and structure.

Participation in exercise during early life (i.e., childhood through adolescence) enhances response inhibition; however, it is unclear whether participation in exercise during early life positively predicts response inhibition in later life. This historical cohort study was designed to clarify whether participation in exercise (e.g., structured sports participation) during early life predicts response inhibition in adulthood and if so, to reveal the brain connectivity and cortical structures contributing to this association. We analyzed data derived from 214 participants (women = 104, men = 110; age: 26‒69 years). Results indicated that participation in exercise during childhood (before entering junior high school; ≤ 12 years old) significantly predicted better response inhibition. No such association was found if exercise participation took place in early adolescence or later (junior high school or high school; ≥ 12 years old). The positive association of exercise participation during childhood with response inhibition was moderated by decreased structural and functional connectivity in the frontoparietal (FPN), cingulo-opercular (CON), and default mode networks (DMN), and increased inter-hemispheric structural networks. Greater cortical thickness and lower levels of dendritic arborization and density in the FPN, CON, and DMN also moderated this positive association. Our results suggest that participation in exercise during childhood positively predicts response inhibition later in life and that this association can be moderated by changes in neuronal circuitry, such as increased cortical thickness and efficiency, and strengthened inter-hemispheric connectivity.

Fluid dynamics analyses of the intrahepatic portal vein tributaries using 7-T MRI.

BACKGROUND: Assessing portal vein (PV) hemodynamics is an essential part of liver disease management/liver surgery, yet the optimal methods of assessing intrahepatic PV flow have not yet been established. This study investigated the usefulness of 7-Tesla MRI with hemodynamic analysis for detecting small flow changes within narrow intrahepatic PV branches. METHODS: Flow data in the main PV was obtained by two methods, two-dimensional cine phase contrast-MRI (2D cine PC-MRI) and three-dimensional non-cine phase contrast-MRI (3D PC-MRI). Hemodynamic parameters, such as flow volume rate, flow velocity, and wall shear stress in intrahepatic PV branches were calculated before and after a meal challenge using 3D PC-MRI and hemodynamic analysis. RESULTS: The hemodynamic parameters obtained using 3D PC-MRI and 2D cine PC-MRI were similar. All intrahepatic PV branches were clearly depicted in eight planes, and significant changes in flow volume rate were seen in three planes. Average and maximum velocities, cross-sectional area, and wall shear stress were similar between before and after a meal challenge in all planes. CONCLUSION: 7-Tesla 3D PC-MRI combined with hemodynamic analysis is a promising tool for assessing intrahepatic PV flow and enables future studies in small animals to investigate PV hemodynamics associated with liver disease/postoperative liver recovery.

Statistical deformation reconstruction using multi-organ shape features for pancreatic cancer localization.

Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were generated by shape matching their region labels defined on four-dimensional computed tomography images. A total of 250 volumes were measured from 25 pancreatic cancer patients. This paper also proposes a per-region-based deformation learning using the non-linear kernel model to predict the displacement of pancreatic cancer for adaptive radiotherapy. The experimental results show that the proposed concept estimates deformations better than general per-patient-based learning models and achieves a clinically acceptable estimation error with a mean distance of 1.2  ±  0.7 mm and a Hausdorff distance of 4.2  ±  2.3 mm throughout the respiratory motion.

Photoacoustic in vivo 3D imaging of tumor using a highly tumor-targeting probe under high-threshold conditions.

Three-dimensional (3D) representation of a tumor with respect to its size, shape, location, and boundaries is still a challenge in photoacoustic (PA) imaging using artificial contrast agents as probes. We carried out PA imaging of tumors in mice using 800RS-PMPC, which was obtained by coupling of 800RS, a near-infrared cyanine dye, with PMPC, a highly selective tumor-targeting methacrylate polymer having phosphorylcholine side chains, as a probe. The conjugate 800RS-PMPC forms compact nanoparticles (dDLS = 14.3 nm), retains the biocompatibility of the parent polymer (PMPC) and exhibits unprecedented PA performance. When applied to mice bearing a 6 × 3 × 3 mm3 tumor buried 6 mm beneath the skin, the probe 800RS-PMPC selectively accumulates in the tumor and emits PA signals that are strong enough to be unambiguously distinguished from noise signals of endogenous blood/hemoglobin. The PA image thus obtained under high-threshold conditions allows 3D characterization of the tumor in terms of its size, shape, location, and boundaries.

Deep Learning Based Lung Region Segmentation with Data Preprocessing by Generative Adversarial Nets.

In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve safety. For organs that do not deform much during surgery, preoperative computed tomography (CT) images can be used to understand their three-dimensional structure, however, deformation estimation is necessary for organs that deform substantially. Even though the intraoperative deformation estimation of organs has been widely studied, two-dimensional organ region segmentations from camera images are necessary to perform this estimation. In this paper, we propose a region segmentation method using U-net for the lung, which is an organ that deforms substantially during surgery. Because the accuracy of the results for smoker lungs is lower than that for non-smoker lungs, we improved the accuracy by translating the texture of the lung surface using a CycleGAN.

X-ray2Shape: Reconstruction of 3D Liver Shape from a Single 2D Projection Image.

Computed tomography (CT) and magnetic resonance imaging (MRI) scanners measure three-dimensional (3D) images of patients. However, only low-dimensional local two-dimensional (2D) images may be obtained during surgery or radiotherapy. Although computer vision techniques have shown that 3D shapes can be estimated from multiple 2D images, shape reconstruction from a single 2D image such as an endoscopic image or an X-ray image remains a challenge. In this study, we propose X-ray2Shape, which permits a deep learning-based 3D organ mesh to be reconstructed from a single 2D projection image. The method learns the mesh deformation from a mean template and deep features computed from the individual projection images. Experiments with organ meshes and digitally reconstructed radiograph (DRR) images of abdominal regions were performed to confirm the estimation performance of the methods.