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Although most children with coronavirus disease 2019 (COVID-19) infection present with mild symptoms, a few pediatric patients develop severe neurological manifestations. Herein, we describe the case of a pediatric patient who presented with rapidly progressive diffuse and fatal cerebral edema associated with COVID-19. A previously healthy 6-year-old boy was diagnosed with acute fulminant cerebral edema (AFCE), which resulted in transtentorial downward herniation within 48 hours after the initial onset of fever. Detailed history-taking, close monitoring of the consciousness level with serial neurological examinations, and prompt diagnosis and treatment are required in patients suspected to have AFCE. Further research is needed to identify the pathogenesis of AFCE associated with COVID-19 and the related risk factors.

Abstract Background Androgenetic alopecia (AGA) is a common form of hair loss. Androgens, such as testosterone and dihydrotestosterone, are the main causes of AGA. Extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs) can reduce AGA. However, preparing therapeutic doses of MSCs for clinical use is challenging. Induced pluripotent stem cell-derived MSCs (iMSCs) are homogenous and easily expandable, enabling scalable production of EVs. Hyaluronic acid (HA) can exert various functions including free radical scavenging, immune regulation, and cell migration. Herein, we examined whether hyaluronic acid (HA) stimulation of iMSCs could produce EVs with enhanced therapeutic outcomes for AGA. Methods EVs were collected from iMSCs primed with HA (HA–iMSC–EVs) or without HA (iMSC–EVs). The characteristics of EVs were examined using dynamic light scattering, cryo-transmission electron microscopy, immunoblotting, flow cytometry, and proteomic analysis. In vitro, we compared the potential of EVs in stimulating the survival of hair follicle dermal papilla cells undergoing testosterone-mediated AGA. Additionally, the expression of androgen receptor (AR) and relevant growth factors as well as key proteins of Wnt/β-catenin signaling pathway (β-catenin and phosphorylated GSK3β) was analyzed. Subsequently, AGA was induced in male C57/BL6 mice by testosterone administration, followed by repeated injections of iMSC–EVs, HA–iMSC–EVs, finasteride, or vehicle. Several parameters including hair growth, anagen phase ratio, reactivation of Wnt/β-catenin pathway, and AR expression was examined using qPCR, immunoblotting, and immunofluorescence analysis. Results Both types of EVs showed typical characteristics for EVs, such as size distribution, markers, and surface protein expression. In hair follicle dermal papilla cells, the mRNA levels of AR, TGF-β, and IL-6 increased by testosterone was blocked by HA–iMSC–EVs, which also contributed to the augmented expression of trophic genes related to hair regrowth. However, no notable changes were observed in the iMSC–EVs. Re-activation of Wnt/β-catenin was observed in HA–iMSC–EVs but not in iMSC–EVs, as shown by β-catenin stabilization and an increase in phosphorylated GSK3β. Restoration of hair growth was more significant in HA–iMSC–EVs than in iMSC–EVs, and was comparable to that in mice treated with finasteride. Consistently, the decreased anagen ratio induced by testosterone was reversed by HA–iMSC–EVs, but not by iMSC–EVs. An increased expression of hair follicular β-catenin protein, as well as the reduction of AR was observed in the skin tissue of AGA mice receiving HA–iMSC–EVs, but not in those treated with iMSC–EVs. Conclusions Our results suggest that HA–iMSC–EVs have potential to improve AGA by regulating growth factors/cytokines and stimulating AR-related Wnt/β-catenin signaling.

Early-life experiences play a crucial role in the development of the fronto-limbic regions, influencing both macro- and microstructural changes in the brain. These alterations profoundly impact cognitive, social-emotional functions. Recently, early limbic structural alterations have been associated with numerous neurological and psychiatric morbidities. Although identifying normative developmental trajectories is essential for determining brain alterations, only a few studies have focused on examining the normative trajectories in the fronto-limbic regions during preschool-aged children. The aim of this study was to investigate the structural-developmental trajectory of the fronto-limbic regions using the cortical thickness, volume, and subcortical volume in 57 healthy and typical preschool-aged children between 1 and 5 years and examined the early lateralization patterns during the development of the fronto-limbic regions. Regarding brain lateralization, remarkable asymmetry was detected in the volume of thalamus and the cortical regions excluding the lateral orbitofrontal cortex in the fronto-limbic regions. This study of preschool-aged children may fill the knowledge gaps regarding the developmental patterns and hemispheric asymmetries of the fronto-limbic regions between newborns and adolescents.

Owing to the low detection accuracy of camera-based object detection models, various fusion techniques with Light Detection and Ranging (LiDAR) have been attempted. This has resulted in improved detection of objects that are difficult to detect due to partial occlusion by obstacles or unclear silhouettes. However, the detection performance remains limited in low-light environments where small pedestrians are located far from the sensor or pedestrians have difficult-to-estimate shapes. This study proposes an object detection model that employs a Gaussian–Sobel filter. This filter combines Gaussian blurring, which suppresses the effects of noise, and a Sobel mask, which accentuates object features, to effectively utilize depth maps generated by LiDAR for object detection. The model performs independent pedestrian detection using the real-time object detection model You Only Look Once v4, based on RGB images obtained using a camera and depth maps preprocessed by the Gaussian–Sobel filter, and estimates the optimal pedestrian location using non-maximum suppression. This enables accurate pedestrian detection while maintaining a high detection accuracy even in low-light or external-noise environments, where object features and contours are not well defined. The test evaluation results demonstrated that the proposed method achieved at least 1–7% higher average precision than the state-of-the-art models under various environments.

Abstract Sensory processing is the process by which the central nervous system gathers, interprets, and regulates sensory stimuli in response to environmental cues. However, our understanding of the genetic factors and neuroanatomical correlations that influence sensory processing is limited. The vasotocin system modulates sensory input responsiveness, making it a potential candidate for further investigation. Additionally, human neuroimaging studies have demonstrated that the ability to modulate sensory stimuli is related to neuroanatomical features such as cortical thickness. Therefore, this study aimed to examine the relationship between functional polymorphisms in vasotocin receptor (VTR) genes, sensory profiles, and neuroanatomical correlations. We used structural magnetic resonance imaging (MRI) and the Adolescent/Adult Sensory Profile (AASP) questionnaire in 98 healthy adult participants to assess sensory processing and identified seven single nucleotide polymorphisms. We found that A-allele carriers of rs1042615 in VTR had higher scores for “sensory sensitivity” and “sensation avoiding”. Moreover, higher scores for three AASP subscales were associated with decreased cortical thickness in various regions, including the right precentral, paracentral, and fusiform gyri, as well as bilateral inferior temporal gyri. This study sheds light on the potential role of genetic variations in the VTR in modulating sensory processing and correlation with cortical thickness which has future implications for better understanding sensory abnormalities in neurodevelopmental disorders.

Abstract Background Atopic dermatitis (AD) is a chronic inflammatory skin disorder characterized by immune dysregulation, pruritus, and abnormal epidermal barrier function. Compared with conventional mesenchymal stem cell (MSC), induced pluripotent stem cell (iPSC)-derived mesenchymal stem cell (iMSC) is recognized as a unique source for producing extracellular vesicles (EVs) because it can be obtained in a scalable manner with an enhanced homogeneity. Stimulation of iMSCs with inflammatory cytokines can improve the immune-regulatory, anti-inflammatory, and tissue-repairing potential of iMSC-derived EVs. Results Proteome analysis showed that IFN-γ-iMSC-EVs are enriched with protein sets that are involved in regulating interferon responses and inflammatory pathways. In AD mice, expression of interleukin receptors for Th2 cytokines (IL-4Rα/13Rα1/31Rα) and activation of their corresponding intracellular signaling molecules was reduced. IFN-γ-iMSC-EVs decreased itching, which was supported by reduced inflammatory cell infiltration and mast cells in AD mouse skin; reduced IgE receptor expression and thymic stromal lymphopoietin and NF-kB activation; and recovered impaired skin barrier, as evidenced by upregulation of key genes of epidermal differentiation and lipid synthesis. Conclusions IFN-γ-iMSC-EVs inhibit Th2-induced immune responses, suppress inflammation, and facilitate skin barrier restoration, contributing to AD improvement.

Deep neural networks (DNNs) have been adopted widely as classifiers for functional magnetic resonance imaging (fMRI) data, advancing beyond traditional machine learning models. Consequently, transfer learning of the pre-trained DNN becomes crucial to enhance DNN classification performance, specifically by alleviating an overfitting issue that occurs when a substantial number of DNN parameters are fitted to a relatively small number of fMRI samples. In this study, we first systematically compared the two most popularly used, unsupervised pretraining models for resting-state fMRI (rfMRI) volume data to pre-train the DNNs, namely autoencoder (AE) and restricted Boltzmann machine (RBM). The group in-brain mask used when training AE and RBM displayed a sizable overlap ratio with Yeo's seven functional brain networks (FNs). The parcellated FNs obtained from the RBM were fine-grained compared to those from the AE. The pre-trained AE and RBM served as the weight parameters of the first of the two hidden DNN layers, and the DNN fulfilled the task classifier role for fMRI (tfMRI) data in the Human Connectome Project (HCP). We tested two transfer learning schemes: (1) fixing and (2) fine-tuning the DNN's pre-trained AE or RBM weights. The DNN with transfer learning was compared to a baseline DNN, trained using random initial weights. Overall, DNN classification performance from the transfer learning proved superior when the pre-trained RBM weights were fixed and when the pre-trained AE weights were fine-tuned (average error rates: 14.8% for fixed RBM, 15.1% fine-tuned AE, and 15.5% for the baseline model) compared to the alternative scenarios of DNN transfer learning schemes. Moreover, the optimal transfer learning scheme between the fixed RBM and fine-tuned AE varied according to seven task conditions in the HCP. Nonetheless, the computational load reduced substantially for the fixed-weight-based transfer learning compared to the fine-tuning-based transfer learning (e.g., the number of weight parameters for the fixed-weight-based DNN model reduced to 1.9% compared with a baseline/fine-tuned DNN model). Our findings suggest that weight initialization at the DNN's first layer using RBM-based pre-trained weights provides the most promising approach when the whole-brain fMRI volume supports associated task classification. We believe that our proposed scheme could be applied to a variety of task conditions to improve their classification performance and to utilize computational resources efficiently using our AE/RBM-based pre-trained weights compared to random initial weights for DNN training.

Introduction Neuroimaging studies on attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have demonstrated differences in extensive brain structure, activity and network. However, there remains heterogeneity and inconsistency across these findings, presumably because of the diversity of the disorders themselves, small sample sizes, and site and parameter differences in MRI scanners, and their overall pathogenesis remains unclear. To address these gaps in the literature, we will apply the travelling-subject approach to correct site differences in MRI scanners and clarify brain structure and network characteristics of children with ADHD and ASD using large samples collected in a multi-centre collaboration. In addition, we will investigate the relationship between these characteristics and genetic, epigenetic, biochemical markers, and behavioural and psychological measures.Methods and analysis We will collect resting-state functional MRI (fMRI) and T1-weighted and diffusion-weighted MRI data from 15 healthy adults as travelling subjects and 300 children (ADHD, n=100; ASD, n=100; and typical development, n=100) with multi-dimensional assessments. We will also apply data from more than 1000 samples acquired in our previous neuroimaging studies on ADHD and ASD.Ethics and dissemination The study protocol has been approved by the Research Ethics Committee of the University of Fukui Hospital (approval no: 20220601). Our study findings will be submitted to scientific peer-reviewed journals and conferences.

Airborne bathymetric LiDAR (ABL) acquires waveform data with better accuracy and resolution and greater user control over data processing than discrete returns. The ABL waveform is a mixture of reflections from the water surface and bottom, water column backscattering, and noise, and it can be separated into individual components through waveform decomposition. Because the point density and positional accuracy of the point cloud are dependent on waveform decomposition, an effective decomposition technique is required to improve ABL measurement. In this study, a new progressive waveform decomposition technique based on Gaussian mixture models was proposed for universal applicability to various types of ABL waveforms and to maximize the observation of seafloor points. The proposed progressive Gaussian decomposition (PGD) estimates potential peaks that are not detected during the initial peak detection and progressively decomposes the waveform until the Gaussian mixture model sufficiently represents the individual waveforms. Its performance is improved by utilizing a termination criterion based on the time difference between the originally detected and estimated peaks of the approximated model. The PGD can be universally applied to various waveforms regardless of water depth or underwater environment. To evaluate the proposed approach, it was applied to the waveform data acquired from the Seahawk sensor developed in Korea. In validating the PGD through comparative evaluation with the conventional Gaussian decomposition method, the root mean square error was found to decrease by approximately 70%. In terms of point cloud extractability, the PGD extracted 14–18% more seafloor points than the Seahawk’s data processing software.

The caudate nucleus has been thought to be involved in the control of motor commands by the cerebrum, and recent studies suggest that it may play a role in the control of attachment behavior, cognition, emotion, and mental functions. Implied by the basal ganglia's involvement in the execution, planning and control of movement, the caudate nucleus functions in a situation-dependent manner where processing of external stimuli is important on the basis of learning and memory. Sensory processing, which determines the response to external stimuli, has been shown to be related to various brain regions but it remains unknown how sensory processing is associated with the structure of the caudate nucleus and white matter microstructures of the caudate. Using four diffusion parameters derived from diffusion tensor imaging (DTI) (i.e., fractional anisotropy (FA), mean diffusivity (MD), axonal diffusivity (AD), and radial diffusivity (RD)) and the Adolescent/Adult Sensory Profile (AASP) questionnaire of 99 healthy subjects [42 males and 57 females; mean age:26.9 years, standard deviation 6.9], we investigated the relationship between white matter structure in the caudate nucleus and sensory processing. In consistent with what had been suggested by the results of previous studies, we found significant correlations between AD, MD and tactile sensation. Furthermore, we found a significant correlation between AD, MD and tactile sensory avoidance, the AASP sub-scores regarding the tactile senses. To the best of our knowledge, this is the first study to show that DTI diffusion parameters correlate with AASP scores in specific brain regions.

Abstract Individuals with autism spectrum disorders (ASDs) exhibit atypical sensory characteristics, impaired social skills, deficits in verbal and nonverbal communication, and restricted and repetitive behaviors. The relationship between sensory characteristics and brain morphological changes in ASD remains unclear. In this study, we investigated the association between brain morphological changes and sensory characteristics in individuals with ASD using brain image analysis and a sensory profile test. Forty-three adults with ASD and 84 adults with typical development underwent brain image analysis using FreeSurfer. The brain cortex was divided into 64 regions, and the cortical thickness and volume of the limbic system were calculated. The sensory characteristics of the participants were evaluated using the Adolescent/Adult Sensory Profile (AASP). Correlation analysis was performed for cortical thickness, limbic area volume, and AASP scores. In the ASD group, there was a significant positive correlation between visual sensory sensitivity scores and the right lingual cortical thickness (r = 0.500). There were also significant negative correlations between visual sensation avoiding scores and the right lateral orbitofrontal cortical thickness (r = −0.513), taste/smell sensation avoiding scores and the right hippocampal volume (r = −0.510), and taste/smell sensation avoiding scores and the left hippocampal volume (r = −0.540). The study identified associations among the lingual cortical thickness, lateral orbitofrontal cortical thickness, and hippocampal volume and sensory characteristics. These findings suggest that brain morphological changes may trigger sensory symptoms in adults with ASD.

In Korea, several national cross-sectional surveys monitor the diet, nutritional status, and health status of children. This continual dedicated national surveillance system contributes to the identification of nutritional and health issues, establishment of public health policies, and development of nutrition recommendations. This paper provides recent information about the Korea National Health and Nutrition Examination Survey and the Korean Youth Risk Behavior Web-based Survey and describes key nationwide survey findings published in the last 5 years on infant feeding practices and the dietary intake and nutritional status of Korean infants, children, and adolescents. There have been increasing trends in children, and teenagers who skip breakfast, eat fast food, consume sugary drinks, have vitamin D deficiency, and are obese. This review will inform pediatricians, nutritionists, and other health care practitioners who track children’s growth and development. It may also help researchers and policymakers identify diet-related policies and strategies for chronic disease prevention in Korean infants, children, and adolescents.

Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the accurate analysis of the topographic changes. This paper proposes a new scalable method to alleviate the problematic systematic errors with a high degree of automation in consideration of the practical limitations raised when processing the rapidly increasing amount of large-scale lidar datasets. The proposed method focused on estimating the displacements caused by vertical positioning errors, which are the most critical error source, and adjusting the DTMs already produced as basic lidar products without access to the point cloud and raw data from the laser scanner. The feasibility and effectiveness of the proposed method were evaluated with experiments with county-level multitemporal airborne lidar datasets in Indiana, USA. The experimental results demonstrated that the proposed method could estimate the vertical displacement reasonably along the flight paths and improve the county-level lidar differencing results by reducing the problematic errors and increasing consistency across the flight paths. The improved differencing results presented in this paper are expected to provide more consistent information about topographic changes in Indiana. In addition, the proposed method can be a feasible solution to upcoming problems induced by rapidly increasing large-scale multitemporal lidar given recent active government-driven lidar data acquisition programs, such as the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP).

Image super-resolution (SR) is a significant technique in image processing as it enhances the spatial resolution of images, enabling various downstream applications. Based on recent achievements in SR studies in computer vision, deep-learning-based SR methods have been widely investigated for remote sensing images. In this study, we proposed a two-stage approach called bicubic-downsampled low-resolution (LR) image-guided generative adversarial network (BLG-GAN) for remote sensing image super-resolution. The proposed BLG-GAN method divides the image super-resolution procedure into two stages: LR image transfer and super-resolution. In the LR image transfer stage, real-world LR images are restored to less blurry and noisy bicubic-like LR images using guidance from synthetic LR images obtained through bicubic downsampling. Subsequently, the generated bicubic-like LR images are used as inputs to the SR network, which learns the mapping between the bicubic-like LR image and the corresponding high-resolution (HR) image. By approaching the SR problem as finding optimal solutions for subproblems, the BLG-GAN achieves superior results compared to state-of-the-art models, even with a smaller overall capacity of the SR network. As the BLG-GAN utilizes a synthetic LR image as a bridge between real-world LR and HR images, the proposed method shows improved image quality compared to the SR models trained to learn the direct mapping from a real-world LR image to an HR image. Experimental results on HR satellite image datasets demonstrate the effectiveness of the proposed method in improving perceptual quality and preserving image fidelity.

Abstract Sensory processing and behaviors are altered during the development of connectivity between the sensory cortices and multiple brain regions in an experience-dependent manner. To reveal the relationship between sensory processing and brain white matter, we investigated the association between the Adolescent/Adult Sensory Profile (AASP) and neural connectivity in the white matter tracts of 84 healthy young adults using diffusion tensor imaging (DTI). We observed a positive relationship between AASP scores (i.e., sensory sensitivity, sensation avoiding, activity level)/subscores (i.e., sensory sensitivity–activity level, sensation avoiding–touch) and DTI parameters in the cingulum–cingulate gyrus bundle (CCG) and between AASP subscores (i.e., sensory sensitivity–auditory) and a diffusion parameter in the uncinate fasciculus (UNC). The diffusion parameters that correlated with AASP scores/subscores and AASP quadrant scores (i.e., sensory avoiding and sensitivity) were axonal diffusivity (AD) and mean diffusivity (MD) in the CCG and MD in the UNC. Moreover, the increased sensory avoiding and sensitivity scores represent the sensitization of sensory processing, and the level of diffusivity parameters indicates white matter microstructure variability, such as axons and myelin from diffusivity of water molecules. Thus, the present study suggests that the CCG and UNC are critical white matter microstructures for determining the level of sensory processing in young adults.

Abstract Background Despite the high workload of mechanical ventilation, there has been a lack of studies on the association between nurse workforce and mortality in mechanically ventilated patients. We evaluated the association of the bed-to-nurse ratio with mortality in ventilated pediatric patients admitted to an intensive care unit (ICU). Methods We conducted a nationwide retrospective analysis by using the Korean National Health Insurance database, which categorizes the bed-to-nurse ratio into 9 grades according to the number of beds divided by the number of full-time equivalent registered nurses in a unit. Patients of ages between 28 days and 18 years were enrolled. Multiple admissions and transfers from other hospitals were excluded. We evaluated the odds ratios (ORs) of in-hospital mortality using 4 groups (Grade 1: bed-to-nurse 0.77) with adjustment of patient factors, hospital factors, and treatment requirements. Results Of the 27,849 patients admitted to ICU, 11,628 (41.8%) were on mechanical ventilation. The overall in-hospital mortality rates in Grade 1, Grade 2, Grade 3, and Grade 4 or above group were 4.5%, 6.8%, 6.9%, and 4.7%, respectively. The adjusted ORs (95% CI) for in-hospital mortality of mechanically ventilated patients in the Grade 2, Grade 3, and Grade 4 or above compared to those in Grade 1 were 2.73 (95% CI 1.51–4.95), 4.02 (95% CI 2.23–7.26), and 7.83 (4.07–15.07), respectively. However, for patients without mechanical ventilation, the adjusted ORs of in-hospital mortality were not statistically significant. Conclusion In mechanically ventilated patients, the adjusted mortality rate increased significantly, as the bed-to-nurse ratio of the ICU increased. Policies that limit the number of ventilated patients per nurse should be considered. Trial registration retrospectively registered

Background: We aimed to evaluate the influence of cold airflow from the air conditioner on skin barrier function and filaggrin degradation products (FDPs) in children with atopic deramtitis (AD). Methods: In a case-control study, 28 children with AD and 12 normal children without AD were exposed to one of two air conditioner modes (conventional or wind-free) for 2 h. Skin temperature, transepidermal water loss (TEWL), and skin pH were measured on right cheek and forearm at pre- and post-exposure time points. We also measured filaggrin and FDPs from the volar surface of the forearm. Results: In AD patients, skin temperature on the forearm decreased after exposure to the conventional and wind-free modes (P

Abstract Background Multimodal PET/MRI image data simultaneously obtained from patients with early-stage of Alzheimer’s disease (eAD) were assessed in order to observe pathophysiologic and functional changes, as well as alterations of morphology and connectivity in the brain. Fifty-eight patients with mild cognitive impairment and early dementia (29 males, 69 ± 12 years) underwent [11C]Pittsburgh compound-B (PiB) PET/MRI with 70-min PET and MRI scans. Sixteen age-matched healthy controls (CTL) (9 males, 68 ± 11 years) were also studied with the same scanning protocol. Cerebral blood flow (CBF) was calculated from the early phase PET images using the image-derived input function method. A standardized uptake value ratio (SUVr) was calculated from 50 to 70 min PET data with a reference region of the cerebellar cortex. MR images such as 3D-T1WI, resting-state functional MRI (RS-fMRI), diffusion tensor image (DTI), and perfusion MRI acquired during the dynamic PET scan were also analyzed to evaluate various brain functions on MRI. Results Twenty-seven of the 58 patients were determined as eAD based on the results of PiB-PET and clinical findings, and a total of 43 subjects’ data including CTL were analyzed in this study. PiB SUVr values in all cortical regions of eAD were significantly greater than those of CTL. The PiB accumulation intensity was negatively correlated with cognitive scores. The regional PET-CBF values of eAD were significantly lower in the bilateral parietal lobes and right temporal lobe compared with CTL, but not in MRI perfusion; however, SPM showed regional differences on both PET- and MRI-CBF. SPM analysis of RS-fMRI delineated regional differences between the groups in the anterior cingulate cortex and the left precuneus. VBM analysis showed atrophic changes in the AD group in a part of the bilateral hippocampus; however, analysis of fractional anisotropy calculated from DTI data did not show differences between the two groups. Conclusion Multimodal analysis conducted with various image data from PiB-PET/MRI scans showed differences in regional CBF, cortical volume, and neuronal networks in different regions, indicating that pathophysiologic and functional changes in the AD brain can be observed from various aspects of neurophysiologic parameters. Application of multimodal brain images using PET/MRI would be ideal for investigating pathophysiologic changes in patients with dementia and other neurodegenerative diseases.

Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area of the input sample and moves across the sample to provide a feature map for the subsequent layers. In our study, we hypothesized that a 3D-CNN model with down-sampling operations such as pooling and/or stride would have the ability to extract robust feature maps from the shifted and scaled neuronal activations in a single functional MRI (fMRI) volume for the classification of task information associated with that volume. Thus, the 3D-CNN model would be able to ameliorate the potential misalignment of neuronal activations and over-/under-activation in local brain regions caused by imperfections in spatial alignment algorithms, confounded by variability in blood-oxygenation-level-dependent (BOLD) responses across sessions and/or subjects. To this end, the fMRI volumes acquired from four sensorimotor tasks (left-hand clenching, right-hand clenching, auditory attention, and visual stimulation) were used as input for our 3D-CNN model to classify task information using a single fMRI volume. The classification performance of the 3D-CNN was systematically evaluated using fMRI volumes obtained from various minimal preprocessing scenarios applied to raw fMRI volumes that excluded spatial normalization to a template and those obtained from full preprocessing that included spatial normalization. Alternative classifier models such as the 1D fully connected DNN (1D-fcDNN) and support vector machine (SVM) were also used for comparison. The classification performance was also assessed for several k-fold cross-validation (CV) schemes, including leave-one-subject-out CV (LOOCV). Overall, the classification results of the 3D-CNN model were superior to that of the 1D-fcDNN and SVM models. When using the fully-processed fMRI volumes with LOOCV, the mean error rates (± the standard error of the mean) for the 3D-CNN, 1D-fcDNN, and SVM models were 2.1% (± 0.9), 3.1% (± 1.2), and 4.1% (± 1.5), respectively (p = 0.041 from a one-way ANOVA). The error rates for 3-fold CV were higher (2.4% ± 1.0, 4.2% ± 1.3, and 10.1% ± 2.0; p < 0.0003 from a one-way ANOVA). The mean error rates also increased considerably using the raw fMRI 3D volume data without preprocessing (26.2% for the 3D-CNN, 75.0% for the 1D-fcDNN, and 75.0% for the SVM). Furthermore, the ability of the pre-trained 3D-CNN model to handle shifted and scaled neuronal activations was demonstrated in an online scenario for five-class classification (i.e., four sensorimotor tasks and the resting state) using the real-time fMRI of three participants. The resulting classification accuracy was 78.5% (± 1.4), 26.7% (± 5.9), and 21.5% (± 3.1) for the 3D-CNN, 1D-fcDNN, and SVM models, respectively. The superior performance of the 3D-CNN compared to the 1D-fcDNN was verified by analyzing the resulting feature maps and convolution filters that handled the shifted and scaled neuronal activations and by utilizing an independent public dataset from the Human Connectome Project.

Abstract The cerebellum, although traditionally considered a motor structure, has been increasingly recognized to play a role in regulating executive function, the dysfunction of which is a factor in attention-deficit/hyperactivity disorder (ADHD). Additionally, catechol-O-methyltransferase (COMT) polymorphism has been reported to be associated with executive function. We examined whether the cortico-cerebellar executive function network is altered in children with ADHD and whether COMT polymorphism is associated with the altered network. Thirty-one children with ADHD and thirty age- and IQ-matched typically developing (TD) controls underwent resting-state functional MRI, and functional connectivity of executive function-related Crus I/II in the cerebellum was analysed. COMT Val158Met genotype data were also obtained from children with ADHD. Relative to TD controls, children with ADHD showed significantly lower functional connectivity of the right Crus I/II with the left dorsolateral prefrontal cortex. Additionally, the functional connectivity of children with ADHD was modulated by COMT polymorphism, with Met-carriers exhibiting significantly lower functional connectivity than the Val/Val genotype. These results suggest the existence of variations, such as ethnic differences, in COMT genetic effects on the cortico-cerebellar executive function network. These variations contribute to heterogeneity in ADHD. Further neuroimaging genetics study might lead to the development of fundamental therapies that target ADHD pathophysiology.

Evidence from functional imaging studies points to a role for gender in language ability. However, recent studies suggest that sex differences in the neural basis of language are still unclear, reflecting a complex interaction between sex and language ability. We used diffusion weighted magnetic resonance imaging and global probabilistic tractography to investigate white matter (WM) pathways between 32 male and 35 age- and IQ-matched female adult participants in relation to their verbal abilities. Males showed higher fractional anisotropy (FA) in the left anterior thalamic radiations (ATR), right cingulum-angular bundle, right corticospinal tract, bilateral superior longitudinal fasciculus-temporal terminations, bilateral uncinate fasciculus (UNC), and corpus callosum-forceps minor when compared with the female group. In contrast, females showed higher radial diffusivity (RD) in the left ATR and left UNC when compared to the male group. The relationship between WM metrics and verbal ability also differed across the two groups: a negative correlation between verbal comprehension index (VCI) and FA as well as axial diffusivity (AD) in left cingulum-cingulate gyrus (CCG) supracallosal bundle in males but not in females; a negative correlation between verbal IQ (VIQ) and FA in the right corticospinal tract (CST), and a positive correlation between VCI and RD in corpus callosum-forceps minor in the female but not in the male group. A direct comparison of these correlation coefficients yielded significant differences between the groups for the VCI-AD and VIQ -FA associations. The findings may reflect sex differences in WM related to language ability.

Individuals with autism spectrum disorder (ASD) often lack cognitive empathy, so they experience difficulty in empathizing with others. Although deficits in social abilities, such as empathy, have been demonstrated in previous studies, most stimuli used in previous studies were developed for typically developing (TD) individuals. Previous studies have demonstrated that adults with and without ASD display empathetic responses toward similar others. Adults with ASD (n = 22, 7 women and 15 men, mean age = 26.8 years) and intelligence- and age-matched TD adults (n = 20, 8 women and 12 men, mean age = 24.0 years) participated in the study. They were instructed to read 24 stories (12 stories featured protagonists with characteristics of ASD, and the other 12 featured TD protagonists) and respond to the following questions: “How did the protagonist feel?” and “Would you help if the protagonist were in trouble?” After controlling for alexithymia and AQ based on multiple regression analyses, individuals with ASD empathize with other people who have ASD and are motivated to help other people with ASD. Additionally, social skills and attention to detail were associated with decreased helping motivation for story characters with ASD. Social skills among AQ subscales (social skills, attention switching, attention to detail, communication, and imagination) were the most potent predictor of decreased helping motivation. These findings suggest that the reason why individuals with ASD are considered to have limited cognitive empathy and helping motivation could be related to alexithymia and the lack of social skills and attention to detail, which are related to atypical perception.

Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of visual networks in cLBP patients and the feasibility of distinguishing cLBP patients from healthy controls using machine learning methods. cLBP (n = 90) and control individuals (n = 74) were enrolled and underwent resting-state BOLD fMRI scans. Primary, dorsal, and ventral visual networks derived from independent component analysis were used as regions of interest to compare resting state functional connectivity changes between the cLBP patients and healthy controls. We then applied a support vector machine classifier to distinguish the cLBP patients and control individuals. These results were further verified in a new cohort of subjects. We found that the functional connectivity between the primary visual network and the somatosensory/motor areas were significantly enhanced in cLBP patients. The rsFC between the primary visual network and S1 was negatively associated with duration of cLBP. In addition, we found that the rsFC of the visual network could achieve a classification accuracy of 79.3% in distinguishing cLBP patients from HCs, and these results were further validated in an independent cohort of subjects (accuracy = 66.7%). Our results demonstrate significant changes in the rsFC of the visual networks in cLBP patients. We speculate these alterations may represent an adaptation/self-adjustment mechanism and cross-model interaction between the visual, somatosensory, motor, attention, and salient networks in response to cLBP. Elucidating the role of the visual networks in cLBP may shed light on the pathophysiology and development of the disorder. Keywords: Chronic low back pain, Vision system, fMRI, Cross-modal perception, Attention, Resting state functional connectivity

Household peanut exposure via skin in infants with impaired skin barrier function is a risk factor for peanut allergy development. The aim of this study is to investigate the peanut consumption of Koreans using national representative data. We used data from the Korean National Health and Nutrition Examination Survey 2012–2016, consisting of data from 17,625 adults who complete the survey. Peanut intake was assessed using a 24-h recall method. Of the study population, 10,552 (59.9%), 6726 (38.2%), and 347 (1.9%) subjects were categorized into non-intake, intermittent intake, and frequent intake group, respectively. Ordered logistic regression models were used to examine the association between sociodemographic and dietary factors and the frequency of peanut intake. After adjusting for confounders, increasing age (adjusted odds ratio (aOR) 1.03; 95% confidence interval (CI) 1.03–1.04), higher education (high school graduates: aOR 1.75, 95 CI 1.39–2.19; higher than college: aOR 2.11, 95% CI 1.65–2.70), and prudent dietary scores in the second (aOR 1.71; 95% CI 1.47–1.99), third (aOR 2.53; 95% CI 2.16–2.97) and the fourth quartiles (aOR 3.72; 95%CI 3.16–4.40) were associated with a high frequency of peanut consumption. This information may be helpful not only in public health research for nutrition but also in personal management for the prevention of peanut allergy in Korea.

Background Child maltreatment is a major risk factor for psychopathology, including reactive attachment disorder (RAD). Aims To examine whether neural activity during reward processing was altered in children and adolescents with RAD. Method Sixteen children and adolescents with RAD and 20 typically developing (TD) individuals performed tasks with high and low monetary rewards while undergoing functional magnetic resonance imaging. Results Significantly reduced activity in the caudate and nucleus accumbens was observed during the high monetary reward condition in the RAD group compared with the TD group (P=0.015, family-wise error-corrected cluster level). Significant negative correlations between bilateral striatal activity and avoidant attachment were observed in the RAD and TD groups. Conclusions Striatal neural reward activity in the RAD group was markedly decreased. The present results suggest that dopaminergic dysfunction occurs in the striatum of children and adolescents with RAD, leading towards potential future risks for psychopathology.

Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by analyzing combined pre-disaster very high resolution (VHR) optical data and post-disaster polarimetric SAR (PolSAR) data, which has rarely been used in previous research because the two data have extremely different characteristics. To overcome these differences and effectively compare VHR optical data and PolSAR data, a technique to simulate polarization orientation angles (POAs) in built-up areas was developed using building orientations extracted from VHR optical data. The POA is an intrinsic parameter of PolSAR data and has a physical relationship with building orientation. A damage level indicator was also proposed, based on the consideration of diminished homogeneity of POA values by damaged buildings. The indicator is the difference between directional dispersions of the pre and post-disaster POA values. Damage assessment in urban areas was conducted by using the indicator calculated with the simulated pre-disaster POAs from VHR optical data and the derived post-disaster PolSAR POAs. The proposed method was validated on the case study of the 2011 tsunami in Japan using pre-disaster KOMPSAT-2 data and post-disaster ALOS/PALSAR-1 data. The experimental results demonstrated that the proposed method accurately simulated the POAs with a root mean square error (RMSE) value of 2.761° and successfully measured the level of damage in built-up areas. The proposed method can facilitate efficient and fast damage assessment in built-up areas by comparing pre-disaster VHR optical data and post-disaster PolSAR data.

Autism spectrum traits exist on a continuum and are more common in males than in females, but the basis for this sex difference is unclear. To this end, the present study draws on the extreme male brain theory, investigating the relationship between sex difference and the default mode network (DMN), both known to be associated with autism spectrum traits. Resting-state functional magnetic resonance imaging (MRI) was carried out in 42 females (mean age ± standard deviation, 22.4 ± 4.2 years) and 43 males (mean age ± standard deviation, 23.8 ± 3.9 years) with typical development. Using a combination of different analyses (viz., independent component analysis (ICA), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and seed-based analyses), we examined sex differences in the DMN and the relationship to autism spectrum traits as measured by autism-spectrum quotient (AQ) scores. We found significant differences between female and male subjects in DMN brain regions, with seed-based analysis revealing a significant negative correlation between default-mode resting state functional connectivity of the anterior medial prefrontal cortex seed (aMPFC) and AQ scores in males. However, there were no relationships between DMN sex differences and autism spectrum traits in females. Our findings may provide important insight into the skewed balance of functional connectivity in males compared to females that could serve as a potential biomarker of the degree of autism spectrum traits in line with the extreme male brain theory.

Over the past decade, research on the aftereffects of stressful or traumatic events has emphasized the negative outcomes from these experiences. However, the positive outcomes deriving from adversity are increasingly being examined, and such positive changes are described as posttraumatic growth (PTG). To investigate the relationship between basal whole-brain functional connectivity and PTG, we employed resting-state functional magnetic resonance imaging and analyzed the neural networks using independent component analysis in a sample of 33 healthy controls. Correlations were calculated between the network connectivity strength and the Posttraumatic Growth Inventory (PTGI) score. There were positive associations between the PTGI scores and brain activation in the rostral prefrontal cortex and superior parietal lobule (SPL) within the left central executive network (CEN) (respectively, r = 0.41, p < 0.001; r = 0.49, p < 0.001). Individuals with higher psychological growth following adverse experiences had stronger activation in prospective or working memory areas within the executive function network than did individuals with lower psychological growth (r = 0.40, p < 0.001). Moreover, we found that individuals with higher PTG demonstrated stronger connectivity between the SPL and supramarginal gyrus (SMG). The SMG is one of the brain regions associated with the ability to reason about the mental states of others, otherwise known as mentalizing. These findings suggest that individuals with higher psychological growth may have stronger functional connectivity between memory functions within the CEN and social functioning in the SMG, and that their better sociality may result from using more memory for mentalizing during their daily social interactions.

Background: Oral immunotherapy (OIT) can impose psychological burdens on patients and their parents due to the necessary preparations and repeated adverse reactions. Objective: To investigate changes in quality of life (QoL) and psychological burden in parents of children receiving OIT for food allergy (FA). Methods: Children aged 3-13 years with FA were enrolled. Parents were asked tojill out the Korean versions of the Food Allergy Quality of L(fe-Parental Burden (FAQL-PB). the Korean versions Of the Food Allergy Quality of Life-Parental Form (K-FAQLQPF), the Korean versions of the Beck Anxiety Inventory (K-BAI), and the Korean version of the Patient Health Questionnaire-9 (PHQ-9) for depression before OIT (Tl), after 2 months of updosing (T2), and after the end of the updosingphase (T3). Results: A total Of 111 parents were enrolled. The total FAQL-PB scores were decreased at TZ and T3 compared with those at Tl Call p < 0.001). Greater improvement in the total FAQL-PB score at T2 was noted in parents with a higher parental burden (FAQL-PB score 2 74 points) at baseline than in those with a lower parental burden (p= 0.001). Among the K-FAQLQ-PF domains, "food anxiety" scores were decreased at TZ and T3 compared with those at Tl (p = 0.049 and p = 0.030, respectively), whereas there was no change in "socialand dietary limitation" and "emotional impact" scores between Tl and T2 and between Tl and T3. However, no differences were observed in K-BAI and PHQ-9 scores between Tl and TZ and between Tl and T3. Conclusion: Our results suggest that OfT improves parental burden and QoL in parents of children with FA. [ABSTRACT FROM AUTHOR]