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Abstract Background Somatic Symptom Disorder (SSD) is a condition often linked to excessive health anxiety and somatic symptoms. In recent years, studies have found associations between the cerebellum and various mental illnesses, including SSD. However, the microstructure of cerebellar subregions in SSD using diffusion magnetic resonance imaging has not been fully defined. Methods This is a cross-sectional study, that included 30 SSD patients and 30 age- and gender-matched healthy controls to investigate the microstructure of the cerebellum using diffusion magnetic resonance imaging. SSD diagnosis followed DSM-5 criteria, excluding major psychiatric comorbidities, while healthy controls underwent rigorous screening to exclude psychiatric or neurological histories. Clinical evaluations utilized standardized scales to assess depressive, anxiety, and cognitive symptoms. MRI data were acquired using a 3T Siemens Prisma scanner, including T1-weighted and diffusion-weighted imaging (30 directions, b = 1000/2000 s/mm²). Multi-compartment diffusion magnetic resonance imaging metrics from free water elimination diffusion tensor imaging and neurite orientation dispersion and density imaging were used to observe microstructural changes in the cerebellum’s white matter and gray matter subregions in SSD patients. Results Compared to the control group, patients with SSD exhibited significant alterations in white matter microstructure. These changes were characterized by increased free water-eliminated fractional anisotropy and neurite density index, as well as decreased free water-eliminated mean diffusivity and radial diffusivity. Furthermore, the cerebellum displayed varying microstructural changes across 26 gray matter subregions. These changes included reduced mean diffusivity, free water-eliminated axial diffusivity, and free water-eliminated radial diffusivity, alongside increased neurite density index and orientation dispersion index. Importantly, the study identified significant correlations between these microstructural changes and clinical symptoms. Specifically, Vermis X and the left lobule VIIb showed significant associations with both depression and anxiety scores. Conclusions The findings suggest greater neurite density and enhanced diffusion restriction in the cerebellum of patients with SSD, which may indicate possible adaptive changes associated with chronic stress.

BackgroundThere are limited studies exploring the relationship between physical activity (PA), cognitive function, and the brain processing characteristics in healthy older adults.MethodsA total of 41 participants (42.7 ± 20.5 years, 56.1% males) were included in the data analysis. The International Physical Activity Questionnaire Short Form was used to assess PA levels, and the Chinese version of the Montreal Cognitive Assessment-Basic and the Flanker task were employed to evaluate cognitive function. Furthermore, fMRI technology was utilized to examine brain activation patterns.ResultsThe cognitive function of the older adults was found to be significantly lower compared to the young adults. Within the older adults, those with high levels of PA exhibited significantly higher cognitive function than those with low and medium PA levels. The fMRI data showed significant differences in brain activation patterns among young adults across the different PA levels. However, such difference was not observed among older adults.ConclusionA decline in cognitive function was observed among older adults. There was a significant correlation between the levels of PA and cognitive function in healthy older adults. The study demonstrated significant effects of PA levels on brain activation patterns in inhibitory control-related regions among young adults, while not significant among older adults. The findings suggest that neurological mechanisms driving the relationship between PA and cognitive function may differ between older and young adults.

(1) Background: This study investigates the resting-state brain characteristics of skeleton athletes compared to healthy age-matched non-athletes, using resting-state fMRI to investigate long-term skeleton-training-related changes in the brain. (2) Methods: Eleven skeleton athletes and twenty-three matched novices with no prior experience with skeleton were recruited. Amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity analyses were explored to investigate resting-state functional magnetic resonance imaging (rs-fMRI) data, aiming to elucidate differences in resting-state brain function between the two groups. (3) Results: Compared to the control group, skeleton athletes exhibited significantly higher ALFF in the left fusiform, left inferior temporal gyrus, right inferior frontal gyrus, left middle temporal gyrus, left and right insula, left Rolandic operculum, left inferior frontal gyrus, and left superior temporal gyrus. Skeleton athletes exhibit stronger functional connectivity in brain regions associated with cognitive and motor control (superior frontal gyrus, insula), as well as those related to reward learning (putamen), visual processing (precuneus), spatial cognition (inferior parietal), and emotional processing (amygdala), during resting-state brain function. (4) Conclusions: The study contributes to understanding how motor training history shapes skeleton athletes’ brains, which have distinct neural characteristics compared to the control population, indicating potential adaptations in brain function related to their specialized training and expertise in the sport.

Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand‐crafted features, which are not sufficiently robust to a complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)‐based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy.