In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized with various factors, such as regional volume, surface area and curvature. Moreover, most of morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28) each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20%-40%), and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78) indicates the robustness of the present method. Results demonstrate that the multiple morphometric features can be applied to form a rational reproducible individual-based morphological brain network.

Background: Abnormalities in prespecified and empirical white matter tracts in young patients with anxiety-related disorders have been reported in some diffusion tensor imaging (DTI) studies. However, with few literatures examining the association between the integrity of whole brain white matter and trait anxiety levels in the non-clinical populations, whether white matter changes arise in young healthy individuals with high trait anxiety remains unknown. Methods: We examined whole brain white matter alterations in young healthy individuals with high anxiety but without history of neurological or psychiatric disorders via DTI technology. Group comparison of tract-based spatial statistics (TBSS) was performed to investigate the microstructural diffusion alterations in 38 high anxious subjects in comparison with 34 low anxious subjects matched with age, gender, and degree of education. These analyses controlled for depression to establish specificity to trait anxiety. Results: Young healthy subjects with high trait anxiety had significantly decreased fractional anisotropy (FA) values in multiple clusters, including corona radiate (CR), anterior thalamic radiation (ATR), inferior fronto-occipital fasciculus (IFOF), bilaterally, body, genu, and splenium of corpus callosum (CC) and forceps minor, compared with low trait anxious subjects. For the abnormal FA regions, the other diffusion metrics were also altered slightly. Conclusions: Non-clinical individuals with high anxiety already have white matter alterations in the thalamus-cortical circuit and some emotion-related areas that were widely reported in anxiety-related disorders. The altered white matter may be a vulnerability marker in individuals at high risk of clinical anxiety. These findings can deepen our understanding of the pathological mechanism of anxiety and further support the need for preventive interventions in high anxiety individuals.

Objective. The purpose of this study was to find the correlations between B‐mode ultrasonic tissue texture features and tissue temperature in microwave ablation. Methods. A total of 20 in vitro porcine liver samples were used for microwave ablation experiments. B‐mode ultrasonic images under various temperatures were acquired. The texture features of the differential images based on the gray level histogram, including the mean of the gray scale (MGS), standard deviation of the gray scale, and entropy of the gray scale (ENT), and those based on the gray level co‐occurrence matrix, including the contrast (CON), angular second moment (ASM), inverse difference moment (IDM), and correlation, were extracted. Correlations between the features and liver sample temperature were analyzed. In addition, water bath heating experiments were also performed on 15 in vitro porcine liver samples for analysis validation. Results. The correlation coefficients across the MGS, ENT, and ASM in 4 directions (0°, 45°, 90°, and 135°), the CON and IDM in 3 directions (45°, 90°, and 135°), and a temperature range of 15°C to 90°C were high and greater than 0.9 during microwave ablation. All texture features of the differential B‐mode ultrasonic images changed with rising temperature from 25°C to 60°C during water bath heating. Conclusions. Changes in image features reflect changes in tissue temperature during microwave ablation.

Purpose: Quantitative computed tomography (QCT) techniques, focusing on airway anatomy and emphysema, may help to detect early structural changes of COPD disease. This retrospective study aims to identify high-risk COPD participants by using QCT measurements. Patients and Methods: We enrolled 140 participants from the Second Affiliated Hospital of Shenyang Medical College who completed inspiratory high-resolution CT scans, pulmonary function tests (PFTs), and clinical characteristics recorded. They were diagnosed Non-COPD by PFT value of FEV1/FVC > 70% and divided into two groups according percentage predicted FEV1 (FEV1%), low-risk COPD group: FEV1% ≥ 95%, high-risk group: 80% < FEV1% < 95%. The QCT measurements were analyzed by the Student’s t-test (or Mann–Whitney U-test) method. Then, feature candidates were identified using the LASSO method. Meanwhile, the correlation between QCT measurements and PFTs was assessed by the Spearman rank correlation test. Furthermore, support vector machine (SVM) was performed to identify high-risk COPD participants. The performance of the models was evaluated in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-score, and area under the ROC curve (AUC), with p < 0.05 considered statistically significant. Results: The SVM based on QCT measurements achieved good performance in identifying high-risk COPD patients with 85.71% of ACC, 88.34% of SEN, 84.00% of SPE, 83.33% of F1-score, and 0.93 of AUC. Further, QCT measurements integration of clinical data improved the performance with an ACC of 90.48%. The emphysema index (%LAA_{− 950}) of left lower lung was negatively correlated with PFTs (P < 0.001). The airway anatomy indexes of lumen diameter (LD) were correlated with PFTs. Conclusion: QCT measurements combined with clinical information could provide an effective tool for an early diagnosis of high-risk COPD. The QCT indexes can be used to assess the pulmonary function status of high-risk COPD.

Alzheimer’s disease (AD) is one of the most common forms of dementia that has slowly negative impacts on memory and cognition. With the assistance of multimodal brain networks and graph-based analysis approaches, AD-related network disruptions support the hypothesis that AD can be identified as a dysconnectivity syndrome. However, as the recent emerging of individual-based morphological network research of AD, the utilization of multiple morphometric features may provide a broader horizon for locating the lesions. Therefore, the present study applied the newly proposed individual morphological brain network with five commonly used morphometric features (cortical thickness, regional volume, surface area, mean curvature, and fold index) to explore the topological aberrations and their relationship with cognitive functioning alterations in the early stage of AD. A total of 40 right-handed participants were selected from Open Access Series of Imaging Studies Database with 20 AD patients (age ranged from 70 to 79, CDR = 0.5) and 20 age/gender-matched healthy controls. The significantly affected connections (p < 0.05 with FDR correction) were observed across multiple regions, both enhanced and attenuated correlations, primarily related to the left entorhinal cortex (ENT). In addition, profoundly changed Mini Mental State Examination (MMSE) score and global efficiency (p < 0.05) were noted in the AD patients, as well as the pronounced inter-group distinctions of betweenness centrality, global and local efficiency (p < 0.05) in the higher MMSE score zone (28–30), which indicating the potential role of graphic properties in determination of early-stage AD patients. Moreover, the reservations (regions in the occipital and frontal lobes) and alterations (regions in the right temporal lobe and cingulate cortex) of hubs were also detected in the AD patients. Overall, the findings further confirm the selective AD-related disruptions in morphological brain networks and also suggest the feasibility of applying the morphological graphic properties in the discrimination of early-stage AD patients.