Aberrant Brain Triple-Network Effective Connectivity Patterns in Type 2 Diabetes Mellitus.

INTRODUCTION: Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type 2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines. METHODS: In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected. RESULTS: Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = - 0.353, p = 0.001; r = 0.377, p = 0.004). CONCLUSION: These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction.

Detection and Semiquantitative Analysis of Cardiomegaly, Pneumothorax, and Pleural Effusion on Chest Radiographs.

PURPOSE: To develop and evaluate deep learning models for the detection and semiquantitative analysis of cardiomegaly, pneumothorax, and pleural effusion on chest radiographs. MATERIALS AND METHODS: In this retrospective study, models were trained for lesion detection or for lung segmentation. The first dataset for lesion detection consisted of 2838 chest radiographs from 2638 patients (obtained between November 2018 and January 2020) containing findings positive for cardiomegaly, pneumothorax, and pleural effusion that were used in developing Mask region-based convolutional neural networks plus Point-based Rendering models. Separate detection models were trained for each disease. The second dataset was from two public datasets, which included 704 chest radiographs for training and testing a U-Net for lung segmentation. Based on accurate detection and segmentation, semiquantitative indexes were calculated for cardiomegaly (cardiothoracic ratio), pneumothorax (lung compression degree), and pleural effusion (grade of pleural effusion). Detection performance was evaluated by average precision (AP) and free-response receiver operating characteristic (FROC) curve score with the intersection over union greater than 75% (AP75; FROC score75). Segmentation performance was evaluated by Dice similarity coefficient. RESULTS: The detection models achieved high accuracy for detecting cardiomegaly (AP75, 98.0%; FROC score75, 0.985), pneumothorax (AP75, 71.2%; FROC score75, 0.728), and pleural effusion (AP75, 78.2%; FROC score75, 0.802), and they also weakened boundary aliasing. The segmentation effect of the lung field (Dice, 0.960), cardiomegaly (Dice, 0.935), pneumothorax (Dice, 0.827), and pleural effusion (Dice, 0.826) was good, which provided important support for semiquantitative analysis. CONCLUSION: The developed models could detect cardiomegaly, pneumothorax, and pleural effusion, and semiquantitative indexes could be calculated from segmentations.Keywords: Computer-Aided Diagnosis (CAD), Thorax, CardiacSupplemental material is available for this article.© RSNA, 2021.

Altered Dynamic Neural Activity in the Default Mode Network in Lung Cancer Patients After Chemotherapy.

BACKGROUND Few studies have examined functional brain changes specifically associated with chemotherapy (CTx) in patients with lung cancer. This prospective longitudinal research aimed to explore the change in intrinsic brain activity by investigating patients with lung cancer after CTx. MATERIAL AND METHODS Sixteen patients and 20 healthy individuals were enrolled in this study. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), dynamic amplitude of low-frequency fluctuation (dALFF), and dynamic regional homogeneity (dReHo) were computed. The group differences in resting state functional magnetic resonance imaging (rs-fMRI) parameters were compared. Alterations in the rs-fMRI parameters from before CTx to after CTx were assessed using the paired t-test. We performed correlation analyses between rs-fMRI parameters and Montreal Cognitive Assessment (MoCA) scores. RESULTS We found statistically significant differences in MoCA scores before CTx and after CTx. Compared to the healthy group, rs-fMRI values decreased in the frontal regions as well as parietal regions compared to values before CTx. In addition, we found significantly decreased rs-fMRI values in the default-mode network (DMN) region of the brain before CTx compared to after CTx. We found no significant correlations between altered intrinsic activity values and MoCA scores. CONCLUSIONS The current study indicated that patients with lung cancer after CTx had decreased dynamic brain activity in the DMN region, and the DMN is vulnerable when patients undergoing CTx.

Does the maxillary anterior ratio in Korean adults follow the Golden Proportion?

PURPOSE: The purpose of this study was to determine the effect of changes in the horizontal plane angle on the mesiodistal width ratios of the maxillary anterior teeth during the acquisition of frontal view photographs, derive these ratios for Korean adults on the basis of the data obtained, and analyze them using the Golden Proportion as a reference. MATERIALS AND METHODS: In experiment I, 30 plaster casts were mounted on an articulator and positioned on the angle-measuring device with a center setting of 0°. The device was rotated to 10° in 1° increments in a counterclockwise direction. At each angle, photographs were obtained and analyzed. Experiment II was based on 60 patients who visited the Department of Prosthodontics at Kyungpook National University Dental Hospital from February 2012 to February 2015. The patients were divided into three groups [Male (M), Female (F), Total (M + F)]. Frontal views were obtained for all groups and analyzed. RESULTS: From 1° to 10°, the relative mesiodistal width ratios for the maxillary anterior teeth showed no significant differences from those at 0°. In all three groups, the relative width ratio of the maxillary central incisor was smaller than that specified in the Golden Proportion; the opposite was true for the canine. CONCLUSION: Our results suggest that the mesiodistal width ratios of the maxillary anterior teeth do not follow the Golden Proportion in Korean adults, and that a change in the horizontal plane angle from 1° to 10° during frontal photography does not affect these ratios.