Item-Wise Interindividual Brain-Behavior Correlation in Task Neuroimaging Analysis

Brain-behavior correlations are commonly used to explore the associations between the brain and human behavior in cognitive neuroscience studies. There are many critics of the correlation approach, however. Most problems associated with correlation approaches originate in the weak statistical power of traditional correlation procedures (i.e., the mean-wise interindividual brain-behavior correlation). This paper proposes a new correlation procedure, the item-wise interindividual brain-behavior correlation, which enhances statistical power via testing the significance of small correlation coefficients from trials against zero rather than simply pursuing the highest correlation coefficient. The item-wise and mean-wise correlation were compared in simulations and an fMRI experiment on mathematical problem-solving. Simulations show that the item-wise correlation relative to the mean-wise correlation results in higher t-values when signal-to-noise ratio is equal to or larger than 6%. Item-wise correlation displayed more voxels with significant brain-behavior correlation than did mean-wise correlation. Analyses with item-wise (rather than mean-wise) correlation showed significant brain-behavior correlation at the threshold of p < 0.05 corrected. Cross validation showed that odd- and even-ordered trials have greater stability in terms of the item-wise correlation (r = 0.918) than the mean-wise correlation (r = 0.686). The simulations and example analyses altogether demonstrate the effectiveness of the proposed correlation procedure for task neuroimaging studies

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides state-of-the-art performance and has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs by selecting more valuable examples from the unlabeled data pool. However, their application in medical image segmentation tasks is limited, and there is currently no effective and universal AL-based method specifically designed for 3D medical image segmentation. To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks. We extensively validated our proposed active learning method on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our PCDAL can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The codes of this study are available at https://github.com/ortonwang/PCDAL

Mapping the scientific research on integrated care: a bibliometric and social network analysis

BackgroundIntegrated care (IC) is the cornerstone of the sustainable development of the medical and health system. A thorough examination of the existing scientific literature on IC is essential for assessing the present state of knowledge on this subject. This review seeks to offer an overview of evidence-based knowledge, pinpoint existing knowledge gaps related to IC, and identify areas requiring further research.MethodsData were retrieved from the Web of Science Core Collection, from 2010 to 2020. Bibliometrics and social network analysis were used to explore and map the knowledge structure, research hotspots, development status, academic groups and future development trends of IC.ResultsA total of 7,501 articles were obtained. The number of publications on IC was rising in general. Healthcare science services were the most common topics. The United States contributed the highest number of articles. The level of collaboration between countries and between authors was found to be relatively low. The keywords were stratified into four clusters: IC, depression, integrative medicine, and primary health care. In recent years, complementary medicine has become a hotspot and will continue to be a focus.ConclusionThe study provides a comprehensive analysis of global research hotspots and trends in IC, and highlights the characteristics, challenges, and potential solutions of IC. To address resource fragmentation, collaboration difficulties, insufficient financial incentives, and poor information sharing, international collaboration needs to be strengthened to promote value co-creation and model innovation in IC. The contribution of this study lies in enhancing people’s understanding of the current state of IC research, guiding scholars to discover new research perspectives, and providing valuable references for researchers and policymakers in designing and implementing effective IC strategies

Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation

Supervised learning algorithms based on Convolutional Neural Networks have become the benchmark for medical image segmentation tasks, but their effectiveness heavily relies on a large amount of labeled data. However, annotating medical image datasets is a laborious and time-consuming process. Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation. We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Additionally, we enforce the consistency between different scales in the decoder module of the segmentation network and propose a loss function suitable for evaluating the consistency. Moreover, we incorporate a sharpening operation on the predicted results, further enhancing the accuracy of the segmentation. Extensive experiments on three publicly available datasets demonstrate that the PLGDF framework can largely improve performance by incorporating the unlabeled data. Meanwhile, our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods. The codes of this study are available at https://github.com/ortonwang/PLGDF

Neural correlates of quantity processing of numeral classifiers.

ObjectiveClassifiers play an important role in describing the quantity information of objects. Few studies have been conducted to investigate the brain organization for quantity processing of classifiers. In the current study, we investigated whether activation of numeral classifiers was specific to the bilateral inferior parietal areas, which are believed to process numerical magnitude.MethodUsing functional MRI, we explored the neural correlates of numeral classifiers, as compared with those of numbers, dot arrays, and nonquantity words (i.e., tool nouns).ResultsOur results showed that numeral classifiers and tool nouns elicited greater activation in the left inferior frontal lobule and left middle temporal gyrus than did numbers and dot arrays, but numbers and dot arrays had greater activation in the middle frontal gyrus, precuneus, and the superior and inferior parietal lobule in the right hemisphere. No differences were found between numeral classifiers and tool nouns.ConclusionThe results suggest that quantity processing of numeral classifiers is independent of that of numbers and dot arrays, supporting the notation-dependent hypothesis of quantity processing

Language Ability Accounts for Ethnic Difference in Mathematics Achievement

The mathematics achievement of minority students has always been a focal point of educators in China. This study investigated the differences in mathematics achievement between Han and minority pupils to determine if there is any cognitive mechanism that can account for the discrepancy. We recruited 236 Han students and 272 minority students (including Uygur and Kazak) from the same primary schools. They were tested on mathematics achievement, language abilities, and general cognitive abilities. The results showed that Han pupils had better mathematics achievement scores and better Chinese language ability than minority students. After controlling for age, gender, and general cognitive abilities, there were still significant differences in mathematics achievement between Han and minority students. However, these differences disappeared after controlling for language ability. These results suggest that the relatively poor levels of mathematics achievement observed in minority students is related to poor Chinese language skills

Quantitative Measurement of the Three-dimensional Structure of the Vocal Folds and Its Application in Identifying the Type of Cricoarytenoid Joint Dislocation

Summary(#br)Objective(#br)The objective of this study was to quantitatively measure the three-dimensional (3D) structure of the vocal folds in normal subjects and in patients with different types of cricoarytenoid dislocation. We will analyze differences in parameters between the groups and also determine if any morphologic parameters possess utility in distinguishing the type and the degree of cricoarytenoid dislocation.(#br)Study Design(#br)This retrospective study was conducted using university hospital data.(#br)Methods(#br)Subjects’ larynges were scanned using dual-source computed tomography (CT). The normal subjects were divided into deep-inhalation and phonation groups, and patients with cricoarytenoid joint dislocation were divided into anterior-dislocation and posterior-dislocation groups. Membranous vocal fold length and width were measured directly on the thin-section CT images. Vocal fold and airway 3D models were constructed using Mimics software and used in combination to measure vocal fold thickness, subglottal convergence angle, and oblique angle of the vocal folds.(#br)Results(#br)The phonation group displayed a greater vocal fold width, greater oblique angle, thinner vocal folds, and a smaller subglottal convergence angle than those of the deep-inhalation group ( P < 0.05). The anterior-dislocation group displayed a smaller oblique angle and subglottal convergence angle than the posterior-dislocation group ( P < 0.05).(#br)Conclusions(#br)The 3D structure of the vocal folds during deep inhalation and phonation can be accurately measured using dual-source CT and laryngeal 3D reconstruction. As the anterior-dislocation group yielded negative values for the oblique angle and the posterior-dislocation group yielded positive values, the oblique angle of the vocal folds may possess utility for distinguishing the type and for quantitatively determining the degree of cricoarytenoid dislocation

Quantitative Measurement of the Three-dimensional Structure of the Vocal Folds and Its Application in Identifying the Type of Cricoarytenoid Joint Dislocation.

OBJECTIVE(#br)The objective of this study was to quantitatively measure the three-dimensional (3D) structure of the vocal folds in normal subjects and in patients with different types of cricoarytenoid dislocation. We will analyze differences in parameters between the groups and also determine if any morphologic parameters possess utility in distinguishing the type and the degree of cricoarytenoid dislocation.(#br)STUDY DESIGN(#br)This retrospective study was conducted using university hospital data.(#br)METHODS(#br)Subjects’ larynges were scanned using dual-source computed tomography (CT). The normal subjects were divided into deep-inhalation and phonation groups, and patients with cricoarytenoid joint dislocation were divided into anterior-dislocation and posterior-dislocation groups. Membranous vocal fold length and width were measured directly on the thin-section CT images. Vocal fold and airway 3D models were constructed using Mimics software and used in combination to measure vocal fold thickness, subglottal convergence angle, and oblique angle of the vocal folds.(#br)RESULTS(#br)The phonation group displayed a greater vocal fold width, greater oblique angle, thinner vocal folds, and a smaller subglottal convergence angle than those of the deep-inhalation group (P < 0.05). The anterior-dislocation group displayed a smaller oblique angle and subglottal convergence angle than the posterior-dislocation group (P < 0.05).(#br)CONCLUSIONS(#br)The 3D structure of the vocal folds during deep inhalation and phonation can be accurately measured using dual-source CT and laryngeal 3D reconstruction. As the anterior-dislocation group yielded negative values for the oblique angle and the posterior-dislocation group yielded positive values, the oblique angle of the vocal folds may possess utility for distinguishing the type and for quantitatively determining the degree of cricoarytenoid dislocation

The Factorial Structure of Spatial Abilities in Russian and Chinese Students

Background: Recent research suggested a unifactorial structure of spatial ability (SA). Research is needed to replicate this finding in different populations. Objective: This study aims to explore the factorial structure of SA in samples of 921 Russian and 229 Chinese university students. Design: A gamified spatial abilities battery was administered to all participants. The battery consists of 10 different domains of SA, including 2D and 3D visualization, mental rotation, spatial pattern assembly, spatial relations, spatial planning, mechanical reasoning, spatial orientation and spatial decision making speed and flexibility. Results: The results of the factor analysis showed a somewhat different pattern for different samples. In the Russian sample, the unifactorial structure, shown previously in a large UK sample (Rimfeld et al., 2017), was replicated. A single factor explained 40% of the variance. In the Chinese sample two factors emerged: first factor explained 26% of the variance and the second factor, including only Mechanical reasoning and Cross-Sections tests, explained 14%. The results also showed that the Chinese sample significantly outperformed the Russian sample in 5 out of the 10 tests. Russian students showed better performance only in two of the tests. The effects of all group comparisons were small. Conclusion: Overall, a similar amount of variance in the 10 tests was explained in the two samples, replicating results from the UK sample. Future research is needed to explain the observed differences in the structure of SA