Electroconvulsive Therapy Regulates the Interhemispheric Functional Connectivity of the Dorsomedial Prefrontal Cortex in Depressive Patients: Evidence from 2 Independent Samples.

Background: The dorsomedial prefrontal cortex (dmPFC) is considered a crucial node in emotional and cognitive processes. Voxel-mirrored homotopic connectivity (VMHC) is a validated methodology for investigating interhemispheric coordination. This study aims to elucidate the effects of electroconvulsive therapy (ECT) on the interhemispheric connectivity of the dmPFC in patients with depression, using VMHC as a measure of bilateral neural coordination.

Cross-Subject Lifelong Learning for Continuous Estimation From Surface Electromyographic Signal.

The employment of surface electromyographic (sEMG) signals in the estimation of hand kinematics represents a promising non-invasive methodology for the advancement of human-machine interfaces. However, the limitations of existing subject-specific methods are obvious as they confine the application to individual models that are custom-tailored for specific subjects, thereby reducing the potential for broader applicability. In addition, current cross-subject methods are challenged in their ability to simultaneously cater to the needs of both new and existing users effectively.

Tensor Learning Meets Dynamic Anchor Learning: From Complete to Incomplete Multiview Clustering.

Multiview clustering (MVC), which can dexterously uncover the underlying intrinsic clustering structures of the data, has been particularly attractive in recent years. However, previous methods are designed for either complete or incomplete multiview only, without a unified framework that handles both tasks simultaneously. To address this issue, we propose a unified framework to efficiently tackle both tasks in approximately linear complexity, which integrates tensor learning to explore the inter-view low-rankness and dynamic anchor learning to explore the intra-view low-rankness for scalable clustering (TDASC).

Smooth Tensor Qatar Riyal Decomposition for Dynamic MRI Reconstruction.

Dynamic magnetic resonance imaging (dMRI) speed and imaging quality have always been a crucial issue in medical imaging research. Most existing methods characterize the tensor rank-based minimization to reconstruct dMRI from sampling k- t space data. However, (1) these approaches that unfold the tensor along each dimension destroy the inherent structure of dMR images.

A Riemannian Framework for Structurally Curated Functional Clustering of Brain White Matter Fibers.

White matter (WM) consists of fibers that transmit information from one brain region to another, and functional fiber clustering that combines diffusion and functional MRI provides a novel perspective for exploring the functional architecture of axonal fibers. However, existing methods only concern functional signals in gray matter (GM), whereas the connecting fibers may not transmit relevant functional signals. There has been growing evidence that neural activity is encoded in WM BOLD signals as well, which provides rich multimodal information for fiber clustering.

RV-ESA: A novel computer-aided elastic shape analysis system for retinal vessels in diabetic retinopathy.

Diabetic retinopathy (DR), one of the most common and serious complications of diabetes, has become one of the main blindness diseases. The retinal vasculature is the only part of the human circulatory system that allows direct noninvasive visualization of the body's microvasculature, which provides the opportunity to detect the structural and functional changes before DR becomes unable to intervene. For decades, as the fundamental step in computer-assisted analysis of retinopathy, retinal vascular extraction methods have been largely developed.

A BERT Based Method for Continuous Estimation of Cross-Subject Hand Kinematics From Surface Electromyographic Signals.

Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a non-invasive human-machine interface. This approach is usually subject-specific, so that the training on one individual does not generalise to different subjects. In this paper, we propose a method based on Bidirectional Encoder Representation from Transformers (BERT) structure to predict the movement of hands from the root mean square (RMS) feature of the sEMG signal following μ -law normalization.

Statistical analysis of the community lockdown for COVID-19 pandemic.

As the global pandemic of the COVID-19 continues, the statistical modeling and analysis of the spreading process of COVID-19 have attracted widespread attention. Various propagation simulation models have been proposed to predict the spread of the epidemic and the effectiveness of related control measures. These models play an indispensable role in understanding the complex dynamic situation of the epidemic.

Elastic statistical analysis of interval-valued time series.

We investigate the problem of statistical analysis of interval-valued time series data - two nonintersecting real-valued functions, representing lower and upper limits, over a period of time. Specifically, we pay attention to the two concepts of phase (or horizontal) variability and amplitude (or vertical) variability, and propose a phase-amplitude separation method. We view interval-valued time series as elements of a function (Hilbert) space and impose a Riemannian structure on it.

A Labeling Method for Financial Time Series Prediction Based on Trends.

Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic.

Statistical regression analysis of functional and shape data.

We develop a multivariate regression model when responses or predictors are on nonlinear manifolds, rather than on Euclidean spaces. The nonlinear constraint makes the problem challenging and needs to be studied carefully. By performing principal component analysis (PCA) on tangent space of manifold, we use principal directions instead in the model.

Elastic Functional Coding of Riemannian Trajectories.

Visual observations of dynamic phenomena, such as human actions, are often represented as sequences of smoothly-varying features. In cases where the feature spaces can be structured as Riemannian manifolds, the corresponding representations become trajectories on manifolds. Analysis of these trajectories is challenging due to non-linearity of underlying spaces and high-dimensionality of trajectories.

Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories.

We study the problem of classifying actions of human subjects using depth movies generated by Kinect or other depth sensors. Representing human body as dynamical skeletons, we study the evolution of their (skeletons’) shapes as trajectories on Kendall’s shape manifold. The action data is typically corrupted by large variability in execution rates within and across subjects and, thus, causing major problems in statistical analyses.