AI Article Synopsis
- Graphical models based on brain connectomes from fMRI are essential for understanding network processes, but traditional methods often struggle with accurate graph recovery.
- By utilizing additional information, such as the spatial positions of measurements, the authors develop a new approach that enhances the accuracy of neighborhood selection through the inclusion of pairwise distances.
- This improved method not only offers computational efficiency and a Bayesian interpretation but also reveals significant insights into brain connectivity in Alzheimer's patients, highlighting increased connectivity in the cerebellum and the importance of different brain lobes.
Article Abstract
Graphical models such as brain connectomes derived from functional magnetic resonance imaging (fMRI) data are considered a prime gateway to understanding network-type processes. We show, however, that standard methods for graphical modeling can fail to provide accurate graph recovery even with optimal tuning and large sample sizes. We attempt to solve this problem by leveraging information that is often readily available in practice but neglected, such as the spatial positions of the measurements. This information is incorporated into the tuning parameter of neighborhood selection, for example, in the form of pairwise distances. Our approach is computationally convenient and efficient, carries a clear Bayesian interpretation, and improves standard methods in terms of statistical stability. Applied to data about Alzheimer's disease, our approach allows us to highlight the central role of lobes in the connectivity structure of the brain and to identify an increased connectivity within the cerebellum for Alzheimer's patients compared to other subjects.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1515/ijb-2020-0133 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Graphical Feature Construction-Based Deep Learning Model for Fatigue Life Prediction of AM Alloys.
Materials (Basel)
December 2024
School of Science, Harbin Institute of Technology, Shenzhen 518055, China.
Fatigue failure poses a serious challenge for ensuring the operational safety of critical components subjected to cyclic/random loading. In this context, various machine learning (ML) models have been increasingly explored, due to their effectiveness in analyzing the relationship between fatigue life and multiple influencing factors. Nevertheless, existing ML models hinge heavily on numeric features as inputs, which encapsulate limited information on the fatigue failure process of interest.
View Article and Find Full Text PDFFractional and stochastic modeling of breast cancer progression with real data validation.
PLoS One
January 2025
Biology Department, Faculty of Science, Islamic University of Madinah, Madinah, Saudi Arabia.
This study presents a novel approach to modeling breast cancer dynamics, one of the most significant health threats to women worldwide. Utilizing a piecewise mathematical framework, we incorporate both deterministic and stochastic elements of cancer progression. The model is divided into three distinct phases: (1) initial growth, characterized by a constant-order Caputo proportional operator (CPC), (2) intermediate growth, modeled by a variable-order CPC, and (3) advanced stages, capturing stochastic fluctuations in cancer cell populations using a stochastic operator.
View Article and Find Full Text PDFEnabling Uniform Computer Interaction Experience for Blind Users through Large Language Models.
ASSETS
October 2024
Department of Computer Science, Old Dominion University, United States.
Blind individuals, who by necessity depend on screen readers to interact with computers, face considerable challenges in navigating the diverse and complex graphical user interfaces of different computer applications. The heterogeneity of various application interfaces often requires blind users to remember different keyboard combinations and navigation methods to use each application effectively. To alleviate this significant interaction burden imposed by heterogeneous application interfaces, we present Savant, a novel assistive technology powered by large language models (LLMs) that allows blind screen reader users to interact uniformly with any application interface through natural language.
View Article and Find Full Text PDFNonlinear dynamic wave properties of travelling wave solutions in in (3+1)-dimensional mKdV-ZK model.
PLoS One
January 2025
Department of Mathematics, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
The (3+1)-dimensional mKdV-ZK model is an important framework for studying the dynamic behavior of waves in mathematical physics. The goal of this study is to look into more generic travelling wave solutions (TWSs) for the generalized ion-acoustic scenario in three dimensions. These solutions exhibit a combination of rational, trigonometric, hyperbolic, and exponential solutions that are concurrently generated by the new auxiliary equation and the unified techniques.
View Article and Find Full Text PDFShinyGS-a graphical toolkit with a serial of genetic and machine learning models for genomic selection: application, benchmarking, and recommendations.
Front Plant Sci
December 2024
Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, China.
Genomic prediction is a powerful approach for improving genetic gain and shortening the breeding cycles in animal and crop breeding programs. A series of statistical and machine learning models has been developed to increase the prediction performance continuously. However, the application of these models requires advanced R programming skills and command-line tools to perform quality control, format input files, and install packages and dependencies, posing challenges for breeders.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!