Dynamic Functional Connectivity
원문 초록
Most generally, dynamic functional connectivity (FC) refers to the non-instantaneous couplings across timeseries from a set of brain areas, here as measured by fMRI. This is in contrast to static FC, which is defined as purely instantaneous relations. In this chapter, we provide a hands-on description of a non-exhaustive selection of different methods used to estimate dynamic FC (such as sliding windows, clustering approaches, Hidden Markov Models, and multivariate autoregressive models), and we explain, using practical examples, how data should be prepared for dynamic FC analyses and how models of dynamic FC can be evaluated. We also discuss current developments in the dynamic FC research field, including challenges of reliability and reproducibility, and perspectives of using dynamic FC for prediction.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Brain Age Group Classification Based on Resting State Functional Connectivity Metrics
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
HERCULES: Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization
MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.