Literature DB >> 29292136

A common framework for the problem of deriving estimates of dynamic functional brain connectivity.

William Hedley Thompson1, Peter Fransson2.   

Abstract

The research field of dynamic functional connectivity explores the temporal properties of brain connectivity. To date, many methods have been proposed, which are based on quite different assumptions. In order to understand in which way the results from different techniques can be compared to each other, it is useful to be able to formulate them within a common theoretical framework. In this study, we describe such a framework that is suitable for many of the dynamic functional connectivity methods that have been proposed. Our overall intention was to derive a theoretical framework that was constructed such that a wide variety of dynamic functional connectivity techniques could be expressed and evaluated within the same framework. At the same time, care was given to the fact that key features of each technique could be easily illustrated within the framework and thus highlighting critical assumptions that are made. We aimed to create a common framework which should serve to assist comparisons between different analytical methods for dynamic functional brain connectivity and promote an understanding of their methodological advantages as well as potential drawbacks.
Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Keywords:  Brain connectivity; Dynamic functional connectivity; Resting-state; Sliding-window; fMRI

Mesh:

Year:  2017        PMID: 29292136     DOI: 10.1016/j.neuroimage.2017.12.057

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  9 in total

1.  Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study.

Authors:  Young-Beom Lee; Kwangsun Yoo; Jee Hoon Roh; Won-Jin Moon; Yong Jeong
Journal:  Brain Topogr       Date:  2019-06-03       Impact factor: 3.020

2.  Weighted Graph Regularized Sparse Brain Network Construction for MCI Identification.

Authors:  Renping Yu; Lishan Qiao; Mingming Chen; Seong-Whan Lee; Xuan Fei; Dinggang Shen
Journal:  Pattern Recognit       Date:  2019-01-08       Impact factor: 7.740

Review 3.  Statistical model for dynamically-changing correlation matrices with application to brain connectivity.

Authors:  Shih-Gu Huang; S Balqis Samdin; Chee-Ming Ting; Hernando Ombao; Moo K Chung
Journal:  J Neurosci Methods       Date:  2019-11-21       Impact factor: 2.390

4.  Simulations to benchmark time-varying connectivity methods for fMRI.

Authors:  William Hedley Thompson; Craig Geoffrey Richter; Pontus Plavén-Sigray; Peter Fransson
Journal:  PLoS Comput Biol       Date:  2018-05-29       Impact factor: 4.475

5.  Dynamic visual cortical connectivity analysis based on functional magnetic resonance imaging.

Authors:  Le Zhao; Weiming Zeng; Yuhu Shi; Weifang Nie; Jiajun Yang
Journal:  Brain Behav       Date:  2020-06-07       Impact factor: 2.708

6.  Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis.

Authors:  Yuhui Du; Hui Hao; Shuhua Wang; Godfrey D Pearlson; Vince D Calhoun
Journal:  Neuroimage Clin       Date:  2020-05-26       Impact factor: 4.881

7.  Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation.

Authors:  William Hedley Thompson; Granit Kastrati; Karolina Finc; Jessey Wright; James M Shine; Russell A Poldrack
Journal:  Hum Brain Mapp       Date:  2020-02-14       Impact factor: 5.038

8.  Behavioural relevance of spontaneous, transient brain network interactions in fMRI.

Authors:  D Vidaurre; A Llera; S M Smith; M W Woolrich
Journal:  Neuroimage       Date:  2021-01-06       Impact factor: 7.400

Review 9.  Brain functional network modeling and analysis based on fMRI: a systematic review.

Authors:  Zhongyang Wang; Junchang Xin; Zhiqiong Wang; Yudong Yao; Yue Zhao; Wei Qian
Journal:  Cogn Neurodyn       Date:  2020-08-31       Impact factor: 3.473

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.