Literature DB >> 23583357

Learning and comparing functional connectomes across subjects.

Gaël Varoquaux1, R Cameron Craddock.   

Abstract

Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes.
Copyright © 2013 Elsevier Inc. All rights reserved.

Keywords:  Connectome; Effective connectivity; Functional connectivity; Group study; Resting-state; fMRI

Mesh:

Year:  2013        PMID: 23583357     DOI: 10.1016/j.neuroimage.2013.04.007

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


  64 in total

Review 1.  Connectivity-based parcellation: Critique and implications.

Authors:  Simon B Eickhoff; Bertrand Thirion; Gaël Varoquaux; Danilo Bzdok
Journal:  Hum Brain Mapp       Date:  2015-09-27       Impact factor: 5.038

2.  Multiple Matrix Gaussian Graphs Estimation.

Authors:  Yunzhang Zhu; Lexin Li
Journal:  J R Stat Soc Series B Stat Methodol       Date:  2018-06-14       Impact factor: 4.488

3.  Multivariate Heteroscedasticity Models for Functional Brain Connectivity.

Authors:  Christof Seiler; Susan Holmes
Journal:  Front Neurosci       Date:  2017-12-12       Impact factor: 4.677

4.  Testing group differences in brain functional connectivity: using correlations or partial correlations?

Authors:  Junghi Kim; Jeffrey R Wozniak; Bryon A Mueller; Wei Pan
Journal:  Brain Connect       Date:  2015-02-25

5.  Compact and informative representation of functional connectivity for predictive modeling.

Authors:  Raif M Rustamov; David Romano; Allan L Reiss; Leonidas J Guibas
Journal:  Med Image Comput Comput Assist Interv       Date:  2014

6.  Path and Directionality Discovery in Individual Dynamic Models: A Regularized Unified Structural Equation Modeling Approach for Hybrid Vector Autoregression.

Authors:  Ai Ye; Kathleen M Gates; Teague Rhine Henry; Lan Luo
Journal:  Psychometrika       Date:  2021-04-11       Impact factor: 2.500

7.  Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction.

Authors:  Mehdi Rahim; Bertrand Thirion; Claude Comtat; Gaël Varoquaux
Journal:  IEEE J Sel Top Signal Process       Date:  2016-08-15       Impact factor: 6.856

8.  A multivariate distance-based analytic framework for connectome-wide association studies.

Authors:  Zarrar Shehzad; Clare Kelly; Philip T Reiss; R Cameron Craddock; John W Emerson; Katie McMahon; David A Copland; F Xavier Castellanos; Michael P Milham
Journal:  Neuroimage       Date:  2014-02-28       Impact factor: 6.556

9.  Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage.

Authors:  Amanda F Mejia; Mary Beth Nebel; Anita D Barber; Ann S Choe; James J Pekar; Brian S Caffo; Martin A Lindquist
Journal:  Neuroimage       Date:  2018-02-14       Impact factor: 6.556

10.  Topographical Information-Based High-Order Functional Connectivity and Its Application in Abnormality Detection for Mild Cognitive Impairment.

Authors:  Han Zhang; Xiaobo Chen; Feng Shi; Gang Li; Minjeong Kim; Panteleimon Giannakopoulos; Sven Haller; Dinggang Shen
Journal:  J Alzheimers Dis       Date:  2016-10-04       Impact factor: 4.472

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