Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns from fMRI Data

2020 ◽  
pp. 1-1
Author(s):  
Li Xiao ◽  
Aiying Zhang ◽  
Biao Cai ◽  
Julia M. Stephen ◽  
Tony W. Wilson ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Fmri Data ◽  
Graph Learning ◽  
Connectivity Patterns

Groupwise spatial normalization of fMRI data based on multi-range functional connectivity patterns

NeuroImage ◽  
2013 ◽  
Vol 82 ◽  
pp. 355-372 ◽  
Author(s):  
Di Jiang ◽  
Yuhui Du ◽  
Hewei Cheng ◽  
Tianzi Jiang ◽  
Yong Fan
Keyword(s):  
Functional Connectivity ◽  
Fmri Data ◽  
Spatial Normalization ◽  
Connectivity Patterns

scholarly journals A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

2017 ◽  
Author(s):  
Jeremy R. Manning ◽  
Xia Zhu ◽  
Theodore L. Willke ◽  
Rajesh Ranganath ◽  
Kimberly Stachenfeld ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Probabilistic Approach ◽  
Covariance Structure ◽  
Fmri Data ◽  
Cognitive State ◽  
Bayesian Hierarchical ◽  
Connectivity Patterns ◽  
Hierarchical Matrix

AbstractRecent research shows that the covariance structure of functional magnetic resonance imaging (fMRI) data - commonly described as functional connectivity - can change as a function of the participant’s cognitive state (for review see [35]). Here we present a Bayesian hierarchical matrix factorization model, termed hierarchical topographic factor analysis (HTFA), for efficiently discovering full-brain networks in large multi-subject neuroimaging datasets. HTFA approximates each subject’s network by first re-representing each brain image in terms of the activities of a set of localized nodes, and then computing the covariance of the activity time series of these nodes. The number of nodes, along with their locations, sizes, and activities (over time) are learned from the data. Because the number of nodes is typically substantially smaller than the number of fMRI voxels, HTFA can be orders of magnitude more efficient than traditional voxel-based functional connectivity approaches. In one case study, we show that HTFA recovers the known connectivity patterns underlying a collection of synthetic datasets. In a second case study, we illustrate how HTFA may be used to discover dynamic full-brain activity and connectivity patterns in real fMRI data, collected as participants listened to a story. In a third case study, we carried out a similar series of analyses on fMRI data collected as participants viewed an episode of a television show. In these latter case studies, we found that the HTFA-derived activity and connectivity patterns can be used to reliably decode which moments in the story or show the participants were experiencing. Further, we found that these two classes of patterns contained partially non-overlapping information, such that decoders trained on combinations of activity-based and dynamic connectivity-based features performed better than decoders trained on activity or connectivity patterns alone. We replicated this latter result with two additional (previously developed) methods for efficiently characterizing full-brain activity and connectivity patterns.

scholarly journals Determining functional connectivity using fMRI data with diffusion-based anatomical weighting

NeuroImage ◽  
2012 ◽  
Vol 62 (3) ◽  
pp. 1769-1779 ◽  
Author(s):  
F. DuBois Bowman ◽  
Lijun Zhang ◽  
Gordana Derado ◽  
Shuo Chen
Keyword(s):  
Functional Connectivity ◽  
Fmri Data

Decoding sound categories based on whole-brain functional connectivity patterns

2018 ◽  
Vol 14 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Jinliang Zhang ◽  
Gaoyan Zhang ◽  
Xianglin Li ◽  
Peiyuan Wang ◽  
Bin Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Whole Brain ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity
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