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Biao Cai
Tulane University
Pattern recognitionFunctional magnetic resonance imagingDynamic functional connectivityComputer scienceResting state fMRI
9Publications
1H-index
8Citations
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Publications 11
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#1Biao CaiH-Index: 1
#2Julia M. StephenH-Index: 22
Last. Yu-Ping WangH-Index: 22
view all 5 authors...
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#1Biao Cai (Tulane University)H-Index: 1
#2Gemeng Zhang (Tulane University)
Last. Yu-Ping Wang (Tulane University)H-Index: 22
view all 8 authors...
Abstract Background: Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In particular, time-varying connectivity analysis has emerged as an important measure to uncover essential knowledge within the network. On the other hand, independent component analysis (ICA) has served as a powerful tool to preprocess fMRI data before performing network analysis. Together, they may lead to novel findings. Methods: We propose a new framework (GICA-TVGL) tha...
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#1Gemeng Zhang (Tulane University)
#2Biao Cai (Tulane University)H-Index: 1
Last. Yu-Ping Wang Wang (Tulane University)
view all 7 authors...
Estimating dynamic functional network connectivity (dFNC) of the brain from functional magnetic resonance imaging (fMRI) data can reveal both spatial and temporal organization and can be applied to track the developmental trajectory of brain maturity as well as to study mental illness. Resting state fMRI (rs-fMRI) is regarded as a promising task since it reflects the spontaneous brain activity without an external stimulus. The sliding window method has been successfully used to extract dFNC but ...
1 CitationsSource
#1Aiying Zhang (Tulane University)
#2Biao Cai (Tulane University)H-Index: 1
Last. Yu-Ping Wang (Tulane University)H-Index: 22
view all 9 authors...
Adolescence is a transitional period between child-hood and adulthood with physical changes, as well as increasing emotional development. Studies have shown that emotional sensitivity is related to a second period of rapid brain growth. However, there is little focus on the trend of brain development during this period. In this paper, we aim to track functional brain connectivity development from late childhood to young adulthood. Mathematically, this problem can be modeled via the estimation of...
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#1Biao Cai (Tulane University)H-Index: 1
#2Gemeng Zhang (Tulane University)
Last. Yu-Ping Wang (Tulane University)H-Index: 22
view all 10 authors...
Source
#1Biao CaiH-Index: 1
Last. Yu-Ping Wang (Tulane University)H-Index: 22
view all 7 authors...
Functional connectivity (FC) within the human brain evaluated through functional magnetic resonance imaging (fMRI) data has attracted increasing attention and has been employed to study the development of the brain or health conditions of the brain. Many different approaches have been proposed to estimate FC from fMRI data, whereas many of them rely on an implicit assumption that functional connectivity should be static throughout the fMRI scan session. Recently, the fMRI community has realized ...
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#1Wenxing Hu (Tulane University)H-Index: 2
#2Aiying Zhang (Tulane University)
Last. Yu-Ping Wang (Tulane University)H-Index: 22
view all 5 authors...
Distance correlation is a measure that can detect both linear and nonlinear associations. However, applying distance correlation to imaging genetic studies often needs multiple testing correction due to the large number of multiple inferences. As a result, the sensitivity of its detection may be low. We propose a new model, distance canonical correlation analysis (DCCA), which overcomes this problem by searching a combination of features with the highest distance correlation. This is achieved by...
Source
#1Biao Cai (Tulane University)H-Index: 1
#2Julia M. Stephen (The Mind Research Network)H-Index: 22
Last. Yu-Ping Wang (Tulane University)H-Index: 22
view all 5 authors...
Source
#1Wenxing Hu (Tulane University)H-Index: 2
#2Biao Cai (Tulane University)H-Index: 1
Last. Yu-Ping Wang (Tulane University)H-Index: 22
view all 5 authors...
Multi-modal fMRI imaging has been used to study brain development such as the difference of functional connectivities (FCs) between different ages. Canonical correlation analysis (CCA) has been used to find correlations between multiple imaging modalities. However, it is unrelated to phenotypes. On the other hand, regression models can identify phenotype related imaging features but overlook the cross-modal data correlation. Collaborative regression (CR) is thus introduced to incorporate correla...
Source
#1Wenxing Hu (Tulane University)H-Index: 2
#2Biao Cai (Tulane University)H-Index: 1
Last. Yu-Ping Wang (Tulane University)H-Index: 22
view all 4 authors...
Functional connectivities in the brain explain how different brain regions interact with each other when conducting a specific activity. Canonical correlation analysis (CCA) based models, have been used to detect correlations and to analyze brain connectivities which further help explore how the brain works. However, the data representation of CCA lacks label related information and may be limited when applied to functional connectivity study. Collaborative regression was proposed to address the...
1 CitationsSource
12