Vince D. Calhoun
University of New Mexico
CognitionFunctional magnetic resonance imagingComputer visionSensor fusionComputer science
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Publications 28
Last. Vince D. CalhounH-Index: 2
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#1Biao CaiH-Index: 1
Last. Yu-Ping WangH-Index: 22
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#1Li Xiao (Tulane University)H-Index: 1
#2Julia M. Stephen (The Mind Research Network)H-Index: 22
Last. Yu-Ping Wang (Tulane University)H-Index: 22
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Objective: Multi-modal brain functional connectivity (FC) data have shown great potential for providing insights into individual variations in behavioral and cognitive traits. The joint learning of multi-modal data can utilize the intrinsic association, and thus can boost the learning performance. Although several multi-task based learning models have already been proposed by viewing the feature learning on each modality as one task, most of them ignore the structural information inherent across...
#1Rajikha RajaH-Index: 2
#2Arvind CaprihanH-Index: 4
Last. Vince D. CalhounH-Index: 2
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#1Junqi WangH-Index: 1
Last. Yu-Ping WangH-Index: 22
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#1Li XiaoH-Index: 1
Last. Yu-Ping WangH-Index: 22
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#1Kuaikuai Duan (Georgia Institute of Technology)
#2Rogers F. Silva (Georgia Institute of Technology)H-Index: 9
Last. Jingyu Liu (Georgia Institute of Technology)H-Index: 30
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Multimodal data fusion is a topic of great interest. Several fusion methods have been proposed to investigate coherent patterns and corresponding linkages across modalities, such as joint independent component analysis (jICA), multiset canonical correlation analysis (mCCA), mCCA+jICA, and parallel ICA. JICA exploits source independence but assumes shared loading parameters. MCCA maximizes correlation linkage across modalities directly but is limited to orthogonal features. While there is no theo...
#1Na Luo (CAS: Chinese Academy of Sciences)H-Index: 2
#2Jing Sui (CAS: Chinese Academy of Sciences)H-Index: 31
Last. Vince D. Calhoun (Georgia Institute of Technology)H-Index: 2
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Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of structural-functional (S-F) component maps, distributing across 9 functional domains, in both a discovery cohort (n=6005) and a replic...
1 CitationsSource
#1Tianye Jia (Fudan University)H-Index: 14
#2Congying Chu ('KCL': King's College London)H-Index: 2
Last. Sylvane Desrivières ('KCL': King's College London)H-Index: 25
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DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as...
2 CitationsSource
#1Usman Mahmood (GSU: Georgia State University)
#2Mahfuzur Rahman (GSU: Georgia State University)
Last. Sergey M. Plis (GSU: Georgia State University)H-Index: 2
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Differentiating multivariate dynamic signals is a difficult learning problem as the feature space may be large yet often only a few training examples are available. Traditional approaches to this problem either proceed from handcrafted features or require large datasets to combat the m >> n problem. In this paper, we show that the source of the problem---signal dynamics---can be used to our advantage and noticeably improve classification performance on a range of discrimination tasks when traini...