Rogers F. Silva
Georgia Institute of Technology
Machine learningBlind signal separationPattern recognitionComputer scienceIndependent component analysis
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Publications 29
#1Bradley T. Baker (Georgia Institute of Technology)
#2Eswar Damaraju (Georgia Institute of Technology)H-Index: 1
Last. Vince D. Calhoun (Georgia Institute of Technology)H-Index: 93
view all 5 authors...
As neuroimaging data increase in complexity and related analytical problems follow suite, more researchers are drawn to collaborative frameworks that leverage data sets from multiple data-collection sites to balance out the complexity with an increased sample size. Although centralized data-collection approaches have dominated the collaborative scene, a number of decentralized approaches-those that avoid gathering data at a shared central store-have grown in popularity. We expect the prevalence ...
#1Anees Abrol (Georgia Institute of Technology)H-Index: 2
#2Zening Fu (Georgia Institute of Technology)H-Index: 8
Last. Vince D. Calhoun (Georgia Institute of Technology)H-Index: 2
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Previous successes of deep learning (DL) approaches on several complex tasks have hugely inflated expectations of their power to learn subtle properties of complex brain imaging data, and scale to large datasets. Perhaps as a reaction to this inflation, recent critical commentaries unfavorably compare DL with standard machine learning (SML) approaches for the analysis of brain imaging data. Yet, their conclusions are based on pre-engineered features which deprives DL of its main advantage: repre...
1 CitationsSource
#1Maziar Yaesoubi (Georgia Institute of Technology)
#1Maziar YaesoubiH-Index: 8
Last. Vince D. CalhounH-Index: 93
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Many brain imaging modalities reveal interpretable patterns after the data dimensionality is reduced and summarized via data-driven approaches. In functional magnetic resonance imaging (fMRI) studies, such summarization is often achieved through independent component analysis (ICA). ICA transforms the original data into a relatively small number of interpretable bases in voxel space (referred to as ICA spatial components, or spatial maps) and corresponding bases in the time domain (referred to a...
1 CitationsSource
#1Thomas P. Deramus (Georgia Institute of Technology)H-Index: 1
#2Rogers F. Silva (Georgia Institute of Technology)H-Index: 9
Last. Godfrey D. Pearlson (Yale University)H-Index: 110
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The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying compone...
#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...
#1Rogers F. SilvaH-Index: 9
#2Sergey M. PlisH-Index: 2
Last. Vince D. CalhounH-Index: 93
view all 5 authors...
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of information available in multiple datasets. Multidatasets, on the other hand, yield joint solutions otherwise unavailable in isolation, with a potential for pivotal insights into complex systems. To take advantage of the complex multidimensional subspace structur...
1 Citations
#1Hafiz ImtiazH-Index: 9
#2Jafar MohammadiH-Index: 4
Last. Vince D. CalhounH-Index: 93
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Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. In order to leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at a single site. A recently proposed algorithm uses message-passing between sites and a central aggregator to perform a decentralize...
#1Shile Qi (Georgia Institute of Technology)H-Index: 5
#2Jing Sui (CAS: Chinese Academy of Sciences)H-Index: 31
Last. Vince D. CalhounH-Index: 93
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Author(s): Qi, Shile; Sui, Jing; Chen, Jiayu; Liu, Jingyu; Jiang, Rongtao; Silva, Rogers; Iraji, Armin; Damaraju, Eswar; Salman, Mustafa; Lin, Dongdong; Fu, Zening; Zhi, Dongmei; Turner, Jessica A; Bustillo, Juan; Ford, Judith M; Mathalon, Daniel H; Voyvodic, James; McEwen, Sarah; Preda, Adrian; Belger, Aysenil; Potkin, Steven G; Mueller, Bryon A; Adali, Tulay; Calhoun, Vince D | Abstract: There is growing evidence that rather than using a single brain imaging modality to study its association w...
#1Kuaikuai Duan (UNM: University of New Mexico)H-Index: 1
#2Rogers F. Silva (The Mind Research Network)H-Index: 9
Last. Jingyu Liu (UNM: University of New Mexico)H-Index: 30
view all 6 authors...
Independent component analysis has been widely applied to brain imaging and genetic data analyses for its ability to identify interpretable latent sources. Nevertheless, leveraging source sparsity in a more granular way may further improve its ability to optimize the solution for certain data types. For this purpose, we propose a sparse infomax algorithm based on nonlinear Hoyer projection, leveraging both sparsity and statistical independence of latent sources. The proposed algorithm iterativel...
1 CitationsSource
#1Bradley T. Baker (UNM: University of New Mexico)H-Index: 3
#2Anees Abrol (UNM: University of New Mexico)H-Index: 4
Last. Sergey M. Plis (UNM: University of New Mexico)H-Index: 20
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Abstract The field of neuroimaging has recently witnessed a strong shift towards data sharing; however, current collaborative research projects may be unable to leverage institutional architectures that collect and store data in local, centralized data centers. Additionally, though research groups are willing to grant access for collaborations, they often wish to maintain control of their data locally. These concerns may stem from research culture as well as privacy and accountability concerns. ...
1 CitationsSource