Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

Silva, Rogers F.; Plis, Sergey M.; Sui, Jing; Pattichis, Marios S.; Calhoun, Vince D.

doi:10.1109/jstsp.2016.2594945

Cited by 21 publications

(13 citation statements)

References 180 publications

(237 reference statements)

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“…Existing multivariate data-driven multimodal fusion methods have in most cases been based on blind source separation (BSS) techniques [6]. Specifically, multi-set canonical correlation analysis (CCA) and sparse CCA maximize the inter-modality covariance across multiple data sets, thus enable the identification of the co-varying multimodal components with similar individual variabilities, but their associated spatial maps may not be sufficiently unique [1].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

Calhoun

Erp

et al. 2018

IEEE Trans. Med. Imaging

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Multimodal fusion is an effective approach to take advantage of cross-information among multiple imaging data to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. To date, there is increasing interest to uncover the neurocognitive mapping of specific behavioral measurement on enriched brain imaging data; hence, a supervised, goal-directed model that enables a priori information as a reference to guide multimodal data fusion is in need and a natural option. Here we proposed a fusion with reference model, called “multi-site canonical correlation analysis with reference plus joint independent component analysis” (MCCAR+jICA), which can precisely identify co-varying multimodal imaging patterns closely related to reference information, such as cognitive scores. In a 3-way fusion simulation, the proposed method was compared with its alternatives on estimation accuracy of both target component decomposition and modality linkage detection. MCCAR+jICA outperforms others with higher precision. In human imaging data, working memory performance was utilized as a reference to investigate the covarying functional and structural brain patterns among 3 modalities and how they are impaired in schizophrenia. Two independent cohorts (294 and 83 subjects respectively) were used. Interestingly, similar brain maps were identified between the two cohorts, with substantial overlap in the executive control networks in fMRI, salience network in sMRI, and major white matter tracts in dMRI. These regions have been linked with working memory deficits in schizophrenia in multiple reports, while MCCAR+jICA further verified them in a repeatable, joint manner, demonstrating the potential of such results to identify potential neuromarkers for mental disorders.

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Section: Introductionmentioning

confidence: 99%

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

Calhoun

Erp

et al. 2018

IEEE Trans. Med. Imaging

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“…IVA is an approach that allows corresponding sources from different subjects to be similar rather than identical. IVA enables the subject source maps to contain unique information, yet still be linked across different subjects ( Kim et al , 2006 ; Silva et al , 2016 ).…”

Section: Methods and Use Casesmentioning

confidence: 99%

COINSTAC: Decentralizing the future of brain imaging analysis

et al. 2017

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In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.

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“…100 The extension of this to additional subspaces to better capture other types of variability is an interesting future direction. 101 …”

Section: Number 10: Independent Component Analysis Algorithm Developmmentioning

confidence: 99%

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

Calhoun

Lacy

2017

Neuroimaging Clinics of North America

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108

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Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

Cited by 21 publications

References 180 publications

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

COINSTAC: Decentralizing the future of brain imaging analysis

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

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