TY - JOUR
T1 - Template Independent Component Analysis
T2 - Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors
AU - Mejia, Amanda F.
AU - Nebel, Mary Beth
AU - Wang, Yikai
AU - Caffo, Brian S.
AU - Guo, Ying
N1 - Funding Information:
National Center for Advancing Translational Sciences;National Institute of Biomedical Imaging and Bioengineering;National Institute of Mental Health;National Institute of Neurological Disorders and Stroke;
Publisher Copyright:
© 2019 American Statistical Association.
PY - 2020/7/2
Y1 - 2020/7/2
N2 - Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical “template” ICA model where source signals—including known population brain networks and subject-specific signals—are represented as latent variables. For estimation, we derive an expectation–maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75–250% higher intra-subject reliability. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
AB - Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical “template” ICA model where source signals—including known population brain networks and subject-specific signals—are represented as latent variables. For estimation, we derive an expectation–maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75–250% higher intra-subject reliability. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
KW - Applications and case studies
KW - Bayesian methods
KW - Computationally intensive methods
KW - Expectation–maximization
KW - Neuroimaging
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U2 - 10.1080/01621459.2019.1679638
DO - 10.1080/01621459.2019.1679638
M3 - Article
C2 - 33060872
AN - SCOPUS:85075361398
SN - 0162-1459
VL - 115
SP - 1151
EP - 1177
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 531
ER -