Template Independent Component Analysis with Spatial Priors for Accurate Subject-Level Brain Network Estimation and Inference

Abstract

Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates. However, this and other hierarchical ICA models assume unrealistically that subject effects are spatially independent. Here, we propose spatial template ICA (stICA), which incorporates spatial priors into the template ICA framework for greater estimation efficiency. Additionally, the joint posterior distribution can be used to identify brain regions engaged in each network using an excursions set approach. By leveraging spatial dependencies and avoiding massive multiple comparisons, stICA has high power to detect true effects. We derive an efficient expectation-maximization algorithm to obtain maximum likelihood estimates of the model parameters and posterior moments of the latent fields. Based on analysis of simulated data and fMRI data from the Human Connectome Project, we find that stICA produces estimates that are more accurate and reliable than benchmark approaches, and identifies larger and more reliable areas of engagement. The algorithm is computationally tractable, achieving convergence within 12 hr for whole-cortex fMRI analysis. Supplementary materials for this article are available online.

KEYWORDS:

• Bayesian methods
• Computationally intensive methods
• Expectation-maximization
• Neuroimaging
• Spatial analysis

Supplementary Materials

The supplementary materials include detailed derivations, more details and sensitivity analyses around the computational approach, and additional data analysis results.

Disclosure Statement

The authors report that there are no competing interests to declare.

Notes

1 Note that we use the terms “independent component (IC)” and “spatial source signal” interchangeably in this article. We also use the term “functional brain networks” to refer to ICs that have a neuronal origin, as some ICs may represent artifacts

2 The variance of the model on ${ℝ}^d$ with parameters $\alpha ,\kappa$ and τ is $\Gamma (\nu ){\left[\Gamma (\alpha ){(4\pi )}^{d/2}{\kappa }^{2\nu }{\tau }^2\right]}^{-1}$, where $\nu =\alpha -d/2$.

3 A FWHM of f is equivalent to a standard deviation of $\sigma =f/\sqrt{8\ln (2)}$.

Additional information

Funding

This work was supported by the NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) (R01EB027119 to A.F.M., R01EB029977 to B.S.C. and P41EB031771 to B.S.C.), National Institute of Neurological Disorders and Stroke (NINDS) (R01NS060910 to B.S.C.), National Institute of Mental Health (NIMH) (K01MH109766 to M.B.N.), and National Institute on Drug Abuse (NIDA) (U54DA049110 to B.S.C.).