Independent component analysis (ICA) has found wide application
in a variety of areas, and analysis of functional magnetic resonance
imaging (fMRI) data has been a particularly fruitful one. Maximum
likelihood provides a natural formulation for ICA and allows one to
take into account multiple statistical properties of the data—forms
of diversity. While use of multiple types of diversity allows for additional
flexibility, it comes at a cost, leading to high variability in the
solution space. In this paper, using simulated as well as fMRI-like
data, we provide insight into the trade-offs between estimation accuracy
and algorithmic consistency with or without deviations from
the assumed model and assumptions such as the statistical independence.
Additionally, we propose a new metric, cross inter-symbol
interference, to quantify the consistency of an algorithm across different
runs, and demonstrate its desirable performance for selecting
consistent run compared to other metrics used for the task.
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The role of diversity in data‐driven analysis of multi‐subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics
Data‐driven methods have been widely used in functional magnetic resonance imaging (fMRI) data analysis. They extract latent factors, generally, through the use of a simple generative model. Independent component analysis (ICA) and dictionary learning (DL) are two popular data‐driven methods that are based on two different forms of diversity—statistical properties of the data—statistical independence for ICA and sparsity for DL. Despite their popularity, the comparative advantage of emphasizing one property over another in the decomposition of fMRI data is not well understood. Such a comparison is made harder due to the differences in the modeling assumptions between ICA and DL, as well as within different ICA algorithms where each algorithm exploits a different form of diversity. In this paper, we propose the use of objective global measures, such as time course frequency power ratio, network connection summary, and graph theoretical metrics, to gain insight into the role that different types of diversity have on the analysis of fMRI data. Four ICA algorithms that account for different types of diversity and one DL algorithm are studied. We apply these algorithms to real fMRI data collected from patients with schizophrenia and healthy controls. Our results suggest that no one particular method has the best performance using all metrics, implying that the optimal method will change depending on the goal of the analysis. However, we note that in none of the scenarios we test the highly popular Infomax provides the best performance, demonstrating the cost of exploiting limited form of diversity.
more » « less- NSF-PAR ID:
- 10075808
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Human Brain Mapping
- Volume:
- 40
- Issue:
- 2
- ISSN:
- 1065-9471
- Page Range / eLocation ID:
- p. 489-504
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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