Format

Send to

Choose Destination

See 1 citation found by title matching your search:

J Neurosci Methods. 2019 Jan 1;311:267-276. doi: 10.1016/j.jneumeth.2018.10.008. Epub 2018 Oct 30.

A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.

Author information

1
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, United States. Electronic address: ylevins1@umbc.edu.
2
The Mind Research Network, Albuquerque, NM 87106, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, United States.
3
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, United States.

Abstract

BACKGROUND:

The widespread application of data-driven factorization-based methods, such as independent component analysis (ICA), to functional magnetic resonance imaging data facilitates the study of neural function and how it is disrupted by psychiatric disorders such as schizophrenia. While the increasing number of these methods motivates a comparison of their relative performance, such a comparison is difficult to perform on real fMRI data, since the ground truth is, relatively, unknown and the alignment of factors across different methods is impractical and imprecise.

NEW METHOD:

We present a novel method, global difference maps (GDMs), to compare the results of different fMRI analysis techniques on real fMRI data, quantify their relative performances, and highlight the differences between the decompositions visually.

COMPARISON WITH EXISTING METHODS:

We apply this method to compare the performances of two different factorization-based methods, ICA and its multiset extension independent vector analysis (IVA), for the analysis of fMRI data from 109 patients with schizophrenia and 138 healthy controls during the performance of three tasks.

RESULTS:

Through this application of GDMs, we find that IVA can determine regions that are more discriminatory between patients and controls than ICA, though IVA is less effective at emphasizing regions found in only a subset of the tasks.

CONCLUSIONS:

These results demonstrate that GDMs are an effective way to compare the performances of different factorization-based methods as well as regression-based analyses.

KEYWORDS:

ICA; Schizophrenia; fMRI

PMID:
30389489
PMCID:
PMC6258321
[Available on 2020-01-01]
DOI:
10.1016/j.jneumeth.2018.10.008

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center