Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data

Robert E. Kelly, George S. Alexopoulos, Zhishun Wang, Faith M. Gunning, Christopher F. Murphy, Sarah Shizuko Morimoto, Dora Kanellopoulos, Zhiru Jia, Kelvin O. Lim, Matthew J. Hoptman

Research output: Contribution to journalArticlepeer-review

239 Scopus citations

Abstract

Artifacts in functional magnetic resonance imaging (fMRI) data, primarily those related to motion and physiological sources, negatively impact the functional signal-to-noise ratio in fMRI studies, even after conventional fMRI preprocessing. Independent component analysis' demonstrated capacity to separate sources of neural signal, structured noise, and random noise into separate components might be utilized in improved procedures to remove artifacts from fMRI data. Such procedures require a method for labeling independent components (ICs) as representing artifacts to be removed or neural signals of interest to be spared. Visual inspection is often considered an accurate method for such labeling as well as a standard to which automated labeling methods are compared. However, detailed descriptions of methods for visual inspection of ICs are lacking in the literature. Here we describe the details of, and the rationale for, an operationalized fMRI data denoising procedure that involves visual inspection of ICs (96% inter-rater agreement). We estimate that dozens of subjects/sessions can be processed within a few hours using the described method of visual inspection. Our hope is that continued scientific discussion of and testing of visual inspection methods will lead to the development of improved, cost-effective fMRI denoising procedures.

Original languageEnglish (US)
Pages (from-to)233-245
Number of pages13
JournalJournal of Neuroscience Methods
Volume189
Issue number2
DOIs
StatePublished - Jun 2010

Bibliographical note

Funding Information:
This work was supported by NIMH grants R01 MH065653 , P30 MH085943 , T32 MH019132 (to George S. Alexopoulos, M.D.), K23 MH067702 (to Christopher F. Murphy, Ph.D.), K23 MH074818 (to Faith M. Gunning, Ph.D.), the Sanchez and TRU Foundations , and Forest Pharmaceuticals, Inc. Dr. Alexopoulos has received research grants by Forest Pharmaceuticals, Inc. and Cephalon and participated in scientific advisory board meetings of Forest Pharmaceuticals. He has given lectures supported by Forest, Bristol Meyers, Janssen, and Lilly and has received support from Comprehensive Neuroscience, Inc. for the development of treatment guidelines in late-life psychiatric disorders. All other authors report no competing interests. The authors thank Michael Greicius, M.D. for the default mode network spatial map used in our demonstration with synthetic data. We also thank Raj Sangoi (RT) (R) (MR) for his work as Chief MRI Research Technologist, Nathan Kline Institute; and at Columbia University, Stephen Dashnaw for his work as Imaging Supervisor, and Joy Hirsch, Ph.D. for fMRI instruction and use of her imaging lab.

Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.

Keywords

  • Artifacts
  • Denoising
  • FMRI
  • Independent component (IC) labeling
  • Independent component analysis (ICA)
  • Structured noise
  • Visual inspection

Fingerprint

Dive into the research topics of 'Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data'. Together they form a unique fingerprint.

Cite this