On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fMRI data are characterised by small activation fluctuations in a background of noise. Depending on how the signal of interest and the noise are identified, signal-to-noise ratio for fMRI data is reported by using many different definitions. Since each definition comes with a different scale, interpreting and comparing signal-to-noise ratio values for fMRI data can be a very challenging job. In this paper, we provide an overview of existing definitions. Further, the relationship with activation detection power is investigated. Reference tables and conversion formulae are provided to facilitate comparability between fMRI studies.

Related collections

Most cited references 49

Record: found
Abstract: found
Article: not found

Distributed and overlapping representations of faces and objects in ventral temporal cortex.

J Haxby, M. I. Gobbini, M. Furey … (2001)

The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.

0 comments Cited 760 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Dynamic causal modelling.

L. Harrison, W. Penny, W Penny (2003)

In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.

0 comments Cited 565 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns.

Rainer Goebel, Giancarlo Valente, Elia Formisano … (2008)

In functional brain mapping, pattern recognition methods allow detecting multivoxel patterns of brain activation which are informative with respect to a subject's perceptual or cognitive state. The sensitivity of these methods, however, is greatly reduced when the proportion of voxels that convey the discriminative information is small compared to the total number of measured voxels. To reduce this dimensionality problem, previous studies employed univariate voxel selection or region-of-interest-based strategies as a preceding step to the application of machine learning algorithms. Here we employ a strategy for classifying functional imaging data based on a multivariate feature selection algorithm, Recursive Feature Elimination (RFE) that uses the training algorithm (support vector machine) recursively to eliminate irrelevant voxels and estimate informative spatial patterns. Generalization performances on test data increases while features/voxels are pruned based on their discrimination ability. In this article we evaluate RFE in terms of sensitivity of discriminative maps (Receiver Operative Characteristic analysis) and generalization performances and compare it to previously used univariate voxel selection strategies based on activation and discrimination measures. Using simulated fMRI data, we show that the recursive approach is suitable for mapping discriminative patterns and that the combination of an initial univariate activation-based (F-test) reduction of voxels and multivariate recursive feature elimination produces the best results, especially when differences between conditions have a low contrast-to-noise ratio. Furthermore, we apply our method to high resolution (2 x 2 x 2 mm(3)) data from an auditory fMRI experiment in which subjects were stimulated with sounds from four different categories. With these real data, our recursive algorithm proves able to detect and accurately classify multivoxel spatial patterns, highlighting the role of the superior temporal gyrus in encoding the information of sound categories. In line with the simulation results, our method outperforms univariate statistical analysis and statistical learning without feature selection.

0 comments Cited 146 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Essa Yacoub: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2013

Publication date (Electronic): 6 November 2013

Volume: 8

Issue: 11

Electronic Location Identifier: e77089

Affiliations

[1]Department of Data Analysis, Ghent University, Gent, Belgium

University of Minnesota, United States of America

Author notes

* E-mail: Marijke.Welvaert@ 123456Ugent.be

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: MW YR. Performed the experiments: MW. Analyzed the data: MW. Contributed reagents/materials/analysis tools: MW. Wrote the paper: MW YR.

Article

Publisher ID: PONE-D-13-09648

DOI: 10.1371/journal.pone.0077089

PMC ID: 3819355

PubMed ID: 24223118

SO-VID: c5f3bcc8-f9fc-481f-8842-6ad37f3effb1

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 8 March 2013

Date accepted : 6 September 2013

Page count

Pages: 10

Funding

The authors have no support or funding to report.

Comments

Comment on this article

scite_

Cited by 118

See all cited by

Most referenced authors 408

See all reference authors

On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data

Read this article at

Abstract

Related collections

PLOS Climate

Most cited references 49

Distributed and overlapping representations of faces and objects in ventral temporal cortex.

Dynamic causal modelling.

Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns.

Author and article information

Contributors

Journal

Affiliations

Author notes

Article

History

Page count

Funding

Categories

Comments

Comment on this article

Similar content 298

Cited by 118

Most referenced authors 408