Automated detection of datasets with artefactual signal in large n studies using Shannon entropy

McGonigle John; Malizia Andrea L.; Majid Mirmehdi

Automated detection of datasets with artefactual signal in large n studies using Shannon entropy

McGonigle John
, Malizia Andrea L.
, Majid Mirmehdi

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

The recent public release of more than 1200 resting state BOLD MRI (R-fMRI) datasets as part of the 1000 Functional Connectomes Project provides the community with the opportunity to apply and test analysis techniques on a much larger number of subjects than may be available locally. With the potential to examine data from many sources comes the issue of how the characteristics of this data vary between site, and also between studies at the same site. Here we explore the use of examining standard deviation and Shannon entropy distributions for common voxels in a subset of the released data with similar parameters in order to automatically identify outlier datasets. Removal of these is prudent before carrying out further data-driven methods, as are often used in the analysis of R-fMRI.

Translated title of the contribution	Automated detection of datasets with artefactual signal in large n studies using Shannon entropy
Original language	English
Title of host publication	16th Annual Meeting of the Organization for Human Brain Mapping
Publication status	Published - 2010

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Conference Proceedings/Title of Journal: 16th Annual Meeting of the Organization for Human Brain Mapping
Other identifier: 2001204

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@inproceedings{2cc07a3530684b46a07e66886d7c5a4e,

title = "Automated detection of datasets with artefactual signal in large n studies using Shannon entropy",

abstract = "The recent public release of more than 1200 resting state BOLD MRI (R-fMRI) datasets as part of the 1000 Functional Connectomes Project provides the community with the opportunity to apply and test analysis techniques on a much larger number of subjects than may be available locally. With the potential to examine data from many sources comes the issue of how the characteristics of this data vary between site, and also between studies at the same site. Here we explore the use of examining standard deviation and Shannon entropy distributions for common voxels in a subset of the released data with similar parameters in order to automatically identify outlier datasets. Removal of these is prudent before carrying out further data-driven methods, as are often used in the analysis of R-fMRI. ",

author = "McGonigle John and \{Andrea L.\}, Malizia and Majid Mirmehdi",

note = "Other page information: - Conference Proceedings/Title of Journal: 16th Annual Meeting of the Organization for Human Brain Mapping Other identifier: 2001204",

year = "2010",

language = "English",

booktitle = "16th Annual Meeting of the Organization for Human Brain Mapping",

}

TY - GEN

T1 - Automated detection of datasets with artefactual signal in large n studies using Shannon entropy

AU - John, McGonigle

AU - Andrea L., Malizia

AU - Mirmehdi, Majid

N1 - Other page information: - Conference Proceedings/Title of Journal: 16th Annual Meeting of the Organization for Human Brain Mapping Other identifier: 2001204

PY - 2010

Y1 - 2010

N2 - The recent public release of more than 1200 resting state BOLD MRI (R-fMRI) datasets as part of the 1000 Functional Connectomes Project provides the community with the opportunity to apply and test analysis techniques on a much larger number of subjects than may be available locally. With the potential to examine data from many sources comes the issue of how the characteristics of this data vary between site, and also between studies at the same site. Here we explore the use of examining standard deviation and Shannon entropy distributions for common voxels in a subset of the released data with similar parameters in order to automatically identify outlier datasets. Removal of these is prudent before carrying out further data-driven methods, as are often used in the analysis of R-fMRI.

AB - The recent public release of more than 1200 resting state BOLD MRI (R-fMRI) datasets as part of the 1000 Functional Connectomes Project provides the community with the opportunity to apply and test analysis techniques on a much larger number of subjects than may be available locally. With the potential to examine data from many sources comes the issue of how the characteristics of this data vary between site, and also between studies at the same site. Here we explore the use of examining standard deviation and Shannon entropy distributions for common voxels in a subset of the released data with similar parameters in order to automatically identify outlier datasets. Removal of these is prudent before carrying out further data-driven methods, as are often used in the analysis of R-fMRI.

M3 - Conference Contribution (Conference Proceeding)

BT - 16th Annual Meeting of the Organization for Human Brain Mapping

ER -

Automated detection of datasets with artefactual signal in large n studies using Shannon entropy

Abstract

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