### Abstract

One of the most important considerations in any hypothesis based fMRI data analysis is to choose the appropriate threshold to construct the activation maps, which is usually based on p-values. However, in fMRI data, there are three factors which necessitate severe corrections in the process of estimating the p-values. First, the fMRI time series at an individual voxel has strong temporal autocorrelation which needs to be estimated to obtain the corrected parametric p-value. The second factor is the multiple comparisons problem arising from simultaneously testing tens of thousands of voxels for activation. A common way in the statistical literature to account for multiple testing is to consider the family-wise error rate (FWE) which is related to the distribution of the maximum observed value over all voxels. The third problem, which is not mentioned frequently in the context of adjusting the p-value, is the effect of inherent low frequency processes present even in resting-state data that may introduce a large number of false positives without proper adjustment. In this article, a novel and efficient semi-parametric method, using resampling of normalized spacings of order statistics, is introduced to address all the three problems mentioned above. The new method makes very few assumptions and demands minimal computational effort, unlike other existing resampling methods in fMRI. Furthermore, it will be demonstrated that the correction for temporal autocorrelation is not critical in implementing the proposed method. Results using the proposed method are compared with SPM2.

Original language | English |
---|---|

Pages (from-to) | 1562-1576 |

Number of pages | 15 |

Journal | NeuroImage |

Volume | 34 |

Issue number | 4 |

DOIs | |

State | Published - 15 Feb 2007 |

### Fingerprint

### Keywords

- FWE
- Low frequency
- Multiple comparisons problem
- Order statistics
- Resampling
- Resting-state
- Temporal autocorrelation
- fMRI

### Cite this

*NeuroImage*,

*34*(4), 1562-1576. https://doi.org/10.1016/j.neuroimage.2006.10.025

}

*NeuroImage*, vol. 34, no. 4, pp. 1562-1576. https://doi.org/10.1016/j.neuroimage.2006.10.025

**A semi-parametric approach to estimate the family-wise error rate in fMRI using resting-state data.** / Nandy, Rajesh Ranjan; Cordes, Dietmar.

Research output: Contribution to journal › Article

TY - JOUR

T1 - A semi-parametric approach to estimate the family-wise error rate in fMRI using resting-state data

AU - Nandy, Rajesh Ranjan

AU - Cordes, Dietmar

PY - 2007/2/15

Y1 - 2007/2/15

N2 - One of the most important considerations in any hypothesis based fMRI data analysis is to choose the appropriate threshold to construct the activation maps, which is usually based on p-values. However, in fMRI data, there are three factors which necessitate severe corrections in the process of estimating the p-values. First, the fMRI time series at an individual voxel has strong temporal autocorrelation which needs to be estimated to obtain the corrected parametric p-value. The second factor is the multiple comparisons problem arising from simultaneously testing tens of thousands of voxels for activation. A common way in the statistical literature to account for multiple testing is to consider the family-wise error rate (FWE) which is related to the distribution of the maximum observed value over all voxels. The third problem, which is not mentioned frequently in the context of adjusting the p-value, is the effect of inherent low frequency processes present even in resting-state data that may introduce a large number of false positives without proper adjustment. In this article, a novel and efficient semi-parametric method, using resampling of normalized spacings of order statistics, is introduced to address all the three problems mentioned above. The new method makes very few assumptions and demands minimal computational effort, unlike other existing resampling methods in fMRI. Furthermore, it will be demonstrated that the correction for temporal autocorrelation is not critical in implementing the proposed method. Results using the proposed method are compared with SPM2.

AB - One of the most important considerations in any hypothesis based fMRI data analysis is to choose the appropriate threshold to construct the activation maps, which is usually based on p-values. However, in fMRI data, there are three factors which necessitate severe corrections in the process of estimating the p-values. First, the fMRI time series at an individual voxel has strong temporal autocorrelation which needs to be estimated to obtain the corrected parametric p-value. The second factor is the multiple comparisons problem arising from simultaneously testing tens of thousands of voxels for activation. A common way in the statistical literature to account for multiple testing is to consider the family-wise error rate (FWE) which is related to the distribution of the maximum observed value over all voxels. The third problem, which is not mentioned frequently in the context of adjusting the p-value, is the effect of inherent low frequency processes present even in resting-state data that may introduce a large number of false positives without proper adjustment. In this article, a novel and efficient semi-parametric method, using resampling of normalized spacings of order statistics, is introduced to address all the three problems mentioned above. The new method makes very few assumptions and demands minimal computational effort, unlike other existing resampling methods in fMRI. Furthermore, it will be demonstrated that the correction for temporal autocorrelation is not critical in implementing the proposed method. Results using the proposed method are compared with SPM2.

KW - FWE

KW - Low frequency

KW - Multiple comparisons problem

KW - Order statistics

KW - Resampling

KW - Resting-state

KW - Temporal autocorrelation

KW - fMRI

UR - http://www.scopus.com/inward/record.url?scp=33846573748&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2006.10.025

DO - 10.1016/j.neuroimage.2006.10.025

M3 - Article

C2 - 17196400

AN - SCOPUS:33846573748

VL - 34

SP - 1562

EP - 1576

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 4

ER -