Neuroinformatics

, Volume 11, Issue 1, pp 105–118

A Least Trimmed Square Regression Method for Second Level fMRI Effective Connectivity Analysis

  • Xingfeng Li
  • Damien Coyle
  • Liam Maguire
  • Thomas Martin McGinnity
Original Article

DOI: 10.1007/s12021-012-9168-8

Cite this article as:
Li, X., Coyle, D., Maguire, L. et al. Neuroinform (2013) 11: 105. doi:10.1007/s12021-012-9168-8

Abstract

We present a least trimmed square (LTS) robust regression method to combine different runs/subjects for second/high level effective connectivity analysis. The basic idea of this method is to treat the extreme nonlinear model variability as outliers if they exceed a certain threshold. A bootstrap method for the LTS estimation is employed to detect model outliers. We compared the LTS robust method with a non-robust method using simulated and real datasets. The difference between LTS and the non-robust method for second level effective connectivity analysis is significant, suggesting the conventional non-robust method is easily affected by the model variability from the first level analysis. In addition, after these outliers are detected and excluded for the high level analysis, the model coefficients of the second level are combined within the framework of a mixed model. The variance of the mixed model is estimated using the Newton–Raphson (NR) type Levenberg-Marquardt algorithm. Three sets of real data are adopted to compare conventional methods which do not include random effects in the analysis with a mixed model for second level effective connectivity analysis. The results show that the conventional method is significantly different from the mixed model when greater model variability exists, suggesting there is a strong random effect, and the mixed model should be employed for the second level effective connectivity analysis.

Keywords

Robust estimation Outlier detection Least trimmed squares fMRI effective connectivity Second level analysis 

Supplementary material

12021_2012_9168_MOESM1_ESM.docx (23 kb)
ESM 1(DOCX 23 kb)

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Xingfeng Li
    • 1
  • Damien Coyle
    • 1
  • Liam Maguire
    • 1
  • Thomas Martin McGinnity
    • 1
  1. 1.Intelligent Systems Research Centre, University of Ulster at MageeDerryUK

Personalised recommendations