FMRI BASELINE DRIFT ESTIMATION METHOD BY MDL PRINCIPLE

This paper introduces a new method for estimating and removing baseline drift in the fMRI signal. We propose the technique of minimum description length (MDL) to the problem of fMRI drift analysis. The proposed method is based on the iterative estimation of the activation level and the drift component, using least square estimation and the concept of MDL denoising. Simulation results show that the proposed algorithm can estimate the drift term without having any prior knowledge or assuming an overly restrictive model. The algorithm is tested on both simulated and real fMRI data. The performance is compared with that of polynomial detrending method used in AFNI.

• Publication year

  2007

• Venue

  IEEE International Symposium on Biomedical Imaging

• Publication date

  2007-04-12

• Fields of study

  Medicine, Computer Science, Mathematics

• Identifiers
  DOI 10.1109/ISBI.2007.356891
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• MDL Denoising Revisited

  2006cited by this paper
• Penalized partially linear models using sparse representations with an application to fMRI time series

  2005cited by this paper
• Adaptive analysis of fMRI data

  2003cited by this paper
• Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series

  2003cited by this paper
• Functional MRI : an introduction to methods

  2002cited by this paper
• To Smooth or Not to Smooth?: Bias and Efficiency in fMRI Time-Series Analysis

  2000cited by this paper
• ROC analysis of statistical methods used in functional MRI: individual subjects.

  1999cited by this paper
• Deconvolution of impulse response in event-related BOLD fMRI.

  1999cited by this paper
• Activation detection in functional MRI using subspace modeling and maximum likelihood estimation

  1999cited by this paper
• Comparison of filtering methods for fMRI datasets.

  1999cited by this paper
• Statistical methods of estimation and inference for functional MR image analysis

  1996cited by this paper
• Processing strategies for time‐course data sets in functional mri of the human brain

  1993cited by this paper
• Modeling By Shortest Data Description*

  1978influential reference

• Subspace Least Square Approach for Drift Removal with Application to Herschel Data

  2013cites this paper
• Concurrent bias correction in hemodynamic data assimilation

  2012cites this paper
• Detrend-free hemodynamic data assimilation of two-stage Kalman estimator.

  2011cites this paper
• Detrend-Free Hemodynamic Data Assimilation of Two-Stage Kalman Estimator

  2011cites this paper
• Spatio-temporal representations and analysis of brain function from fmri

  2011cites this paper
• Model Based Principal Component Analysis with Application to Functional Magnetic Resonance Imaging.

  2007cites this paper
• Smooth Principal Component Analysis with Application to Functional Magnetic Resonance Imaging

  2006cites this paper