Adaptive analysis of functional MRI data

This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.

[1]  Zhiping Lin,et al.  Multidimensional signal processing of NMR data , 2004 .

[2]  T. W. Anderson.  An Introduction to Multivariate Statistical Analysis , 1959 .

[3]  T A Carpenter,et al.  Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains , 2001, Human brain mapping.

[4]  Karl J. Friston,et al.  Anatomically Informed Basis Functions , 2000, NeuroImage.

[5]  Christopher R. Genovese.  A Bayesian Time-Course Model for Functional Magnetic Resonance Imaging Data: Rejoinder , 2000 .

[6]  P. Bandettini,et al.  Spatial Heterogeneity of the Nonlinear Dynamics in the FMRI BOLD Response , 2001, NeuroImage.

[7]  K. J. Worsley,et al.  Rotation Space: Detecting Functional Activation by Searching Over Rotated and Scaled Filters , 1998, NeuroImage.

[8]  A. Constantine.  Some Non-Central Distribution Problems in Multivariate Analysis , 1963 .

[9]  Hans Knutsson,et al.  Learning multidimensional signal processing , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[10]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[11]  R. Wilson,et al.  Anisotropic Nonstationary Image Estimation and Its Applications: Part I - Restoration of Noisy Images , 1983, IEEE Transactions on Communications.

[12]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[13]  B. Ripley,et al.  A new statistical approach to detecting significant activation in functional MRI , 2000, NeuroImage.

[14]  K J Worsley,et al.  An overview and some new developments in the statistical analysis of PET and fMRI data , 1997, Human brain mapping.

[15]  Karl J. Friston,et al.  Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.

[16]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[17]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited , 1995, NeuroImage.

[18]  R. Weisskoff,et al.  Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel‐level false‐positive rates in fMRI , 1998, Human brain mapping.

[19]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[20]  Thomas M. Talavage,et al.  Simulation of human respiration in fMRI with a mechanical model , 2002, IEEE Transactions on Biomedical Engineering.

[21]  Dietmar Cordes,et al.  Novel ROC‐type method for testing the efficiency of multivariate statistical methods in fMRI , 2003, Magnetic resonance in medicine.

[22]  G. Glover.  Deconvolution of Impulse Response in Event-Related BOLD fMRI1 , 1999, NeuroImage.

[23]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[24]  J. -B. Poline,et al.  Estimating the Delay of the fMRI Response , 2002, NeuroImage.

[25]  Karl J. Friston,et al.  Event-related fMRI , 1997 .

[26]  R. Buxton,et al.  Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.

[27]  A. Macovski.  Noise in MRI , 1996, Magnetic resonance in medicine.

[28]  Hans Knutsson,et al.  Detection of Neural Activity in fMRI Using Maximum Correlation Modeling , 2002, NeuroImage.

[29]  Thomas E. Nichols,et al.  Controlling the familywise error rate in functional neuroimaging: a comparative review , 2003, Statistical methods in medical research.

[30]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[31]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[32]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[34]  I Kanno,et al.  Statistical methods for detecting activated regions in functional MRI of the brain. , 1998, Magnetic resonance imaging.

[35]  Jun Huang,et al.  A DCT-based fast enhancement technique for robust speech recognition in automobile usage , 1999, EUROSPEECH.

[36]  Alan C. Evans,et al.  A general statistical analysis for fMRI data , 2000, NeuroImage.

[37]  Karl J. Friston,et al.  Characterizing the Response of PET and fMRI Data Using Multivariate Linear Models , 1997, NeuroImage.

[38]  Niels V. Hartvig,et al.  PARAMETRIC MODELLING OF FUNCTIONAL MAGNETIC RESONANCE IMAGING DATA , 2000 .

[39]  H. Knutsson,et al.  Detection of neural activity in functional MRI using canonical correlation analysis , 2001, Magnetic resonance in medicine.

[40]  X Hu,et al.  Wavelet transform‐based Wiener filtering of event‐related fMRI data , 2000, Magnetic resonance in medicine.

[41]  D. Tank,et al.  Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited—Again , 1995, NeuroImage.

[43]  Mark Woolrich,et al.  Lowpass temporal filtering in FMRI time series , 2000, NeuroImage.

[44]  Louis L. Scharf,et al.  Matched subspace detectors , 1994, IEEE Trans. Signal Process..

[45]  Karl J. Friston,et al.  Combining Spatial Extent and Peak Intensity to Test for Activations in Functional Imaging , 1997, NeuroImage.

[46]  D. Heeger,et al.  Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1 , 1996, The Journal of Neuroscience.

[47]  V. Koivunen,et al.  Identifiability and Separability of Linear Ica Models Revisited , 2003 .

[48]  T. Sejnowski,et al.  Human Brain Mapping 6:368–372(1998) � Independent Component Analysis of fMRI Data: Examining the Assumptions , 2022 .

[49]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[50]  Karl J. Friston,et al.  To Smooth or Not to Smooth? Bias and Efficiency in fMRI Time-Series Analysis , 2000, NeuroImage.

[51]  M. D’Esposito,et al.  The Variability of Human, BOLD Hemodynamic Responses , 1998, NeuroImage.

[52]  Emery N. Brown,et al.  Locally Regularized Spatiotemporal Modeling and Model Comparison for Functional MRI , 2001, NeuroImage.

[53]  Jody Tanabe,et al.  See Blockindiscussions, Blockinstats, Blockinand Blockinauthor Blockinprofiles Blockinfor Blockinthis Blockinpublication Comparison Blockinof Blockindetrending Blockinmethods Blockinfor Optimal Blockinfmri Blockinpreprocessing , 2022 .

[54]  E. Bullmore,et al.  Statistical methods of estimation and inference for functional MR image analysis , 1996, Magnetic resonance in medicine.

[55]  L. K. Hansen,et al.  On Clustering fMRI Time Series , 1999, NeuroImage.

[56]  Guillermo Sapiro,et al.  Anisotropic 2-D and 3-D averaging of fMRI signals , 2001, IEEE Transactions on Medical Imaging.

[57]  Ari Visa,et al.  Reproducibility of fMRI: Effect of the Use of Contextual Information , 2001, NeuroImage.

[58]  Chris Tofallis.  Model Building with Multiple Dependent Variables and Constraints , 1999 .

[59]  Karl J. Friston.  Modes or models: a critique on independent component analysis for fMRI , 1998, Trends in Cognitive Sciences.

[60]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[61]  J. Gary Lutz,et al.  The Relationship between Canonical Correlation Analysis and Multivariate Multiple Regression , 1994 .

[62]  Pranab Kumar Sen,et al.  Asymptotic Distribution of Restricted Canonical Correlations and Relevant Resampling Methods , 1996 .

[63]  Andreas Ziehe,et al.  TDSEP { an e(cid:14)cient algorithm for blind separation using time structure , 1998 .

[64]  M. McKeown.  Detection of Consistently Task-Related Activations in fMRI Data with Hybrid Independent Component Analysis , 2000, NeuroImage.

[65]  Ravi S. Menon,et al.  Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. , 1993, Biophysical journal.

[66]  Hans Knutsson,et al.  Exploratory fMRI Analysis by Autocorrelation Maximization , 2002, NeuroImage.

[67]  J. Duyn,et al.  Investigation of Low Frequency Drift in fMRI Signal , 1999, NeuroImage.

[68]  M. D’Esposito,et al.  Empirical Analyses of BOLD fMRI Statistics , 1997, NeuroImage.

[69]  Paul M. Matthews,et al.  Functional magnetic resonance imaging: An introduction to methods , 2001 .

[70]  M. Lowe,et al.  Spatially filtering functional magnetic resonance imaging data , 1997, Magnetic resonance in medicine.

[71]  P. J. Jennings,et al.  Time series analysis in the time domain and resampling methods for studies of functional magnetic resonance brain imaging , 1997, Human brain mapping.

[72]  K. Worsley,et al.  Local Maxima and the Expected Euler Characteristic of Excursion Sets of χ 2, F and t Fields , 1994, Advances in Applied Probability.

[73]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[74]  John C. Gore,et al.  ROC Analysis of Statistical Methods Used in Functional MRI: Individual Subjects , 1999, NeuroImage.

[75]  Christopher R. Genovese.  Statistical Inference in Functional Magnetic Resonance Imaging , 1997 .

[76]  Matjaž Omladič,et al.  More on restricted canonical correlations , 2000 .

[77]  Xavier Descombes,et al.  fMRI Signal Restoration Using a Spatio-Temporal Markov Random Field Preserving Transitions , 1998, NeuroImage.

[78]  Jagath C. Rajapakse,et al.  Bayesian approach to segmentation of statistical parametric maps , 2001, IEEE Transactions on Biomedical Engineering.

[79]  Schuster,et al.  Separation of a mixture of independent signals using time delayed correlations. , 1994, Physical review letters.

[80]  James V. Stone.  Blind Source Separation Using Temporal Predictability , 2001, Neural Computation.

[81]  Hans Knutsson,et al.  Signal processing for computer vision , 1994 .

[82]  Yoshio Takane,et al.  Canonical correlation analysis with linear constraints , 1992 .

[83]  P. Mansfield.  Multi-planar image formation using NMR spin echoes , 1977 .

[84]  J. Rajapakse,et al.  Human Brain Mapping 6:283–300(1998) � Modeling Hemodynamic Response for Analysis of Functional MRI Time-Series , 2022 .

[85]  Hans Knutsson,et al.  A canonical correlation approach to blind source separation , 2001 .

[86]  Jörg Polzehl,et al.  Functional and dynamic magnetic resonance imaging using vector adaptive weights smoothing , 2001 .

[87]  Karl J. Friston,et al.  Analysis of functional MRI time‐series , 1994, Human Brain Mapping.

[88]  Antonio M. Peinado,et al.  Diagonalizing properties of the discrete cosine transforms , 1995, IEEE Trans. Signal Process..

[89]  Karl J. Friston,et al.  Classical and Bayesian Inference in Neuroimaging: Applications , 2002, NeuroImage.

[90]  D. Y. von Cramon,et al.  Comparison of Filtering Methods for fMRI Datasets , 1999, NeuroImage.

[91]  Hans Knutsson,et al.  Hierarchical temporal blind source separation of fMRI data , 2002 .

[92]  Iwao Kanno,et al.  Activation detection in functional MRI using subspace modeling and maximum likelihood estimation , 1999, IEEE Transactions on Medical Imaging.

[93]  A. Andersen,et al.  Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework. , 1999, Magnetic resonance imaging.

[94]  P. Lauterbur,et al.  Image Formation by Induced Local Interactions: Examples Employing Nuclear Magnetic Resonance , 1973, Nature.

[95]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[96]  Alan C. Evans,et al.  Searching scale space for activation in PET images , 1996, Human brain mapping.

[97]  R. Turner,et al.  Characterizing Evoked Hemodynamics with fMRI , 1995, NeuroImage.

[98]  R Baumgartner,et al.  Wavelet domain de-noising of time-courses in MR image sequences. , 2000, Magnetic resonance imaging.

[99]  P. Sen,et al.  Restricted canonical correlations , 1994 .

[100]  Ravi S. Menon,et al.  Submillimeter functional localization in human striate cortex using BOLD contrast at 4 Tesla: Implications for the vascular point‐spread function , 1999, Magnetic resonance in medicine.

[101]  Jean-Baptiste Poline,et al.  Ambiguous Results in Functional Neuroimaging Data Analysis Due to Covariate Correlation , 1999, NeuroImage.

[102]  D. Noll,et al.  Nonlinear Aspects of the BOLD Response in Functional MRI , 1998, NeuroImage.

[103]  Scott L. Zeger,et al.  Non‐linear Fourier Time Series Analysis for Human Brain Mapping by Functional Magnetic Resonance Imaging , 1997 .

[104]  R W Cox,et al.  Software tools for analysis and visualization of fMRI data , 1997, NMR in biomedicine.