Optimal Design for Functional Magnetic Resonance Imaging Experiments: Methodology, Challenges, and Future Perspectives

This paper provides an overview of optimal design for functional magnetic resonance imaging (fMRI) studies. We present the main types of fMRI designs, namely blocked and event-related designs, and common objectives of fMRI experiments, for example, localization of task-related activity in the human brain. Furthermore, we present an introduction into the methodology for analysis and optimization of fMRI experiments, for instance common analysis models and applied optimality criteria. We outline some of the problems encountered when optimizing fMRI experiments, for example, the temporal autocorrelation between measurements in fMRI data. The most important results for optimization of blocked and event-related designs with regard to the different design objectives are presented and explained. Finally, we conclude with future perspectives and challenges for optimization of fMRI experiments.

[1]  Thomas J. Grabowski,et al.  Adaptive pacing of visual stimulation for fMRI studies involving overt speech , 2006, NeuroImage.

[2]  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.

[3]  Daniel Gallichan,et al.  Real‐time adaptive sequential design for optimal acquisition of arterial spin labeling MRI data , 2010, Magnetic resonance in medicine.

[4]  Rainer Goebel,et al.  Robustness of optimal design of fMRI experiments with application of a genetic algorithm , 2010, NeuroImage.

[5]  S. Petersen,et al.  Characterizing the Hemodynamic Response: Effects of Presentation Rate, Sampling Procedure, and the Possibility of Ordering Brain Activity Based on Relative Timing , 2000, NeuroImage.

[6]  R. Goebel,et al.  Optimization of Blocked Designs in fMRI Studies , 2009, NeuroImage.

[7]  Nikos K Logothetis,et al.  Interpreting the BOLD signal. , 2004, Annual review of physiology.

[8]  Thomas T. Liu,et al.  Efficiency, power, and entropy in event-related FMRI with multiple trial types Part I: theory , 2004, NeuroImage.

[9]  N. Kanwisher,et al.  The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception , 1997, The Journal of Neuroscience.

[10]  R. Cox,et al.  Event‐related fMRI contrast when using constant interstimulus interval: Theory and experiment , 2000, Magnetic resonance in medicine.

[11]  Thomas E. Nichols,et al.  Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation , 2008, NeuroImage.

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

[13]  Steven E. Petersen,et al.  The mixed block/event-related design , 2012, NeuroImage.

[14]  R. Buxton,et al.  Detection Power, Estimation Efficiency, and Predictability in Event-Related fMRI , 2001, NeuroImage.

[15]  Thomas E. Nichols,et al.  Optimization of experimental design in fMRI: a general framework using a genetic algorithm , 2003, NeuroImage.

[16]  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.

[17]  Abhyuday Mandal,et al.  Constrained multiobjective designs for functional magnetic resonance imaging experiments via a modified non‐dominated sorting genetic algorithm , 2012 .

[18]  Sharon L. Thompson-Schill,et al.  The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs , 2012, NeuroImage.

[19]  Thomas T. Liu,et al.  The development of event-related fMRI designs , 2012, NeuroImage.

[20]  Peter A. Bandettini,et al.  From neuron to BOLD: new connections , 2001, Nature Neuroscience.

[21]  O Josephs,et al.  Event-related functional magnetic resonance imaging: modelling, inference and optimization. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[22]  Tim Curran,et al.  Optimization of contrast detection power with probabilistic behavioral information , 2012, NeuroImage.

[23]  Mark D'Esposito,et al.  Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.

[24]  J. Stufken,et al.  Efficient Designs for Event-Related Functional Magnetic Resonance Imaging with Multiple Scanning Sessions , 2009 .

[25]  Shein-Chung Chow,et al.  Adaptive design methods in clinical trials – a review , 2008, Orphanet journal of rare diseases.

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

[27]  Abhyuday Mandal,et al.  Multi-objective optimal experimental designs for event-related fMRI studies , 2009, NeuroImage.

[28]  Karl J. Friston,et al.  Some Limit Results for Efficiency in Stochastic fMRI Designs , 2002 .

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

[30]  Karl J. Friston,et al.  Stochastic Designs in Event-Related fMRI , 1999, NeuroImage.

[31]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[32]  David I Donaldson,et al.  Parsing brain activity with fMRI and mixed designs: what kind of a state is neuroimaging in? , 2004, Trends in Neurosciences.

[33]  Christian Keysers,et al.  The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machines , 2011, NeuroImage.

[34]  G. Barker,et al.  Study design in fMRI: Basic principles , 2006, Brain and Cognition.

[35]  Thomas T. Liu,et al.  Part II: design of experiments , 2022 .

[36]  K. Chaloner,et al.  Bayesian Experimental Design: A Review , 1995 .

[37]  K. Worsley,et al.  Unified univariate and multivariate random field theory , 2004, NeuroImage.

[38]  J. Sanes,et al.  Improved Detection of Event-Related Functional MRI Signals Using Probability Functions , 2001, NeuroImage.

[39]  Richard B. Buxton.  Efficient Design of BOLD Experiments , 2002 .

[40]  Richard N. Henson,et al.  CHAPTER 15 – Efficient Experimental Design for fMRI , 2007 .

[41]  Russell A. Poldrack,et al.  The future of fMRI in cognitive neuroscience , 2012, NeuroImage.

[42]  Jeffrey S. Spence,et al.  Key properties of D‐optimal designs for event‐related functional MRI experiments with application to nonlinear models , 2012, Statistics in medicine.

[43]  R. Buxton,et al.  Sorting out event-related paradigms in fMRI: the distinction between detecting an activation and estimating the hemodynamic response , 2000, NeuroImage.

[44]  Gary H Glover,et al.  Estimating sample size in functional MRI (fMRI) neuroimaging studies: Statistical power analyses , 2002, Journal of Neuroscience Methods.

[45]  Scott A. Huettel,et al.  Event-related fMRI in cognition , 2012, NeuroImage.

[46]  Kerstin Preuschoff,et al.  Optimizing Experimental Design for Comparing Models of Brain Function , 2011, PLoS Comput. Biol..

[47]  F. Giesel,et al.  Methodische Grundlagen der Optimierung funktioneller MR-Experimente , 2005, Der Radiologe.

[48]  A M Dale,et al.  Optimal experimental design for event‐related fMRI , 1999, Human brain mapping.

[49]  Rainer Goebel,et al.  Optimal design of multi-subject blocked fMRI experiments , 2011, NeuroImage.

[50]  A M Dale,et al.  Estimation and detection of event‐related fMRI signals with temporally correlated noise: A statistically efficient and unbiased approach , 2000, Human brain mapping.

[51]  M. D’Esposito,et al.  Experimental Design for Brain fMRI , 2000 .

[52]  J.A. Mumford,et al.  Modeling and inference of multisubject fMRI data , 2006, IEEE Engineering in Medicine and Biology Magazine.

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

[54]  Peter A. Bandettini,et al.  Detection versus Estimation in Event-Related fMRI: Choosing the Optimal Stimulus Timing , 2002, NeuroImage.

[55]  M. Lindquist.  The Statistical Analysis of fMRI Data. , 2008, 0906.3662.

[56]  Jean-Baptiste Poline,et al.  Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses , 2007, NeuroImage.

[57]  N. Kriegeskorte,et al.  Revealing representational content with pattern-information fMRI--an introductory guide. , 2009, Social cognitive and affective neuroscience.

[58]  Geoffrey M. Boynton,et al.  Efficient Design of Event-Related fMRI Experiments Using M-Sequences , 2002, NeuroImage.

[59]  R. Goebel,et al.  Optimal design for nonlinear estimation of the hemodynamic response function , 2012, Human brain mapping.

[60]  Douglas C. Noll,et al.  Accounting for nonlinear BOLD effects in fMRI: parameter estimates and a model for prediction in rapid event-related studies , 2005, NeuroImage.

[61]  Anthony C. Atkinson,et al.  Optimum Experimental Designs, with SAS , 2007 .