Optimizing Complexity Measures for fMRI Data: Algorithm, Artifact, and Sensitivity

Denis Rubin,Tomer Fekete,Lilianne R Mujica-Parodi,Alejandro Raul Hernandez Montoya

doi:10.1371/journal.pone.0063448

Denis Rubin, Tomer Fekete + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0063448

Copy DOI

Journal: PloS one	Publication Date: May 21, 2013
Citations: 37	License type: CC BY 4.0

Affiliation: Stony Brook University, State University of New York

Abstract

IntroductionComplexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. However, application of complexity measures to fMRI time-series, which are short, sparse, and have low signal/noise, requires careful modality-specific optimization.MethodsHere we use both simulated and real data to address two fundamental issues: choice of algorithm and degree/type of signal processing. Methods were evaluated with regard to resilience to acquisition artifacts common to fMRI as well as detection sensitivity. Detection sensitivity was quantified in terms of grey-white matter contrast and overlap with activation. We additionally investigated the variation of complexity with activation and emotional content, optimal task length, and the degree to which results scaled with scanner using the same paradigm with two 3T magnets made by different manufacturers. Methods for evaluating complexity were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range, Higuchi’s estimate of fractal dimension, aggregated variance, and detrended fluctuation analysis. To permit direct comparison across methods, all results were normalized to Hurst exponents.ResultsPower-spectrum, Higuchi’s fractal dimension, and generalized Hurst exponent based estimates were most successful by all criteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated variance, and rescaled range.ConclusionsFunctional MRI data have artifacts that interact with complexity calculations in nontrivially distinct ways compared to other physiological data (such as EKG, EEG) for which these measures are typically used. Our results clearly demonstrate that decisions regarding choice of algorithm, signal processing, time-series length, and scanner have a significant impact on the reliability and sensitivity of complexity estimates.

Highlights

Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders
Shimizu et al [20] applied a multifractal version of the Hurst exponent to fMRI data to show that active voxels are clearly distinct from both non-active ones and white matter
HFFT and HpWelch are not intrinsically bounded at 0.5 since there is no natural upper limit on the slope of the log-powerspectrum: rather the upper bound of the spectrum exists due to the way wavelet-based fractional Brownian motion (fBm) time series are generated

Summary

Introduction

Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. The prevalence of power-law or scale-free behavior in natural processes is well-established [1], with theoretical justification for the complexity found in neurobiological and physiological systems [2,3]. These include considerations of robustness, in which redundancies maximize system integrity in the event of damage, as well as adaptability: operating on the edge of chaos, complex systems position themselves for optimal responsivity to inputs, as well as ability to maintain homeostatic regulation. Recent fMRI experiments demonstrate that brain signal fluctuations differ from other scale-free natural processes, suggestive of different underlying mechanisms [8], and that fMRI time series exhibit complexity that is functionally significant [9]. Between-group differences in time series complexity either focused on a particular disorder, such as Alzheimer’s disease [17], autism [16], and schizophrenia [18], or individual variability across psychophysiological variables such as trait anxiety, heart rate variability [23], age, cholinergic effects, and cognitive performance [24,25]

Methods

Results

Discussion

Conclusion