Exploiting Complexity Information for Brain Activation Detection

We present a complexity-based approach for the analysis of fMRI time series, in which sample entropy (SampEn) is introduced as a quantification of the voxel complexity. Under this hypothesis the voxel complexity could be modulated in pertinent cognitive tasks, and it changes through experimental paradigms. We calculate the complexity of sequential fMRI data for each voxel in two distinct experimental paradigms and use a nonparametric statistical strategy, the Wilcoxon signed rank test, to evaluate the difference in complexity between them. The results are compared with the well known general linear model based Statistical Parametric Mapping package (SPM12), where a decided difference has been observed. This is because SampEn method detects brain complexity changes in two experiments of different conditions and the data-driven method SampEn evaluates just the complexity of specific sequential fMRI data. Also, the larger and smaller SampEn values correspond to different meanings, and the neutral-blank design produces higher predictability than threat-neutral. Complexity information can be considered as a complementary method to the existing fMRI analysis strategies, and it may help improving the understanding of human brain functions from a different perspective.

[1]  B. Xu,et al.  The Increase of the Functional Entropy of the Human Brain with Age , 2013, Scientific Reports.

[2]  Andrew L. Rukhin,et al.  Approximate entropy for testing randomness , 2000, Journal of Applied Probability.

[3]  Kai-Hsiang Chuang,et al.  Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy C-means , 1999, IEEE Transactions on Medical Imaging.

[4]  Marc M. Van Hulle,et al.  fMRI bold signal analysis using a novel nonparametric statistical method. , 2007 .

[5]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[6]  D. Wolfe,et al.  Nonparametric Statistical Methods. , 1974 .

[7]  Hai-yan Wang,et al.  [Effect of subanaesthetic dose of ketamine on mechanical stimulus on brain regions]. , 2013, Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae.

[8]  S. Smith,et al.  Combining ICA and GLM: a hybrid approach to FMRI analysis , 2000, NeuroImage.

[9]  Ian J. Deary,et al.  Inter-individual Differences in fMRI Entropy Measurements in Old Age , 2011, IEEE Transactions on Biomedical Engineering.

[10]  Stephen C. Strother,et al.  Evaluation and comparison of GLM- and CVA-based fMRI processing pipelines with Java-based fMRI processing pipeline evaluation system , 2008, NeuroImage.

[11]  Steven M. Pincus,et al.  Approximate entropy: Statistical properties and applications , 1992 .

[12]  Lee M. Miller,et al.  Measuring temporal dynamics of functional networks using phase spectrum of fMRI data , 2005, NeuroImage.

[13]  M L Johnson,et al.  Enhanced basal and disorderly growth hormone secretion distinguish acromegalic from normal pulsatile growth hormone release. , 1994, The Journal of clinical investigation.

[14]  M. V. Van Hulle,et al.  fMRI bold signal analysis using a novel nonparametric statistical method. , 2007, Journal of magnetic resonance.

[15]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[16]  Weizhong Guo,et al.  A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction , 2010 .

[17]  Moses O. Sokunbi,et al.  Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets , 2014, Front. Neuroinform..

[18]  Moses O. Sokunbi,et al.  Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span. , 2015, Medical engineering & physics.

[19]  R. Cuninghame-Green,et al.  Applied Linear Algebra , 1979 .

[20]  Moses O. Sokunbi,et al.  Nonlinear Complexity Analysis of Brain fMRI Signals in Schizophrenia , 2014, PloS one.

[21]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[22]  J. Detre,et al.  Brain Entropy Mapping Using fMRI , 2014, PloS one.

[23]  Philip H. Ramsey.  Nonparametric Statistical Methods , 1974, Technometrics.

[24]  Steven M. Pincus.  Assessing Serial Irregularity and Its Implications for Health , 2001, Annals of the New York Academy of Sciences.

[25]  Hong-Bo Xie,et al.  Fuzzy Approximate Entropy Analysis of Chaotic and Natural Complex Systems: Detecting Muscle Fatigue Using Electromyography Signals , 2010, Annals of Biomedical Engineering.

[26]  Nathaniel H. Hunt,et al.  The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets , 2012, Annals of Biomedical Engineering.

[27]  Jean-Baptiste Poline,et al.  Multivariate Model Specification for fMRI Data , 2002, NeuroImage.

[28]  Zhenghui Hu,et al.  Regularity and Complexity of Human Electroencephalogram Dynamics: Applications to Diagnosis of Alzheimers Disease , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[29]  Chad M. Miller,et al.  Adaptive computation of approximate entropy and its application in integrative analysis of irregularity of heart rate variability and intracranial pressure signals. , 2008, Medical engineering & physics.

[30]  Wolf Singer,et al.  The Brain, a Complex Self-organizing System , 2009, European Review.

[31]  T. Sejnowski,et al.  Single-Trial Variability in Event-Related BOLD Signals , 2002, NeuroImage.

[32]  Jessie VanSwearingen,et al.  An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks , 2014, Journal of Neuroscience Methods.

[33]  M. P. Griffin,et al.  Sample entropy analysis of neonatal heart rate variability. , 2002, American journal of physiology. Regulatory, integrative and comparative physiology.

[34]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Weiting Chen,et al.  Measuring complexity using FuzzyEn, ApEn, and SampEn. , 2009, Medical engineering & physics.

[36]  Steven M. Pincus,et al.  Quantification of hormone pulsatility via an approximate entropy algorithm. , 1992, The American journal of physiology.

[37]  Walter H. Backes,et al.  Spatial heterogeneity analysis of brain activation in fMRI , 2014, NeuroImage: Clinical.

[38]  Roberto Hornero,et al.  Optimal parameters study for sample entropy-based atrial fibrillation organization analysis , 2010, Comput. Methods Programs Biomed..