Detecting spatiotemporal nonlinear dynamics in resting state of human brain based on fMRI datasets
ABSTRACT In this work, a nonlinear dynamics method, coupled map lattices, was applied to functional magnetic resonance imaging (fMRI) datasets to examine the spatiotemporal properties of resting state blood oxygen level-dependent (BOLD) fluctuations. Spatiotemporal Lyapunov Exponent (SPLE) was calculated to study the deterministic nonlinearity in resting state human brain of nine subjects based on fMRI datasets. The results show that there is nonlinearity and determinism in resting state human brain. Furthermore, the results demonstrate that there is a spatiotemporal chaos phenomenon in resting state brain, and suggest that fluctuations of fMRI data in resting state brain cannot be fully attributed to nuclear magnetic resonance noise. At the same time, the spatiotemporal chaos phenomenon suggests that the correlation between voxels varies with time and there is a dynamic functional connection or network in resting state human brain.
- [Show abstract] [Hide abstract]
ABSTRACT: The upper and lower bounds of the linear variance decay (LVD) dimension density are analytically deduced using multivariate series with uncorrelated and perfectly correlated component series. Then, the normalized LVD dimension density (δnormLVD) is introduced. In order to measure the complexity of a scalar series with δnormLVD, a pseudo-multivariate series was constructed from the scalar time series using time-delay embedding. Thus, δnormLVD is used to characterize the complexity of the pseudo-multivariate series. The results from the model systems and fMRI data of anxiety subjects reveal that this method can be used to analyze short and noisy time series.Highlights► Deducing the upper and lower bounds of δLVD dimension density analytically. ► Proposing the normalized LVD dimension density (δnormLVD). ► Measuring the complexity of a scalar time series by δnormLVD. ► Voxel-base analysis of fMRI data set of anxiety disease by δnormLVD.Physics Letters A 04/2011; 375(17):1789-1795. DOI:10.1016/j.physleta.2011.03.003 · 1.68 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Background: Dysfunctional integration of distributed brain networks is believed to be the cause of schizophrenia, and resting-state functional connectivity analyses of schizophrenia have attracted considerable attention in recent years. Unfortunately, existing functional connectivity analyses of schizophrenia have been mostly limited to linear associations. Objective: The objective of the present study is to evaluate the discriminative power of non-linear functional connectivity and identify its changes in schizophrenia. Method: A novel measure utilizing the extended maximal information coefficient was introduced to construct non-linear functional connectivity. In conjunction with multivariate pattern analysis, the new functional connectivity successfully discriminated schizophrenic patients from healthy controls with relative higher accuracy rate than the linear measure. Result: We found that the strength of the identified non-linear functional connections involved in the classification increased in patients with schizophrenia, which was opposed to its linear counterpart. Further functional network analysis revealed that the changes of the non-linear and linear connectivity have similar but not completely the same spatial distribution in human brain. Conclusion: The classification results suggest that the non-linear functional connectivity provided useful discriminative power in diagnosis of schizophrenia, and the inverse but similar spatial distributed changes between the non-linear and linear measure may indicate the underlying compensatory mechanism and the complex neuronal synchronization underlying the symptom of schizophrenia.Frontiers in Human Neuroscience 10/2013; 7:702. DOI:10.3389/fnhum.2013.00702 · 2.99 Impact Factor