Mr. Gopikrishna Deshpande Profile

Gopikrishna Deshpande

at Emory Univ

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Proceedings Article | 14 April 2005 Paper

Spatial embedding of fMRI for investigating local coupling in human brain

Gopikrishna Deshpande, Stephen LaConte, Scott Peltier, Xiaoping Hu

Proceedings Volume 5746, (2005) https://doi.org/10.1117/12.595262

KEYWORDS: Functional magnetic resonance imaging, Brain, Image segmentation, Chaos, Brain mapping, Tissues, Data acquisition, Complex systems, Electroencephalography, Systems modeling

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In this paper, we have investigated local spatial couplings in the human brain by applying nonlinear dynamical techniques on fMRI data. We have recorded BOLD-contrast echo-planar fMRI data along with high-resolution T1-weighted anatomical images from the resting brain of healthy human subjects and performed physiological correction on the functional data. The corrected data from resting subjects is spatially embedded into its phase space and the largest Lyapunov exponent of the resulting attractor is calculated and whole slice maps are obtained. In addition, we segment the high-resolution anatomical image and obtain a down sampled mask corresponding to gray and white matter, which is used to obtain mean indices of the exponents for both the tissues separately. The results show the existence of local couplings, its tissue specificity (more local coupling in gray matter than white matter) and dependence on the size of the neighborhood (larger the neighborhood, lesser the coupling). We believe that these techniques capture the information of a nonlinear and evolving system like the brain that may not be evident from static linear methods. The results show that there is evidence of spatio-temporal chaos in the brain, which is a significant finding hitherto not reported in literature to the best of our knowledge. We try to interpret our results from healthy resting subjects based on our knowledge of the native low frequency fluctuations in the resting brain and obtain a better understanding of the local spatial behavior of fMRI. This exploratory study has demonstrated the utility of nonlinear dynamical techniques like spatial embedding in analyzing fMRI data to gain meaningful insights into the working of human brain.

Proceedings Article | 30 April 2004 Paper

Detecting nonlinear dynamics of functional connectivity

Stephen LaConte, Scott Peltier, Yasser Kadah, Shing-Chung Ngan, Gopikrishna Deshpande, Xiaoping Hu

Proceedings Volume 5369, (2004) https://doi.org/10.1117/12.535804

KEYWORDS: Functional magnetic resonance imaging, Brain, Physiology, Nonlinear dynamics, Signal to noise ratio, Brain mapping, Dynamical systems, Time metrology, Magnetic resonance imaging, Neuroimaging

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Functional magnetic resonance imaging (fMRI) is a technique that is sensitive to correlates of neuronal activity. The application of fMRI to measure functional connectivity of related brain regions across hemispheres (e.g. left and right motor cortices) has great potential for revealing fundamental physiological brain processes. Primarily, functional connectivity has been characterized by linear correlations in resting-state data, which may not provide a complete description of its temporal properties. In this work, we broaden the measure of functional connectivity to study not only linear correlations, but also those arising from deterministic, non-linear dynamics. Here the delta-epsilon approach is extended and applied to fMRI time series. The method of delays is used to reconstruct the joint system defined by a reference pixel and a candidate pixel. The crux of this technique relies on determining whether the candidate pixel provides additional information concerning the time evolution of the reference. As in many correlation-based connectivity studies, we fix the reference pixel. Every brain location is then used as a candidate pixel to estimate the spatial pattern of deterministic coupling with the reference. Our results indicate that measured connectivity is often emphasized in the motor cortex contra-lateral to the reference pixel, demonstrating the suitability of this approach for functional connectivity studies. In addition, discrepancies with traditional correlation analysis provide initial evidence for non-linear dynamical properties of resting-state fMRI data. Consequently, the non-linear characterization provided from our approach may provide a more complete description of the underlying physiology and brain function measured by this type of data.

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