An Informed Multitask Diffusion Adaptation Approach to Study Tremor in Parkinson's Disease

In this paper, a network-based approach for studying the relation between the tremor intensity and the brain connectivity of Parkinson's patients is introduced. We propose an adaptive multitask diffusion strategy to estimate the underlying model between the gait information and the electroencephalography signals. Furthermore, the method incorporates an S-transform-based connectivity measure that performs well even on a single-trial basis. The estimated connectivity values are then combined with the combination weights of the multitask diffusion strategy to model the relation between tremor and the brain signals. The outcome is an enhanced brain connectivity measure representing its time-space relation to the tremor. The results show how the differences between the connectivity values of patients with mild and severe hand tremor are most distinguishable when using the proposed method.

[1]  Ali H. Sayed,et al.  Adaptive regularized diffusion adaptation over multitask networks , 2015, 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP).

[2]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[3]  Jing Li,et al.  Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation , 2010, NeuroImage.

[4]  Delong Zhang,et al.  Widespread Increase of Functional Connectivity in Parkinson’s Disease with Tremor: A Resting-State fMRI Study , 2015, Front. Aging Neurosci..

[5]  Jie Chen,et al.  Adaptive clustering for multitask diffusion networks , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[6]  Ali H. Sayed,et al.  Distributed Clustering and Learning Over Networks , 2014, IEEE Transactions on Signal Processing.

[7]  Jie Chen,et al.  Multitask Diffusion Adaptation Over Networks , 2013, IEEE Transactions on Signal Processing.

[8]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[9]  Bin He,et al.  Estimation of Time-Varying Connectivity Patterns Through the Use of an Adaptive Directed Transfer Function , 2008, IEEE Transactions on Biomedical Engineering.

[10]  Ali H. Sayed,et al.  Modelling brain cortical connectivity using diffusion adaptation , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[12]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[13]  C. Tanner,et al.  Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030 , 2007, Neurology.

[14]  Saeid Sanei,et al.  Discrimination of task-related eeg signals using diffusion adaptation and S-transform coherency , 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

[15]  Manuel M. Vindiola,et al.  Comparing parametric and nonparametric methods for detecting phase synchronization in EEG , 2013, Journal of Neuroscience Methods.

[16]  Robert Oostenveld,et al.  An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias , 2011, NeuroImage.

[17]  Asuman E. Ozdaglar,et al.  Distributed Subgradient Methods for Multi-Agent Optimization , 2009, IEEE Transactions on Automatic Control.

[18]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[19]  Dimitri P. Bertsekas,et al.  Incremental Subgradient Methods for Nondifferentiable Optimization , 2001, SIAM J. Optim..

[20]  Sim Heng Ong,et al.  Advances in bacteria motility modelling via diffusion adaptation , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[21]  Ali H. Sayed,et al.  Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation Over Adaptive Networks , 2012, IEEE Transactions on Signal Processing.

[22]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[23]  Alfred O. Hero,et al.  Diffusion LMS for multitask problems with overlapping hypothesis subspaces , 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

[24]  Marcus E. Raichle,et al.  The Restless Brain , 2011, Brain Connect..

[25]  Xi-Nian Zuo,et al.  Shared and Distinct Intrinsic Functional Network Centrality in Autism and Attention-Deficit/Hyperactivity Disorder , 2013, Biological Psychiatry.

[26]  Stephen P. Boyd,et al.  Fast linear iterations for distributed averaging , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[27]  Jie Chen,et al.  Diffusion LMS Over Multitask Networks , 2014, IEEE Transactions on Signal Processing.

[28]  Ali H. Sayed,et al.  Distributed Pareto Optimization via Diffusion Strategies , 2012, IEEE Journal of Selected Topics in Signal Processing.

[29]  O. Sporns,et al.  Network centrality in the human functional connectome. , 2012, Cerebral cortex.

[30]  Kuncheng Li,et al.  Changes of functional connectivity of the motor network in the resting state in Parkinson's disease , 2009, Neuroscience Letters.

[31]  Ali H. Sayed,et al.  Adaptive Networks , 2014, Proceedings of the IEEE.

[32]  Ali Sayed,et al.  Adaptation, Learning, and Optimization over Networks , 2014, Found. Trends Mach. Learn..

[33]  Ali H. Sayed,et al.  Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks , 2011, IEEE Transactions on Signal Processing.

[34]  Cornelis J. Stam,et al.  Increased cortico-cortical functional connectivity in early-stage Parkinson's disease: An MEG study , 2008, NeuroImage.

[35]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[36]  Mitra Basu,et al.  Gaussian-based edge-detection methods - a survey , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[37]  D O Walter,et al.  Changes in brain functional connectivity in Alzheimer-type and multi-infarct dementia. , 1992, Brain : a journal of neurology.

[38]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..