Semi-blind Functional Source Separation Algorithm from Non-invasive Electrophysiology to Neuroimaging

Neuroimaging, investigating how specific brain sources play a particular role in a definite cognitive or sensorimotor process, can be achieved from non-invasive electrophysiological (EEG, EMG, MEG) and multimodal (concurrent EEG-fMRI) recordings. However, especially for the non-invasive electrophysiological techniques, the signals measured at the scalp are a mixture of the contributions from multiple generators or sources added to background activity and system noise, meaning that it is often difficult to identify the dynamic activity of generators of interest starting from the electrode/sensor recordings. Although the most common method of overcoming this limitation is time-domain averaging with or without source localization, blind source separation (BSS) algorithms are becoming increasingly widely accepted as a way of extracting the different neuronal sources that contribute to the measured scalp signals without trial exclusion. The advantage of BSS or semi-blind source separation (semi-BSS) techniques compared to methods such as time-domain averaging lies in their ability to extract sources exploring the whole time evolving data. Taking into account the whole signal without averaging, it also provides a means suitable to investigate non-phase locked oscillatory processes and single-trial behaviour. This characteristic becomes a crucial issue when investigating combined EEG-fMRI data, particularly when the focus is on neurovascular coupling definitely dependent on single trial variability of the two datasets. In this context, this chapter describes a semi-BSS technique, Functional Source Separation (FSS), which is a tool to identify cerebral sources by exploiting a priori knowledge, such as spectral or evoked activity, which cannot be expressed by sources other than the one to be identified (functional fingerprint). In other words, FSS allows the identification of specific neuronal pools on the bases of their functional roles, independent of their spatial position.

[1]  Dirk Ostwald,et al.  An information theoretic approach to EEG–fMRI integration of visually evoked responses , 2010, NeuroImage.

[2]  A. Engel,et al.  Single-trial EEG–fMRI reveals the dynamics of cognitive function , 2006, Trends in Cognitive Sciences.

[3]  A. Kleinschmidt,et al.  Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[4]  R. Hari,et al.  Neuromagnetic studies of somatosensory system: Principles and examples , 1985, Progress in Neurobiology.

[5]  Makoto Takahashi,et al.  The neural basis of the hemodynamic response nonlinearity in human primary visual cortex: Implications for neurovascular coupling mechanism , 2006, NeuroImage.

[6]  A. Engel,et al.  Trial-by-Trial Coupling of Concurrent Electroencephalogram and Functional Magnetic Resonance Imaging Identifies the Dynamics of Performance Monitoring , 2005, The Journal of Neuroscience.

[7]  Paolo Maria Rossini,et al.  High-gamma band activity of primary hand cortical areas: A sensorimotor feedback efficiency index , 2008, NeuroImage.

[8]  P. Rossini,et al.  Hand sensory–motor cortical network assessed by functional source separation , 2008, Human brain mapping.

[9]  Franca Tecchio,et al.  Primary sensory and motor cortex activities during voluntary and passive ankle mobilization by the SHADE orthosis , 2011, Human brain mapping.

[10]  J. Kaas Plasticity of sensory and motor maps in adult mammals. , 1991, Annual review of neuroscience.

[11]  P. Rossini,et al.  Hand somatosensory subcortical and cortical sources assessed by functional source separation: An EEG study , 2009, Human brain mapping.

[12]  C. Porcaro,et al.  Physiological Aging Impacts the Hemispheric Balances of Resting State Primary Somatosensory Activities , 2012, Brain Topography.

[13]  J. R. Rosenberg,et al.  Coherent cortical and muscle discharge in cortical myoclonus. , 1999, Brain : a journal of neurology.

[14]  J. Rothwell,et al.  Cortical correlate of the Piper rhythm in humans. , 1998, Journal of neurophysiology.

[15]  G. Barnes,et al.  Statistical flattening of MEG beamformer images , 2003, Human brain mapping.

[16]  P. Rossini,et al.  Functional source separation from magnetoencephalographic signals , 2006, Human brain mapping.

[17]  Dirk Ostwald,et al.  Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent EEG-fMRI , 2010, NeuroImage.

[18]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[19]  P. Rossini,et al.  Combined Analysis of Cortical (EEG) and Nerve Stump Signals Improves Robotic Hand Control , 2012, Neurorehabilitation and neural repair.

[20]  T. Sejnowski,et al.  Electroencephalographic Brain Dynamics Following Manually Responded Visual Targets , 2004, PLoS biology.

[21]  Paolo Maria Rossini,et al.  Brain plasticity in recovery from stroke: An MEG assessment , 2006, NeuroImage.

[22]  Paolo Maria Rossini,et al.  Interhemispheric asymmetry of primary hand representation and recovery after stroke: A MEG study , 2007, NeuroImage.

[23]  Gareth R. Barnes,et al.  The relationship between the visual evoked potential and the gamma band investigated by blind and semi-blind methods , 2011, NeuroImage.

[24]  Robert Turner,et al.  A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI , 2000, NeuroImage.

[25]  Viviana Betti,et al.  Synchronous with Your Feelings: Sensorimotor γ Band and Empathy for Pain , 2009, The Journal of Neuroscience.

[26]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[27]  Paolo Maria Rossini,et al.  Choice of multivariate autoregressive model order affecting real network functional connectivity estimate , 2009, Clinical Neurophysiology.

[28]  Kenneth Hugdahl,et al.  Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Gareth R. Barnes,et al.  Stimuli of varying spatial scale induce gamma activity with distinct temporal characteristics in human visual cortex , 2007, NeuroImage.

[30]  Giancarlo Zito,et al.  Intra-cortical connectivity in multiple sclerosis: a neurophysiological approach. , 2008, Brain : a journal of neurology.

[31]  C. Porcaro,et al.  Movement-induced uncoupling of primary sensory and motor areas in focal task-specific hand dystonia , 2013, Neuroscience.

[32]  E. Oja,et al.  Independent Component Analysis , 2013 .

[33]  Christopher J. James,et al.  Extracting Rhythmic Brain Activity for Brain-Computer Interfacing through Constrained Independent Component Analysis , 2007, Comput. Intell. Neurosci..

[34]  C. Porcaro,et al.  Multiple frequency functional connectivity in the hand somatosensory network: An EEG study , 2013, Clinical Neurophysiology.

[35]  Franca Tecchio,et al.  Functional source separation applied to induced visual gamma activity , 2008, Human brain mapping.

[36]  K. Reilly,et al.  The moving phantom: Motor execution or motor imagery? , 2012, Cortex.

[37]  Stephen J. Jones,et al.  Potentials evoked in human and monkey cerebral cortex by stimulation of the median nerve. A review of scalp and intracranial recordings. , 1991, Brain : a journal of neurology.

[38]  Derek K. Jones,et al.  Visual gamma oscillations and evoked responses: Variability, repeatability and structural MRI correlates , 2010, NeuroImage.

[39]  Louis Lemieux,et al.  Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction , 1998, NeuroImage.

[40]  L. Zollo,et al.  Inter-hemispheric coupling changes associate with motor improvements after robotic stroke rehabilitation. , 2012, Restorative neurology and neuroscience.

[41]  Gian Luca Romani,et al.  Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis , 2007, NeuroImage.

[42]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[43]  R. Oostenveld,et al.  Validating the boundary element method for forward and inverse EEG computations in the presence of a hole in the skull , 2002, Human brain mapping.

[44]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[45]  Wei Lu,et al.  Approach and applications of constrained ICA , 2005, IEEE Transactions on Neural Networks.

[46]  M. Cynader,et al.  Somatosensory cortical map changes following digit amputation in adult monkeys , 1984, The Journal of comparative neurology.

[47]  G. Barbati,et al.  Functional source separation and hand cortical representation for a brain–computer interface feature extraction , 2007, The Journal of physiology.

[48]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[49]  K. Reilly,et al.  Disentangling motor execution from motor imagery with the phantom limb. , 2012, Brain : a journal of neurology.

[50]  M. Roth,et al.  Single‐trial analysis of oddball event‐related potentials in simultaneous EEG‐fMRI , 2007, Human brain mapping.

[51]  P M Rossini,et al.  A neurally-interfaced hand prosthesis tuned inter-hemispheric communication. , 2012, Restorative neurology and neuroscience.

[52]  H. Freund,et al.  Cortico‐muscular synchronization during isometric muscle contraction in humans as revealed by magnetoencephalography , 2000, The Journal of physiology.

[53]  Robert Oostenveld,et al.  The five percent electrode system for high-resolution EEG and ERP measurements , 2001, Clinical Neurophysiology.

[54]  K. Reilly,et al.  Mapping phantom movement representations in the motor cortex of amputees. , 2006, Brain : a journal of neurology.

[55]  S. Makeig,et al.  Mining event-related brain dynamics , 2004, Trends in Cognitive Sciences.

[56]  Giancarlo Valente,et al.  Somatosensory dynamic gamma-band synchrony: A neural code of sensorimotor dexterity , 2007, NeuroImage.

[57]  A. Engel,et al.  Spectral fingerprints of large-scale neuronal interactions , 2012, Nature Reviews Neuroscience.

[58]  K. Reilly,et al.  Persistent hand motor commands in the amputees' brain. , 2006, Brain : a journal of neurology.

[59]  M. Corbetta,et al.  The Dynamical Balance of the Brain at Rest , 2011, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[60]  M. Mishkin,et al.  Massive cortical reorganization after sensory deafferentation in adult macaques. , 1991, Science.

[61]  Tae-Seong Kim,et al.  Robust extraction of P300 using constrained ICA for BCI applications , 2012, Medical & Biological Engineering & Computing.