Functional source separation from magnetoencephalographic signals

Barbati, Giulia; Sigismondi, Roberto; Zappasodi, Filippo; Porcaro, Camillo; Graziadio, Sara; Valente, Giancarlo; Balsi, Marco; Rossini, Paolo Maria; Tecchio, Franca

doi:10.1002/hbm.20232

Cited by 47 publications

(44 citation statements)

References 37 publications

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“…Moreover, since prior information on the sources may also be described by a nondifferentiable function, the new contrast function F is optimized by means of simulated annealing. This procedure does not require the use of derivatives and allows to achieve global optimization, while gradient-based algorithms usually employed in ICA only guarantee local optimization (Barbati et al, 2006). To identify the specific contribution of different neuronal sources (which are assumed to be nonindependent/noncorrelated), the FSS source-specific functional constraints are applied to the original data.…”

Section: Data Handlingmentioning

confidence: 99%

Synchronous with Your Feelings: Sensorimotor γ Band and Empathy for Pain

Betti¹,

Zappasodi²,

Rossini³

et al. 2009

J. Neurosci.

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Neuroscience studies on the social sharing of observed or imagined pain focused on whether empathic pain resonance is linked to affective or sensory nodes of the pain matrix. However, empathy, like other complex cognitive processes, is inherently linked to the activation of functional networks rather than of separate brain areas. Here, we used magnetoencephalography (MEG) to explore the relationship between empathy and functional coupling of neuronal activity in primary somatosensory (SI) and motor (MI) cortices. MEG recording was performed while healthy participants observed movie-clips depicting the static hand of a stranger model, the same hand deeply penetrated by a needle, or gently touched by a Q-tip. Subjects were asked to rate the movie-derived sensations attributed to self or to the model. For each type of clip observation, we analyzed spectral power and coherence values in ␣, ␤, and ␥ frequency bands. While spectral power indexes separate neural activity in SI and MI, coherence values index functional cross-talk between these two areas. No power changes of SI or MI sources were induced by observation conditions in any of the frequency bands. Crucially, ␥-band coherence values were significantly higher during needle-in-hand than touch and static hand observation and correlated with self-and otherreferred pain ratings derived from needle-in-hand movies observation. Thus, observation of others' pain increases neuronal synchronization and cross-talk between the onlookers' sensory and motor cortices, indicating that empathic resonance relies upon the activity of functional networks more than of single areas.

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Section: Data Handlingmentioning

confidence: 99%

Synchronous with Your Feelings: Sensorimotor γ Band and Empathy for Pain

Betti¹,

Zappasodi²,

Rossini³

et al. 2009

J. Neurosci.

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“…The applicability of ICA to ERP analyses has been shown repeatedly in a context in which it is possible to attribute a physiological meaning to one or more components (Makeig et al 2002;Debener et al 2004), for artifacts detection (Vigario 1997) and more generally for EEG pattern recognition and classification (Naeem et al 2006;De Lucia et al 2008). Numerous other approaches have been developed in the area of blind source separation (Belouchrani et al 1997;Tang et al 2005; Barbati et al 2006), and several methods at the level of single waveforms either based on assuming stationary stereotypic wave shapes (Knuth et al 2006) or on filtering and de-noising (Quiroga and Garcia 2003;Georgiadis et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Comparing ICA-based and Single-Trial Topographic ERP Analyses

2010

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Single-trial analysis of human electroencephalography (EEG) has been recently proposed for better understanding the contribution of individual subjects to a group-analyis effect as well as for investigating singlesubject mechanisms. Independent Component Analysis (ICA) has been repeatedly applied to concatenated singletrial responses and at a single-subject level in order to extract those components that resemble activities of interest. More recently we have proposed a single-trial method based on topographic maps that determines which voltage configurations are reliably observed at the event-related potential (ERP) level taking advantage of repetitions across trials. Here, we investigated the correspondence between the maps obtained by ICA versus the topographies that we obtained by the single-trial clustering algorithm that best explained the variance of the ERP. To do this, we used exemplar data provided from the EEGLAB website that are based on a dataset from a visual target detection task. We show there to be robust correpondence both at the level of the activation time courses and at the level of voltage configurations of a subset of relevant maps. We additionally show the estimated inverse solution (based on lowresolution electromagnetic tomography) of two corresponding maps occurring at approximately 300 ms poststimulus onset, as estimated by the two aforementioned approaches. The spatial distribution of the estimated sources significantly correlated and had in common a right parietal activation within Brodmann's Area (BA) 40. Despite their differences in terms of theoretical bases, the consistency between the results of these two approaches shows that their underlying assumptions are indeed compatible.

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“…Prior MEG based BCI studies have been conducted based on the sensor domain, focusing mainly on the source identification problem [41][42][43]. In [44], a source based MEG analysis was proposed using a novel blind source separation method called functional source separation (FSS) to identify sources of activation and source time courses for potential BCI use.…”

Section: Synthetic Aperture Magnetometry (Sam) and Source Space Bcimentioning

confidence: 99%