Hand somatosensory subcortical and cortical sources assessed by functional source separation: An EEG study

Abstract

We propose a novel electroencephalographic application of a recently developed cerebral source extraction method (Functional Source Separation, FSS), which starts from extracranial signals and adds a functional constraint to the cost function of a basic independent component analysis model without requiring solutions to be independent. Five ad-hoc functional constraints were used to extract the activity reflecting the temporal sequence of sensory information processing along the somatosensory pathway in response to the separate left and right median nerve galvanic stimulation. Constraints required only the maximization of the responsiveness at specific latencies following sensory stimulation, without taking into account that any frequency or spatial information. After source extraction, the reliability of identified FS was assessed based on the position of single dipoles fitted on its retroprojected signals and on a discrepancy measure. The FS positions were consistent with previously reported data (two early subcortical sources localized in the brain stem and thalamus, the three later sources in cortical areas), leaving negligible residual activity at the corresponding latencies. The high-frequency component of the oscillatory activity (HFO) of the extracted component was analyzed. The integrity of the low amplitude HFOs was preserved for each FS. On the basis of our data, we suggest that FSS can be an effective tool to investigate the HFO behavior of the different neuronal pools, recruited at successive times after median nerve galvanic stimulation. As FSs are reconstructed along the entire experimental session, directional and dynamic HFO synchronization phenomena can be studied.

Removing speech artifacts from electroencephalographic recordings during overt picture naming

“…An Independent Component Analysis (ICA) procedure, like many other blind source separation (BSS) techniques, decomposes the EEG data into sources with independent time course on the basis of the statistical properties of the generated signal (Makeig et al, 2004a;Medaglia et al, 2009;Porcaro et al, 2006Porcaro et al, , 2009Porcaro et al, , 2011. Following ICA model application, introduced for example in the context of fMRI (Beckmann and Smith, 2004;Porcaro et al, 2010) and Fetal Magnetoencephalography (Porcaro et al, 2006), we applied an automatic ICA procedure (an appropriately modified version of Barbati et al, 2004) to the Raw Data to identify and classify artifactual non-cerebral activities, i.e.…”

“…from 500 ms before the picture presentation onset till 2300 ms afterwards. We then used averaged trials, single trials, topographical distributions and the localizations of the components to manually classify all ICs (Delorme and Makeig, 2004;Makeig et al, 2004a,b;Medaglia et al, 2009;Porcaro et al, 2009) into the following 5 clusters (see Fig. 3): Cluster 1 -Evoked Responses (ER) of the Corrected Data (i.e.…”

Movement-induced uncoupling of primary sensory and motor areas in focal task-specific hand dystonia

“…For the purpose of separately assessing S1 and M1 activities, we have applied a source extraction method called Functional Source Separation (FSS; Barbati et al, 2006;Tecchio et al, 2007aTecchio et al, , 2008aPorcaro et al, 2008Porcaro et al, , 2009a, developed in our laboratory, which is based on a standard Independent Component Analysis (ICA) algorithm and biases the extraction toward the source of interest by adding a functional constraint to the standard cost function. FSS is capable of providing the activity of a particular source in a variety of different experimental conditions based on specific information about that source, which can be gained by exploiting a 'fingerprint behavior' arising from a given experimental condition.…”

Contradiction in universal and particular reasoning

“…This algorithm is able to identify the brain sources supporting the investigated function by exploiting the most accurate information provided by the electrophysiological techniques, i.e. the dynamical properties of the recorded signal [Barbati et al, 2004;Jung et al, 2000;Makeig et al, 2002;Porcaro et al, 2009]. Beyond separating stereotyped nonbrain artifact signals including eye movements, line noise, cardiac artifacts, and muscle activities [Makeig et al, 2004], ICA can identify large or small neuronal pool activities with diverse physiological and functional roles [Hyvärinen and Oja, 2000;Makeig et al, 1999;Vorobyov and Cichocki, 2002].…”