Electrical neuroimaging based on biophysical constraints

Abstract

This paper proposes and implements biophysical constraints to select a unique solution to the bioelectromagnetic inverse problem. It first shows that the brain's electric fields and potentials are predominantly due to ohmic currents. This serves to reformulate the inverse problem in terms of a restricted source model permitting noninvasive estimations of Local Field Potentials (LFPs) in depth from scalprecorded data. Uniqueness in the solution is achieved by a physically derived regularization strategy that imposes a spatial structure on the solution based upon the physical laws that describe electromagnetic fields in biological media. The regularization strategy and the source model emulate the properties of brain activity's actual generators. This added information is independent of both the recorded data and head model and suffices for obtaining a unique solution compatible with and aimed at analyzing experimental data. The inverse solution's features are evaluated with event-related potentials (ERPs) from a healthy subject performing a visuo-motor task. Two aspects are addressed: the concordance between available neurophysiological evidence and inverse solution results, and the functional localization provided by fMRI data from the same subject under identical experimental conditions. The localization results are spatially and temporally concordant with experimental evidence, and the areas detected as functionally activated in both imaging modalities are similar, providing indices of localization accuracy. We conclude that biophysically driven inverse solutions offer a novel and reliable possibility for studying brain function with the temporal resolution required to advance our understanding of the brain's functional networks. D

Balanced bilinguals favor lexical processing in their opaque language and conversion system in their shallow language

“…A distributed linear inverse solution and the local autoregressive average (LAURA) regularization approach were used to estimate intracranial sources of the scalp-recorded data (Grave de Peralta, Gonzalez, Lantz, Michel, & Landis, 2001; Grave de Peralta, Murray, Michel, Martuzzi, & Gonzalez Andino, 2004). Intracranial sources were estimated and statistically processed over the periods showing a topographic and/or a GFP modulation.…”

Task relevance effects in electrophysiological brain activity: Early, but not first

“…We estimated electrical sources in the brain using a distributed linear inverse solution and the local autoregressive average (LAURA) regularization approach (Grave de Peralta Menendez et al, 2004; see also Michel et al, 2004 for a comparison of inverse solution methods). LAURA selects the sources of configuration that follow best the biophysical behaviour of electric fields (i.e., activity at one point depends on the activity at neighbouring points according to electromagnetic law).…”

Methods for Determining Frequency- and Region-Dependent Relationships Between Estimated LFPs and BOLD Responses in Humans

“…This analysis was conducted on the eLFPs (Grave de Peralta Menendez et al 2000Menendez et al , 2004 see following text for further details).…”

“…We then estimated intracranial LFPs (eLFP) within the whole brain volume (Grave de Peralta Menendez et al 2000 to explore the spatial and spectral dependencies between these signals. Comparable activation maps from both the EEG and fMRI data sets were obtained, without losing sensitivity to high-frequency oscillations, through the analysis of single-trial spectral power.…”