Localizing movement-related primary sensorimotor cortices with multi-band EEG frequency changes and functional MRI.

Electroencephalographic (EEG) oscillations in multiple frequency bands can be observed during functional activity of the cerebral cortex. An important question is whether activity of focal areas of cortex, such as during finger movements, is tracked by focal oscillatory EEG changes. Although a number of studies have compared EEG changes to functional MRI hemodynamic responses, we can find no previous research that relates the fMRI hemodynamic activity to localization of the multiple EEG frequency changes observed in motor tasks. In the present study, five participants performed similar thumb and finger movement tasks in parallel EEG and functional MRI studies. We examined changes in five frequency bands (from 5-120 Hz) and localized them using 256 dense-array EEG (dEEG) recordings and high-resolution individual head models. These localizations were compared with fMRI localizations in the same participants. Results showed that beta-band (14-30 Hz) desynchronizations (power decreases) were the most robust effects, appearing in all individuals, consistently localized to the hand region of the primary motor cortex, and consistently aligned with fMRI localizations.

A 3D finite-difference BiCG iterative solver with the Fourier-Jacobi preconditioner for the anisotropic EIT/EEG forward problem.

The Electrical Impedance Tomography (EIT) and electroencephalography (EEG) forward problems in anisotropic inhomogeneous media like the human head belongs to the class of the three-dimensional boundary value problems for elliptic equations with mixed derivatives. We introduce and explore the performance of several new promising numerical techniques, which seem to be more suitable for solving these problems. The proposed numerical schemes combine the fictitious domain approach together with the finite-difference method and the optimally preconditioned Conjugate Gradient- (CG-) type iterative method for treatment of the discrete model. The numerical scheme includes the standard operations of summation and multiplication of sparse matrices and vector, as well as FFT, making it easy to implement and eligible for the effective parallel implementation. Some typical use cases for the EIT/EEG problems are considered demonstrating high efficiency of the proposed numerical technique.

Minimal Information for Neural Electromagnetic Ontologies (MINEMO): A standards-compliant method for analysis and integration of event-related potentials (ERP) data.

We present MINEMO (Minimal Information for Neural ElectroMagnetic Ontologies), a checklist for the description of event-related potentials (ERP) studies. MINEMO extends MINI (Minimal Information for Neuroscience Investigations)to the ERP domain. Checklist terms are explicated in NEMO, a formal ontology that is designed to support ERP data sharing and integration. MINEMO is also linked to an ERP database and web application (the NEMO portal). Users upload their data and enter MINEMO information through the portal. The database then stores these entries in RDF (Resource Description Framework), along with summary metrics, i.e., spatial and temporal metadata. Together these spatial, temporal, and functional metadata provide a complete description of ERP data and the context in which these data were acquired. The RDF files then serve as inputs to ontology-based labeling and meta-analysis. Our ultimate goal is to represent ERPs using a rich semantic structure, so results can be queried at multiple levels, to stimulate novel hypotheses and to promote a high-level, integrative account of ERP results across diverse study methods and paradigms.

Computational modeling of human head electromagnetics for source localization of milliscale brain dynamics.

Understanding the milliscale (temporal and spatial) dynamics of the human brain activity requires high-resolution modeling of head electromagnetics and source localization of EEG data. We have developed an automated environment to construct individualized computational head models from image segmentation and to estimate conductivity parameters using electrical impedance tomography methods. Algorithms incorporating tissue inhomogeneity and impedance anisotropy in electromagnetics forward simulations have been developed and parallelized. The paper reports on the application of the environment in the processing of realistic head models, including conductivity inverse estimation and lead field generation for use in EEG source analysis.