High-resolution EEG source imaging of one-year-old children

Abstract

Recently we described an iterative skull conductivity and source location estimation (SCALE) algorithm for simultaneously estimating head tissue conductivities and brain source locations. SCALE uses a realistic FEM forward problem head model and scalp maps of 10 or more near-dipolar sources identified by independent component analysis (ICA) decomposition of sufficient high-density EEG data. In this study, we applied SCALE to 20 minutes of 64-channel EEG data and magnetic resonance (MR) head images from four twelve-months-of-age infants. For each child, we selected 15–16 near-dipolar independent components from multiple-model adaptive mixture ICA (AMICA) decomposition of their EEG data. SCALE converged to brain-to-skull conductivity ratio (BSCR) estimates in the 10–12 range and mostly compact gyral or sulcal cortical distributions for the IC sources.

Change detection to tone pairs during the first year of life – Predictive longitudinal relationships for EEG-based source and time-frequency measures

“…The equation for the exponential function was 0.064*e-0.195*age in years giving an estimate of 0.0581 at 6 months and 0.0527 at 12 months. This estimate is close to that provided by the SCALE algorithm (Acar et al, 2016).…”

Variation in reported human head tissue electrical conductivity values

“…Assuming the brain to have homogeneous and isotropic conductivity, as in research considering the brain as a whole, is therefore insufficient for accurate conductivity profiles. Assuming the brain as a homogeneous conductor consequently results in considerable EEG and MEG source localisation errors (Acar et al, 2016;Awada et al, 1998;Cohen & Cuffin, 1983b).…”

“…In vivo n=1 adult healthy 0.774 ± 0.01 (Lee et al, 2015) MREIT In vivo n=2 adult healthy 0.787 (Ropella and Noll, 2017) MREIT in vivo n=4 adult healthy 0.028 ± 0.052 (Dabek et al, 2016) EIT in vivo 2 n=9 (4m) 32.5±10 healthy 0.627 ±0.037 (Akhtari et al, 2016) DAC in vitro 10 n=24 paediatric epilepsy 0.698 ±0.212 (Acar et al, 2016) E/MEG in vivo 0 n=2 (m) 21.5±2.12 healthy 0.718 ±0.019 (Gurler and Ider, 2017) MREIT in vivo n=1 23 healthy 0.817 ±0.218 (Lee et al, 2016) MREIT in vivo n=1 adult healthy 0.561 ± 0.382 (Koessler et al, 2017) EIT in vivo 50 n=15 (10m) 38±10 epilepsy 0.643 ± 0.0478 (Huang et al, 2017) EIT (Michel et al, 2017) MREIT in vivo n=1 29 healthy 0.89 ± 0.028 (Tha et al, 2018) MREIT in vivo n=30 (14m) 50.7 ± 18.2 tumour 0.486 (Chauhan et al, 2018) DTI in vivo 10 n=2 (m) healthy 0.939…”

Structural templates for imaging EEG cortical sources in infants

“…For example, all dipolar contributions can be postulated to sum linearly and be represented with equivalent current dipoles (ECD) whose position and orientation are estimated from scalp EEG activity. Such an approach presents some analytic advantages (e.g., mathematical tractability when used with spherical head models) and can be motivated when one or a few specific sources of activity clearly dominates like in the context of epileptic activity ( Ebersole, 1994 ) or for localized independent components ( Acar et al, 2016 ). By generalizing the ECD approach to a large number of dipoles and using sophisticated finite element models (FEM) of the head, we can also perform current density reconstruction (CDR).…”