Development of infant sustained attention and its relation to EEG oscillations: an EEG and cortical source analysis study

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

The current study examined the relation between infant sustained attention and infant EEG oscillations. Fifty-nine infants were tested at 6 (N = 15), 8 (N = 17), 10 (N = 14), and 12 (N = 13) months. Three attention phases, stimulus orienting, sustained attention, and attention termination, were defined based on infants' heart rate changes. Frequency analysis using simultaneously recorded EEG focused on infant theta (2-6 Hz), alpha (6-9 Hz), and beta (9-14 Hz) rhythms. Cortical source analysis of EEG oscillations was conducted with realistic infant MRI models. Theta synchronization was found over fontal pole, temporal, and parietal electrodes during infant sustained attention for 10 and 12 months. Alpha desynchronization was found over frontal, central and parietal electrodes during sustained attention. This alpha effect started to emerge at 10 months and became well established by 12 months. No difference was found for the beta rhythm between different attention phases. The theta synchronization effect was localized to the orbital frontal, temporal pole, and ventral temporal areas. The alpha desynchronization effect was localized to the brain regions composing the default mode network including the posterior cingulate cortex and precuneus, medial prefrontal cortex, and inferior parietal gyrus. The alpha desynchronization effect was also localized to the pre- and post-central gyri. The present study demonstrates a connection between infant sustained attention and EEG oscillatory activities.

Related collections

Most cited references 41

Record: found
Abstract: found
Article: not found

Electrophysiological signatures of resting state networks in the human brain.

D. Mantini, M G Perrucci, C Del Gratta … (2007)

Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.

0 comments Cited 589 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Construction of a 3D probabilistic atlas of human cortical structures.

David W. Shattuck, Mubeena Mirza, Vitria Adisetiyo … (2008)

We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998) paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al., 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines.

0 comments Cited 305 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Assessing interactions in the brain with exact low-resolution electromagnetic tomography.

Roberto D. Pascual-Marqui, Dietrich Lehmann, Martha Koukkou … (2011)

Scalp electric potentials (electroencephalogram; EEG) are contingent to the impressed current density unleashed by cortical pyramidal neurons undergoing post-synaptic processes. EEG neuroimaging consists of estimating the cortical current density from scalp recordings. We report a solution to this inverse problem that attains exact localization: exact low-resolution brain electromagnetic tomography (eLORETA). This non-invasive method yields high time-resolution intracranial signals that can be used for assessing functional dynamic connectivity in the brain, quantified by coherence and phase synchronization. However, these measures are non-physiologically high because of volume conduction and low spatial resolution. We present a new method to solve this problem by decomposing them into instantaneous and lagged components, with the lagged part having almost pure physiological origin.