AUTHOR=Fahimi Hnazaee Mansoureh , Khachatryan Elvira , Van Hulle Marc M. 

TITLE=Semantic Features Reveal Different Networks During Word Processing: An EEG Source Localization Study

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 12 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2018.00503

DOI=10.3389/fnhum.2018.00503

ISSN=1662-5161

ABSTRACT=The neural principles behind semantic category representation are still under debate. Dominant theories mostly focus on distinguishing concrete from abstract concepts but in such theories divisions into categories for concrete concepts are more developed than for their abstract counterparts. An encompassing theory on semantic category representation could be within reach when charting the semantic attributes that are capable of describing both concept types. A good candidate are the three semantic dimensions defined by Osgood (potency, valence, activity). However, to show to what extent they affect semantic processing, specific neuroimaging tools are required. 
Electroencephalography (EEG) is on par with the temporal resolution of cognitive behaviour and source reconstruction, using high-density set-ups, is able to yield a spatial resolution in the scale of millimetres, sufficient to identify anatomical brain parcellations that could differentially contribute to semantic category representation. Cognitive neuroscientists traditionally focus on scalp domain analysis and turn to source reconstruction when an effect in scalp domain has been detected. Traditional methods will potentially miss out on fine-grained effects of semantic features possibly obscured by the mixing of source activity due to volume conduction.  For this reason we have developed a mass-univariate analysis in the source domain using a mixed linear effect model. Our analyses reveal distinct networks of sources for different semantic features that are active during different stages of lexico-semantic processing of single words. With our method we identified differences in the spatio-temporal activation patterns of abstract and concrete words, high and low potency words, high and low valence words, and high and low activity words, and in this way shed light on how word categories are represented in the brain.