Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method

Abstract

Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection method based on the sequential forward floating search algorithm was used to identify an ''optimal'' combination of EEG channel locations, where the corresponding GBR feature subset could obtain the highest accuracy in discriminating pairwise mental states influenced by each experiment factor. The integrative results from multi-factor selections suggest that the right-lateral fronto-parietal system is highly involved in neural efficiency of the math-gifted brain, primarily including the bilateral superior frontal, right inferior frontal, right-lateral central and right temporal regions. By means of the localization method based on single-trial classification of mental states, new GBR features and EEG channel-based brain regions related to mathematical giftedness were identified, which could be useful for the brain function improvement of children/ adolescents in mathematical learning through brain-computer interface systems.

What Expertise Can Tell About Mathematical Learning and Cognition

“…This reduced activation, which has been associated with neural efficiency, marks the presence of more efficient and automatized processing (Chein & Schneider, ; Shiffrin & Schneider, ), and has been shown to be causally related to mathematical performance (Snowball et al, ). Accordingly, math‐gifted adolescents show a rapid reduction of neural activity compared to peers when learning how to solve number reasoning tasks (Zhang, Gan, & Wang, ). More broadly, age‐related changes in mental arithmetic have been related to increased functional specialization of the left inferior parietal cortex and a reduction in activation of the hippocampus and dorsal basal ganglia, which characterized younger individuals who still heavily relied on memory and attentional resources when solving arithmetic problems (Rivera, Reiss, Eckert, & Menon, ).…”

“…Cognitive Excellent computational estimation skills and use of arithmetic strategies (Dowker, 1992;Dowker et al, 1996;Obersteiner et al, 2013) Rapid number comparison (Castronovo & Göbel, 2012) Accurate spatial mapping of numbers (Sella et al, 2016) Flexible spatial representation of numbers (Cipora et al, 2015;Hoffmann et al, 2014) Neural Increased gray matter in the parietal cortex (Aydin et al, 2007) Larger surface area and thinner cortex in the frontal and parietal regions (Navas-Sánchez et al, 2014, 2016 Neural efficiency in frontal areas (Jeon & Friederici, 2016;Zhang, Gan, & Wang, 2015) Greater frontoparietal and interhemispheric connectivity (Prescott, Gavrilescu, Cunnington, O'Boyle, & Egan, 2010) Enhanced activation in frontal and parietal areas during reasoning and executive function tasks (Desco et al, 2011) 2007), which constitutes the primary brain area responsible for processing of numerical and visuospatial information (Cohen Kadosh, Lammertyn, & Izard, 2008;Dehaene, Piazza, Pinel, & Cohen, 2003;Klingberg, Forssberg, & Westerberg, 2002) (see Table 1). Significant differences also emerge in the frontoparietal network, which plays a crucial role in the development of arithmetical and mathematical competencies (Evans et al, 2015;Kucian et al, 2014;Rotzer et al, 2008;Tsang, Dougherty, Deutsch, Wandell, & Ben-Shachar, 2009).…”

Cognitive and Neural Correlates of Mathematical Giftedness in Adults and Children: A Review

“…In a combined neuroimaging and behavioral Zhang et al ( 2015a ) tested adolescents—math experts ( n = 8) and normal controls ( n = 7)—who were given tasks with varying cognitive demands. The first task involved inducing and applying an arithmetic rule (addition or subtraction) when presented with three triangles, each of which contained three single-digit numbers inside at each vertex.…”

Regularized common spatial patterns with subject-to-subject transfer of EEG signals

“…In this paper, we develop a novel framework of spatial filter learning by introducing the transfer learning strategy into the classical common spatial patterns (CSP) technique (Blankertz et al 2008b;Ramoser et al 2000). The CSP method seeks spatial filters that maximize the discriminability of two classes of signals so as to extract discriminative features (Blankertz et al 2006;Yue et al 2012;Zhang et al 2015). However, the conventional CSP method does not incorporate other subjects' information engaged in the same task as the subject of interest.…”