The brain in development
Martin Edwards (1) and Eva Van den Bussche (2)
(1) UCLouvain; (2) KU Leuven
Abstract to follow
Speaker 1: The emergence of the white matter network for language in infancy
Jolijn Vanderauwera (1,2,3), Jennifer Zuk (1,2), Ted Turesky (1,2), Ally Lee (1,2), Jade Dunstan (1,2), Ellen Grant (1,2) and Nadine Gaab (1,2)
(1) Boston Children’s Hospital, USA; (2) Harvard Medical School, USA; (3) UCLouvain
The white matter pathways underpinning language have been well specified in adults and children. Yet, it remains unclear how the neural basis of language manifests in infancy, since core language pathways are characterized by a protracted developmental trajectory within the first year of life. Therefore, the present study investigates the relation between white matter organization and language skills in 80 infants (mean age: 8.6 mo, range: 2.5-17.3). More specifically, we studied how structural organization as characterized by fractional anisotropy (FA) fluctuates along the course of four intra-hemispheric and two inter-hemispheric tracts. Partial correlations controlled for infant age revealed significant relations between clusters of FA-values in bilateral intra-hemispheric tracts and inter-hemispheric connections. Interestingly, in addition to core language tracts such as the left arcuate fasciculus, relations with language skills were observed in early maturing white matter pathways, predominantly the inter-hemispheric corpus callosum and bilateral corticospinal tract. Hence, these results indicate that language skills in infancy seem to be underpinned by a network of tracts that extends beyond the core tracts and have a stronger bilateral distribution.
Speaker 2: Learning a language effortlessly like a child: Enhanced online neural monitoring of
language sequences in adults with a disrupted prefrontal cortex
language sequences in adults with a disrupted prefrontal cortex
Eleonore Smalle (1), Tatsuya Daikoku (2), Cristina Pancotto (3), Arnaud Szmalec (1, 4), Wouter Duyck (1) and Riikka Möttönen (3)
(1) UGent; (2) Max Planck Institute Leipzig, Germany; (3) University of Nottingham, UK; (4) UCLouvain
Paradoxically, adults are less effective in learning new languages than children although they have superior cognitive skills. From a very young age humans are able to acquire linguistic knowledge (e.g., words or grammatical rules) from continuous streams of speech sounds through statistical learning. Why language learning outcomes decline during development is an unresolved question. Here, we tested a hypothesis that late-developing cognitive control mechanisms compete with implicit statistical learning mechanisms that contribute to early language acquisition. In agreement with this hypothesis, we found that depleting the cognitive system by non-invasive brain stimulation or demanding cognitive tasks boosted auditory statistical learning abilities in adults. These findings suggest that adults are able to acquire linguistic knowledge effortlessly like children when their cognitive resources are depleted. The findings shed new light on the cognitive architecture of language learning and open new avenues to enhance language outcomes learning in adults.
Speaker 3: The role vision plays in shaping the brain representation of number: Insights from blindness
Virginie Crollen (1)
Numbers play an important role in our daily lives, they are used in a variety of contexts (e.g., to use mobile phones, cook, deal with money, tell time, etc.) and are most of the time accessed and processed through the visual modality. This supremacy of vision in accessing numerical information has led some researchers to assume that number was a fundamental visual attribute principally processed through the neural recycling of some visuo-spatial brain areas. If this assumption is true, then the lack of early visual experience should impede the development of good numerical abilities. In this talk, I will review recent behavioural and imaging data examining numerical cognition in congenitally blind individuals. I will show that the lack of visual experience does not prevent the development of good numerical abilities, but nevertheless shapes some qualitative properties of the number representation. Within this context, the study of visually deprived individuals represents a unique opportunity to test the intrinsic relation between numerical cognition and vision and also provides important insights into the role played by visual experience in shaping the neural foundations of arithmetic reasoning.
Speaker 4: The added value of structural brain imaging data in predicting individual differences in children’s arithmetic
Bert De Smedt (1)
(1) KU Leuven
Most of the developmental brain imaging studies in the field of mathematical development have focused on identifying networks of brain activity during arithmetic, yet much less is known about the structural properties of these networks. We therefore investigated which structural brain imaging measures, comprising both measures of grey matter (MRI) and white matter (dMRI) correlated with individual differences in children’s arithmetic. The combination of these two measures additionally allowed us to verify the predictive value of both structural brain imaging measures on top of each other as well as on top of well-known behavioral predictors that have been related to in individual differences in children’s arithmetic. Participants were 47 typically developing 9-10-year-olds. Grey matter structure was investigated by looking at volume through voxel-based morphometry, and at cortical complexity through fractal dimensionality. White matter structure was analyzed via spherical deconvolution. Behavioral measures assessed symbolic number processing, working memory, rapid automatized naming (RAN). The white matter integrity of the right inferior longitudinal fasciculus and the cortical complexity of the left postcentral gyrus emerged as neuroanatomical predictors of arithmetic. Symbolic number processing and RAN emerged as critical behavioral measures. All measures were subsequently added to a series of multiple regression models. These models revealed that the neuro-anatomical measures still predicted unique variance in individual differences in arithmetic, highlighting the value of structural brain imaging measures for the prediction of cognitive skills.