Biology subjects

Goucha, T.

Publications and source records attributed to Goucha, T..

4 recordsLinked to original sources

bioRxiv2023

White matter plasticity during second language learning within and across hemispheres

Adult second language (L2) learning is a challenging enterprise inducing neuroplastic changes in the human brain. However, it remains unclear how the structural language connectome and its subnetworks change during adult L2-learning. The current study investigated longitudinal changes in white matter (WM) language networks in each hemisphere, as well as their interconnection, in a large group of Arabic-speaking adults who learned German intensively for six months. We found a significant increase in WM-connectivity within bilateral temporal-parietal semantic and phonological subnetworks and right temporal-frontal pathways mainly in the second half of the learning period. At the same time, WM-connectivity between the two hemispheres decreased significantly. Crucially, these changes in WM-connectivity are correlated with L2 performance. The observed changes in subnetworks of the two hemispheres suggest a network reconfiguration due to lexical learning. The reduced interhemispheric connectivity may indicate a key role of the corpus callosum in L2-learning by reducing the inhibition of the language-dominant left hemisphere. Our study highlights the dynamic changes within and across hemispheres in adult language-related networks driven by L2 learning. SignificanceThe neuroplastic changes induced by learning a second language (L2) in adulthood open up new perspectives for understanding brain function. The current study shows structural changes in the language network of Arabic native speakers who learned German intensively in two phases of three months each. We found a marked change in the left-hemispheric lexical-semantic language system and the right fronto-temporal pathway, accompanied by a decrease in white matter connectivity in the corpus callosum during L2 learning, which occurred mainly in the second period of L2 acquisition. The reduced interhemispheric connectivity suggests that the inhibitory role of the corpus callosum, relevant for native language processing, is reduced by L2 learning. Our findings demonstrate a clear experience-dependent structural plasticity in the human brain during L2 learning.

neuroscience↗

bioRxiv2022

Children's syntax is supported by the maturation of BA44 at 4 years, but of the posterior STS at 3 years of age

Within the first years of life, children learn major aspects of their native language. However, the ability to process complex sentence structures, a core faculty in human language called syntax, has been found to emerge only slowly. A milestone in the acquisition of syntax is reached around the age of 4, when children learn a variety of syntactic concepts, including, for example, subordinate clauses. Here, we ask which maturational changes in the childs brain underlie the emergence of syntactic abilities around this critical age. We relate markers of cortical brain maturation to 3- and 4-year-olds syntactic in contrast to other language abilities. Our results show that distinct cortical brain areas support syntax in the two age groups: While 3-year-old childrens syntactic abilities were associated with increased surface area in the most posterior part of the left superior temporal sulcus, 4-year-old children showed an association with cortical thickness in the left posterior part of Brocas area, i.e. BA44. The present findings suggest that syntactic abilities rely on the maturation of distinct cortical regions in 3- compared to 4-year-olds. The observed shift to more mature regions involved in syntax may underlie the behavioral milestones in syntax acquisition around 4 years of age.

neuroscience↗

bioRxiv2022

Native language leaves distinctive traces in brain connections

The worlds languages differ substantially in their sounds, grammatical rules, and expression of semantic relations. While starting from a shared neural substrate, the developing brain must therefore have the plasticity to accommodate to the specific processing needs of each language. However, there is little research on how language-specific differences impacts brain function and structure. Here, we show that speaking typologically different languages leaves unique traces in the brains white matter connections of monolingual speakers of English (fixed word order language), German (with grammatical marking), and Chinese (tonal language). Using machine learning, we classified with high accuracy the mother tongue based on the participants patterns of structural connectivity obtained with probabilistic tractography. More importantly, connectivity differences between groups could be traced back to relevant processing characteristics of each native tongue. Our results show that the life-long use of a certain language leaves distinct traces in a speakers neural network.

neuroscience↗

bioRxiv2022

Assessing Quantitative MRI Techniques using Multimodal Comparisons

The study of brain structure and change in neuroscience is commonly conducted using macroscopic morphological measures of the brain such as regional volume or cortical thickness, providing little insight into the microstructure and physiology of the brain. In contrast, quantitative MRI allows the monitoring of microscopic brain change non-invasively in-vivo, and provides normative values for comparisons between tissues, regions, and individuals. To support the development and common use of qMRI for cognitive neuroscience, we analysed a set of qMRI metrics (R1, R2*, Magnetization Transfer saturation, Proton Density saturation, Fractional Anisotropy, Mean Diffusivity) in 101 healthy young adults. Here we provide a comprehensive descriptive analysis of these metrics and their linear relationships to each other in grey and white matter to develop a more complete understanding of the relationship to tissue microstructure. Furthermore, we provide evidence that combinations of metrics may uncover informative gradients across the brain by showing that lower variance components of PCA may be used to identify cortical gradients otherwise hidden within individual metrics. We discuss these results within the context of microstructural and physiological neuroscience research.

neuroscience↗