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Adamson, H.

Publications and source records attributed to Adamson, H..

2 recordsLinked to original sources

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↗

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↗