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Brain activation for language and its relationship to cognitive and linguistic measures: a multimodal exploration
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Poster C29 in Poster Session C, Wednesday, October 25, 10:15 am - 12:00 pm CEST, Espace Vieux-Port
This poster is part of the Sandbox Series.
Irene Balboni1,4, Alessandra Rampinini4, Olga Kepinska2,3, Raphael Berthele1, Narly Golestani2,3,4; 1Institute of Multilingualism, University of Fribourg, Fribourg, Switzerland, 2Brain and Language Lab, Cognitive Science Hub, University of Vienna, Vienna, Austria, 3Department of Behavioral and Cognitive Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria, 4Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
Language learning and use both require a complex set of skills spanning from auditory perception to higher-order syntactic planning. Abilities related to memory, pattern recognition, and motor control all participate in language functions. Brain correlates for language abilities (Golestani, 2012; A. G. Hervais-Adelman et al., 2011; Kepinska et al., 2017; Reiterer, 2018; Turker et al., 2017), language experience (A. Hervais-Adelman et al., 2018; Jouravlev et al., 2021; Malik-Moraleda et al., 2022), as well as other language-relevant cognitive skills (Brissenden & Somers, 2019; Earle et al., 2020; Imaging et al., 2000; Ullman, 2004; Zhang et al., 2017) have been established by a wealth of previous publications. However, a multimodal and multivariate investigation linking cognitive, motor, other domain-specific skills with language abilities and brain activation for language is still lacking. The objective of the present work is to carry out a data-driven investigation of the key dimensions that underlie language learning, their subcomponents and their relationship with language-related brain activation. This work analyses of a subset of the data collected for the Aptitude WP (https://evolvinglanguage.ch/research/#biological). We obtained behavioural and brain data from 150 participants with a wide multilingual background (with self-reported knowledge of 1 to 50 languages). A subsample (N=29) had previously been diagnosed with dyslexia. We included general cognition measures such as fluid intelligence, attention, pattern recognition, and memory (verbal, procedural, visuospatial, declarative) as well as measures of arithmetic, musicality, and fine motor skills. Additionally, all the participants were assessed on language-specific tests, spanning from traditional language aptitude measures (phoneme perception and production, rote learning, grammar analytic ability) to tests used for diagnosing dyslexia (phonological awareness, auditory working memory, rapid naming, spelling and decoding). The participants also completed questionnaires regarding motivation for language learning, reading history, language experience, and musical training. For each participant, fMRI data were collected using a 3-Tesla Siemens Prisma scanner. Functional activation maps for language were obtained using an adapted version of the AliceLoc localizer from (Malik-Moraleda et al., 2022). In the localizer, participants listened to 24 passages (18 s each) from the book ‘Alice in Wonderland’, read by a female native speaker of their first language (L1). There was also a baseline condition, comprising 24 degraded versions of the passages, following the procedure from (Malik-Moraleda et al., 2022). The above work has resulted in two datasets: a behavioural dataset, comprising around 50 variables derived from the most relevant scores on the tasks and questionnaires, and a brain dataset comprising voxel-wise brain activation for the L1. These multimodal data will be analysed using partial least square correlations (PLS) which allows for the identification of the main dimensions explaining variation within each dataset, and in a second step, to uncover multivariate patterns underlying common features between the two. This exploratory work is expected to uncover the main relationships between cognition and language, and their links to language-relevant brain activity.
Topic Areas: Multilingualism, Speech Perception