Sign-language learning evokes linguistic-domain-specific functional alterations
Poster No:
998
Submission Type:
Abstract Submission
Authors:
Yael Coldham1, Neta Haluts1, Eden Elbaz1, Tamar Ben David1, Nell Racabi1, Shachar Gal1, Naama Friedmann1, Ido Tavor1
Institutions:
1Tel Aviv University, Tel Aviv, Israel
First Author:
Co-Author(s):
Introduction:
Language processing is a complex neural mechanism encompassing various regions of the human brain. Previous studies have described language comprehension as a multi-stage framework with separate phonological, semantic, and syntactic components (Bibbs et al., 2000; Gvion & Friedmann, 2012), and specific brain regions have been associated with the different linguistic domains (Friederici, 2012). In the current study we investigate the functional changes evoked by learning a novel language in a new modality, and focus on the processing of each linguistic component of the newly-learned language. We hypothesize that learning-induced functional alterations will be unique per linguistic component, driving distinct activation patterns following learning.
Methods:
Seventy-nine naive hearing participants (ages 21-37, 50 females) completed a comprehensive Israeli Sign Language (ISL) course. They underwent task-fMRI scans at two time-points, pre- and post-learning, in which they watched ISL content in four conditions: sentences, learned words, unlearned words and matched non-linguistic gestures. Syntax processing is expected to be involved in ISL sentences, semantics in sentences and learned words, phonology in all three linguistic components, and no linguistic components in non-linguistic gestures. Thus, contrasting neural activation in different conditions is expected to yield activation maps corresponding with the processing of specific linguistic components (e.g., the contrast sentences>learned words would correspond with syntactic processing). First-level analysis yielded contrast maps of all condition pairs, per participant per time-point. For each task contrast, a one-tailed paired t-test on the pre- and post-learning contrast maps was used to detect group-level regions of increased activity following sign-language learning (p<0.05, FDR corrected). Moreover, a searchlight representational similarity analysis (RSA, Fig.2A) (Kriegeskorte et al., 2008) was applied on the whole-brain BOLD signal in all task conditions post-learning, using CoSMo Multivariate Pattern Analysis (CoSMoMVPA; Oosterhof et al., 2016), to identify cortical regions associated with the three linguistic components. This analysis resulted in maps of brain regions significantly associated with syntactic, semantic and phonological processing (10,000 iterations permutation testing with threshold-free cluster enhancement, p<0.001 (Smith and Nichols, 2009; Stelzer et al., 2013)). A Dice coefficient was calculated between each pair of RSA-generated maps to assess the overlap between them. We additionally calculated a representational dissimilarity matrix (RDM) between the whole-brain BOLD signal, averaged across trials, of each pair of task conditions with the dissimilarity measure of 1-Pearson's r.
Results:
We found increased (p<0.05, FDR corrected) group-level brain activity in all task contrasts (Fig.1), reflecting significant functional alterations following sign-language learning in the processing of all three linguistic components. Additionally, low Dice coefficients were found between the RSA-generated syntax and semantics maps (0.21), semantics and phonology maps (0.18) and syntax and phonology maps (0.04, Fig.2B), indicating minimal overlap between the neural regions engaged in the processing of the three linguistic domains post-learning. This is further supported by high dissimilarity scores between the whole-brain BOLD signal in different conditions, ranging from 1.18 to 1.94 (values of 0 mean perfect correlation, Fig.2C).
·Figure 1. Changes in task-induced activation following sign-language learning
·Figure 2. Representational Similarity Analysis
Conclusions:
In the current work we show the dissimilarity between the functional alterations associated with syntactic, semantic and phonological processing in a newly-learned language of a novel modality. Based on the language comprehension framework, our findings support the attribution of unique neural representations to the different stages of language processing and demonstrate its manifestation within a learning process.
Language:
Language Acquisition 1
Language Comprehension and Semantics
Learning and Memory:
Skill Learning 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Keywords:
Cognition
Data analysis
FUNCTIONAL MRI
Language
Learning
MRI
Plasticity
1|2Indicates the priority used for review
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