Imaging neural correlates of syntactic complexity in a naturalistic context

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2008.Includes bibliographical references (p. 253-280).The aim of this thesis, and the research project within which it is embedded, is to delineate a neural model of grammatical competence. For this purpose, we develop here a novel integrated, multi-disciplinary experimental paradigm that endorses the fundamental premise of generative grammar, that the study of language is in essence, the study of the mind. We use functional Magnetic Resonance Imaging (fMRI) to monitor brain activation while subjects listen to short narratives. The texts have been written so as to introduce various syntactic complexities (relative clauses, embedded questions, etc.) not usually found (in such density) in actual corpora. We have calculated a number of complexity measures (both at the level of the single word and at that of the phrase) based on current linguistic and psycholinguistic theory and with the use of a computationally implemented probabilistic parser. By correlating these measures with observed brain activity, we are able to identify the different brain networks that support linguistic processing and characterize their particular function. Conversely, we use the rich brain data to inform our cognitive, and linguistic, theory. We report here the neural correlates of surprisal (based on contextual predictions), syntactic complexity, structural ambiguity and disambiguation, Theory of Mind and non-local dependencies. This work made use of novel solutions to compute numerical predictions for these linguistic dimensions, which are often tested only qualitatively, and of a novel parametric fMRI design that allowed for the use of single subject unaveraged data as the dependent variable. The thesis ends with a synthesis of the results in the form of a blue print for a neural model of grammatical competence.by Asaf Bachrach.Ph.D

Contributions to the functional neuroanatomy of morphosyntactic processing in L2

Studies about bilingualism and second language acquisition (SLA) have a long tradition within linguistic and psycholinguistic research. With the global population becoming more and more multilingual and the recent proliferation of research in cognitive neuroscience, an increasing number of studies examining the way our brain is able to learn, represent, and handle more than one language at the same time are currently available. However, few attempts have been made to transpose psycholinguistic models of SLA into functional neuroanatomic models. An important problem that arises when pursuing this goal is partially due to the delay in the development of cognitive neuroscience of language compared to psycholinguistics. In general, neurolinguistic models focus on very broad and general questions about bilingualism, whereas psycholinguistic research is already at the stage of addressing more specific and fine-tuned questions. This Granularity Mismatch Problem (Poeppel & Embick, 2005) in the degree of zooming into this research topic is not exclusive of L2 research, but it is present in language research in general (Hauser & Bever, 2008). In either case, it often becomes difficult to put together the results from these different perspectives into one integrated model

Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities

In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca’s region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners’ differences. We conclude that in terms of functional connectivity, the engagement of brain’s networks during grammar acquisition is coupled with one’s language learning abilities.Theoretical and Experimental Linguistic

Longitudinal changes in resting-state fMRI from age 5 to age 6 years covary with language development

Resting-state functional magnetic resonance imaging is a powerful technique to study the whole-brain neural connectivity that underlies cognitive systems. The present study aimed to define the changes in neural connectivity in their relation to language development. Longitudinal resting-state functional data were acquired from a cohort of preschool children at age 5 and one year later, and changes in functional connectivity were correlated with language performance in sentence comprehension. For this, degree centrality, a voxel-based network measure, was used to assess age-related differences in connectivity at the whole-brain level. Increases in connectivity with age were found selectively in a cluster within the left posterior superior temporal gyrus and sulcus (STG/STS). In order to further specify the connection changes, a secondary seed-based functional connectivity analysis on this very cluster was performed. The correlations between resting-state functional connectivity (RSFC) and language performance revealed developmental effects with age and, importantly, also dependent on the advancement in sentence comprehension ability over time. In children with greater advancement in language abilities, the behavioral improvement was positively correlated with RSFC increase between left posterior STG/STS and other regions of the language network, i.e., left and right inferior frontal cortex. The age-related changes observed in this study provide evidence for alterations in the language network as language develops and demonstrates the viability of this approach for the investigation of normal and aberrant language development