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J Neurosci. 2017 Nov 15;37(46):11101-11113. doi: 10.1523/JNEUROSCI.1720-17.2017. Epub 2017 Oct 12.

Strength of Temporal White Matter Pathways Predicts Semantic Learning.

Author information

1
Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute, and.
2
Department of Cognition, Development and Education Psychology, Campus Bellvitge, University of Barcelona, L'Hospitalet de Llobregat, 08097 Barcelona, Spain.
3
Poeppel Laboratory, Department of Psychology, New York University, New York, New York 10003.
4
Institute of Psychology I, University of Lübeck, 23562 Lübeck, Germany.
5
Aphasia Research Laboratory, Department of Speech, Language, and Hearing Sciences, Boston University, Boston, Massachusetts 02215.
6
Department of Neurology, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany.
7
Department of Biological Psychology, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany.
8
Center for Behavioral Brain Sciences, D-39106 Magdeburg, Germany, and.
9
Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute, and arfornells@gmail.com.
10
Catalan Institution for Research and Advanced Studies, Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Spain.

Abstract

Learning the associations between words and meanings is a fundamental human ability. Although the language network is cortically well defined, the role of the white matter pathways supporting novel word-to-meaning mappings remains unclear. Here, by using contextual and cross-situational word learning, we tested whether learning the meaning of a new word is related to the integrity of the language-related white matter pathways in 40 adults (18 women). The arcuate, uncinate, inferior-fronto-occipital and inferior-longitudinal fasciculi were virtually dissected using manual and automatic deterministic fiber tracking. Critically, the automatic method allowed assessing the white matter microstructure along the tract. Results demonstrate that the microstructural properties of the left inferior-longitudinal fasciculus predict contextual learning, whereas the left uncinate was associated with cross-situational learning. In addition, we identified regions of special importance within these pathways: the posterior middle temporal gyrus, thought to serve as a lexical interface and specifically related to contextual learning; the anterior temporal lobe, known to be an amodal hub for semantic processing and related to cross-situational learning; and the white matter near the hippocampus, a structure fundamental for the initial stages of new-word learning and, remarkably, related to both types of word learning. No significant associations were found for the inferior-fronto-occipital fasciculus or the arcuate. While previous results suggest that learning new phonological word forms is mediated by the arcuate fasciculus, these findings show that the temporal pathways are the crucial neural substrate supporting one of the most striking human abilities: our capacity to identify correct associations between words and meanings under referential indeterminacy.SIGNIFICANCE STATEMENT The language-processing network is cortically (i.e., gray matter) well defined. However, the role of the white matter pathways that support novel word learning within this network remains unclear. In this work, we dissected language-related (arcuate, uncinate, inferior-fronto-occipital, and inferior-longitudinal) fasciculi using manual and automatic tracking. We found the left inferior-longitudinal fasciculus to be predictive of word-learning success in two word-to-meaning tasks: contextual and cross-situational learning paradigms. The left uncinate was predictive of cross-situational word learning. No significant correlations were found for the arcuate or the inferior-fronto-occipital fasciculus. While previous results showed that learning new phonological word forms is supported by the arcuate fasciculus, these findings demonstrate that learning new word-to-meaning associations is mainly dependent on temporal white matter pathways.

KEYWORDS:

cross-situational learning; meaning; semantic; temporal pathways; tractography; word learning

PMID:
29025925
DOI:
10.1523/JNEUROSCI.1720-17.2017
[Indexed for MEDLINE]
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