Effects of Early Language Deprivation on Brain Connectivity: Language Pathways in Deaf Native and Late First-Language Learners of American Sign Language

Abstract

Previous research has identified ventral and dorsal white matter tracts as being crucial for language processing; their maturation correlates with increased language processing capacity. Unknown is whether the growth or maintenance of these language-relevant pathways is shaped by language experience in early life. To investigate the effects of early language deprivation and the sensory-motor modality of language on white matter tracts, we examined the white matter connectivity of language-relevant pathways in congenitally deaf people with or without early access to language. We acquired diffusion tensor imaging (DTI) data from two groups of individuals who experienced language from birth, twelve deaf native signers of American Sign Language, and twelve hearing L2 signers of ASL (native English speakers), and from three, well-studied individual cases who experienced minimal language during childhood. The results indicate that the sensory-motor modality of early language experience does not affect the white matter microstructure between crucial language regions. Both groups with early language experience, deaf and hearing, show leftward laterality in the two language-related tracts. However, all three cases with early language deprivation showed altered white matter microstructure, especially in the left dorsal arcuate fasciculus (AF) pathway.

A whole-cortex probabilistic diffusion tractography connectome

“…The lateralization we observed may then reflect that of the arcuate and inferior longitudinal fasciculi which connect the same structures and show significant left lateralization in humans but not macaques (Eichert et al, 2019;Panesar, Yeh, Jacquesson, Hula, & Fernandez-Miranda, 2018). Left-lateralization of the arcuate fasciculus develops late (Lebel & Beaulieu, 2011), and is sensitive to the presence, quality and quantity of early language experience (Cheng, Roth, Halgren, & Mayberry, 2019;Romeo et al, 2018). More generally, many of the connectivity patterns observed here could be the indirect result of co-activation of the connected parcels (Mount & Monje, 2017).…”

Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network

“…Timing could also play a role. The perisylvian fronto-temporal language network may have a sensitive period of development during which it is most capable of learning ( Cheng et al, 2019 ; Mayberry et al, 2018 ; Cheng et al, 2020 ; Ferjan Ramirez et al, 2016 ) By the time people learn to code, the network may be incapable of taking on new cognitive functions. Indeed, even acquiring a second language late in life leads to lower levels of proficiency and responses outside the perisylvian fronto-temporal system ( Hartshorne et al, 2018 ; Johnson and Newport, 1989 ; ).…”

Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network

“…Timing could also play a role. The perisylvian fronto-temporal language network may have a sensitive period of development during which it is most capable of learning (Cheng, Roth, Halgren, & Mayberry, 2019;Mayberry, Davenport, Roth, & Halgren, 2018;Ramirez et al, 2016). By the time people learn to code, the network may be incapable of taking on new cognitive functions.…”