Older adults tend to have a broader vocabulary compared to younger adults - indicating a richer storage of semantic knowledge - but their retrieval abilities decline with age. Recent advances in quantitative methods based on network science have investigated the effect of aging on semantic memory structure. However, it is yet to be determined how this aging effect on semantic memory structure relates to its overall flexibility. Percolation analysis provides a quantitative measure of the flexibility of a semantic network, by examining how a semantic memory network is resistant to "attacks" or breaking apart. In this study, we incorporated percolation analyses to examine how semantic networks of younger and older adults break apart to investigate potential age-related differences in language production. We applied the percolation analysis to 3 independent sets of data (total N = 78 younger, 78 older adults) from which we generated semantic networks based on verbal fluency performance. Across all 3 datasets, the percolation integrals of the younger adults were larger than older adults, indicating that older adults' semantic networks were less flexible and broke down faster than the younger adults'. Our findings provide quantitative evidence for diminished flexibility in older adults' semantic networks, despite the stability of semantic knowledge across the lifespan. This may be one contributing factor to age-related differences in language production.

Previous research has documented change in white matter tract integrity with increasing age. Both interhemispheric and intrahemispheric tracts that underlie language processing are susceptible to these age-related changes. The aim of the current study was to explore age and white matter integrity in language-related tracts as predictors of cognitive task performance in younger and older adults. To this end, we carried out principal component analyses of white matter tracts and confirmatory factor analysis of neuropsychological measures. We next carried out a series of regression analyses that used white matter components to predict scores on each of the neuropsychological components. For both younger and older adults, age was a significant predictor of processing speed and working memory. However, white matter integrity did not contribute independently toward these models. In older adults only, both age and a white matter component that included the bilateral frontal aslant tract and left superior longitudinal fasciculus were significant predictors of working memory. Taken together, these results extend our understanding of the contributions of language-related white matter structure to cognitive processing and highlight the effects of age-related differences in both frontal and dorsal tracts.

A briefly exposed visual stimulus may not be consciously perceived if it is preceded and followed by a dissimilar visual pattern or mask. Despite the subject's lack of awareness, prior behavioral studies have shown that such masked stimuli, nevertheless, engage domain-specific processes [Dehaene, S., Naccache, L., Cohen, L., Le Bihan, D., Mangin, J.-F., Poline, J.-B., et al. Cerebral mechanisms of word masking and unconscious repetition priming. Nature Neuroscience, 4, 752-758, 2001; Bar, M., & Biederman, I. Subliminal visual priming. Psychological Science, 9, 464-469, 1998; Dehaene, S., Naccache, L., Le Clec'H, G., Koechlin, E., Mueller, M., Dehaene-Lambertz, G., et al. Imaging unconscious semantic priming. Nature, 395, 597-600, 1998; Whalen, P. J., Rauch, S. L., Etcoff, N. L., McInerney, S. C., Lee, M. B., & Jenike, M. A. Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. Journal of Neuroscience, 18, 411-418, 1998; Marcel, A. J. Conscious and unconscious perception: Experiments on visual masking and word recognition. Cognitive Psychology, 15, 197-237, 1983]. Masking thus provides a method for identifying language processes that are preattentive and automatic. Functional magnetic resonance imaging used in concert with masking may identify brain regions engaged by these unconscious language processes. In an adaptation design, subjects viewed a continuous stream of masked words and masked nonwords while performing an unrelated detection task, in which they were asked to make a response to a visible colored nonword stimulus (i.e., ampersands in red or blue font). Most trials were masked nonwords and masked words were presented once every 12-15 sec. The task ensured participant engagement, while the masked nonword baseline controlled for perceptual and orthographic processing. Participants were naïve to the purpose of the experiment and testing indicated that they did not consciously perceive either the words or nonwords. Masked words, but not masked nonwords, strongly activated left hemisphere language regions, including Broca's area, the angular gyrus, and the lateral temporal lobe. Differential activation of the posterior corpus callosum was also observed.

Morphological brain changes as a consequence of new learning have been widely established. Learning a second language (L2) is one such experience that can lead to rapid structural neural changes. However, still relatively little is known about how levels of proficiency in the L2 and the age at which the L2 is learned influence brain neuroplasticity. The goal of this study is to provide novel evidence for the effect of bilingualism on white matter structure in relatively proficient but late L2 learners who acquired the second language after early childhood. Overall, the results demonstrate a significant effect on white matter fractional anisotropy (FA) as a function of L2 learning. Higher FA values were found in a broad white matter network including the anterior thalamic radiation (ATR), the inferior fronto-occipital fasciculus (IFOF), the Uncinate Fasciculus (UF), and the inferior longitudinal fasciculus (ILF). Moreover, FA values were correlated with age of L2 acquisition, suggesting that learning an L2, even past childhood, induces neural changes. Finally, these results provide some initial evidence that variability in the age of L2 acquisition has important consequences for neural plasticity.