• 2018-07
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  • 2019-10
  • Other investigators have also interpreted structural differe


    Other investigators have also interpreted structural differences between lower- and higher-SES children, in the absence of data concerning the participants’ language functioning, as evidence of a neural basis for language delays in the former group. For example, Jednoróg et al. (2012) studied a sample of 8- to 10-year-old children and found a positive correlation between SES and gray matter volume of bilateral middle temporal gyri, left fusiform gyrus, and right inferior occipito-temporal region. Because these regions have been found in previous studies to be associated with the processing of written language, Jednoróg et al. (2012) suggest that their obtained volumetric differences are indicative of poorer reading and writing skills in lower-SES children. They found no significant correlation, however, between SES and tests of phonological skill or non-verbal IQ, nor did they include any tests of reading and writing skills in the children whom they studied. Therefore, it is difficult to evaluate the validity of their interpretations of their study. Two recent studies have made significant headway in investigating the interactions among SES, diazoxide Supplier structure, and language development. In a groundbreaking study, Noble et al. (2015) analyzed data from the brains of over 1000 children from a number of sites across the United States. These investigators found that after controlling for genetic ancestry, there was a logarithmic association between SES and cortical surface area, such that children whose families earned the lowest income had significantly reduced cortical surface area, particularly in temporal regions that have been posited to support language. Although this measure of cortical surface area partially mediated the relation between SES and a test of executive function, it was not associated with any measures of language. In a separate study, Hair et al. (2015) assessed a large sample of socioeconomically diverse children. They found that between ages 4–22 years, children below the poverty line were below developmental norms in regional gray matter volume, particularly in the temporal lobe. As expected, these structural differences partially mediated the link between SES and standardized tests thought to measure intelligence and academic achievement (Hair et al., 2015). Hair et al. (2015) did not, however, include any direct measures of language comprehension or production. Moreover, the developmental norms were modeled from their sample, which was predominantly higher-SES; thus, the trajectories may be more representative of the trajectories of higher-SES children. These two studies are groundbreaking within the cognitive neuroscience of SES in both their sample size and scope. Both are also limited, however, in furthering our understanding of hypothesized language deficits associated with SES: neither study elucidates specifically what it is about poverty that drives these neurodevelopmental differences. These two studies also rely on standardized tests, which, due to the associations among SES, race, ethnicity, and native language, as we noted above, may produce artificially low scores in lower-SES children (Walton and Spencer, 2009).
    SES, language, and brain function Using task-based functional MRI (fMRI), investigators have assessed whether brain function underlies SES-related differences in language skill. FMRI permits the examination of brain activity during language tasks, which allows researchers to probe which regions of the brain are specifically recruited for particular components of language processing. In an early study, Noble et al. (2006) attempted to elucidate how brain activation differs by level of SES by controlling for children’s language skill. They recruited children who had been identified in New York Public Schools as delayed readers. Children from both high- and low-SES families in this sample tested similarly on a task meant to measure phonological awareness, a skill crucial for reading proficiency. During the task, the lower-SES children activated the left fusiform region, a region that adults and normally-reading children generally recruit for such a task. As would be expected given these past findings, left fusiform activation was related to these children’s performance on the task. The higher-SES children, however, showed a different pattern of activation: at the same levels of phonological awareness, they were less likely than were lower-SES children to activate the left fusiform region. Furthermore, the extent to which they recruited this region was not related to their phonological awareness. Thus, there was a stronger brain-behavior relation in the lower- than in the higher-SES children (Noble et al., 2006).