The application of neuroimaging to social inequity and language disparity: A cautionary examination.
The application of neuroimaging to social inequity and language disparity: A cautionary examination.
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DOI:
10.1016/j.dcn.2016.10.001
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发表时间:
2016-12
影响因子:
4.7
通讯作者:
Gotlib, Ian H.
中科院分区:
文献类型:
--
作者:
Ellwood-Lowe, Monica E.;Sacchet, Matthew D.;Gotlib, Ian H.
Researchers rely on reverse inference to interpret SES-related brain data. Neural differences related to SES are posited to be indicative of language delays. SES is confounded with cultural language use, bilingualism, and first-language. Cognitive tests may under-predict abilities of some children. Future neuroimaging studies must examine aspects of children’s language directly. In the nascent field of the cognitive neuroscience of socioeconomic status (SES), researchers are using neuroimaging to examine how growing up in poverty affects children’s neurocognitive development, particularly their language abilities. In this review we highlight difficulties inherent in the frequent use of reverse inference to interpret SES-related abnormalities in brain regions that support language. While there is growing evidence suggesting that SES moderates children’s developing brain structure and function, no studies to date have elucidated explicitly how these neural findings are related to variations in children’s language abilities, or precisely what it is about SES that underlies or contributes to these differences. This issue is complicated by the fact that SES is confounded with such linguistic factors as cultural language use, first language, and bilingualism. Thus, SES-associated differences in brain regions that support language may not necessarily indicate differences in neurocognitive abilities. In this review we consider the multidimensionality of SES, discuss studies that have found SES-related differences in structure and function in brain regions that support language, and suggest future directions for studies in the area of cognitive neuroscience of SES that are less reliant on reverse inference.
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