Beyond the 30-Million-Word Gap: Children’s Conversational Exposure Is Associated With Language-Related Brain Function – ScienceOpen
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      Beyond the 30-Million-Word Gap: Children’s Conversational Exposure Is Associated With Language-Related Brain Function

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          Abstract

          Children’s early language exposure impacts their later linguistic skills, cognitive abilities, and academic achievement, and large disparities in language exposure are associated with family socioeconomic status (SES). However, there is little evidence about the neural mechanisms underlying the relation between language experience and linguistic and cognitive development. Here, language experience was measured from home audio recordings of 36 SES-diverse 4- to 6-year-old children. During a story-listening functional MRI task, children who had experienced more conversational turns with adults—independently of SES, IQ, and adult-child utterances alone—exhibited greater left inferior frontal (Broca’s area) activation, which significantly explained the relation between children’s language exposure and verbal skill. This is the first evidence directly relating children’s language environments with neural language processing, specifying both an environmental and a neural mechanism underlying SES disparities in children’s language skills. Furthermore, results suggest that conversational experience impacts neural language processing over and above SES or the sheer quantity of words heard.

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          Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

          Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.
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            Talking to children matters: early language experience strengthens processing and builds vocabulary.

            Infants differ substantially in their rates of language growth, and slow growth predicts later academic difficulties. In this study, we explored how the amount of speech directed to infants in Spanish-speaking families low in socioeconomic status influenced the development of children's skill in real-time language processing and vocabulary learning. All-day recordings of parent-infant interactions at home revealed striking variability among families in how much speech caregivers addressed to their child. Infants who experienced more child-directed speech became more efficient in processing familiar words in real time and had larger expressive vocabularies by the age of 24 months, although speech simply overheard by the child was unrelated to vocabulary outcomes. Mediation analyses showed that the effect of child-directed speech on expressive vocabulary was explained by infants' language-processing efficiency, which suggests that richer language experience strengthens processing skills that facilitate language growth.
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              The Neuroscience of Socioeconomic Status: Correlates, Causes, and Consequences

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                Author and article information

                Journal
                Psychol Sci
                Psychol Sci
                PSS
                sppss
                Psychological Science
                SAGE Publications (Sage CA: Los Angeles, CA )
                0956-7976
                1467-9280
                14 February 2018
                May 2018
                14 February 2019
                : 29
                : 5
                : 700-710
                Affiliations
                [1 ]Division of Medical Sciences, Harvard University
                [2 ]McGovern Institute for Brain Research, Massachusetts Institute of Technology
                [3 ]Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
                [4 ]Harvard Graduate School of Education, Harvard University
                [5 ]Department of Psychology, University of Pennsylvania
                Author notes
                [*]Rachel R. Romeo, Massachusetts Institute of Technology, Office 46-4037, 43 Vassar St., Cambridge, MA 02139 E-mail: rromeo@ 123456mit.edu
                Author information
                https://orcid.org/0000-0002-0315-4385
                Article
                PMC5945324 PMC5945324 5945324 10.1177_0956797617742725
                10.1177/0956797617742725
                5945324
                29442613
                8e31c0d2-effd-46e3-8b7f-82ccb0269040
                © The Author(s) 2018
                History
                : 6 July 2017
                : 24 October 2017
                Funding
                Funded by: Walton Family Foundation, FundRef https://doi.org/10.13039/100010536;
                Award ID: 2014-1057
                Funded by: Eunice Kennedy Shriver National Institute of Child Health and Human Development, FundRef https://doi.org/10.13039/100009633;
                Award ID: F31HD086957
                Categories
                Research Articles
                Custom metadata
                open-data
                open-materials

                open materials,open data,turn taking,LENA,fMRI,socioeconomic status,language

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