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Spencer, J. P.

Publications and source records attributed to Spencer, J. P..

2 recordsLinked to original sources

Language exposure and brain myelination in early development

The language environment to which children are exposed has an impact on later language and cognitive abilities as well as on brain development; however, it is unclear how early such impacts emerge. This study investigates the effects of childrens early language environment and socioeconomic status (SES) on brain structure in infancy at both 6 and 30 months of age. We used magnetic resonance imaging (MRI) to quantify concentrations of myelin in specific fiber tracts in the brain. Our central question was whether Language ENvironment Analysis (LENA) measures from in-home recording devices and SES measures of maternal education and family income predicted myelin concentrations over development. Results show relationships between amount of in-home adult input and myelination in the white matter tracts most associated with language. Right hemisphere regions also show an association with SES, with older children from a higher SES background who were exposed to more adult input showing greater concentrations of myelin in language-related areas. We discuss these results in relation with the current literature and implications for future language research and intervention. Significance StatementThis is the first study to look at how brain myelination is impacted by language input and socioeconomic status early in development. We find robust relationships of both factors in language-related brain areas at 30 months of age.

neuroscience↗

A processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy

AimWe demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space. ApproachWe synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis. ResultsThis pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data. ConclusionsThe pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field. SignificanceImage reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present a novel integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy.

neuroscience↗