RESUMO
Supplemental material is available for this article.
Assuntos
Neoplasias Encefálicas , Glioma , Imageamento por Ressonância Magnética , Humanos , Glioma/diagnóstico por imagem , Glioma/patologia , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/patologia , Feminino , Masculino , Pessoa de Meia-Idade , Adulto , Estudos Longitudinais , São Francisco , IdosoRESUMO
Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.
Assuntos
Escolaridade , Substância Branca , Humanos , Substância Branca/diagnóstico por imagem , Substância Branca/crescimento & desenvolvimento , Masculino , Feminino , Criança , Fatores Socioeconômicos , Encéfalo/crescimento & desenvolvimento , Encéfalo/diagnóstico por imagem , Pré-Escolar , Imagem de Tensor de DifusãoRESUMO
Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.
Assuntos
Imageamento por Ressonância Magnética , Neuroimagem , Humanos , Recém-Nascido , Encéfalo/diagnóstico por imagem , Cabeça , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Neuroimagem/métodos , Crânio , Estudos Multicêntricos como AssuntoRESUMO
Supplemental material is available for this article.
Assuntos
Neoplasias Encefálicas , Radiocirurgia , Humanos , São Francisco , Neoplasias Encefálicas/secundário , Imageamento por Ressonância MagnéticaRESUMO
Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.