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Bloom, D.

Publications and source records attributed to Bloom, D..

3 recordsLinked to original sources

White matter and literacy: a dynamic system in flux

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 individuals 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 reading skills over development 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 single-observation 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.

neuroscience↗

Development of the visual white matter pathways mediates development of electrophysiological responses in visual cortex

The latency of neural responses in the visual cortex changes systematically across the lifespan. Here we test the hypothesis that development of visual white matter pathways mediates maturational changes in the latency of visual signals. Thirty-eight children participated in a cross-sectional study including diffusion MRI and MEG sessions. During the MEG acquisition, participants performed a lexical decision and a fixation task on words presented at varying levels of contrast and noise. For all stimuli and tasks, early evoked fields were observed around 100 ms after stimulus onset (M100), with slower and lower amplitude responses for low as compared to high contrast stimuli. The optic radiations and optic tracts were identified in each individuals brain based on diffusion MRI tractography. The diffusion properties of the optic radiations predicted M100 responses, especially for high contrast stimuli. Higher optic radiation fractional anisotropy (FA) values were associated with faster and larger M100 responses. Over this developmental window, the M100 responses to high contrast stimuli became faster with age and the optic radiation FA mediated this effect. These findings suggest that the maturation of the optic radiations over childhood accounts for individual variations observed in the developmental trajectory of visual cortex responses.

neuroscience↗

Evaluating the reliability of human brain white matter tractometry

The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on reliability of the analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to demonstrate the reliability of computational analysis pipelines in neuroimaging.

neuroscience↗