Ethan Roy

All Publications

• Differences in educational opportunity predict white matter development. Developmental cognitive neuroscience Roy, E., Van Rinsveld, A., Nedelec, P., Richie-Halford, A., Rauschecker, A. M., Sugrue, L. P., Rokem, A., McCandliss, B. D., Yeatman, J. D. 2024; 67: 101386

  Abstract

  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.

  View details for DOI 10.1016/j.dcn.2024.101386

  View details for PubMedID 38676989

• White matter and literacy: A dynamic system in flux. Developmental cognitive neuroscience Roy, E., Richie-Halford, A., Kruper, J., Narayan, M., Bloom, D., Nedelec, P., Rauschecker, A. M., Sugrue, L. P., Brown, T. T., Jernigan, T. L., McCandliss, B. D., Rokem, A., Yeatman, J. D. 2024; 65: 101341

  Abstract

  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.

  View details for DOI 10.1016/j.dcn.2024.101341

  View details for PubMedID 38219709

• Development of the alpha rhythm is linked to visual white matter pathways and visual detection performance. The Journal of neuroscience : the official journal of the Society for Neuroscience Caffarra, S., Kanopka, K., Kruper, J., Richie-Halford, A., Roy, E., Rokem, A., Yeatman, J. D. 2023

  Abstract

  Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase of alpha frequency over childhood and adulthood. Here we tested the hypothesis that these changes of alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large dMRI-EEG dataset (dMRI n=2,747, EEG n=2,561) of children and adolescents of either sex (age range: 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.Significance statement The present work shows that the maturation of visual white matter pathways (optic radiations) specifically accounts for the developmental increase of brain oscillations frequency (alpha), which is ultimately related to an enhancement of visual perception during childhood and adolescence. The present findings are an example of how relating white matter properties to functional aspects of the brain can help us reach a more complete understanding of the link between development of brain connectivity, changes in electrophysiology, and visual perception.

  View details for DOI 10.1523/JNEUROSCI.0684-23.2023

  View details for PubMedID 38124006

• Groupitizing reflects conceptual developments in math cognition and inequities in math achievement from childhood through adolescence. Child development Guillaume, M., Roy, E., Van Rinsveld, A., Starkey, G. S., Uncapher, M. R., McCandliss, B. D. 2022

  Abstract

  Understanding the cognitive processes central to mathematical development is crucial to addressing systemic inequities in math achievement. We investigate the "Groupitizing" ability in 1209 third to eighth graders (mean age at first timepoint = 10.48, 586 girls, 39.16% Asian, 28.88% Hispanic/Latino, 18.51% White), a process that captures the ability to use grouping cues to access the exact value of a set. Groupitizing improves each year from late childhood to early adolescence (d = 3.29), is a central predictor of math achievement (beta weight = .30), is linked to conceptual processes in mathematics (minimum d = 0.69), and helps explain the dynamic between the ongoing development of non-symbolic number concepts, systemic educational inequities in school associated with SES, and mathematics achievement (minimum beta weight = .11) in ways that explicit symbolic measures may miss.

  View details for DOI 10.1111/cdev.13859

  View details for PubMedID 36484357

• An analysis-ready and quality controlled resource for pediatric brain white-matter research. Scientific data Richie-Halford, A., Cieslak, M., Ai, L., Caffarra, S., Covitz, S., Franco, A. R., Karipidis, I. I., Kruper, J., Milham, M., Avelar-Pereira, B., Roy, E., Sydnor, V. J., Yeatman, J. D., Fibr Community Science Consortium, Satterthwaite, T. D., Rokem, A., Abbott, N. J., Anderson, J. A., Gagana, B., Bleile, M., Bloomfield, P. S., Bottom, V., Bourque, J., Boyle, R., Brynildsen, J. K., Calarco, N., Castrellon, J. J., Chaku, N., Chen, B., Chopra, S., Coffey, E. B., Colenbier, N., Cox, D. J., Crippen, J. E., Crouse, J. J., David, S., Leener, B. D., Delap, G., Deng, Z., Dugre, J. R., Eklund, A., Ellis, K., Ered, A., Farmer, H., Faskowitz, J., Finch, J. E., Flandin, G., Flounders, M. W., Fonville, L., Frandsen, S. B., Garic, D., Garrido-Vasquez, P., Gonzalez-Escamilla, G., Grogans, S. E., Grotheer, M., Gruskin, D. C., Guberman, G. I., Haggerty, E. B., Hahn, Y., Hall, E. H., Hanson, J. L., Harel, Y., Vieira, B. H., Hettwer, M. D., Hobday, H., Horien, C., Huang, F., Huque, Z. M., James, A. R., Kahhale, I., Kamhout, S. L., Keller, A. S., Khera, H. S., Kiar, G., Kirk, P. A., Kohl, S. H., Korenic, S. A., Korponay, C., Kozlowski, A. K., Kraljevic, N., Lazari, A., Leavitt, M. J., Li, Z., Liberati, G., Lorenc, E. S., Lossin, A. J., Lotter, L. D., Lydon-Staley, D. M., Madan, C. R., Magielse, N., Marusak, H. A., Mayor, J., McGowan, A. L., Mehta, K. P., Meisler, S. L., Michael, C., Mitchell, M. E., Morand-Beaulieu, S., Newman, B. T., Nielsen, J. A., O'Mara, S. M., Ojha, A., Omary, A., Ozarslan, E., Parkes, L., Peterson, M., Pines, A. R., Pisanu, C., Rich, R. R., Sahoo, A. K., Samara, A., Sayed, F., Schneider, J. T., Shaffer, L. S., Shatalina, E., Sims, S. A., Sinclair, S., Song, J. W., Hogrogian, G. S., Tooley, U. A., Tripathi, V., Turker, H. B., Valk, S. L., Wall, M. B., Walther, C. K., Wang, Y., Wegmann, B., Welton, T., Wiesman, A. I., Wiesman, A. G., Wiesman, M., Winters, D. E., Yuan, R., Zacharek, S. J., Zajner, C., Zakharov, I., Zammarchi, G., Zhou, D., Zimmerman, B., Zoner, K. 2022; 9 (1): 616

  Abstract

  We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N=2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC=0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

  View details for DOI 10.1038/s41597-022-01695-7

  View details for PubMedID 36224186

• Evaluating the Reliability of Human Brain White Matter Tractometry. Aperture neuro Kruper, J., Yeatman, J. D., Richie-Halford, A., Bloom, D., Grotheer, M., Caffarra, S., Kiar, G., Karipidis, I. I., Roy, E., Chandio, B. Q., Garyfallidis, E., Rokem, A. 1800; 1 (1)

  Abstract

  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 the reliability of 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 establish the reliability of computational analysis pipelines in neuroimaging.

  View details for DOI 10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669

  View details for PubMedID 35079748

• Testosterone and Adult Neurogenesis. Biomolecules Spritzer, M. D., Roy, E. A. 2020; 10 (2)

  Abstract

  It is now well established that neurogenesis occurs throughout adulthood in select brain regions, but the functional significance of adult neurogenesis remains unclear. There is considerable evidence that steroid hormones modulate various stages of adult neurogenesis, and this review provides a focused summary of the effects of testosterone on adult neurogenesis. Initial evidence came from field studies with birds and wild rodent populations. Subsequent experiments with laboratory rodents have tested the effects of testosterone and its steroid metabolites upon adult neurogenesis, as well as the functional consequences of induced changes in neurogenesis. These experiments have provided clear evidence that testosterone increases adult neurogenesis within the dentate gyrus region of the hippocampus through an androgen-dependent pathway. Most evidence indicates that androgens selectively enhance the survival of newly generated neurons, while having little effect on cell proliferation. Whether this is a result of androgens acting directly on receptors of new neurons remains unclear, and indirect routes involving brain-derived neurotrophic factor (BDNF) and glucocorticoids may be involved. In vitro experiments suggest that testosterone has broad-ranging neuroprotective effects, which will be briefly reviewed. A better understanding of the effects of testosterone upon adult neurogenesis could shed light on neurological diseases that show sex differences.

  View details for DOI 10.3390/biom10020225

  View details for PubMedID 32028656