Adam Richie-Halford’s research while affiliated with Stanford University and other places

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Publications (38)


Fig. 1 Rapid Online Assessment of Reading Single Word Recognition (ROAR-SWR) trial structure. Task instructions are narrated as part of a story to keep young participants engaged. After completing
Fig. 2 Characteristics of each sample. Box plots show age, response time (RT), ability (theta), and standard error of measurement for each sample. Flower names were assigned to each sample to keep the participating schools anonymous. Ability distributions differ dra-
Fig. 3 Item difficulty parameters are highly correlated across the four samples. The item difficulty parameters derived from the Calibration group exhibit strong correlations with item difficulty parameters obtained from the three school samples
Fig. 4 Item response times are highly correlated across the four samples. The mean log RT patterns in milliseconds (ms) from each group are highly correlated with the observed RT patterns from the calibration group
Fig. 5 Item difficulty parameters. The new item bank calibrated by the 1PL model with a fixed lower asymptote of 0.5 consisted of 246 items, with an equal distribution of 123 real words and 123 pseudo words

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ROAR-CAT: Rapid Online Assessment of Reading ability with Computerized Adaptive Testing
  • Article
  • Full-text available

January 2025

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31 Reads

Behavior Research Methods

Wanjing Anya Ma

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Adam Richie-Halford

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Amy K. Burkhardt

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[...]

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The Rapid Online Assessment of Reading (ROAR) is a web-based lexical decision task that measures single-word reading abilities in children and adults without a proctor. Here we study whether item response theory (IRT) and computerized adaptive testing (CAT) can be used to create a more efficient online measure of word recognition. To construct an item bank, we first analyzed data taken from four groups of students (N = 1960) who differed in age, socioeconomic status, and language-based learning disabilities. The majority of item parameters were highly consistent across groups (r = .78–.94), and six items that functioned differently across groups were removed. Next, we implemented a JavaScript CAT algorithm and conducted a validation experiment with 485 students in grades 1–8 who were randomly assigned to complete trials of all items in the item bank in either (a) a random order or (b) a CAT order. We found that, to achieve reliability of 0.9, CAT improved test efficiency by 40%: 75 CAT items produced the same standard error of measurement as 125 items in a random order. Subsequent validation in 32 public school classrooms showed that an approximately 3-min ROAR-CAT can achieve high correlations (r = .89 for first grade, r = .73 for second grade) with alternative 5–15-min individually proctored oral reading assessments. Our findings suggest that ROAR-CAT is a promising tool for efficiently and accurately measuring single-word reading ability. Furthermore, our development process serves as a model for creating adaptive online assessments that bridge research and practice. Supplementary Information The online version contains supplementary material available at 10.3758/s13428-024-02578-y.

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Development and validation of a rapid and precise online sentence reading efficiency assessment

December 2024

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38 Reads

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2 Citations

Introduction The speed at which students can accurately read and understand connected text is at the foundation of reading development. Timed reading measures go under a variety of names (e.g., reading fluency, reading efficiency, etc) and involve different levels of demands on comprehension, making it hard to interpret the extent to which scores reflect differences in reading efficiency versus comprehension. Methods Here we define a new measure of silent sentence reading efficiency (SRE) and explore key aspects of item development for an unproctored, online SRE assessment (ROAR-SRE). In doing so, we set forth an argument for developing sentences that are simple assertions, with an unambiguous answer, requiring minimal background knowledge and vocabulary. We then run a large-scale validation study to document convergent validity between ROAR-SRE and other measures of reading. Finally we validate the reliability and accuracy of using artificial intelligence (AI) to generate matched test forms. Results We find that a short, one-minute SRE assessment is highly correlated with other reading measures and has exceptional reliability. Moreover, AI can automatically generate test forms that are matched to manually-authored test forms. Discussion Together these results highlight the potential for regular screening and progress monitoring at scale with ROAR-SRE.


Figure 1 A. Average estimated tract profiles for MD in the left arcuate fasciculus generated by the GAMM for four different quartiles of reading score change (reading state). B. The estimated smoothing effect of time elapsed since the first study observation on average MD in the left arcuate. This effect was not significant (p = 0.137) C. Relationship between overall mean Woodcock-Johnson reading scores and MD in the left arcuate at each time point in the study.
Figure 4: Path diagram illustrating the mlVAR model examining the relationship between the time series of reading development and MD avg in the left arcuate. The values along each path represent the beta-weights estimated by the model. All paths were significant, with the exception of the path of previous MD avg predicting current reading skill.
Summary of parametric coefficients and smooth terms for the final GAMM modeling the development of mean diffusivity (MD) across the length of the left arcuate.
Development of the Left Arcuate Fasciculus is Linked to Learning Gains in Reading, but not Math

November 2024

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40 Reads

Past studies leveraging cross-sectional data have raised questions surrounding the relationship between diffusion properties of the white matter and academic skills. Some studies have suggested that white matter properties serve as static predictors of academic skills, whereas other studies have observed no such relationship. On the other hand, longitudinal studies have suggested that within-individual changes in the white matter are linked to learning gains over time. In the present study, we look to replicate and extend the previous longitudinal results linking longitudinal changes in the white matter properties of the left arcuate fasciculus to individual differences in reading development. To do so, we analyzed diffusion MRI data, along with reading and mathematics scores in a longitudinal sample of 340 students as they progressed from 1st grade into 4th grade. Longitudinal growth models revealed that year-to-year within-individual changes in reading scores, but not math, were related to the development of the left arcuate fasciculus. These findings provide further evidence linking the dynamics of white matter development and learning in a unique sample and highlight the importance of longitudinal designs.


Shared and Unique Influences of Phonological Processing on Reading and Math

August 2024

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35 Reads

The relationship between phonological awareness and reading development is well documented. There is less research on the relationship between phonological awareness and math. Studies have shown that phonological awareness is specifically related with arithmetic problems that employ retrieval strategies, but these studies have primarily focused on English speaking populations with relatively small sample sizes. Here, we tested a) whether the relationship between phonological awareness, reading, and math extends to transparent orthographies and b) whether phonological awareness is more strongly associated with reading or equally related to reading and math. Through the Rapid Online Assessment Dashboard (ROAD), measures of reading, arithmetic, and phonological awareness were administered in a large sample of Spanish speaking, third to sixth grade children in Colombia (n=1337). We employed correlational analysis, multiple regression analysis, and structural equation modeling. The results supported prior studies in English populations on the relationship between phonological awareness and retrieval-based arithmetic. Moreover, we found a relationship between phonological awareness and calculation ability. The results from structural equation modeling suggested that phonological awareness is related to both reading and math, showing a slightly stronger relationship with reading. This provides support for phonological awareness as a shared cognitive ability of reading and math. These findings further inform research on comorbidity, assessment, and intervention of reading and math disabilities across diverse populations.


Assessing white matter plasticity in a randomized controlled trial of early literacy training in preschoolers

August 2024

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24 Reads

Reading is a cognitive skill that requires our brain to go through a myriad of changes during learning. While many studies have described how reading acquisition shapes childrens' brain function, less is known about the impact of reading on brain structure. Here we examined short-term causal effects of reading training on preschoolers' behavior and white matter structure. Forty-eight English-speaking preschoolers (4y10m to 6y2m) participated in a randomized controlled trial where they were randomly assigned to two training programs: the Letter training program was focused on key skills for reading (e.g., decoding and letter knowledge), while the Language training program strengthened oral language comprehension skills without exposure to text. Longitudinal behavioral data showed that only the Letter Training group increased letter knowledge and decoding skills after the two-week training. Diffusion MRI measures (FA and MD) of eighteen white matter pathways (including the left arcuate and the left inferior longitudinal fasciculus) did not reveal any statistically significant changes for either group despite high degrees of scan-rescan reliability across sessions. These findings suggest that a two-week reading training program can cause changes in preschoolers' letter knowledge and decoding abilities, without being accompanied by measurable changes in the diffusion properties of the major white matter pathways of the reading network. We conclude highlighting possible constraints (i.e., age, training onset and duration, cognitive profile) to reading-related white matter plasticity.


Fig. 1 | Tract profiles in individuals with glaucoma and healthy controls. Thick lines show the mean tract profiles in the left hemisphere of all bundle and tissue property combinations. The medium-thickness lines hugging the thick lines show the 95% confidence interval. The thin lines show interquartile ranges (n = 895 in each group). Positions in OR are from anterior to posterior (A → P), in the corticospinal tract (CST) are from inferior to superior (I → S), and in the uncinate (UNC) from posterior to anterior (P → A).
Convolutional neural network-based classification of glaucoma using optic radiation tissue properties

April 2024

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150 Reads

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7 Citations

Communications Medicine

Background Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. Methods We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. Results We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. Conclusions Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.


Fig. 1. A. Correlation matrix illustrating the univariate relationships between mean FA in the left arcuate, SEDA intercept, and other demographic and socioeconomic factors. Coefficients in bold represent correlations where FDR-corrected p<0.05.B. Beta-weights for linear mixed-effects models predicting mean FA in the left and right arcuate from a single predictor, specified on the x-axis. Each model included a random effects structure of family structure nested within scanner site. The colors of each bar denote each predictor variable. Error bars represent the standard error of each beta-coefficient. Bars that are bolded illustrate the beta-weights with FDR-corrected p<0.05.
Fig. 3. Left: Growth trajectories for Diffusion Kurtosis (DKI) FA in the left and right arcuate across the first two observations of the ABCD study. The red and blue lines represent the average DKI FA growth trajectories for individuals in high (Intercept = 1) or low SEDA (Intercept = 1) intercept schools, respectively. Gray lines represent the observed changes in FA in the left and right arcuate for each individual present in the dataset. Right: Mean residual values for the model predicting Brain-Age Gap from a reduced model that excludes SEDA intercept as a predictor, but retains all other random and fixed-effects. Each bar represents either the top (red) or bottom (blue) 20% of participants based on their SEDA intercept scores. Error bars represent one standard error from the mean.
Educational Environment and White Matter Development in Early Adolescence

April 2024

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35 Reads

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3 Citations

Developmental Cognitive Neuroscience

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.


White matter and literacy: A dynamic system in flux

January 2024

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79 Reads

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10 Citations

Developmental Cognitive Neuroscience

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.


Figure 3. A, Example of a power spectrum from a 5-year-old male participant (in black) with closed eyes (EC). The corresponding FOOOF model fit is displayed in red and it corresponds to the sum of the periodic (Gaussian function included in the purple square) and the aperiodic signal (dashed line). Three different estimates are extracted from the periodic signal within the alpha frequency range: power, central frequency, and bandwidth. B, Examples of original power spectra and corresponding full FOOOF model fits (periodic + aperiodic components) in the EC condition. Data come from two representative male participants of 12 years of age with high and low FA average values (high FA = 0.55; low FA = 0.50; median FA = 0.53). C, Relationship between alpha frequency (after correcting for the aperiodic component) and the FA of the optic radiations in the EC and EO conditions in the full participant sample. Model fits of the periodic signal are shown for high and low FA participants (defined based on a median split). Beta estimates of alpha frequency were calculated based on the following LME model: alpha∼FA + age + (1|sj). Model fits of the periodic signal were derived based on the formula Gaussian = power*e[−(frequency − central frequency)2/(2*bandwidth2)]. The shaded areas represented ± 1 SE.
Figure 4. A, Three-dimensional rendering of the left optic radiation for a single representative participant (5-year-old female). The rendering was derived from the Automated Fiber Quantification software (Yeatman et al., 2012). B, Tract profiles of the optic radiations FA for each alpha frequency group (defined based on a median split) in EC and EO conditions. The plots show FA values estimated based on the beta coefficients extracted from node-by-node LME models. The optic radiations FA was modeled as a function of alpha frequency after accounting for age and site location [i.e., FA∼alpha frequency + age + (1|site)]. The red horizontal lines highlight the nodes where FDR-corrected p-values are below 0.025. The shaded areas represent ± 1 SE. C,D, GAM results for tract profile analyses. We adopted a multi-analysis approach where both LME and GAM models were applied for tract profile analyses [following the guidelines from Wagenmakers et al. (2022)]. GAM models were run on the dMRI-EEG sample (n = 585) using the tractable R package (https://github.com/yeatmanlab/tractable; Richie-Halford et al., 2023, which implements the pipeline reported in Muncy et al., 2022). Our GAM formula was FA∼age + alpha frequency + s(nodeID, by = alpha frequency, k = 64) + s(subjectID, bs = "re"). C, Similar to what was seen in the results from the node-by-node LME models, GAM model fits showed that alpha frequency had an impact on the optic radiations FA, and this effect was mainly localized in the centro-posterior part of the tract (EO, estimate = 0.021, SE = 0.002, t = 12.03; EC, estimate = 0.024, SE = 0.002, t = 13.73). D, Spline differences are shown with a 95% confidence interval.
Figure 9. A, Mediation effect of the optic radiations FA on alpha development. Schematic representation of the mediation analysis results. Standardized coefficients are reported. B, Beta coefficients of the main effects of the mediation analysis represented in A are displayed in red. Yellow bars represent ± 1SE. C, Schematic representation of the mediation analysis of alpha on visual detection development. Standardized coefficients are reported. D, Beta coefficients of the main effects of the mediation analysis of alpha on visual detection development (represented in C).
Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance

December 2023

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45 Reads

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4 Citations

The Journal of Neuroscience : The Official Journal of the Society for Neuroscience

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.


Development and validation of a rapid online sentence reading efficiency assessment

December 2023

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60 Reads

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1 Citation

The speed at which students can accurately read and understand connected text is at the foundation of reading development. Timed reading measures go under a variety of names (e.g., reading fluency, reading efficiency and comprehension, etc) and involve different levels of demands on comprehension, making it hard to interpret the extent to which scores reflect differences in reading efficiency versus comprehension. Here we define a new measure of silent sentence reading efficiency (SRE) and explore key aspects of item development for an unproctored, online SRE assessment (ROAR-SRE). In doing so, we set forth an argument for developing sentences that are simple assertions, with an unambiguous answer, requiring minimal background knowledge and vocabulary. We then run a large-scale validation study to document convergent validity between ROAR-SRE and other measures of reading. Finally we validate the reliability and accuracy of using artificial intelligence (AI) to generate matched test forms. We find that a short, one-minute SRE assessment is highly correlated with other reading measures and has exceptional reliability. Moreover, AI can automatically generate test forms that are almost perfectly matched to manually-authored test forms. Together these results highlight the potential for regular - even weekly - assessment and progress monitoring at scale with ROAR-SRE.


Citations (24)


... Such work has typically shown that log data provides a useful signal to help predict student tests scores, focusing on population measures like Pearson correlation and root mean squared error. Recently, perhaps in part motivated by the significant challenges during the covid-19 pandemic of conducting standard educational assessments when many students are remote, people have been interested in designing much shorter assessments that have similar benefits to existing, much longer assessments -for example, Tran et al. (Tran et al., 2023) developed a reading assessment that calculates each student's score at 10 seconds time intervals which was then correlated against a full 3-minute standardized test scores. In such settings, the assumption is that the assessment is being done to capture static student performance, rather than extracting signal during standard usage of a product designed to support student learning. ...

Reference:

Predicting Long-Term Student Outcomes from Short-Term EdTech Log Data
Development and validation of a rapid and precise online sentence reading efficiency assessment

... (1) individual, (2) interpersonal (family and home), (3) school, (4) community, and (5) societal (Fig. 1). The school context is particularly important for adolescents, given the time they spend in this context and, therefore, the impact it can have on developmental outcomes (Roy et al., 2024). Within each of the levels of influence, we maintain the SDOH domain categories: biological, behavioral, sociocultural, physical/built environment, and health systems (NIMHD, 2023b). ...

Educational Environment and White Matter Development in Early Adolescence

Developmental Cognitive Neuroscience

... The fundus and OCT imaging modalities provide complementary information on the retina. Early stages-based techniques utilized fundus or OCT for diagnosis of AMD based on deep learning models [8,13,18,21,22]. This work focuses on investigating the crucial feature information extracted from Fundus and OCT images, which are integrated with the proposed MCGAEc method to capture the surface and subsurface retinal information for the diagnosis of AMD and glaucoma. ...

Convolutional neural network-based classification of glaucoma using optic radiation tissue properties

Communications Medicine

... The ROI approach was useful to capture the canonical U-shape of the corpus callosum and compare it to existing literature. Lastly, diffusion tractography has been extensively used to study the relationship between white matter development and cognitive functions (Roy et al., 2024;Wang et al., 2017;Yeatman, Dougherty, Ben-Shachar, et al., 2012). We show that using a standard automated pipeline for tractography, the reliability of MRF-derived tract profiles is remarkably high across the tracts examined, and in some cases exceeded the reliability of diffusion-based metrics. ...

White matter and literacy: A dynamic system in flux

Developmental Cognitive Neuroscience

... Cortical locations that exhibited an alpha center frequency deviating from the subject's predicted intrinsic alpha center frequency for that location were stimulated at the projected intrinsic frequency. These person-and region-specific intrinsic alpha center frequencies were as an initial approximation derived from occipital electrode spectral EEG records, since alpha generators occur in the thalamus and the visual cortex, which is occipitally located [63,64]. ...

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

... The silent sentence reading efficiency task consists of presenting 140 sentences that are true or false, requiring minimal background knowledge and using simple vocabulary and syntactic structures as in Yeatman et al. (2023). The sentences are presented in a randomized order and consist of silent reading and the indication of whether they are coherent sentences or absurd sentences (the True/False endorsement). ...

Development and validation of a rapid online sentence reading efficiency assessment
  • Citing Preprint
  • December 2023

... These disruptions in long-range and intrahemispheric connectivity seem to be candidate pathways that increase risk for long-term cognitive deficits and IDD risk that emerges in later childhood. White matter injury related to intraventricular hemorrhage is also common in high-risk preterm babies and can manifest as diminished inter and intrahemispheric connectivity on functional levels (Grotheer et al., 2023;Omidvarnia et al., 2015;Smyser et al., 2013). ...

Human white matter myelinates faster in utero than ex utero

Proceedings of the National Academy of Sciences

... Arguably the most easily actionable is for publications to include rich and detailed specifications of all data-processing software (for example, tool versions, parameters, templates, in-house code) to promote reproducibility, ideally with all code being made available through a public repository (for example, GitHub or Zenodo). This transparency should similarly be applied to quality control practices, where automated tools and quantitative cut-offs for quality control will be increasingly essential as sample sizes continue to grow 56 . Beyond this, there is a need for the field to increase its focus on testing of tools, and on benchmarking of new pipelines against one or more reference pipelines (for example, fMRIPrep-LTS and HCP Pipelines). ...

Author Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research

Scientific Data

... Dean et al., 2014;Deoni et al., 2011;Melbourne et al., 2013) and calculation of T1w/T2w ratio (e.g. Filimonova et al., 2023;Grotheer et al., 2023;Lee et al., 2015;Soun et al., 2017). However, T1 and T2 relaxation are partly determined by iron concentration ( Birkl et al., 2019;Stüber et al., 2014), and T1w/T2w ratio correlations with other myelin-sensitive MRI parameters and histological myelin measurements are low ( Arshad et al., 2017;Sandrone et al., 2023;Uddin et al., 2018). ...

Human white matter myelination rate slows down at birth

... One avenue for such improvements is afforded by harnessing new statistical and machine learning methods with high discriminative accuracy for individual-level prediction. [247][248][249] One challenge of developing accurate machine learning models is that their development often requires very large samples. 249 Fortunately, measurements of very large samples across the human life span are increasingly available through projects such as the Human Connectome Project, 250 256 and the Brain/MINDS beyond human brain MRI project. ...

Incremental improvements in tractometry-based brain-age modeling with deep learning