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ObjectiveReading difficulties frequently co-occur with other neurodevelopmental/behavioral conditions. It is difficult to assess reading routinely in Developmental-Behavioral Pediatrics (DBP) clinical practice due to time/resource constraints. Rapid Online Assessment of Reading (ROAR) is a gamified assessment that children take in a web-browser without adult supervision. This study’s purpose was to evaluate ROAR as a screening tool for reading difficulties in a DBP clinic.MethodPatients, ages 6-14 years, attending a DBP clinic, were invited to participate. Children took ROAR and completed the Woodcock-Johnson Letter-Word Identification (LWID) and Word Attack (WA). Basic Reading Skills (BRS), a standardized aggregate score of LWID and WA, was used as the gold standard assessment. The strength of association between age-adjusted standard score on ROAR and BRS was calculated. BRS scores < 90 (bottom quartile of sample) were deemed poor reader. Receiver Operating Characteristic (ROC) curve analysis was used to assess the quality of ROAR as a screening test.ResultsA total of 41 children, 78% boys, mean age 9.5 years (SD 2.0 years), completed the study. The correlation of ROAR standard score with BRS was r = 0.66, pConclusionROAR is a useful screening tool for children to take before attending a DBP clinic to identify children at high risk for reading difficulties. Assessment of the tool during a busy clinic was challenging and a larger replication is warranted.

Multivariate measurements of human brain white matter (WM) with diffusion MRI (dMRI) provide information about the role of WM in a variety of cognitive functions and in brain health. Statistical models take advantage of the regularities in these data to make inferences about individual differences. For example, dMRI data provide the basis for accurate brain-age models – models that predict the chronological age of participants from WM tissue properties. Deep learning (DL) models are powerful machine learning models, which have been shown to provide benefits in many multivariate analysis settings. We investigated whether DL would provide substantial improvements for brain-age models based on dMRI measurements of WM in a large sample of children and adolescents. We found that some DL models fit the data better than a linear baseline, but the differences are small. In particular, recurrent neural network architectures provide up to ∼6% improvement in accuracy. This suggests that information about WM development is mostly accessible with linear models, and does not require the additional invariance and non-linearity offered by DL models. However, in some applications this incremental improvement may prove critical. We provide open-source software that fits DL models to dMRI data (https://yeatmanlab.github.io/AFQ-Insight).

The formation of myelin, the fatty sheath that insulates nerve fibers, is critical for healthy brain function. A fundamental open question is what is the impact of being born on myelin growth. To address this question, we evaluated a large (n=300) cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP). First, we developed new software for the automated identification of 20 white matter bundles in individuals that is well-suited for large samples. Next, we fit linear models that quantify T1w/T2w, a myelin-sensitive imaging contrast, increases along bundles. We found faster growth of T1w/T2w along the lengths of all bundles before birth than right after birth. Further, in a separate longitudinal sample of preterm infants (N=34), we found lower T1w/T2w at term-equivalent age than in full-term peers. By applying the linear models fit on the cross-section sample to the longitudinal sample of preterm infants, we find that their delay in T1w/T2w growth is well explained by the amount of time preterm infants spend developing in utero and ex utero. These results suggest that being born slows the rate of myelin growths. This reduction in the rate of myelin growth at birth, in turn, explains lower myelin content in individuals born preterm, and could account for long-term cognitive, neurological, and developmental consequences of preterm birth. We hypothesize that closely matching the environment of infants born preterm to what they would have experienced in the womb may reduce delays in myelin growth and hence improve developmental outcomes.

Phonological awareness (PA) is at the foundation of reading development: PA is introduced before formal reading instruction, has predictive value for later reading abilities, is a primary target for early intervention, and is considered one of the core mechanisms in developmental dyslexia. Conventional approaches to assessing PA are time-consuming and resource intensive: assessments must be individually administered, require expertise, and scoring verbal responses is challenging and subjective. Therefore, we introduce a rapid, automated, online measure of PA — The Rapid Online Assessment of Reading - Phonological Awareness (ROAR-PA) — that can be widely implemented in classrooms and research studies without a test administrator. We explored whether this gamified, online task, that relies on touchscreen/click responses, can serve as an accurate and reliable measure of PA and as a good predictor of reading development. We found that ROAR-PA is well correlated with standardized measures of PA (CTOPP-2, r = .80) and reading (Woodcock-Johnson, r = .50), for children from Pre-K through fourth grade and achieves exceptional reliability (𝜶 = .96) in a 12-minute automated, online assessment. Furthermore, validation in 50 first and second grade classrooms shows reliable implementation in large, public school classrooms with predictive value of future reading development.

Changes in sensory input with aging and disease affect brain tissue properties. To establish the link between glaucoma, the most prevalent cause of irreversible blindness, and changes in major brain connections, we characterized white matter tissue properties in diffusion MRI measurements in a large sample of subjects with glaucoma (N=905; age 49-80) and healthy controls (N=5,292; age 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. A convolutional neural network (CNN) accurately classified whether a subject has glaucoma using information from the primary visual connection to cortex (the optic radiations, OR), but not from non-visual brain connections. On the other hand, regularized linear regression could not classify glaucoma, and the CNN did not generalize to classification of age-group or of age-related macular degeneration. This suggests a unique non-linear signature of glaucoma in OR tissue properties.

Purpose: It is generally accepted that adults use visual cues to improve speech intelligibility in noisy environments, but findings regarding visual speech benefit in children are mixed. We explored factors that contribute to audiovisual (AV) gain in young children's speech understanding. We examined whether there is an AV benefit to speech-in-noise recognition in children in first grade and if visual salience of phonemes influences their AV benefit. We explored if individual differences in AV speech enhancement could be explained by vocabulary knowledge, phonological awareness, or general psychophysical testing performance. Method: Thirty-seven first graders completed online psychophysical experiments. We used an online single-interval, four-alternative forced-choice picture-pointing task with age-appropriate consonant–vowel–consonant words to measure auditory-only, visual-only, and AV word recognition in noise at −2 and −8 dB SNR. We obtained standard measures of vocabulary and phonological awareness and included a general psychophysical test to examine correlations with AV benefits. Results: We observed a significant overall AV gain among children in first grade. This effect was mainly attributed to the benefit at −8 dB SNR, for visually distinct targets. Individual differences were not explained by any of the child variables. Boys showed lower auditory-only performances, leading to significantly larger AV gains. Conclusions: This study shows AV benefit, of distinctive visual cues, to word recognition in challenging noisy conditions in first graders. The cognitive and linguistic constraints of the task may have minimized the impact of individual differences of vocabulary and phonological awareness on AV benefit. The gender difference should be studied on a larger sample and age range.

The scientific study of reading has a rich history that spans disciplines from vision science to linguistics, psychology, cognitive neuroscience, neurology, and education. The study of reading can elucidate important general mechanisms in spatial vision, attentional control, object recognition, and perceptual learning, as well as the principles of plasticity and cortical topography. However, literacy also prompts the development of specific neural circuits to process a unique and artificial stimulus. In this review, we describe the sequence of operations that transforms visual features into language, how the key neural circuits are sculpted by experience during development, and what goes awry in children for whom learning to read is a struggle.

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 magnetic resonance imaging (MRI) and magnetoencephalography (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 individual's 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., This study elucidates the relationship between structural and functional properties of the visual pathways and how they change during childhood. Developmental changes of visual white matter pathways account for latency variations in electrophysiological responses of visual cortex. 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 and changes in electrophysiology.

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images. QSIPrep is a software platform for processing of most diffusion MRI datasets and ensures that adequate workflows are used.

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, strong 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 (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.

Brain development and aging are complex processes that unfold in multiple brain regions simultaneously. Recently, models of brain age prediction have aroused great interest, as these models can potentially help to understand neurological diseases and elucidate basic neurobiological mechanisms. We test whether quantitative magnetic resonance imaging can contribute to such age prediction models. Using R1, the longitudinal rate of relaxation, we explore lifespan dynamics in cortical gray matter. We compare R1 with cortical thickness, a well-established biomarker of brain development and aging. Using 160 healthy individuals (6–81 years old), we found that R1 and cortical thickness predicted age similarly, but the regions contributing to the prediction differed. Next, we characterized R1 development and aging dynamics. Compared with anterior regions, in posterior regions we found an earlier R1 peak but a steeper postpeak decline. We replicate these findings: firstly, we tested a subset (N = 10) of the original dataset for whom we had additional scans at a lower resolution; and second, we verified the results on an independent dataset (N = 34). Finally, we compared the age prediction models on a subset of 10 patients with multiple sclerosis. The patients are predicted older than their chronological age using R1 but not with cortical thickness.

Research shows that, on average, children with dyslexia behave less categorically in phoneme categorization tasks. This study investigates three subtle ways that struggling readers may perform differently than their typically developing peers in this experimental context: sensitivity to the frequency distribution from which speech tokens are drawn, bias induced by previous stimulus presentations, and fatigue during the course of the task. We replicate findings that reading skill is related to categorical labeling, but we do not find evidence that sensitivity to the stimulus frequency distribution, the influence of previous stimulus presentations, and a measure of task engagement differs in children with dyslexia. It is, therefore, unlikely that the reliable relationship between reading skill and categorical labeling is attributable to artifacts of the task design, abnormal neural encoding, or executive function. Rather, categorical labeling may index a general feature of linguistic development whose causal relationship to literacy remains to be ascertained.

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 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.

Education sculpts specialized neural circuits for skills like reading that are critical to success in modern society but were not anticipated by the selective pressures of evolution. Does the emergence of brain regions that selectively process novel visual stimuli like words occur at the expense of cortical representations of other stimuli like faces and objects? To answer this question we conducted a randomized controlled trial with pre-school children (five years of age). We found that being taught reading versus oral language skills induced different patterns of change in category-selective regions of visual cortex. Reading instruction enhanced the response to text but did not diminish the response to other categories. How these changes play out over a longer timescale is still unknown but, based on these data, we can surmise that high-level visual cortex undergoes rapid changes as children enter school and begin establishing new skills like literacy.

SUMMARYA person’s cognitive state determines how their brain responds to visual stimuli. The most common such effect is a response enhancement when stimuli are task-relevant and attended rather than ignored. In this fMRI study, we report a surprising twist on such attention effects in the visual word form area (VWFA), a region that plays a key role in reading. We presented participants with strings of letters and visually similar shapes which were either relevant for a specific task (lexical decision or gap localization) or ignored (during a fixation dot color task). In the VWFA, the enhancement of responses to attended stimuli occurred only for letter strings, whereas the shapes evokedsmallerresponses when attended than when ignored. The enhancement of VWFA activity was accompanied by strengthened functional connectivity with higher-level language regions. These task-dependent modulations of response magnitude and functional connectivity were specific to the VWFA and absent in the rest of visual cortex. We suggest that language regions send targeted excitatory feedback into the VWFA only when the observer is trying to read. This feedback enables the discrimination of familiar and nonsense words, and is distinct from generic effects of visual attention.

Diffusion MRI is a powerful tool for imaging brain structure, but it is challenging to discern the biological underpinnings of plasticity inferred from these and other non-invasive MR measurements. Biophysical modeling of the diffusion signal aims to render a more biologically rich image of tissue microstructure, but the application of these models comes with important caveats. A separate approach for gaining biological specificity has been to seek converging evidence from multi-modal datasets. Here we use metrics derived from diffusion kurtosis imaging (DKI) and the white matter tract integrity (WMTI) model along with quantitative MRI measurements of T1 relaxation to characterize changes throughout the white matter during an 8-week, intensive reading intervention (160 total hours of instruction). Behavioral measures, multi-shell diffusion MRI data, and quantitative T1 data were collected at regular intervals during the intervention in a group of 33 children with reading difficulties (7-12 years old), and over the same period in an age-matched non-intervention control group. Throughout the white matter, mean ‘extra-axonal’ diffusivity was inversely related to intervention time. In contrast, model estimated axonal water fraction (AWF), overall diffusion kurtosis, and T1 relaxation time showed no significant change over the intervention period. Both diffusion and quantitative T1 based metrics were correlated with pre-intervention reading performance, albeit with distinct anatomical distributions. These results are consistent with the view that rapid changes in diffusion properties reflect phenomena other than widespread changes in myelin density. We discuss this result in light of recent work highlighting non-axonal factors in experience-dependent plasticity and learning.HighlightsDiffusion MRI measurements in white matter show changes linked to an educational intervention.Tissue modeling results point to changes within the extra-axonal space.Complementary MRI measurements fail to suggest a widespread change in white matter in myelination over the intervention period.Both diffusion and quantitative T1 measures correlate with pre-intervention reading skill.

Published Nov 17, 2021 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. This work was supported through grant 1RF1MH121868- 01 from the National Institute of Mental Health/the BRAIN Initiative, through grant 5R01EB027585-02 to Eleftherios Garyfallidis (Indiana University) from the National Institute of Biomedical Imaging and Bioengineering, through Azure Cloud Computing Credits for Research & Teaching provided through the University of Washington’s Research Computing unit and the University of Washington eScience Institute, and NICHD R21HD092771 to Jason D. Yeatman

Literacy is an essential skill. Learning to read is a requirement for becoming a self-providing human being. However, while spoken language is acquired naturally with exposure to language without explicit instruction, reading and writing need to be taught explicitly. Decades of research have shown that well-structured teaching of phonological awareness, letter knowledge, and letter-to-sound mapping is crucial in building solid foundations for the acquisition of reading. During the COVID-19 pandemic, children worldwide did not have access to consistent and structured teaching and are, as a consequence, predicted to be behind in the development of their reading skills. Subsequent evidence confirms this prediction. With the best evidence-based practice in mind, we developed an online version of a well-structured early literacy training program (Reading Camp) for 5-year-old children. This 2-week online Reading Camp program is designed for pre-K children. It incorporates critical components of the fundamental skills essential to learning to read and is taught online in an interactive, multi-sensory, and peer-learning environment. We measure the participants’ literacy skills and other related skills before and after participating in the online Reading Camp and compare the results to no-treatment controls. Results show that children who participated in the online Reading Camp improved significantly on all parameters in relation to controls. Our results demonstrate that a well-structured evidence-based reading instruction program, even if online and short-term, benefits 5-year-old children in learning to read. With the potential to scale up this online program, the evidence presented here, alongside previous evidence for the efficacy of the in-person program, indicates that the online Reading Camp program is effective and can be used to tackle a variety of questions regarding structural and functional plasticity in the early stages of reading acquisition.

While online experiments have shown tremendous potential to study larger and more diverse participant samples than is possible in the lab, the uncontrolled online environment has prohibited many types of psychophysical studies due to difficulties controlling the viewing distance and stimulus size. We introduce the Virtual Chinrest, a method that measures a participant’s viewing distance in the web browser by detecting a participant’s blind spot location. This makes it possible to automatically adjust stimulus configurations based on an individual’s viewing distance. We validated the Virtual Chinrest in two laboratory studies in which we varied the viewing distance and display size, showing that our method estimates participants’ viewing distance with an average error of 3.25 cm. We additionally show that by using the Virtual Chinrest we can reliably replicate measures of visual crowding, which depends on a precise calculation of visual angle, in an uncontrolled online environment. An online experiment with 1153 participants further replicated the findings of prior laboratory work, demonstrating how visual crowding increases with eccentricity and extending this finding by showing that young children, older adults and people with dyslexia all exhibit increased visual crowding, compared to adults without dyslexia. Our method provides a promising pathway to web-based psychophysical research requiring controlled stimulus geometry.

It is established that individuals with dyslexia are less consistent at auditory phoneme categorization than typical readers. One hypothesis attributes these differences in phoneme labeling to differences in auditory cue integration over time, suggesting that the performance of individuals with dyslexia would improve with longer exposure to informative phonetic cues. Here, the relationship between phoneme labeling and reading ability was investigated while manipulating the duration of steady-state auditory information available in a consonant-vowel syllable. Children with dyslexia obtained no more benefit from longer cues than did children with typical reading skills, suggesting that poor task performance is not explained by deficits in temporal integration or temporal sampling.

Education has faced unprecedented disruption during the COVID pandemic. Understanding how students have adapted as we have entered a different phase of the pandemic and some communities have returned to more typical schooling will inform a suite of policy interventions and subsequent research. We use data from an oral reading fluency (ORF) assessment—a rapid assessment taking only a few minutes that measures a fundamental reading skill—to examine COVID’s effects on children’s reading ability during the pandemic. We find that students in the first 200 days of the 2020–2021 school year tended to experience slower growth in ORF relative to prepandemic years. We also observe slower growth in districts with a high percentage of English language learners and/or students eligible for free and reduced-price lunch. These findings offer valuable insight into the effects of COVID on one of the most fundamental skills taught to children.

Reading requires the correct identification of letters and letter positions within words. Selective attention is, therefore, required to select chunks of the text for sequential processing. Despite the extensive literature on visual attention, the well-known effects of spatial cues in simple perceptual tasks cannot inform us about the role of attention in a task as complex as reading. Here, we systematically manipulate spatial attention in a multi-letter processing task to understand the effects of spatial cues on letter encoding in typical adults. Overall, endogenous (voluntary) cue benefits were larger than exogenous (reflexive). We show that cue benefits are greater in the left than in the right visual field and larger for the most crowded letter positions. Endogenous valid cues reduced errors due to confusing letter positions more than misidentifications, specifically for the most crowded letter positions. Therefore, shifting endogenous attention along a line of text is likely an important mechanism to alleviate the effects of crowding on encoding letters within words. Our results help set the premise for constructing theories about how specific mechanisms of attention support reading development in children. Understanding the link between reading development and attention mechanisms has far-reaching implications for effectively addressing the needs of children with reading disabilities.

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project - a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and grey matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference mplementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and cognitive neuroscience research. It will ease the translation of DKI advantages into clinical applications.

Over the past two decades, researchers have tried to uncover how the human brain can extract linguistic information from a sequence of visual symbols. The description of how the brain’s visual system processes words and enables reading has improved with the progressive refinement of experimental methodologies and neuroimaging techniques. This review provides a brief overview of this research journey. We start by describing classical models of object recognition in non-human primates, which represent the foundation for many of the early models of visual word recognition in humans. We then review functional neuroimaging studies investigating the word-selective regions in visual cortex. This research led to the differentiation of highly specialized areas, which are involved in the analysis of different aspects of written language. We then consider the corresponding anatomical measurements and provide a description of the main white matter pathways carrying neural signals crucial to word recognition. Finally, in an attempt to integrate structural, functional, and electrophysiological findings, we propose a view of visual word recognition, accounting for spatial and temporal facets of word-selective neural processes. This multi-modal perspective on the neural circuitry of literacy highlights the relevance of a posterior-anterior differentiation in ventral occipitotemporal cortex for visual processing of written language and lexical features. It also highlights unanswered questions that can guide us towards future research directions. Bridging measures of brain structure and function will help us reach a more precise understanding of the transformation from vision to language.

Development of myelin, a fatty sheath that insulates nerve fibers, is critical for brain function. Myelination during infancy has been studied with histology, but postmortem data cannot evaluate the longitudinal trajectory of white matter development. Here, we obtained longitudinal diffusion MRI and quantitative MRI measures of R1 in 0, 3 and 6 months-old human infants, and (ii) developed an automated method to identify white matter bundles and quantify their properties in each infant’s brain. We find that R1 increases from newborns to 6-months-olds in all bundles. R1 development is nonuniform: there is faster development in white matter that is less mature in newborns, and along inferior-to-superior as well as anterior-to-posterior spatial gradients. As R1 is linearly related to myelin fraction in white matter bundles, these findings open new avenues to elucidate typical and atypical white matter myelination in early infancy, which has important implications for early identification of neurodevelopmental disorders.

Development of myelin, a fatty sheath that insulates nerve fibers, is critical for brain function. Myelination during infancy has been studied with histology, but postmortem data cannot evaluate the longitudinal trajectory of white matter development. Here, we obtained longitudinal diffusion MRI and quantitative MRI measures of longitudinal relaxation rate (R1) of white matter in 0, 3 and 6 months-old human infants, and developed an automated method to identify white matter bundles and quantify their properties in each infant’s brain. We find that R1 increases from newborns to 6-months-olds in all bundles. R1 development is nonuniform: there is faster development in white matter that is less mature in newborns, and development rate increases along inferior-to-superior as well as anterior-to-posterior spatial gradients. As R1 is linearly related to myelin fraction in white matter bundles, these findings open new avenues to elucidate typical and atypical white matter myelination in early infancy.

Available online 27 January 2021 Skilled reading requires years of practice associating visual symbols with speech sounds. Over the course of the learning process, this association becomes effortless and automatic. Here we test whether automatic activation of spoken-language circuits in response to visual words is a hallmark of skilled reading. Magnetoencephalography was used to measure word-selective responses under multiple cognitive tasks (N = 42, 7–12 years of age). Even when attention was drawn away from the words by performing an attention-demanding fixation task, strong word-selective responses were found in a language region (i.e., superior temporal gyrus) starting at ~300 ms after stimulus onset. Critically, this automatic word-selective response was indicative of reading skill: the magnitude of word-selective responses correlated with individual reading skill. Our results suggest that automatic recruitment of spoken-language circuits is a hallmark of skilled reading; with practice, reading becomes effortless as the brain learns to automatically translate letters into sounds and meaning. This work was supported under the framework of international cooperation program managed by the National Research Foundation of Korea (NRF) (No. 2018K2A9A2A20088926 and 2019R1C1C1009383) to SJJ. This work was also funded by NSF BCS 1551330, NICHD R21HD092771 and R01HD09586101 and Jacobs Foundation Research Fellowship to JDY. SC was funded by the European Commission (H2020-MSCA-IF-2018-837228-ENGRAVING). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 837228

Research on educational technologies for reading instruction is disproportionate to the myriad applications in the marketplace. Here we assess a web-based reading tool, Sound it Out, that assists struggling readers in decoding by annotating vowels with small icons indicating the associate phoneme. Created as a collaboration between researchers and technology developers, the phonemic-image cue was designed to scaffold letter-sound correspondence. Study 1 examined whether Sound it Out provides an immediate benefit to reading performance in thirty struggling readers (ages 8-10) randomly assigned to counterbalanced groups in a single-session. Results showed that, without a period of practice, children were not able to capitalize on the cues for improved text reading. Study 2 utilized a repeated measures randomized controlled trial design to determine if an extended practice period (1 month) produced gains and whether a caregiver supervised practice (“dyadic reading”) enhanced benefits. Seventy-six struggling readers (ages 7-13) were randomly assigned to two intervention groups (independent and dyadic reading) and one control group. Results showed significant, dose-response benefits to decoding accuracy and passage reading accuracy for the combined intervention groups in comparison to controls. Moreover, supervised dyadic reading enhanced the effect of practice. These results highlight the potential for an evidenced-based supplemental learning technology to support both independent and shared reading for struggling readers.

The speed-accuracy tradeoff suggests that responses generated under time constraints will be less accurate. While it has undergone extensive experimental verification, it is less clear whether it applies in settings where time pressures are not being experimentally manipulated (but where respondents still vary in their utilization of time). Using a large corpus of 29 response time datasets containing data from cognitive tasks without experimental manipulation of time pressure, we probe whether the speed-accuracy tradeoff holds across a variety of tasks using idiosyncratic within-person variation in speed. We find inconsistent relationships between marginal increases in time spent responding and accuracy; in many cases, marginal increases in time do not predict increases in accuracy. However, we do observe time pressures (in the form of time limits) to consistently reduce accuracy and for rapid responses to typically show the anticipated relationship (i.e., they are more accurate if they are slower). We also consider analysis of items and individuals. We find substantial variation in the item-level associations between speed and accuracy. On the person side, respondents who exhibit more within-person variation in response speed are typically of lower ability. Finally, we consider the predictive power of a person's response time in predicting out-of-sample responses; it is generally a weak predictor. Collectively, our findings suggest the speed-accuracy tradeoff may be limited as a conceptual model in its application in non-experimental settings and, more generally, offer empirical results and an analytic approach that will be useful as more response time data is collected.

Competing theories of dyslexia posit that reading difficulties arise from impaired visual, auditory, phonological, or statistical learning mechanisms. Importantly, many theories posit that dyslexia reflects a cascade of impairments emanating from a single “core deficit”. Here we report two studies evaluating core deficit and multifactorial models. In Study 1, we use publicly available data from the Healthy Brain Network to test the accuracy of phonological processing measures for predicting dyslexia diagnosis and find that over 30% of cases are misclassified (sensitivity = 66.7%; specificity = 68.2%). In Study 2, we collect a battery of psychophysical measures of visual motion processing and standardized measures of phonological processing in 106 school‐aged children to investigate whether dyslexia is best conceptualized under a core‐deficit model, or as a disorder with heterogenous origins. Specifically, by capitalizing on the drift diffusion model to analyze performance on a visual motion discrimination experiment, we show that deficits in visual motion processing, perceptual decision‐making, and phonological processing manifest largely independently. Based on statistical models of how variance in reading skill is parceled across measures of visual processing, phonological processing, and decision‐making, our results challenge the notion that a unifying deficit characterizes dyslexia. Instead, these findings indicate a model where reading skill is explained by several distinct, additive predictors, or risk factors, of reading (dis)ability., Using predictors from a visual motion processing experiment and linguistic measures, we show that a single‐mechanism model of reading disability cannot account for the range of linguistic and sensory processing outcomes observed in children. We propose an additive risk factor model where different aspects of sensory, cognitive and language function each contribute independently to reading development.

Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as “glass-box” methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

Published18 March 2021 An accurate model of the factors that contribute to individual differences in reading ability depends on data collection in large, diverse and representative samples of research participants. However, that is rarely feasible due to the constraints imposed by standardized measures of reading ability which require test administration by trained clinicians or researchers. Here we explore whether a simple, two-alternative forced choice, time limited lexical decision task (LDT), self-delivered through the webbrowser, can serve as an accurate and reliable measure of reading ability. We found that performance on the LDT is highly correlated with scores on standardized measures of reading ability such as the Woodcock-Johnson Letter Word Identification test (r = 0.91, disattenuated r = 0.94). Importantly, the LDT reading ability measure is highly reliable (r = 0.97). After optimizing the list of words and pseudowords based on item response theory, we found that a short experiment with 76 trials (2–3 min) provides a reliable (r = 0.95) measure of reading ability. Thus, the self-administered, Rapid Online Assessment of Reading ability (ROAR) developed here overcomes the constraints of resourceintensive, in-person reading assessment, and provides an efficient and automated tool for effective online research into the mechanisms of reading (dis)ability. We would like to thank the Pavlovia and PsychoPy team for their support on the browser-based experiments. This work was funded by NIH NICHD R01HD09586101, research grants from Microsoft and Jacobs Foundation Research Fellowship to J.D.Y.

An accurate model of the factors that contribute to individual differences in reading ability depends on data collection in large, diverse and representative samples of research participants. However, that is rarely feasible due to the constraints imposed by standardized measures of reading ability which require test administration by trained clinicians or researchers. Here we explore whether a simple, two-alternative forced choice, time limited lexical decision task (LDT), self-delivered through the web-browser, can serve as an accurate and reliable measure of reading ability. We found that performance on the LDT is highly correlated with scores on standardized measures of reading ability such as the Woodcock-Johnson Letter Word Identification test administered in the lab (r = 0.91, disattenuated r = 0.94). Importantly, the LDT reading ability measure is highly reliable (r = 0.97). After optimizing the list of words and pseudowords based on item response theory, we found that a short experiment with 80 words (2-3 minutes) provides a reliable (r = 0.95) measure of reading ability. Thus, the self-administered, rapid online assessment of reading ability (ROAR) developed here overcomes the constraints of resource-intensive, in-person reading assessment, and provides an efficient and automated tool for effective online research into the mechanisms of reading (dis)ability.

Diffusion MRI tractography is essential for reconstructing white-matter projections in the living human brain. Yet tractography results miss some projections and falsely identify others. A challenging example is the optic radiation (OR) that connects the thalamus and the primary visual cortex. Here, we tested whether OR tractography can be optimized using quantitative T1 mapping. Based on histology, we proposed that myelin-sensitive T1 values along the OR should remain consistently low compared with adjacent white matter. We found that complementary information from the T1 map allows for increasing the specificity of the reconstructed OR tract by eliminating falsely identified projections. This T1-filtering outperforms other, diffusion-based tractography filters. These results provide evidence that the smooth microstructural signature along the tract can be used as constructive input for tractography. Finally, we demonstrate that this approach can be applied in a case of multiple sclerosis, and generalized to the HCP-available MRI measurements. We conclude that multimodal MRI microstructural information can be used to eliminate spurious tractography results in the case of the OR.

Articulation movements help us identify speech in noisy environments. While this has been observed at almost all ages, the size of the perceived benefit and its relationship to development in children is less understood. Here, we focus on exploring audiovisual speech benefit in typically developing children (N = 160) across a wide age range (4–15 years) by measuring performance via an online audiovisual speech performance task that is low in cognitive and linguistic demands. Specifically, we investigated how audiovisual speech benefit develops over age and the impact of some potentially important intrinsic (e.g., gender, phonological skills) and extrinsic (e.g., choice of stimuli) experimental factors. Our results show an increased performance of individual modalities (audio-only, audiovisual, visual-only) as a function of age, but no difference in the size of audiovisual speech enhancement. Furthermore, older children showed a significant impact of visually distracting stimuli (e.g., mismatched video), where this had no additional impact on performance of the youngest children. No phonological or gender differences were found given the low cognitive and linguistic demands of this task.

An advantage of digital media is the flexibility to personalize the presentation of text to an individual’s needs and embed tools that support pedagogy and practice. The goal of this study was to develop a tablet-based reading tool, grounded in the principles of phonics-based instruction, and determine whether struggling readers could leverage this technology to improve their decoding skills. The tool presents a small icon below each vowel to represent its sound. Forty struggling child readers were randomly assigned to an intervention or control group to test the efficacy of the phonemic cues. We found that struggling readers could leverage the cues to improve pseudoword decoding: after two weeks of practice, the intervention group showed greater improvement than controls. This study demonstrates the efficacy of a text annotation, grounded in intervention research, to help children decode novel words. These results highlight the opportunity for educational technologies to support and supplement classroom instruction.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Competing theories of dyslexia posit that reading disability arises from impaired sensory, phonological, or statistical learning mechanisms. Importantly, many theories posit that dyslexia reflects a cascade of impairments emanating from a “core deficit”. Here we collect a battery of psychophysical and language measures in 106 school-aged children to investigate whether dyslexia is best conceptualized under a core-deficit model, or as a disorder with heterogenous origins. Specifically, by capitalizing on the drift diffusion model to separate sensory encoding from task-related influences on performance in a visual motion discrimination experiment, we show that deficits in motion perception, decision making and phonological processing manifest largely independently. Based on statistical models of how variance in reading skill is parceled across measures of sensory encoding, phonological processing and decision-making, our results challenge the notion that a unifying deficit characterizes dyslexia. Instead, these findings indicate a model where reading skill is explained by several distinct, additive predictors, or risk factors, of reading (dis)ability.Research HighlightsOur research provides direct evidence that a single-mechanism, or core-deficit, model of dyslexia cannot account for the range of linguistic and sensory outcomes in children.Individual differences in visual motion processing, perceptual decision making, phonological awareness and rapid naming each account for unique variance in reading skill.Our data support an additive risk-factor model, in which multiple independent dimensions each confer risk for reading difficulties.

Interacting with a cluttered and dynamic environment requires making decisions about visual information at relevant locations while ignoring irrelevant locations. Typical adults can do this with covert spatial attention: prioritizing particular visual field locations even without moving the eyes. Deficits of covert spatial attention have been implicated in developmental dyslexia, a specific reading disability. Previous studies of children with dyslexia, however, have been complicated by group differences in overall task ability that are difficult to distinguish from selective spatial attention. Here, we used a single-fixation visual search task to estimate orientation discrimination thresholds with and without an informative spatial cue in a large sample (N = 123) of people ranging in age from 5 to 70 years and with a wide range of reading abilities. We assessed the efficiency of attentional selection via the cueing effect: the difference in log thresholds with and without the spatial cue. Across our whole sample, both absolute thresholds and the cueing effect gradually improved throughout childhood and adolescence. Compared to typical readers, individuals with dyslexia had higher thresholds (worse orientation discrimination) as well as smaller cueing effects (weaker attentional selection). Those differences in dyslexia were especially pronounced prior to age 20, when basic visual function is still maturing. Thus, in line with previous theories, literacy skills are associated with the development of selective spatial attention.

The arcuate fasciculi are white-matter pathways that connect frontal and temporal lobes in each hemisphere. The arcuate plays a key role in the language network and is believed to be left-lateralized, in line with left hemisphere dominance for language. Measuring the arcuate in vivo requires diffusion magnetic resonance imaging-based tractography, but asymmetry of the in vivo arcuate is not always reliably detected in previous studies. It is unknown how the choice of tractography algorithm, with each method's freedoms, constraints, and vulnerabilities to false-positive and -negative errors, impacts findings of arcuate asymmetry. Here, we identify the arcuate in two independent datasets using a number of tractography strategies and methodological constraints, and assess their impact on estimates of arcuate laterality. We test three tractography methods: a deterministic, a probabilistic, and a tractography-evaluation (LiFE) algorithm. We extract the arcuate from the whole-brain tractogram, and compare it to an arcuate bundle constrained even further by selecting only those streamlines that connect to anatomically relevant cortical regions. We test arcuate macrostructure laterality, and also evaluate microstructure profiles for properties such as fractional anisotropy and quantitative R1. We find that both tractography choice and implementing the cortical constraints substantially impact estimates of all indices of arcuate laterality. Together, these results emphasize the effect of the tractography pipeline on estimates of arcuate laterality in both macrostructure and microstructure.

Research in many fields has become increasingly reliant on large and complex datasets. “Big Data” holds untold promise to rapidly advance science by tackling new questions that cannot be answered with smaller datasets. While powerful, research with Big Data poses unique challenges, as many standard lab protocols rely on experts examining each one of the samples. This is not feasible for large-scale datasets because manual approaches are time-consuming and hence difficult to scale. Meanwhile, automated approaches lack the accuracy of examination by highly trained scientists and this may introduce major errors, sources of noise, and unforeseen biases into these large and complex datasets. Our proposed solution is to 1) start with a small, expertly labelled dataset, 2) amplify labels through web-based tools that engage citizen scientists, and 3) train machine learning on amplified labels to emulate expert decision making. As a proof of concept, we developed a system to quality control a large dataset of three-dimensional magnetic resonance images (MRI) of human brains. An initial dataset of 200 brain images labeled by experts were amplified by citizen scientists to label 722 brains, with over 80,000 ratings done through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on a combination of the citizen scientist labels that accounts for differences in the quality of classification by different citizen scientists. In an ROC analysis (on left out test data), the deep learning network performed as well as a state-of-the-art, specialized algorithm (MRIQC) for quality control of T1-weighted images, each with an area under the curve of 0.99. Finally, as a specific practical application of the method, we explore how brain image quality relates to the replicability of a well established relationship between brain volume and age over development. Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in emerging disciplines where specialized, automated tools do not already exist.