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IntroductionThe 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.MethodsHere 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.ResultsWe 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.DiscussionTogether these results highlight the potential for regular screening and progress monitoring at scale with ROAR-SRE.

Abstract Phonological awareness (PA) is at the foundation of reading development: PA is introduced before formal reading instruction, predicts reading development, is a target for early intervention, and is a core mechanism in dyslexia. Conventional approaches to assessing PA are time-consuming and resource intensive: assessments are individually administered 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—that can be implemented at scale without a test administrator. We explored whether this gamified, online task is an accurate and reliable measure of PA and predicts reading development. We found high correlations with standardized measures of PA (CTOPP-2, r = .80) for children from Pre-K through fourth grade and exceptional reliability (α = .96). Validation in 50 first and second grade classrooms showed reliable implementation in a public school setting with predictive value of future reading development.

Abstract 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.

IntroductionThe Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.MethodsWe used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.ResultsWe released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

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? “Neuronal Recycling” predicts that learning to read should enhance the response to words in ventral occipitotemporal cortex (VOTC) and decrease the response to other visual categories such as faces and objects. To test this hypothesis, and more broadly to understand the changes that are induced by the early stages of literacy instruction, we conducted a randomized controlled trial with pre-school children (five years of age). Children were randomly assigned to intervention programs focused on either reading skills or oral language skills and magnetoencephalography (MEG) data collected before and after the intervention was used to measure visual responses to images of text, faces, and objects. We found that being taught reading versus oral language skills induced different patterns of change in category-selective regions of visual cortex, but that there was not a clear tradeoff between the response to words versus other categories. Within a predefined region of VOTC corresponding to the visual word form area (VWFA) we found that the relative amplitude of responses to text, faces, and objects changed, but increases in the response to words were not linked to decreases in the response to faces or objects. 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.

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.

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.

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.

Myelination in early infancy develops at different rates. Here the authors describe this process whereby the back and top of the brain, as well as sections that are least mature at birth develop the fastest.

Abstract 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.

Abstract 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 (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 resource-intensive, in-person reading assessment, and provides an efficient and automated tool for effective online research into the mechanisms of reading (dis)ability.

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.

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.

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 gray 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 implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.

White matter properties correlate with cognitive performance in a number of domains. Here the authors show that altering a child’s educational environment though a targeted intervention program induces rapid, large-scale changes in the white matter, and that these changes track the learning process.

Data sharing is an important component of reproducible research, but meaningful data sharing can be difficult. Here authors describe a new open source tool, AFQ-Browser, that builds an interactive website allowing visualization and exploratory data analysis of published diffusion MRI data.

In the search for mechanisms that contribute to dyslexia, the term "attention" has been invoked to explain performance in a variety of tasks, creating confusion since all tasks do, indeed, demand "attention." Many studies lack an experimental manipulation of attention that would be necessary to determine its influence on task performance. Nonetheless, an emerging view is that children with dyslexia have an impairment in the exogenous (automatic/reflexive) orienting of spatial attention. Here we investigated the link between exogenous attention and reading ability by presenting exogenous spatial cues in the multi-letter processing task--a task relevant for reading. The task was gamified and administered online to a large sample of children (N = 187) between 6 and 17 years. Children with dyslexia performed worse overall at rapidly recognizing and reporting strings of letters. However, we found no evidence for a difference in the utilization of exogenous spatial cues, resolving two decades of ambiguity in the field. Previous studies that claimed otherwise may have failed to distinguish attention effects from overall task performance or found spurious group differences in small samples.

Abstract It is well established that visual sensitivity to motion is correlated with reading skills. Yet, the causal relationship between motion sensitivity and reading skills has been debated for more than thirty years. One hypothesis posits that dyslexia is caused by deficits in the motion processing pathway. An alternative hypothesis explains the motion processing deficit observed in dyslexia as the consequence of a lack, or poor quality, of reading experience. Here we used an intensive reading intervention program to test the causal relationship between learning to read and motion processing in children. Our data show that, while the reading intervention enhanced reading abilities, learning to read did not affect motion sensitivity. Motion sensitivity remained stable over the course of the intervention. Furthermore, the motion sensitivity deficit did not negatively impact the learning process. Children with poor motion sensitivity showed the same improvement in reading skills as children with typical motion sensitivity. Our findings call into question the view that motion processing deficits are due to poor reading experience. We propose that the correlation between the two measures arises from other common mechanisms, or that motion processing deficits are among a collection of correlated risk factors for reading difficulties.

A major achievement of reading research has been the development of effective intervention programs for struggling readers. Most intervention studies employ a pre-post design, to examine efficacy, but this precludes the study of growth curves over the course of the intervention program. Determining the time-course of improvement is essential for cost-effective, evidence-based decisions on the optimal intervention dosage. The goal of this study was to analyze reading growth curves during an intensive summer intervention program. A cohort of 31 children (6–12 years) with reading difficulties (N = 21 with dyslexia diagnosis) were enrolled in 160 h of intervention occurring over 8 weeks of summer vacation. We collected behavioral measures over 4 sessions assessing decoding, oral reading fluency, and comprehension. Mixed-effects modeling of longitudinal measurements revealed a linear dose-response relationship between hours of intervention and improvement in reading ability; there was significant linear growth on every measure of reading skill and none of the measures showed non-linear growth trajectories. Decoding skills showed substantial growth [Cohen’s d = 0.85 (WJ Basic Reading Skills)], with fluency and comprehension growing more gradually [d = 0.41 (WJ Reading Fluency)]. These results highlight the opportunity to improve reading skills over an intensive, short-term summer intervention program, and the linear dose-response relationship between duration and gains enables educators to set reading level goals and design a treatment plan to achieve them.

Big Data promises to advance science through data-driven discovery. However, many standard lab protocols rely on manual examination, which is not feasible for large-scale datasets. Meanwhile, automated approaches lack the accuracy of expert examination. We propose to (1) start with expertly labeled data, (2) amplify labels through web applications that engage citizen scientists, and (3) train machine learning on amplified labels, to emulate the experts. Demonstrating this, we developed a system to quality control brain magnetic resonance images. Expert-labeled data were amplified by citizen scientists through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on citizen scientist labels. Deep learning performed as well as specialized algorithms for quality control (AUC = 0.99). Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in disciplines where specialized, automated tools do not yet exist.

Word-selective neural responses in human ventral occipito-temporal cortex (VOTC) emerge as children learn to read, creating a visual word form area (VWFA) in the literate brain. It has been suggested that the VWFA arises through competition between pre-existing selectivity for other stimulus categories, changing the topography of VOTC to support rapid word recognition. Here, we hypothesized that competition between words and objects would be resolved as children acquire reading skill. Using functional magnetic resonance imaging (fMRI), we examined the relationship between responses to words and objects in VOTC in two ways. First, we defined the VWFA using a words > objects contrast and found that only skilled readers had a region that responded more to words than objects. Second, we defined the VWFA using a words > faces contrast and examined selectivity for words over objects in this region. We found that word selectivity strongly correlated with reading skill, suggesting reading skill-dependent tuning for words. Furthermore, we found that low word selectivity in struggling readers was not due to a lack of response to words, but to a high response to objects. Our results suggest that the fine-tuning of word-selective responses in VOTC is a critical component of skilled reading. Keywords: Visual word form area (VWFA), Dyslexia, Fusiform face area (FFA), Development

Diffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a typical multi-shell diffusion MRI protocol in a group of children (ages 7–12) and use two popular modeling techniques to examine individual differences in white matter structure. We first assess scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon water fraction in poor readers. Both models support this interpretation, highlighting the utility of these approaches for testing specific hypotheses about cognitive development. Keywords: Diffusion MRI, Development, Reading, Dyslexia

Anorexia nervosa (AN) is a serious eating disorder that typically emerges during adolescence and occurs most frequently in females. To date, very few studies have investigated the possible impact of AN on white matter tissue properties during adolescence, when white matter is still developing. The present study evaluated white matter tissue properties in adolescent girls with AN using diffusion MRI with tractography and T1 relaxometry to measure R1 (1/T1), an index of myelin content. Fifteen adolescent girls with AN (mean age = 16.6 years ± 1.4) were compared to fifteen age-matched girls with normal weight and eating behaviors (mean age = 17.1 years ± 1.3). We identified and segmented 9 bilateral cerebral tracts (18) and 8 callosal fiber tracts in each participant's brain (26 total). Tract profiles were generated by computing measures for fractional anisotropy (FA) and R1 along the trajectory of each tract. Compared to controls, FA in the AN group was significantly decreased in 4 of 26 white matter tracts and significantly increased in 2 of 26 white matter tracts. R1 was significantly decreased in the AN group compared to controls in 11 of 26 white matter tracts. Reduced FA in combination with reduced R1 suggests that the observed white matter differences in AN are likely due to reductions in myelin content. For the majority of tracts, group differences in FA and R1 did not occur within the same tract. The present findings have important implications for understanding the neurobiological factors underlying white matter changes associated with AN and invite further investigations examining associations between white matter properties and specific physiological, cognitive, social, or emotional functions affected in AN.

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.

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. 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 decode challenging words. 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 potential 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.

The ability to read a page of text or recognize a person's face depends on category-selective visual regions in ventral temporal cortex (VTC). To understand how these regions mediate word and face recognition, it is necessary to characterize how stimuli are represented and how this representation is used in the execution of a cognitive task. Here, we show that the response of a category-selective region in VTC can be computed as the degree to which the low-level properties of the stimulus match a category template. Moreover, we show that during execution of a task, the bottom-up representation is scaled by the intraparietal sulcus (IPS), and that the level of IPS engagement reflects the cognitive demands of the task. These results provide an account of neural processing in VTC in the form of a model that addresses both bottom-up and top-down effects and quantitatively predicts VTC responses.

Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of commonly used models have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM model-accuracy, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking.

This longitudinal secondary analysis examined which early language and speech abilities are associated with school-aged reading skills, and whether these associations are mediated by cognitive ability. We analyzed vocabulary, syntax, speech sound maturity, and cognition in a sample of healthy children at age 3 years (N=241) in relation to single word reading (decoding), comprehension, and oral reading fluency in the same children at age 9 to 11 years. All predictor variables and the mediator variable were associated with the three reading outcomes. The predictor variables were all associated with cognitive abilities, the mediator. Cognitive abilities partially mediated the effects of language on reading. After mediation, decoding was associated with speech sound maturity; comprehension was associated with receptive vocabulary; and oral fluency was associated with speech sound maturity, receptive vocabulary, and syntax. In summary, all of the effects of language on reading could not be explained by cognition as a mediator. Specific components of language and speech skills in preschool made independent contributions to reading skills 6 to 8 years later. These early precursors to later reading skill represent potential targets for early intervention to improve reading.

Tractography based on diffusion weighted imaging (DWI) data is a method for identifying the major white matter fascicles (tracts) in the living human brain. The health of these tracts is an important factor underlying many cognitive and neurological disorders. In vivo, tissue properties may vary systematically along each tract for several reasons: different populations of axons enter and exit the tract, and disease can strike at local positions within the tract. Hence quantifying and understanding diffusion measures along each fiber tract (Tract Profile) may reveal new insights into white matter development, function, and disease that are not obvious from mean measures of that tract. We demonstrate several novel findings related to Tract Profiles in the brains of typically developing children and children at risk for white matter injury secondary to preterm birth. First, fractional anisotropy (FA) values vary substantially within a tract but the Tract FA Profile is consistent across subjects. Thus, Tract Profiles contain far more information than mean diffusion measures. Second, developmental changes in FA occur at specific positions within the Tract Profile, rather than along the entire tract. Third, Tract Profiles can be used to compare white matter properties of individual patients to standardized Tract Profiles of a healthy population to elucidate unique features of that patient's clinical condition. Fourth, Tract Profiles can be used to evaluate the association between white matter properties and behavioral outcomes. Specifically, in the preterm group reading ability is positively correlated with FA measured at specific locations on the left arcuate and left superior longitudinal fasciculus and the magnitude of the correlation varies significantly along the Tract Profiles. We introduce open source software for automated fiber-tract quantification (AFQ) that measures Tract Profiles of MRI parameters for 18 white matter tracts. With further validation, AFQ Tract Profiles have potential for informing clinical management and decision-making.