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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.

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.

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.

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.

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.

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

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.

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.

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.

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., Author summary The connections between different parts of the brain form networks that are important for information transmission and for brain health. These connections are composed of nerve fibers that travel through the white matter portion of the brain. Thus, mapping tissue properties in white matter pathways can help us understand which connections and what tissue properties are relevant to brain diseases, or explain differences in behavior and cognition between different individuals. We developed a new statistical method that helps map the white matter by automatically identifying the features of the white matter that correspond to individual differences. Our approach relies on incorporating our knowledge of the anatomical sub-divisions of the white matter into the statistical model itself. We demonstrate that the model accurately captures differences between individual’s with amyotrophic lateral sclerosis (ALS) and healthy controls. It also accurately captures brain changes that correspond with the ages of different individuals. We tested our method on four different datasets and found that it is more accurate than previous methods used with these datasets, while also allowing us to highlight which white matter tissue properties and which connections account for individual differences, thus providing interpretable results.

This work evaluates the potential in diagnostic application of a new advanced neuroimaging method, which delineates the profile of tissue properties along the corticospinal tract (CST) in amyotrophic lateral sclerosis (ALS), by means of diffusion tensor imaging (DTI). Twenty-four ALS patients and twenty-four demographically matched healthy subjects were enrolled in this study. The Automated Fiber Quantification (AFQ), a tool for the automatic reconstruction of white matter tract profiles, based on a deterministic tractography algorithm to automatically identify the CST and quantify its diffusion properties, was used. At a group level, the highest non-overlapping DTI-related differences were detected in the cerebral peduncle, posterior limb of the internal capsule, and primary motor cortex. Fractional anisotropy (FA) decrease and mean diffusivity (MD) and radial diffusivity (RD) increases were detected when comparing ALS patients to controls. The machine learning approach used to assess the clinical utility of this DTI tool revealed that, by combining all DTI metrics measured along tract between the cerebral peduncle and the corona radiata, a mean 5-fold cross validation accuracy of 80% was reached in discriminating ALS from controls. Our study provides a useful new neuroimaging tool to characterize ALS-related neurodegenerative processes by means of CST profile. We demonstrated that specific microstructural changes in the upper part of the brainstem might be considered as a valid biomarker. With further validations this method has the potential to be considered a promising step toward the diagnostic utility of DTI measures in ALS. Hum Brain Mapp 38:727-739, 2017. © 2016 Wiley Periodicals, Inc.

Reading-related responses in the lateral ventral temporal cortex (VTC) show a consistent spatial layout across individuals, which is puzzling, since reading skills are acquired during childhood. Here, we tested the hypothesis that white matter fascicles and gray matter microstructure predict the location of reading-related responses in lateral VTC. We obtained functional (fMRI), diffusion (dMRI), and quantitative (qMRI) magnetic resonance imaging data in 30 adults. fMRI was used to map reading-related responses by contrasting responses in a reading task with those in adding and color tasks; dMRI was used to identify the brain’s fascicles and to map their endpoints density in lateral VTC; qMRI was used to measure proton relaxation time (T1), which depends on cortical tissue microstructure. We fit linear models that predict reading-related responses in lateral VTC from endpoint density and T1 and used leave-one-subject-out cross-validation to assess prediction accuracy. Using a subset of our participants (N=10, feature selection set), we find that i) endpoint density of the arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), and vertical occipital fasciculus (VOF) are significant predictors of reading-related responses, and ii) cortical T1 of lateral VTC further improves the predictions of the fascicle model. Next, in the remaining 20 participants (validation set), we showed that a linear model that includes T1, AF, ILF and VOF significantly predicts i) the map of reading-related responses across lateral VTC and ii) the location of the visual word form area, a region critical for reading. Overall, our data-driven approach reveals that the AF, ILF, VOF and cortical microstructure have a consistent spatial relationship with an individual’s reading-related responses in lateral VTC.HighlightsThe ILF, AF, and VOF predict the spatial layout of reading-related responses in VTCGray matter microstructure improves the prediction of reading-related responsesFascicles and gray matter structure together predict the location of the VWFA

In most environments, the visual system is confronted with many relevant objects simultaneously. That is especially true during reading. However, behavioral data demonstrate that a serial bottleneck prevents recognition of more than one word at a time. We used fMRI to investigate how parallel spatial channels of visual processing converge into a serial bottleneck for word recognition. Participants viewed pairs of words presented simultaneously. We found that retinotopic cortex processed the two words in parallel spatial channels, one in each contralateral hemisphere. Responses were higher for attended than for ignored words but were not reduced when attention was divided. We then analyzed two word-selective regions along the occipitotemporal sulcus (OTS) of both hemispheres (subregions of the visual word form area, VWFA). Unlike retinotopic regions, each word-selective region responded to words on both sides of fixation. Nonetheless, a single region in the left hemisphere (posterior OTS) contained spatial channels for both hemifields that were independently modulated by selective attention. Thus, the left posterior VWFA supports parallel processing of multiple words. In contrast, activity in a more anterior word-selective region in the left hemisphere (mid OTS) was consistent with a single channel, showing ( i ) limited spatial selectivity, ( ii ) no effect of spatial attention on mean response amplitudes, and ( iii ) sensitivity to lexical properties of only one attended word. Therefore, the visual system can process two words in parallel up to a late stage in the ventral stream. The transition to a single channel is consistent with the observed bottleneck in behavior.

White matter tissue properties are known to correlate with performance across domains ranging from reading to math, to executive function. Here, we use a longitudinal intervention design to examine experience-dependent growth in reading skills and white matter in grade school-aged, struggling readers. Diffusion MRI data were collected at regular intervals during an 8-week, intensive reading intervention. These measurements reveal large-scale changes throughout a collection of white matter tracts, in concert with growth in reading skill. Additionally, we identify tracts whose properties predict reading skill but remain fixed throughout the intervention, suggesting that some anatomical properties stably predict the ease with which a child learns to read, while others dynamically reflect the effects of experience. These results underscore the importance of considering recent experience when interpreting cross-sectional anatomy–behavior correlations. Widespread changes throughout the white matter may be a hallmark of rapid plasticity associated with an intensive learning experience., 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.

Dyslexia is associated with abnormal performance on many auditory psychophysics tasks, particularly those involving the categorization of speech sounds. However, it is debated whether those apparent auditory deficits arise from (a) reduced sensitivity to particular acoustic cues, (b) the difficulty of experimental tasks, or (c) unmodeled lapses of attention. Here we investigate the relationship between phoneme categorization and reading ability, with special attention to the nature of the cue encoding the phoneme contrast (static versus dynamic), differences in task paradigm difficulty, and methodological details of psychometric model fitting. We find a robust relationship between reading ability and categorization performance, show that task difficulty cannot fully explain that relationship, and provide evidence that the deficit is not restricted to dynamic cue contrasts, contrary to prior reports. Finally, we demonstrate that improved modeling of behavioral responses suggests that the gap between dyslexics and typical readers may be smaller than previously reported.

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

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts., 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.

Human visual cortex comprises many visual field maps organized into clusters. A standard organization separates visual maps into 2 distinct clusters within ventral and dorsal cortex. We combined fMRI, diffusion MRI, and fiber tractography to identify a major white matter pathway, the vertical occipital fasciculus (VOF), connecting maps within the dorsal and ventral visual cortex. We use a model-based method to assess the statistical evidence supporting several aspects of the VOF wiring pattern. There is strong evidence supporting the hypothesis that dorsal and ventral visual maps communicate through the VOF. The cortical projection zones of the VOF suggest that human ventral (hV4/VO-1) and dorsal (V3A/B) maps exchange substantial information. The VOF appears to be crucial for transmitting signals between regions that encode object properties including form, identity, and color and regions that map spatial information.

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., Highlights • AN girls had both increased and decreased FA in 4 white matter tracts. • AN girls had increased R1 in 11 white matter tracts. • White matter differences in AN are likely related to changes in myelin content.

Reading is a complex process that involves low-level visual processing, phonological processing, and higher-level semantic processing. Given that skilled reading requires integrating information among these different systems, it is likely that reading difficulty-known as dyslexia-can emerge from impairments at any stage of the reading circuitry. To understand contributing factors to reading difficulties within individuals, it is necessary to diagnose the function of each component of the reading circuitry. Here, we investigated whether adults with dyslexia who have impairments in visual processing respond to a visual manipulation specifically targeting their impairment. We collected psychophysical measures of visual crowding and tested how each individual's reading performance was affected by increased text-spacing, a manipulation designed to alleviate severe crowding. Critically, we identified a sub-group of individuals with dyslexia showing elevated crowding and found that these individuals read faster when text was rendered with increased letter-, word- and line-spacing. Our findings point to a subtype of dyslexia involving elevated crowding and demonstrate that individuals benefit from interventions personalized to their specific impairments.

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.

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

Here, we describe a quantitative neuroimaging method to estimate the macromolecular tissue volume (MTV), a fundamental measure of brain anatomy. By making measurements over a range of field strengths and scan parameters, we tested the key assumptions and the robustness of the method. The measurements confirm that a consistent quantitative estimate of MTV can be obtained across a range of scanners. MTV estimates are sufficiently precise to enable a comparison between data obtained from an individual subject with control population data. We describe two applications. First, we show that MTV estimates can be combined with T1 and diffusion measurements to augment our understanding of the tissue properties. Second, we show that MTV provides a sensitive measure of disease status in individual patients with multiple sclerosis. The MTV maps are obtained using short clinically appropriate scans that can reveal how tissue changes influence behavior and cognition.

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.

Recent years have seen a growing interest in relating MRI measurements to the structural-biophysical properties of white matter fibers. The fiber g-ratio, defined as the ratio between the inner and outer radii of the axon myelin sheath, is an important structural property of white matter, affecting signal conduction. Recently proposed modeling methods that use a combination of quantitative-MRI signals, enable a measurement of the fiber g-ratio in vivo. Here we use an MRI-based g-ratio estimation to observe the variance of the g-ratio within the corpus callosum, and evaluate sex and age related differences. To estimate the g-ratio we used a model ( Stikov et al., 2011 ; Duval et al., 2017 ) based on two different WM microstructure parameters: the relative amounts of myelin (myelin volume fraction, MVF) and fibers (fiber volume fraction, FVF) in a voxel. We derived the FVF from the fractional anisotropy (FA), and estimated the MVF by using the lipid and macromolecular tissue volume (MTV), calculated from the proton density (Mezer et al., 2013). In comparison to other methods of estimating the MVF, MTV represents a stable parameter with a straightforward route of acquisition. To establish our model, we first compared histological MVF measurements (West et al., 2016) with the MRI derived MTV. We then implemented our model on a large database of 92 subjects (44 males), aged 7 to 81, in order to evaluate age and sex related changes within the corpus callosum. Our results show that the MTV provides a good estimation of MVF for calculating g-ratio, and produced values from the corpus callosum that correspond to those found in animals ex vivo and are close to the theoretical optimum, as well as to published in vivo data. Our results demonstrate that the MTV derived g-ratio provides a simple and reliable in vivo g-ratio-weighted (GR*) measurement in humans. In agreement with theoretical predictions, and unlike other tissue parameters measured with MRI, the g-ratio estimations were found to be relatively stable with age, and we found no support for a significant sexual dimorphism with age.

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

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. DOI: http://dx.doi.org/10.7554/eLife.22341.001, eLife digest As your eyes scan this page, your visual system performs a series of computations that allow you to derive meaning from the printed words. The visual system solves this task with such apparent ease that you may never have thought about the challenges that your brain must overcome for you to read a page of text. The brain must overcome similar challenges to enable you to recognize the faces of your friends. Two factors affect how the neurons in the visual system respond to what you are looking at: the physical features of the object and your cognitive state (for example, your knowledge, past experiences, and the cognitive demands of the task at hand). To figure out exactly how these factors influence the responses of the neurons, Kay and Yeatman used functional magnetic resonance imaging to scan the brains of human volunteers as they viewed different images (some of which were of faces or words). The volunteers had to perform various tasks while viewing the images. These tasks included focusing their attention on a small dot, categorizing the image, and stating whether the image had previously been shown. From the brain imaging data, Kay and Yeatman developed a model of the brain circuits that enable faces and words to be recognized. The model separately characterizes the influence of physical features and the influence of cognitive state, and describes several different types of processing: how the brain represents what is seen, how it makes a decision about how to respond, and how it changes its own activity when carrying out the decision. The model has been made freely available online so that other researchers can reproduce and build upon Kay and Yeatman’s findings. The current model is not perfect, and it does not describe neural activity in fine detail. However, by obtaining new experimental measurements, the model could be systematically improved. DOI: http://dx.doi.org/10.7554/eLife.22341.002

Sensitivity to temporal change places fundamental limits on object processing in the visual system. An emerging consensus from the behavioral and neuroimaging literature suggests that temporal resolution differs substantially for stimuli of different complexity and for brain areas at different levels of the cortical hierarchy. Here, we used steady-state visually evoked potentials to directly measure three fundamental parameters that characterize the underlying neural response to text and face images: temporal resolution, peak temporal frequency, and response latency. We presented full-screen images of text or a human face, alternated with a scrambled image, at temporal frequencies between 1 and 12 Hz. These images elicited a robust response at the first harmonic that showed differential tuning, scalp topography, and delay for the text and face images. Face-selective responses were maximal at 4 Hz, but text-selective responses, by contrast, were maximal at 1 Hz. The topography of the text image response was strongly left-lateralized at higher stimulation rates, whereas the response to the face image was slightly right-lateralized but nearly bilateral at all frequencies. Both text and face images elicited steady-state activity at more than one apparent latency; we observed early (141–160 msec) and late (>250 msec) text- and face-selective responses. These differences in temporal tuning profiles are likely to reflect differences in the nature of the computations performed by word- and face-selective cortex. Despite the close proximity of word- and face-selective regions on the cortical surface, our measurements demonstrate substantial differences in the temporal dynamics of word- versus face-selective responses.

Adolescent survivors of preterm birth experience persistent functional problems that negatively impact academic outcomes, even when standardized measures of cognition and language suggest normal ability. In this fMRI study, we compared the neural activation supporting auditory sentence comprehension in two groups of adolescents (ages 9–16 years); sentences varied in length and syntactic difficulty. Preterms ( n = 18, mean gestational age 28.8 weeks) and full terms ( n = 14) had scores on verbal IQ, receptive vocabulary, and receptive language tests that were within or above normal limits and similar between groups. In early and late phases of the trial, we found interactions by group and length; in the late phase, we also found a group by syntactic difficulty interaction. Post hoc tests revealed that preterms demonstrated significant activation in the left and right middle frontal gyri as syntactic difficulty increased. ANCOVA showed that the interactions could not be attributed to differences in age, receptive language skill, or reaction time. Results are consistent with the hypothesis that preterm birth modulates brain–behavior relations in sentence comprehension as task demands increase. We suggest preterms’ differences in neural processing may indicate a need for educational accommodations, even when formal test scores indicate normal academic achievement.

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 some of the models that are commonly used in analyzing human white matter 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 linear 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, 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.

Properties of human brain tissue change across the lifespan. Here we model these changes in the living human brain by combining quantitative magnetic resonance imaging (MRI) measurements of R1 (1/T1) with diffusion MRI and tractography (N=102, ages 7-85). The amount of R1 change during development differs between white-matter fascicles, but in each fascicle the rate of development and decline are mirror-symmetric; the rate of R1 development as the brain approaches maturity predicts the rate of R1 degeneration in aging. Quantitative measurements of macromolecule tissue volume (MTV) confirm that R1 is an accurate index of the growth of new brain tissue. In contrast to R1, diffusion development follows an asymmetric time-course with rapid childhood changes but a slow rate of decline in old age. Together, the time-courses of R1 and diffusion changes demonstrate that multiple biological processes drive changes in white-matter tissue properties over the lifespan.

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.