Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records

BACKGROUND

Individuals with Down syndrome (DS) have a heightened risk for various co-occurring health conditions, including congenital heart disease (CHD). In this two-part study, electronic medical records (EMRs) were leveraged to examine co-occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2).

METHODS

De-identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one-by-two sex and age matched with samples of case-controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome-disease association study (PheDAS) strategy was employed to reveal co-occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case-controls. Data were included in predictive models and assessed which model-based health conditions, when more prevalent, would increase the likelihood of surgical intervention.

RESULTS

In Study 1, relative to case-controls and those with other IDDs, co-occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co-occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non-rheumatic aortic valve disorders and heart failure (circulatory); acid-base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention.

CONCLUSIONS

Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision-care development.

Stimulus shapes strategy: Effects of stimulus characteristics and individual differences in academic achievement on the neural mechanisms engaged during the N-back task

This fMRI study of 126 youth explored whether the neural mechanisms underlying the N-back task, commonly used to examine executive control over the contents of working memory, are associated with individual differences in academic achievement in reading and math. Moreover, the study explored whether these relationships occur regardless of the nature of the stimulus being manipulated in working memory (letters, numbers, nonsense shapes) or whether these relationships are specific to achievement domain and stimulus type (i.e., letters for reading and numbers for math). The results indicated that higher academic achievement in each of reading and math was associated with greater activation of dorsolateral prefrontal cortex in the N-back task regardless of stimulus type (i.e., did not differ for letters and numbers), suggesting that at least some aspects of the neural mechanisms underlying these academic domains are executive in nature. In addition, regardless of level of academic achievement, prefrontal regions were engaged to a greater degree for letters than numbers than nonsense shapes. In contrast, nonsense shapes yielded greater hippocampal activation than letters and numbers. Potential reasons for this pattern of findings are discussed.

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

Examination of common and unique brain regions for atypical reading and math: a meta-analysis

The purpose of this study is to identify consistencies across functional neuroimaging studies regarding common and unique brain regions/networks for individuals with reading difficulties (RD) and math difficulties (MD) compared to typically developing (TD) individuals. A systematic search of the literature, utilizing multiple databases, yielded 116 functional magnetic resonance imaging and positron emission tomography studies that met the criteria. Coordinates that directly compared TD with either RD or MD were entered into GingerALE (Brainmap.org). An activation likelihood estimate (ALE) meta-analysis was conducted to examine common and unique brain regions for RD and MD. Overall, more studies examined RD (n = 96) than MD (n = 20). Across studies, overactivation for reading and math occurred in the right insula and inferior frontal gyrus for atypically developing (AD) > TD comparisons, albeit in slightly different areas of these regions; however, inherent threshold variability across imaging studies could diminish overlying regions. For TD > AD comparisons, there were no similar or overlapping brain regions. Results indicate there were domain-specific differences for RD and MD; however, there were some similarities in the ancillary recruitment of executive functioning skills. Theoretical and practical implications for researchers and educators are discussed.

Using a semi-automated approach to quantify Unidentified Bright Objects in Neurofibromatosis type 1 and linkages to cognitive and academic outcomes

Neurofibromatosis type 1 (NF1) is an autosomal dominant neurocutaneous syndrome that affects multiple organ systems resulting in widespread symptoms, including cognitive deficits. In addition to the criteria required for an NF1 diagnosis, approximately 70% of children with NF1 present with Unidentified Bright Objects (UBOs) or Focal Areas of Signal Intensity, which are hyperintense bright spots seen on T2-weighted magnetic resonance images and seen more prominently on FLAIR magnetic resonance images (Sabol et al., 2011). Current findings relating the presence/absence, quantities, sizes, and locations of these bright spots to cognitive abilities are mixed. To contribute to and hopefully disentangle some of these mixed findings, we explored potential relationships between metrics related to UBOs and cognitive abilities in a sample of 28 children and adolescents with NF1 (M=12.52 years; SD=3.18 years; 16 male). We used the Lesion Segmentation Tool (LST) to automatically detect and segment the UBOs. The LST was able to qualitatively and quantitatively reliably detect UBOs in images of children with NF1. Using these automatically detected and segmented lesions, we found that while controlling for age, biological sex, perceptual IQ, study, and scanner, "total UBO volume", defined as the sum of all the voxels representing all of the UBOs for each participant, helped explain differences in word reading, phonological awareness, and visuospatial skills. These findings contribute to the emerging NF1 literature and help parse the specific deficits that children with NF1 have, to then help improve the efficacy of reading interventions for children with NF1.

pyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations

Objective

To enable interactive visualization of phenome-wide association studies (PheWAS) on electronic health records (EHR).

Materials and Methods

Current PheWAS technologies require familiarity with command-line interfaces and lack end-to-end data visualizations. pyPheWAS Explorer allows users to examine group variables, test assumptions, design PheWAS models, and evaluate results in a streamlined graphical interface.

Results

A cohort of attention deficit hyperactivity disorder (ADHD) subjects and matched non-ADHD controls is examined. pyPheWAS Explorer is used to build a PheWAS model including sex and deprivation index as covariates, and the Explorer's result visualization for this model reveals known ADHD comorbidities.

Discussion

pyPheWAS Explorer may be used to rapidly investigate potentially novel EHR associations. Broader applications include deployment for clinical experts and preliminary exploration tools for institutional EHR repositories.

Conclusion

pyPheWAS Explorer provides a seamless graphical interface for designing, executing, and analyzing PheWAS experiments, emphasizing exploratory analysis of regression types and covariate selection.

Batch size: go big or go home? Counterintuitive improvement in medical autoencoders with smaller batch size

Batch size is a key hyperparameter in training deep learning models. Conventional wisdom suggests larger batches produce improved model performance. Here we present evidence to the contrary, particularly when using autoencoders to derive meaningful latent spaces from data with spatially global similarities and local differences, such as electronic health records (EHR) and medical imaging. We investigate batch size effects in both EHR data from the Baltimore Longitudinal Study of Aging and medical imaging data from the multimodal brain tumor segmentation (BraTS) challenge. We train fully connected and convolutional autoencoders to compress the EHR and imaging input spaces, respectively, into 32-dimensional latent spaces via reconstruction losses for various batch sizes between 1 and 100. Under the same hyperparameter configurations, smaller batches improve loss performance for both datasets. Additionally, latent spaces derived by autoencoders with smaller batches capture more biologically meaningful information. Qualitatively, we visualize 2-dimensional projections of the latent spaces and find that with smaller batches the EHR network better separates the sex of the individuals, and the imaging network better captures the right-left laterality of tumors. Quantitatively, the analogous sex classification and laterality regressions using the latent spaces demonstrate statistically significant improvements in performance at smaller batch sizes. Finally, we find improved individual variation locally in visualizations of representative data reconstructions at lower batch sizes. Taken together, these results suggest that smaller batch sizes should be considered when designing autoencoders to extract meaningful latent spaces among EHR and medical imaging data driven by global similarities and local variation.

Deep whole brain segmentation of 7T structural MRI

7T magnetic resonance imaging (MRI) has the potential to drive our understanding of human brain function through new contrast and enhanced resolution. Whole brain segmentation is a key neuroimaging technique that allows for region-by-region analysis of the brain. Segmentation is also an important preliminary step that provides spatial and volumetric information for running other neuroimaging pipelines. Spatially localized atlas network tiles (SLANT) is a popular 3D convolutional neural network (CNN) tool that breaks the whole brain segmentation task into localized sub-tasks. Each sub-task involves a specific spatial location handled by an independent 3D convolutional network to provide high resolution whole brain segmentation results. SLANT has been widely used to generate whole brain segmentations from structural scans acquired on 3T MRI. However, the use of SLANT for whole brain segmentation from structural 7T MRI scans has not been successful due to the inhomogeneous image contrast usually seen across the brain in 7T MRI. For instance, we demonstrate the mean percent difference of SLANT label volumes between a 3T scan-rescan is approximately 1.73%, whereas its 3T-7T scan-rescan counterpart has higher differences around 15.13%. Our approach to address this problem is to register the whole brain segmentation performed on 3T MRI to 7T MRI and use this information to finetune SLANT for structural 7T MRI. With the finetuned SLANT pipeline, we observe a lower mean relative difference in the label volumes of ~8.43% acquired from structural 7T MRI data. Dice similarity coefficient between SLANT segmentation on the 3T MRI scan and the after finetuning SLANT segmentation on the 7T MRI increased from 0.79 to 0.83 with p<0.01. These results suggest finetuning of SLANT is a viable solution for improving whole brain segmentation on high resolution 7T structural imaging.

Impact of COVID-19 on Children's Attention Deficit Hyperactivity Disorder Symptomology, Daily Life, and Problem Behavior During Virtual Learning

To explore the impact of COVID-19 on daily life and problem behavior during virtual learning, we created and administered a survey to 64 school-aged children (in 2019, M = 9.84 years; SD = 0.55 years). Results indicated significant increases in hyperactivity (t = -2.259; p = .027) and inattention (t = -2.811; p = .007) from 2019 to 2020. Decreases in sleep were associated with increases in hyperactivity (B = -0.27; p = .04); increases in time exercising were associated with smaller increases in inattention (B = -0.34, p = .01); and higher levels of parent stress, specifically related to virtual learning, were associated with increases in child inattention (B = 0.57, p = .01). Furthermore, hyperactivity predicted problem behavior during virtual learning (B = 0.31, p = .03).

Behavioral and brain anatomical analysis of Foxg1 heterozygous mice

FOXG1 Syndrome (FS) is a devastating neurodevelopmental disorder that is caused by a heterozygous loss-of-function (LOF) mutation of the FOXG1 gene, which encodes a transcriptional regulator important for telencephalic brain development. People with FS have marked developmental delays, impaired ambulation, movement disorders, seizures, and behavior abnormalities including autistic features. Current therapeutic approaches are entirely symptomatic, however the ability to rescue phenotypes in mouse models of other genetic neurodevelopmental disorders such as Rett syndrome, Angelman syndrome, and Phelan-McDermid syndrome by postnatal expression of gene products has led to hope that similar approaches could help modify the disease course in other neurodevelopmental disorders such as FS. While FoxG1 protein function plays a critical role in embryonic brain development, the ongoing adult expression of FoxG1 and behavioral phenotypes that present when FoxG1 function is removed postnatally provides support for opportunity for improvement with postnatal treatment. Here we generated a new mouse allele of Foxg1 that disrupts protein expression and characterized the behavioral and structural brain phenotypes in heterozygous mutant animals. These mutant animals display changes in locomotor behavior, gait, anxiety, social interaction, aggression, and learning and memory compared to littermate controls. Additionally, they have structural brain abnormalities reminiscent of people with FS. This information provides a framework for future studies to evaluate the potential for post-natal expression of FoxG1 to modify the disease course in this severe neurodevelopmental disorder.

The Angular Gyrus as a Hub for Modulation of Language-related Cortex by Distinct Prefrontal Executive Control Regions

It has become clear in recent years that reading, while relying on domain-specific language processing regions, also involves regions that implement executive processes more broadly. Such executive control is generally considered to be implemented by prefrontal regions, which exert control via connectivity that allows them to modulate processing in target brain regions. The present study examined whether three previously identified and distinct executive control regions in the pFC [Wang, K., Banich, M. T., Reineberg, A. E., Leopold, D. R., Willcutt, E. G., Cutting, L. E., et al. Left posterior prefrontal regions support domain-general executive processes needed for both reading and math. Journal of Neuropsychology, 14, 467-495, 2020] show similar patterns of functional connectivity (FC) during a reading comprehension task as compared with a symbol identification condition. Our FC results in a sample of adolescents (n = 120) suggest all three regions commonly show associations with activity in "classic" left hemisphere reading areas, including the angular and supramarginal gyri, yet each exhibits differential connectivity as well. In particular, precentral regions show differential FC to parietal portions of the dorsal language stream, the inferior frontal junction shows differential FC to middle temporal regions of the right hemisphere and other regions involved in semantic processing, and portions of the inferior frontal gyrus show differential FC to an extensive set of right hemisphere prefrontal regions. These results suggest that prefrontal control over language-related regions occurs in a coordinated yet discrete manner.

The influence of regions of interest on tractography virtual dissection protocols: general principles to learn and to follow

Efficient communication across fields of research is challenging, especially when they are at opposite ends of the physical and digital spectrum. Neuroanatomy and neuroimaging may seem close to each other. When neuroimaging studies try to isolate structures of interest, according to a specific anatomical definition, a variety of challenges emerge. It is a non-trivial task to convert the neuroanatomical knowledge to instructions and rules to be executed in neuroimaging software. In the process called "virtual dissection" used to isolate coherent white matter structure in tractography, each white matter pathway has its own set of landmarks (regions of interest) used as inclusion and exclusion criteria. The ability to segment and study these pathways is critical for scientific progress, yet, variability may depend on region placement, and be influenced by the person positioning the region (i.e., a rater). When raters' variability is taken into account, the impact made by each region of interest becomes even more difficult to interpret. A delicate balance between anatomical validity, impact on the virtual dissection and raters' reproducibility emerge. In this work, we investigate this balance by leveraging manual delineation data of a group of raters from a previous study to quantify which set of landmarks and criteria contribute most to variability in virtual dissection. To supplement our analysis, the variability of each pathway with a region-by-region exploration was performed. We present a detailed exploration and description of each region, the causes of variability and its impacts. Finally, we provide a brief overview of the lessons learned from our previous virtual dissection projects and propose recommendations for future virtual dissection protocols as well as perspectives to reach better community agreement when it comes to anatomical definitions of white matter pathways.

Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination

The segmentation of brain structures is a key component of many neuroimaging studies. Consistent anatomical definitions are crucial to ensure consensus on the position and shape of brain structures, but segmentations are prone to variation in their interpretation and execution. White-matter (WM) pathways are global structures of the brain defined by local landmarks, which leads to anatomical definitions being difficult to convey, learn, or teach. Moreover, the complex shape of WM pathways and their representation using tractography (streamlines) make the design and evaluation of dissection protocols difficult and time-consuming. The first iteration of Tractostorm quantified the variability of a pyramidal tract dissection protocol and compared results between experts in neuroanatomy and nonexperts. Despite virtual dissection being used for decades, in-depth investigations of how learning or practicing such protocols impact dissection results are nonexistent. To begin to fill the gap, we evaluate an online educational tractography course and investigate the impact learning and practicing a dissection protocol has on interrater (groupwise) reproducibility. To generate the required data to quantify reproducibility across raters and time, 20 independent raters performed dissections of three bundles of interest on five Human Connectome Project subjects, each with four timepoints. Our investigation shows that the dissection protocol in conjunction with an online course achieves a high level of reproducibility (between 0.85 and 0.90 for the voxel-based Dice score) for the three bundles of interest and remains stable over time (repetition of the protocol). Suggesting that once raters are familiar with the software and tasks at hand, their interpretation and execution at the group level do not drastically vary. When compared to previous work that used a different method of communication for the protocol, our results show that incorporating a virtual educational session increased reproducibility. Insights from this work may be used to improve the future design of WM pathway dissection protocols and to further inform neuroanatomical definitions.

The Contribution of Text Characteristics to Reading Comprehension: Investigating the Influence of Text Emotionality

In the current study, we examined relations between text features (e.g., word concreteness, referential cohesion) and reading comprehension using multilevel logistic models. The sample was 158 native English-speaking students between 8 years 8 months and 11 years 2 months of age with a wide range of reading ability. In line with the simple view of reading, decoding ability and language comprehension were associated with reading comprehension performance. Text characteristics, including indices of word frequency, number of pronouns, word concreteness, and deep cohesion, also predicted unique variance in reading comprehension performance over and above the simple view's components. Additionally, the emotional charge of text (i.e., lexical ratings of arousal) predicted reading comprehension beyond traditional person-level and text-based characteristics. These findings add to a small but growing body of evidence suggesting that it is important to consider emotional charge in addition to person-level and text-based characteristics to better understand reading comprehension performance.

Ultra-high-resolution Mapping of Cortical Layers 3T-Guided 7T MRI

7T MRI provides unprecedented resolution for examining human brain anatomy in vivo. For example, 7T MRI enables deep thickness measurement of laminar subdivisions in the right fusiform area. Existing laminar thickness measurement on 7T is labor intensive, and error prone since the visual inspection of the image is typically along one of the three orthogonal planes (axial, coronal, or sagittal view). To overcome this, we propose a new analytics tool that allows flexible quantification of cortical thickness on a 2D plane that is orthogonal to the cortical surface (beyond axial, coronal, and sagittal views) based on the 3D computational surface reconstruction. The proposed method further distinguishes high quality 2D planes and the low-quality ones by automatically inspecting the angles between the surface normals and slice direction. In our approach, we acquired a pair of 3T and 7T scans (same subject). We extracted the brain surfaces from the 3T scan using MaCRUISE and projected the surface to the 7T scan's space. After computing the angles between the surface normals and axial direction vector, we found that 18.58% of surface points were angled at more than 80° with the axial direction vector and had 2D axial planes with visually distinguishable cortical layers. 15.12% of the surface points with normal vectors angled at 30° or lesser with the axial direction, had poor 2D axial slices for visual inspection of the cortical layers. This effort promises to dramatically extend the area of cortex that can be quantified with ultra-high resolution in-plane imaging methods.

Tracking Familial History of Reading and Math Difficulties in Children's Academic Outcomes

The current study aimed to investigate the extent to which familial history of reading and math difficulties have an impact on children's academic outcomes within a 3-year longitudinal study, which evaluated their core reading and math skills after first (N = 198; 53% girls) and second grades (N = 166), as well as performance on complex academic tasks after second and third grades (N = 148). At baseline, parents were asked to complete the Adult Reading History Questionnaire (ARHQ) and its adaption, Adult Math History Questionnaire (AMHQ), to index familial history of reading and math difficulties, respectively. Preliminary findings established the psychometric properties of the AMHQ, suggesting that it is a reliable and valid scale. Correlation analyses indicated that the ARHQ was negatively associated with children's reading skills, whereas the AMHQ was negatively related to math outcomes. Path results revealed that the ARHQ predicted children's performance on complex reading tasks indirectly via their core reading skills, and the AMHQ was linked to complex math outcomes indirectly via core math abilities. The ARHQ was also found to be negatively correlated with measures of children's math performance, with path findings suggesting that these relations were indirectly explained by differences in their core reading skills. These results suggest that assessing familial risk for academic difficulties may be crucial to understanding comorbid etiological and developmental associations between reading and math differences.

pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis

Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introduce pyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analysis of ICD-9 and ICD-10 billing codes; 3) PheDAS analysis applied to a novel EMR phenotype mapping: current procedural terminology (CPT) codes; and 4) novelty analysis of significant disease-phenotype associations found through PheDAS. The pyPheWAS toolkit is approachable and comprehensive, encapsulating data prep through result visualization all within a simple command-line interface. The toolkit is designed for the ever-growing scale of available EMR data, with the ability to analyze cohorts of 100,000 + patients in less than 2 h. Through a case study of Down Syndrome and other intellectual developmental disabilities, we demonstrate the ability of pyPheWAS to discover both known and potentially novel disease-phenotype associations across different experiment designs and disease groups. The software and user documentation are available in open source at https://github.com/MASILab/pyPheWAS .

Emergent Bilinguals With Specific Reading Comprehension Deficits: A Comparative and Longitudinal Analysis

This study centered on emergent bilingual (EB) students with specific reading comprehension deficits (S-RCD), that is, with poor reading comprehension despite solid word identification skills. The participants were 209 students in Grades 2 to 4, including both EBs and English monolinguals (EMs) with and without S-RCD. Mean comparisons indicated that EBs and EMs with S-RCD showed weaknesses relative to typically developing (TD) readers in oral language, word identification, inference making, and reading engagement, but not in executive functioning. Longitudinal analyses indicated that across two academic years S-RCD persisted for 41% of EBs and EMs alike. Altogether, the study extends research on EBs with S-RCD by identifying variables beyond oral language that may account for their reading comprehension difficulties and providing insight into the extent to which their reading comprehension and word identification performance levels evolve during elementary school. Furthermore, the findings point to the importance of early identification and intervention for weaknesses in reading comprehension and its component elements in both EBs and EMS.

Brief Report: The Characterization of Medical Comorbidity Prior to Autism Diagnosis in Children Before Age Two

In autism spectrum disorder (ASD), medical conditions in infancy could be predictive markers for later ASD diagnosis. In this study, electronic medical records of 579 autistic individuals and 1897 matched controls prior to age 2 were analyzed for potential predictive conditions. Using a novel tool, the relative association of each condition in the autistic group was compared to the control group using logistic regressions across medical records. Generalized convulsive epilepsy, nystagmus, lack of normal physiological development, delayed milestones, and strabismus were more likely in those later diagnosed with ASD while perinatal jaundice was less likely to be associated. Lesser-known conditions, such as strabismus and nystagmus, may point to novel predictive co-occurring condition profiles which could improve screening practices for ASD.

Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography

Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate "regions" rather than as white matter "bundles" or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different automated state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing

It has been challenging to elucidate the differences in brain structure that underlie behavioral features of autism. Prior studies have begun to identify patterns of changes in autism across multiple structural indices, including cortical thickness, local gyrification, and sulcal depth. However, common approaches to local gyrification indexing used in prior studies have been limited by low spatial resolution relative to functional brain topography. In this study, we analyze the aforementioned structural indices, utilizing a new method of local gyrification indexing that quantifies this index adaptively in relation to specific sulci/gyri, improving interpretation with respect to functional organization. Our sample included n = 115 autistic and n = 254 neurotypical participants aged 5-54, and we investigated structural patterns by group, age, and autism-related behaviors. Differing structural patterns by group emerged in many regions, with age moderating group differences particularly in frontal and limbic regions. There were also several regions, particularly in sensory areas, in which one or more of the structural indices of interest either positively or negatively covaried with autism-related behaviors. Given the advantages of this approach, future studies may benefit from its application in hypothesis-driven examinations of specific brain regions and/or longitudinal studies to assess brain development in autism.

Commentary: Dimensionality in environmental adversity, mechanisms of emotional socialization, and children's characteristics and cognitive growth - a reflection on Miller et al. (2020)

Disentangling the dimensionality in environmental adversity offers nuanced insights at both theoretical and practical levels, such as the ways that disadvantaged socioeconomic circumstances during childhood development may contribute to adolescent psychopathology. Miller and colleagues (2020) provide evidence into how early deprivation and threat may exacerbate later psychopathology. Yet, how certain factors in this early environment differentially facilitate children's cognitive and socioemotional growth may modulate the severity of later psychopathology. In this commentary, we reflect on the promising evidence offered by Miller and colleagues and extend additional considerations regarding academic growth, cognitive abilities, and protective environmental factors.

Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.

Using phecode analysis to characterize co-occurring medical conditions in autism spectrum disorder

LAY ABSTRACT

People with autism spectrum disorder often have a number of other medical conditions in addition to autism. These can range from constipation to epilepsy. This study uses medical record data to understand how frequently and how long people with autism have to be seen by a medical professional for these other medical conditions. This study confirmed that people with autism often have a number of other medical conditions and that they have to go see a medical professional about those conditions often. We also looked to see if children diagnosed with autism after age 5 years might have different medical conditions compared to children diagnosed earlier. Children diagnosed later had more conditions like asthma, hearing loss, and mood disorders. This work describes how much medical care people with autism get for different medical conditions and the burden of seeking additional medical care for people with autism and their families.

The relationship between cognitive skills and reading comprehension of narrative and expository texts: A longitudinal study from Grade 1 to Grade 4

Following the increased emphasis on expository text in early grades, this study examined narrative and expository reading comprehension growth in a sample of children who were followed longitudinally from grades 1 to 4, with the goals of explaining potential differences in children's overall performance and growth of narrative and expository text comprehension and identifying the cognitive factors that distinctly contribute to comprehension for each text type. We hypothesized that differences in reading comprehension growth of narrative and expository texts would be explained by various cognitive factors, specifically those related to executive functions (EF; e.g., working memory, planning/organization, shifting, and inhibition). At four annual time points, children (n= 94) read, retold (Recall), and answered questions (CompQ) about expository and narrative passages. Growth curve modeling was used to explore reading comprehension development across the two types of text. On average, results showed that children scored better on reading comprehension of narrative passages than they did on expository passages across all time points. After controlling for socioeconomic status (SES), vocabulary in 1st grade predicted 4th grade comprehension scores (Recall) for both narrative and expository passages, while word reading efficiency (WRE) in 1st grade predicted 4th grade comprehension scores (CompQ) for expository passages only. Additionally, WRE was associated with the growth of expository reading comprehension: children with higher WRE showed a faster growth rate for expository CompQ. The contribution of EF to text comprehension was largely confined to expository text, although planning and organization (measured using a direct cognitive assessment) in 1st grade also predicted 4th grade comprehension scores for narrative text Recall. For expository text comprehens ion, working memory, planning and organization, shifting, and inhibition (measured using a parent rating scale), predicted reading comprehension outcomes. Critically, 1st grade shifting and inhibition not only predicted 4th grade expository text comprehension (CompQ), but also modulated its growth rate: children with stronger shifting and inhibition had faster rates of growth. Together, these findings suggest that expository reading comprehension is (1) more difficult than narrative reading comprehension and (2) is associated with unique cognitive skills.

Association of Intrinsic Brain Architecture With Changes in Attentional and Mood Symptoms During Development

Importance

Understanding the neurodevelopmental trajectory of psychiatric symptoms is important for improving early identification, intervention, and prevention of mental disorders.

Objective

To test whether the strength of the coupling of activation between specific brain regions, as measured by resting-state functional magnetic resonance imaging (fMRI), predicted individual children's developmental trajectories in terms of attentional problems characteristic of attention-deficit/hyperactivity disorder and internalizing problems characteristics of major depressive disorder (MDD).

Design, Setting, and Participants

A community cohort of 94 children was recruited from Vanderbilt University between 2010 and 2013. They were followed up longitudinally for 4 years and the data were analyzed from 2016 to 2019. Based on preregistered hypotheses and an analytic plan, we examined whether specific brain connectivity patterns would be associated with longitudinal changes in scores on the Child Behavior Checklist (CBCL), a parental-report assessment used to screen for emotional, behavioral, and social problems and to predict psychiatric illnesses.

Main Outcomes and Measures

We used the strength of resting-state fMRI connectivity at age 7 years to predict subsequent changes in CBCL measures 4 years later and investigated the mechanisms of change by associating brain connectivity changes with changes in the CBCL.

Results

We analyzed data from a longitudinal brain development study involving children assessed at age 7 years (n = 94; 41 girls [43.6%]) and 11 years (n = 54; 32 girls [59.3%]). As predicted, less positive coupling at age 7 years between the medial prefrontal cortex and dorsolateral prefrontal cortex (DLPFC) was associated with a decrease in attentional symptoms by age 11 years (t49 = 2.38; P = .01; β = 0.32). By contrast, a less positive coupling between a region implicated in mood, the subgenual anterior cingulate cortex (sgACC), and DLPFC at age 7 years was associated with an increase in internalizing (eg, anxiety/depression) behaviors by age 11 years (t49 = -2.4; P = .01; β = -0.30). Logistic regression analyses revealed that sgACC-DLPFC connectivity was a more accurate predictor than baseline CBCL measures for progression to a subclinical score on internalization (t50 = -2.61; P = .01; β = -0.29). We then replicated and extended the sgACC-DLPFC result in an independent sample of children with (n = 25) or without (n = 18) familial risk for MDD.

Conclusions and Relevance

These resting-state fMRI metrics are promising biomarkers for the early identification of children at risk of developing MDD or attention-deficit/hyperactivity disorder.

Executive functions and components of oral reading fluency through the lens of text complexity

As readers struggle to coordinate various reading- and language-related skills during oral reading fluency (ORF), miscues can emerge, especially when processing complex texts. Following a miscue, students often self-correct as a strategy to potentially restore ORF and online linguistic comprehension. Executive functions (EF) are hypothesized to play an interactive role during ORF. Yet, the role of EF in self-corrections while reading complex texts remains elusive. To this end, we evaluated the relation between students' probability of self-correcting miscues-or P(SC)-and their EF profile in a cohort of 143 participants (aged 9-15) who represented a diverse spectrum of reading abilities. Moreover, we used experimentally-manipulated passages (decoding, vocabulary, syntax, and cohesion) and employed a fully cross-classified mixed-effects multilevel regression strategy to evaluate the interplay between components of ORF, EF, and text complexity. Our results revealed that, after controlling for reading and language abilities, increased production of miscues across different passage conditions was explained by worse EF. We also found that students with better EF exhibited greater P(SC) when reading complex texts. While text complexity taxes students' EF and influences their production of miscues, findings suggest that EF may be interactively recruited to restore ORF via self-correcting oral reading errors. Overall, our results suggest that domain-general processes (e.g., EF) are associated with production of miscues and may underlie students' behavior of self-corrections, especially when reading complex texts. Further understanding of the relation between different components of ORF and cognitive processes may inform intervention strategies to improve reading proficiency and overall academic performance.

Relations Among Executive Function, Decoding, and Reading Comprehension: An Investigation of Sex Differences

In the current investigation, we used structural equation mediation modeling to examine the relations between executive function (indexed by measures of working memory, shifting, and inhibition), decoding ability, and reading comprehension in a sample of 298 6- to 8-year-old children (N =132 and 166 for boys and girls, respectively). Results for the full sample indicated that executive function was mediated by decoding ability. When sex was examined as a moderator of these associations, there was evidence for a trend suggesting that direct relations between executive function and reading comprehension were stronger for girls compared to boys; no significant differences were found for other direct and indirect relations. Taken together, these findings highlight the importance of executive function in supporting underlying integrative processes associated with reading comprehension and emphasize the need to further consider the role of executive function in relation to reading.

Readers Recruit Executive Functions to Self-Correct Miscues During Oral Reading Fluency

Reading fluency undoubtedly underlies reading competence; yet, the role of executive functions (EF) is less well understood. Here, we investigated the relationship between children's reading fluency and EF. Children's (n = 82) reading and language performance was determined by standardized assessments and EF by parental questionnaire. Results revealed that production of more miscues was explained by poorer reading and language performance and EF. Yet, self-correcting a miscue was predicted by better EF, beyond reading and language abilities. Intriguingly, EF partially mediated the relationship between reading and self-correction, suggesting that self-correction reflects parallel recruitment and coordination of domain-specific and domain-general processes.

Left posterior prefrontal regions support domain-general executive processes needed for both reading and math

Substantial evidence has suggested that reading and math are supported by executive processes (EP). However, to date little is known about which portion of the neural system underpinning domain-general executive skills works to support reading and math. In this study, we aimed to answer this question using fMRI via two complementary approaches. First, imaging data were acquired whilst a sample of 231 adolescents performed each of three separate tasks designed to assess reading comprehension, numerical magnitude estimation, and EP in working memory (WM), respectively. With careful task designs and conjunction analyses, we were able to isolate cross-domain brain activity specifically related to EP, as opposed to lower-level domain-general processes (e.g., visual processing). Second, the meta-analytic tool Neurosynth was used to independently identify brain regions involved reading, math, and EP. Using a combination of forward and reverse statistical inference and conjunction analyses, we again isolated brain regions specifically supporting domain-general EP. Results from both approaches yielded overlapping activation for reading, math, and EP in the left ventrolateral prefrontal cortex, left inferior frontal junction, and left precentral gyrus. This pattern suggests that posterior regions of the prefrontal cortex, rather than more central regions such as mid-DLPFC, play a leading role in supporting domain-general EP utilized by both reading and math.

Fairy Tales versus Facts: Genre Matters to the Developing Brain

Neurobiological studies of discourse comprehension have almost exclusively focused on narrative comprehension. However, successful engagement in modern society, particularly in educational settings, also requires comprehension with an aim to learn new information (i.e., "expository comprehension"). Despite its prevalence, no studies to date have neurobiologically characterized expository comprehension as compared with narrative. In the current study, we used functional magnetic resonance imaging in typically developing children to test whether different genres require specialized brain networks. In addition to expected activations in language and comprehension areas in the default mode network (DMN), expository comprehension required significantly greater activation in the frontoparietal control network (FPN) than narrative comprehension, and relied significantly less on posterior regions in the DMN. Functional connectivity analysis revealed that, compared with narrative, the FPN robustly correlated with the DMN, and this inter-network communication was higher with increased reading expertise. These findings suggest that, relative to narrative comprehension, expository comprehension shows (1) a unique configuration of the DMN, potentially to support non-social comprehension processes, and (2) increased utilization of top-down regions to help support goal-directed comprehension processes in the DMN. More generally, our findings reveal that different types of discourse-level comprehension place diverse neural demands on the developing brain.

The impact of expressive language development and the left inferior longitudinal fasciculus on listening and reading comprehension

BACKGROUND

During the first 3-years of life, as the brain undergoes dramatic growth, children begin to develop speech and language. Hallmarks of this progression are seen when children reach developmental milestones, forming the foundation of language. Expressive language milestones, such as the production of a child's first word, are delayed in 5-8% of children. While for some children delays in reaching these milestones are harbingers of developmental disorders, for others expressive language delays appear to resolve. Regardless of whether or not early language skills appear resolved, difficulty with later comprehension is a likely outcome. Whether this heightened risk for poor comprehension differs based on text features, individual characteristics, or receipt of intervention remains unknown. Moreover, this relationship between expressive language development and comprehension is not yet linked to neurobiology, though the inferior longitudinal fasciculus (ILF) is a potential neurobiological correlate. Therefore, we investigated the impact of, and interactions between, expressive language development, early intervention, and the ILF on comprehension.

METHODS

Longitudinal recurrent survival analyses predicted the risk of answering a comprehension question incorrectly. Predictors of comprehension included expressive language development, passage features, participant characteristics, fractional anisotropy, receipt of early intervention, and later diagnosis of speech or language disorders.

RESULTS

Children with later expressive language milestones had poorer comprehension. When comprehension text features were examined, children with later milestones had poorer listening and reading comprehension, and poorer narrative and expository comprehension. The left ILF acted as a neurodevelopmental correlate, one that moderated the relationship between expressive language milestones and comprehension. Specifically, the left ILF exacerbated the relationship for those who did not receive early intervention and buffered the relationship for those who received intervention services. Early intervention decreased the risk of poor comprehension by 39% for children later diagnosed with a speech or language disorder.

CONCLUSIONS

Early intervention should be provided for children with delayed expressive language milestones, particularly those who are at risk for speech or language disorders. The ILF plays a critical role in the relationship between expressive language development and comprehension, which may be that of a protective factor for children with the most severe early issues with speech and language.

Direct and Indirect Contributions of Executive Function to Word Decoding and Reading Comprehension in Kindergarten

Extant research is increasingly recognizing the contribution of executive function (EF) to reading comprehension alongside established predictors like word decoding and oral language. The nature of the association between EF and reading comprehension is commonly investigated in older children and in those with reading impairments. However, less is known about this relationship in emerging readers in kindergarten, where word decoding and reading comprehension are highly intertwined. Moreover, a better understanding of the mechanisms by which EF influences reading comprehension is needed. The present study investigated direct contributions of EF to reading comprehension, as well as indirect contributions via word decoding in 97 kindergarteners. Results indicated that there was a significant indirect effect of EF on reading comprehension, with word decoding mediating this association. The direct contribution of EF to reading comprehension was not significant. Implications for reading instruction and intervention for early readers are discussed.

Anatomical context improves deep learning on the brain age estimation task

Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network design to include traditional structural imaging features alongside deep convolutional ones and illustrate this approach on the task of imaging-based age prediction in two separate contexts: T1-weighted brain magnetic resonance imaging (MRI) (N = 5121, ages 4-96, healthy controls) and computed tomography (CT) of the head (N = 1313, ages 1-97, healthy controls). In brain MRI, we can predict age with a mean absolute error of 4.08 years by combining raw images along with engineered structural features, compared to 5.00 years using image-derived features alone and 8.23 years using structural features alone. In head CT, we can predict age with a median absolute error of 9.99 years combining features, compared to 11.02 years with image-derived features alone and 13.28 years with structural features alone. These results show that we can complement traditional feature estimation using deep learning to improve prediction tasks. As the field of medical image processing continues to integrate deep learning, it will be important to use the new techniques to complement traditional imaging features instead of fully displacing them.

Considering the Role of Executive Function in Reading Comprehension: A Structural Equation Modeling Approach

In the present study, we used latent variable structural equation modeling to investigate relations between oral language, decoding, and two components of executive function (cognitive flexibility and working memory) and reading comprehension in a sample of 271 native English-speaking 9.00- to 14.83-year-olds. Results of the mediation analyses indicated that both oral language and decoding fully mediated the relations between working memory and cognitive flexibility and reading comprehension. These findings suggest that executive function is likely associated with reading comprehension through its relation with decoding and oral language and provide additional support for the role of executive function in reading comprehension as a potentially crucial precursor to skilled reading.

Learning Lexical Information Depends Upon Task, Learning Approach, and Reader Subtype

Learning to read relies upon the integration of phonological, orthographic, and semantic information. However, no studies have investigated how children with varying reading abilities learn phonological-orthographic (PO) and semantic aspects of novel words as a function of both learning approach (LA; e.g., learning new words in isolation or context) and outcome (fluency or comprehension). In this study, 45 children participated in three tasks that differentially tested PO and semantic attributes of novel pseudo-words learned through two learning approaches. Children were classified into groups as having dyslexia (DYS), having specific reading comprehension deficits (S-RCDs), or being typically developing readers (TD). Differences were found between groups, with S-RCD poorer than TD on semantic but not PO components of learning. Children with DYS displayed impaired results on both semantic and PO learning but showed an interaction on task by LA performance. Specifically, in the DYS group, isolation LA yielded better performance on PO learning, while context LA was better for semantic learning. These results indicate that (a) children with S-RCDs have a unique learning profile that is dissociable from DYS and TD and (b) reading impairments are not static but rather influence acquisition of reading skill in different ways, depending on reading profile.

3D whole brain segmentation using spatially localized atlas network tiles

Detailed whole brain segmentation is an essential quantitative technique in medical image analysis, which provides a non-invasive way of measuring brain regions from a clinical acquired structural magnetic resonance imaging (MRI). Recently, deep convolution neural network (CNN) has been applied to whole brain segmentation. However, restricted by current GPU memory, 2D based methods, downsampling based 3D CNN methods, and patch-based high-resolution 3D CNN methods have been the de facto standard solutions. 3D patch-based high resolution methods typically yield superior performance among CNN approaches on detailed whole brain segmentation (>100 labels), however, whose performance are still commonly inferior compared with state-of-the-art multi-atlas segmentation methods (MAS) due to the following challenges: (1) a single network is typically used to learn both spatial and contextual information for the patches, (2) limited manually traced whole brain volumes are available (typically less than 50) for training a network. In this work, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks (FCN) for high-resolution whole brain segmentation. To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location. To address the second challenge, auxiliary labels on 5111 initially unlabeled scans were created by multi-atlas segmentation for training. Since the method integrated multiple traditional medical image processing methods with deep learning, we developed a containerized pipeline to deploy the end-to-end solution. From the results, the proposed method achieved superior performance compared with multi-atlas segmentation methods, while reducing the computational time from >30 h to 15 min. The method has been made available in open source (https://github.com/MASILab/SLANTbrainSeg).

Characterizing and decomposing the neural correlates of individual differences in reading ability among adolescents with task-based fMRI

To better characterize the neural correlates of the full spectrum of reading ability, this fMRI study examined how variations in reading ability correlate with task-based brain activity during reading among a large community sample of adolescents (N = 234). In addition, complimentary approaches taking advantage of empirical as well as independent meta-analytic information were employed to isolate neural substrates of domain-general executive processes that are predictive of reading ability. Age-related differences in brain activity were also examined. Better reading was associated with increased activation in left anterior and inferior temporal regions and parts of orbitofrontal cortex, along with reduced activation in the thalamus and left frontal eye field (FEF). Converging evidence suggests that FEF activity corresponds to executive processes during reading. In contrast, activity in temporal regions is likely to reflect cognitive processes specific to reading. Older adolescents also demonstrated increased activation in an orbitofrontal region that overlaps with the aforementioned age-independent, reading-related regions, along with reduced activity in parietal and occipital regions. These results suggest that comparedto poor readers, proficient readers benefit from efficient reading-specific processes and require less executive effort, implemented via the FEF, during a reading comprehension task.

Connections Between Reading Comprehension and Word-Problem Solving via Oral Language Comprehension: Implications for Comorbid Learning Disabilities

In this article, we discuss the approach adopted within the Vanderbilt University Learning Disabilities Innovation Hub, which focuses on students with higher-order comorbidity: students with concurrent difficulty with reading comprehension and word-problem solving. The aim of the Hub's Research Project is to test what we refer to as the higher-order comorbidity hypothesis: that language comprehension plays a critical role in reading comprehension and word-problem solving. In the Hub's study, we test the hypothesize that language comprehension offers a coordinated approach for improving both outcomes and that this approach thus provides direction for understanding higher-order comorbidity and support for the validity of reading comprehension and word-problem solving comorbidity as a learning disabilities subtyping framework. In the first segment of this article, we describe a model that connects reading comprehension and word-problem solving development via oral language comprehension, and we provide a brief overview of prior related research on these connections. This first section provides the basis for the second segment of this article, in which we discuss the Vanderbilt Hub's innovative approach for investigating these connections. This study tests a theoretically-coordinated framework on students' performance in both high-priority domains of academic development, while exploring effects for boys versus girls and for linguistically diverse learners.

Understanding the Influence of Text Complexity and Question Type on Reading Outcomes

In the current study, we examined how student characteristics and cognitive skills, differing levels of text complexity (cohesion, decoding, vocabulary, and syntax), and reading comprehension question types (literal, inferential, critical analysis, and reading strategy) affected different types of reading outcomes (multiple-choice reading comprehension questions, free recall, and oral reading fluency) in a sample of 181 native English-speaking adolescents (9 to 14.83 years). Results from item response theory one-parameter models and multilevel models suggested that different cognitive skills predicted performance across the three reading outcomes. After controlling for student characteristics and cognitive skills, text complexity negatively impacted reading outcomes, particularly oral reading fluency and free recall. Critical analysis and inferential questions emerged as the most difficult types of comprehension questions. The implications of these findings are discussed.

Applying a network framework to the neurobiology of reading and dyslexia

Background

There is a substantial literature on the neurobiology of reading and dyslexia. Differences are often described in terms of individual regions or individual cognitive processes. However, there is a growing appreciation that the brain areas subserving reading are nested within larger functional systems, and new network analysis methods may provide greater insight into how reading difficulty arises. Yet, relatively few studies have adopted a principled network-based approach (e.g., connectomics) to studying reading. In this study, we combine data from previous reading literature, connectomics studies, and original data to investigate the relationship between network architecture and reading.

Methods

First, we detailed the distribution of reading-related areas across many resting-state networks using meta-analytic data from NeuroSynth. Then, we tested whether individual differences in modularity, the brain’s tendency to segregate into resting-state networks, are related to reading skill. Finally, we determined whether brain areas that function atypically in dyslexia, as identified by previous meta-analyses, tend to be concentrated in hub regions.

Results

We found that most resting-state networks contributed to the reading network, including those subserving domain-general cognitive skills such as attention and executive function. There was also a positive relationship between the global modularity of an individual’s brain network and reading skill, with the visual, default mode and cingulo-opercular networks showing the highest correlations. Brain areas implicated in dyslexia were also significantly more likely to have a higher participation coefficient (connect to multiple resting-state networks) than other areas.

Conclusions

These results contribute to the growing literature on the relationship between reading and brain network architecture. They suggest that an efficient network organization, i.e., one in which brain areas form cohesive resting-state networks, is important for skilled reading, and that dyslexia can be characterized by abnormal functioning of hub regions that map information between multiple systems. Overall, use of a connectomics framework opens up new possibilities for investigating reading difficulty, especially its commonalities across other neurodevelopmental disorders.

Electronic supplementary material

The online version of this article (10.1186/s11689-018-9251-z) contains supplementary material, which is available to authorized users.

Structural covariance across the lifespan: Brain development and aging through the lens of inter-network relationships

Recent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within-network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big-data framework and a novel application of covariate-adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7-90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter-network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter-network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter-network structural covariance across the lifespan.

Neurochemistry Predicts Convergence of Written and Spoken Language: A Proton Magnetic Resonance Spectroscopy Study of Cross-Modal Language Integration

Recent studies have provided evidence of associations between neurochemistry and reading (dis)ability (Pugh et al., 2014). Based on a long history of studies indicating that fluent reading entails the automatic convergence of the written and spoken forms of language and our recently proposed Neural Noise Hypothesis (Hancock et al., 2017), we hypothesized that individual differences in cross-modal integration would mediate, at least partially, the relationship between neurochemical concentrations and reading. Cross-modal integration was measured in 231 children using a two-alternative forced choice cross-modal matching task with three language conditions (letters, words, and pseudowords) and two levels of difficulty within each language condition. Neurometabolite concentrations of Choline (Cho), Glutamate (Glu), gamma-Aminobutyric (GABA), and N- acetyl-aspartate (NAA) were then measured in a subset of this sample (n = 70) with Magnetic Resonance Spectroscopy (MRS). A structural equation mediation model revealed that the effect of cross-modal word matching mediated the relationship between increased Glu (which has been proposed to be an index of neural noise) and poorer reading ability. In addition, the effect of cross-modal word matching fully mediated a relationship between increased Cho and poorer reading ability. Multilevel mixed effects models confirmed that lower Cho predicted faster cross-modal matching reaction time, specifically in the hard word condition. These Cho findings are consistent with previous work in both adults and children showing a negative association between Cho and reading ability. We also found two novel neurochemical relationships. Specifically, lower GABA and higher NAA predicted faster cross-modal matching reaction times. We interpret these results within a biochemical framework in which the ability of neurochemistry to predict reading ability may at least partially be explained by cross-modal integration.

Brain basis of cognitive resilience: Prefrontal cortex predicts better reading comprehension in relation to decoding

OBJECTIVE

The ultimate goal of reading is to understand written text. To accomplish this, children must first master decoding, the ability to translate printed words into sounds. Although decoding and reading comprehension are highly interdependent, some children struggle to decode but comprehend well, whereas others with good decoding skills fail to comprehend. The neural basis underlying individual differences in this discrepancy between decoding and comprehension abilities is virtually unknown.

METHODS

We investigated the neural basis underlying reading discrepancy, defined as the difference between reading comprehension and decoding skills, in a three-part study: 1) The neuroanatomical basis of reading discrepancy in a cross-sectional sample of school-age children with a wide range of reading abilities (Experiment-1; n = 55); 2) Whether a discrepancy-related neural signature is present in beginning readers and predictive of future discrepancy (Experiment-2; n = 43); and 3) Whether discrepancy-related regions are part of a domain-general or a language specialized network, utilizing the 1000 Functional Connectome data and large-scale reverse inference from Neurosynth.org (Experiment-3).

RESULTS

Results converged onto the left dorsolateral prefrontal cortex (DLPFC), as related to having discrepantly higher reading comprehension relative to decoding ability. Increased gray matter volume (GMV) was associated with greater discrepancy (Experiment-1). Region-of-interest (ROI) analyses based on the left DLPFC cluster identified in Experiment-1 revealed that regional GMV within this ROI in beginning readers predicted discrepancy three years later (Experiment-2). This region was associated with the fronto-parietal network that is considered fundamental for working memory and cognitive control (Experiment-3).

INTERPRETATION

Processes related to the prefrontal cortex might be linked to reading discrepancy. The findings may be important for understanding cognitive resilience, which we operationalize as those individuals with greater higher-order reading skills such as reading comprehension compared to lower-order reading skills such as decoding skills. Our study provides insights into reading development, existing theories of reading, and cognitive processes that are potentially significant to a wide range of reading disorders.

Prospective relations between resting-state connectivity of parietal subdivisions and arithmetic competence

The present study investigates the relation between resting-state functional connectivity (rsFC) of cytoarchitectonically defined subdivisions of the parietal cortex at the end of 1st grade and arithmetic performance at the end of 2nd grade. Results revealed a dissociable pattern of relations between rsFC and arithmetic competence among subdivisions of intraparietal sulcus (IPS) and angular gyrus (AG). rsFC between right hemisphere IPS subdivisions and contralateral IPS subdivisions positively correlated with arithmetic competence. In contrast, rsFC between the left hIP1 and the right medial temporal lobe, and rsFC between the left AG and left superior frontal gyrus, were negatively correlated with arithmetic competence. These results suggest that strong inter-hemispheric IPS connectivity is important for math development, reflecting either neurocognitive mechanisms specific to arithmetic processing, domain-general mechanisms that are particularly relevant to arithmetic competence, or structural 'cortical maturity'. Stronger connectivity between IPS, and AG, subdivisions and frontal and temporal cortices, however, appears to be negatively associated with math development, possibly reflecting the ability to disengage suboptimal problem-solving strategies during mathematical processing, or to flexibly reorient task-based networks. Importantly, the reported results pertain even when controlling for reading, spatial attention, and working memory, suggesting that the observed rsFC-behavior relations are specific to arithmetic competence.

Prefrontal mediation of the reading network predicts intervention response in dyslexia

A primary challenge facing the development of interventions for dyslexia is identifying effective predictors of intervention response. While behavioral literature has identified core cognitive characteristics of response, the distinction of reading versus executive cognitive contributions to response profiles remains unclear, due in part to the difficulty of segregating these constructs using behavioral outputs. In the current study we used functional neuroimaging to piece apart the mechanisms of how/whether executive and reading network relationships are predictive of intervention response. We found that readers who are responsive to intervention have more typical pre-intervention functional interactions between executive and reading systems compared to nonresponsive readers. These findings suggest that intervention response in dyslexia is influenced not only by domain-specific reading regions, but also by contributions from intervening domain-general networks. Our results make a significant gain in identifying predictive bio-markers of outcomes in dyslexia, and have important implications for the development of personalized clinical interventions.

Neuroanatomical correlates of performance in a state-wide test of math achievement

The development of math skills is a critical component of early education and a strong indicator of later school and economic success. Recent research utilizing population-normed, standardized measures of math achievement suggest that structural and functional integrity of parietal regions, especially the intraparietal sulcus, are closely related to the development of math skills. However, it is unknown how these findings relate to in-school math learning. The present study is the first to address this issue by investigating the relationship between regional differences in grey matter (GM) volume and performance in grade-level mathematics as measured by a state-wide, school-based test of math achievement (TCAP math) in children from 3rd to 8th grade. Results show that increased GM volume in the bilateral hippocampal formation and the right inferior frontal gyrus, regions associated with learning and memory, is associated with higher TCAP math scores. Secondary analyses revealed that GM volume in the left angular gyrus had a stronger relationship to TCAP math in grades 3-4 than in grades 5-8 while the relationship between GM volume in the left inferior frontal gyrus and TCAP math was stronger for grades 5-8. These results suggest that the neuroanatomical architecture related to in-school math achievement differs from that related to math achievement measured by standardized tests, and that the most related neural structures differ as a function of grade level. We suggest, therefore, that the use of school-relevant outcome measures is critical if neuroscience is to bridge the gap to education.

Item response theory analyses of the Delis-Kaplan Executive Function System card sorting subtest

In the current study, we examined the dimensionality of the 16-item Card Sorting subtest of the Delis-Kaplan Executive Functioning System assessment in a sample of 264 native English-speaking children between the ages of 9 and 15 years. We also tested for measurement invariance for these items across age and gender groups using item response theory (IRT). Results of the exploratory factor analysis indicated that a two-factor model that distinguished between verbal and perceptual items provided the best fit to the data. Although the items demonstrated measurement invariance across age groups, measurement invariance was violated for gender groups, with two items demonstrating differential item functioning for males and females. Multigroup analysis using all 16 items indicated that the items were more effective for individuals whose IRT scale scores were relatively high. A single-group explanatory IRT model using 14 non-differential item functioning items showed that for perceptual ability, females scored higher than males and that scores increased with age for both males and females; for verbal ability, the observed increase in scores across age differed for males and females. The implications of these findings are discussed.

Global Approaches to Early Learning Research and Practice: Integrative Commentary

This commentary presents highlights from the seven articles in this volume, along with a synthesis of take-home points that can be used to inform policy and practice. Across each article there is a story of both successes and the challenges of ongoing work that seeks to enhance children's development in diverse and challenging environments across the globe. Although the topics covered in this volume range from development of early self-regulation and executive function to the use of technology to aid literacy acquisition in remote areas, each points to the need for systems-level coordination and sustained commitment to reach children at risk.