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.

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.

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.

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.

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.

Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition.

Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts ("semantic cognition"). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single-modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans (n = 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language comprehension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spatial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ∼300 ms after the sentence final word); a frontotemporal thematic semantic network (∼400 ms); a hippocampal memory update network (∼500 ms); an inferior frontal semantic syntactic reappraisal network (∼600 ms); and nodes of the default mode network associated with conceptual coherence (∼750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains.SIGNIFICANCE STATEMENT The present study identifies a real-time neurobiological model of the meaning processes required during language comprehension (i.e., "semantic cognition"). Using a novel application of fused magnetic resonance imaging and electroencephalography in humans, we found that semantic cognition during language comprehension is supported by a rapid progression of widespread neural networks related to meaning, meaning integration, memory, reappraisal, and conceptual cohesion. Relationships among these systems were predictive of individuals' language comprehension efficiency. Our findings are the first to use fused neuroimaging analysis to elucidate language processes. In so doing, this study provides a new conceptual and methodological framework in which to characterize language processes and guide the treatment of speech and language deficits/disorders.

The behavioral and neurobiological relationships between executive function and reading: A review and preliminary findings.

Despite decades of prior research, the mechanisms for how skilled reading develops remain elusive. Numerous studies have identified word recognition and oral language ability as key components to explain later reading comprehension performance. However, these components alone do not fully explain differences in reading achievement. There is ongoing work exploring other candidate processes important for reading, such as the domain-general cognitive ability of executive function (EF). Here, we summarize our work on the behavioral and neurobiological connections between EF and reading and present preliminary neuroimaging findings from ongoing work. Together, these studies suggest 1) that EF plays a supportive and perhaps indirect role in reading achievement and 2) that EF-related brain regions interface with the reading and language networks. While further work is needed to dissect the specifics of how EF interacts with reading, these studies begin to reveal the complex role that EF plays in reading development.

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.

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.

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.

Contextualizing school achievement among vulnerable learners: Implications for science and practice: Commentary.

The current set of papers in this special issue capture the range of viewpoints, scientific approaches, and populations needed to illuminate and tackle the issues of school achievement among vulnerable learners. This includes providing a framework for researchers to work from relevant policy findings, and literature reviews to small scale studies. The manuscripts also traverse different aspects of scientific inquiry - from data reported by federal and state programs, thus providing a "bird's eye view" of findings, to more granular neurobiological approaches. Across all papers is the clear theme of needing to shift from where we have been in order to establish a path forward for where we need to go to account for learners that have been relatively neglected in scientific studies. To break down barriers of inequity and increase our understanding of causes and consequences of vulnerable learners, there is a need to re-think how we establish policies and allocate funds, as well as broadening our lens as we conduct scientific studies. Each piece in this special issue calls for the need to better understand these issues that vulnerable learners face to address inequities in our educational ecosystems. Together they provide a rich set of insights that have significant implications for science and practice.

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.

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.

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.

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).