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Introduction: The present study examines the relationships between processing speed (PS), mental health disorders, and learning disorders. Prior work has tended to explore relationships between PS deficits and specific diagnoses in isolation of one another. Here, we simultaneously investigated PS associations with five diagnoses (i.e., anxiety, autism, ADHD, depressive, specific learning) in a large-scale, transdiagnostic, community self-referred sample. Method. 843 children, ages 8–16 were included from the Healthy Brain Network (HBN) Biobank. Principal component analysis (PCA) was employed to create a composite measure of four PS tasks, referred to as PC1. Intraclass correlation coefficient (ICC) between the four PS measures, as well as PC1, were calculated to assess reliability. Results. ICCs were moderate between WISC-V tasks (0.663), and relatively modest between NIH Toolbox Pattern Comparison and other PS scales (0.14–0.27). Regression analyses revealed specific significant relationships between PS and reading and math disabilities, ADHD-inattentive presentation (ADHD-I), and ADHD-combined presentation (ADHD-C). After accounting for inattention, the present study did not find a significant relationship with Autism Spectrum Disorder. Discussion. Our examination of PS in a large, transdiagnostic sample suggested more specific associations with ADHD and learning disorders than the literature currently suggests. Implications for understanding how PS interacts with a highly heterogeneous childhood sample are discussed.

Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice. To justify the significant effort required for sharing data, funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a case study that provides direct evidence of the impact of open sharing on brain imaging data use and resulting peer-reviewed publications. We demonstrate that openly shared data can increase the scale of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings dispel the myth that scientific findings using shared data cannot be published in high-impact journals, suggest the transformative power of data sharing for accelerating science, and underscore the need for implementing data sharing universally. Data sharing is recognized as a way to promote scientific collaboration and reproducibility, but some are concerned over whether research based on shared data can achieve high impact. Here, the authors show that neuroimaging papers using shared data are no less likely to appear in top-ranked journals.

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release ( n =664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis). Design Type(s) data integration objective • clinical history design Measurement Type(s) phenotype • brain activity measurement • nuclear magnetic resonance assay Technology Type(s) performing a clinical assessment • electroencephalography • MRI Scanner Factor Type(s) life cycle stage • biological sex • Laterality Sample Characteristic(s) Homo sapiens • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)

There has been a growing interest in developing behavioral tasks to enhance temporal acuity as recent findings have demonstrated changes in temporal processing in a number of clinical conditions. Prior research has demonstrated that perceptual training can enhance temporal acuity both within and across different sensory modalities. Although certain forms of unisensory perceptual learning have been shown to be dependent upon task difficulty, this relationship has not been explored for multisensory learning. The present study sought to determine the effects of task difficulty on multisensory perceptual learning. Prior to and following a single training session, participants completed a simultaneity judgment (SJ) task, which required them to judge whether a visual stimulus (flash) and auditory stimulus (beep) presented in synchrony or at various stimulus onset asynchronies (SOAs) occurred synchronously or asynchronously. During the training session, participants completed the same SJ task but received feedback regarding the accuracy of their responses. Participants were randomly assigned to one of three levels of difficulty during training: easy, moderate, and hard, which were distinguished based on the SOAs used during training. We report that only the most difficult (i.e., hard) training protocol enhanced temporal acuity. We conclude that perceptual training protocols for enhancing multisensory temporal acuity may be optimized by employing audiovisual stimuli for which it is difficult to discriminate temporal synchrony from asynchrony.

Large sequencing data sets are produced and deposited into public archives at unprecedented rates. The availability of tools that can reliably and efficiently generate and store sequencing read summary statistics has become critical. As part of the effort by the Vertebrate Genomes Project (VGP) to generate high-quality reference genomes at scale, we sought to address the community need for efficient sequencing data evaluation by developing rdeval, a standalone tool to quickly compute and dynamically display sequencing read metrics. Rdeval can either run on the fly or store key sequence data metrics in read 'sketches', with dramatic compression gains. Statistics can then be efficiently recalled from sketches for additional processing. Rdeval can convert fa*[.gz] files to and from other popular formats including BAM and CRAM for better compression. Overall, while CRAM achieves the best compression, the gain is marginal, and BAM achieves the best compromise between data compression and accessing speed. Rdeval also generates a detailed visual report with multiple data analytics that can be exported in various formats. We showcase rdeval's functionalities using human and VGP read data from different sequencing platforms and species. For PacBio long-read sequencing, our analysis shows dramatic improvements both in read length and quality over time, and a benefit of additional coverage for genome assembly. Rdeval is implemented in C++ for data processivity and in R for data visualization. Precompiled releases (Linux, MacOS, Windows) and commented source code for rdeval are available under MIT license at https://github.com/vgl-hub/rdeval. Documentation is available using ReadTheDocs (https://rdeval-documentation.readthedocs.io). Rdeval is also available in Bioconda and in Galaxy (https://usegalaxy.org). An automated test workflow ensures the consistency of software updates. Supplementary data are available at Bioinformatics online.

Bird genomes are the smallest among amniotes, but remain challenging to assemble due to their structural complexity. This study presents the first fully phased, diploid, telomere-to-telomere (T2T) reference genome for the zebra finch ( ), a model organism for neuroscience and evolutionary genomics. Combining multiple sequencing strategies resulted in closing nearly all gaps, adding ~90 Mbp of previously missing sequence (7.8%). This includes T2T assemblies for all microchromosomes, including dot chromosomes, and the previously almost entirely missing chr16. The T2T genome is comprehensively annotated for genes, repeats, structural variants, and long-read methylation calls. Complete centromeric structures were assembled and annotated along with kinetochore binding sites. Relative to the previous high-quality reference of the Vertebrate Genomes Project, 2,778 (8.51%) previously unassembled or unannotated genes were identified, of which 9% overlap with segmental duplications. This first complete genome of a songbird, now the new public reference, illuminates avian genome architecture and function.

Background: The Healthy Brain Network is an openly shared pediatric psychiatric biobank with a target of 10,000 participants between the ages of 5 and 21, inclusively. In adding ecological actimetry to the Healthy Brain Network, we intend to use appropriate, accurate, reliable tools. Currently a wide range of personal activity trackers are commercially available, providing a wide variety of sensor configurations. For many of these devices, accelerometry provides the basis of measuring both physical activity and sleep with comparable derivative measures. Results: In order to include an ecological biotracker in the Healthy Brain Network protocol, we first evaluated the specifications of a variety of actimeters available for purchase. We then acquired physical instances of 5 of these devices (ActiGraph wGT3X-BT, Empatica Embrace, Empatica E4, GENEActiv Original, and Wavelet Wristband) and wore each of them in our daily lives, annotating our activities and evaluating the reasonableness of the data from each device and the logistical affordances of each device. Conclusions: We decided that the ActiGraph wGT3X-BT is the most appropriate device for inclusion in the Healthy Brain Network. However, none of the devices we evaluated was clearly superior or inferior to the rest; rather, each device seems to have use cases in which that device excels beyond the others.

Objective: The present study sought to examine the relationships between processing speed (PS), mental health disorders, and learning disorders. Prior work has tended to explore relationships between PS deficits and individual diagnoses (i.e., anxiety, autism, ADHD, depressive) in isolation of one another, often relying on relatively modest sample sizes. In contrast, the present work simultaneously investigated associations between PS deficits and these diagnoses, along with specific learning disabilities (i.e., reading, math), in a large-scale, transdiagnostic, community self-referred sample. Method: A total of 843 children, ages 8-16 were included from the Healthy Brain Network (HBN) Biobank. Given the presence of four PS tasks in HBN, principal component analysis (PCA) was employed to create a composite measure that represented the shared variance of the four PS tasks, referred to as PC1. Intraclass correlation coefficient (ICC) between the four PS measures, as well as PC1, were calculated to assess reliability. We then used multiple linear regression models to assess specific relationships between PS deficits and psychiatric diagnoses. Results. ICCs were moderate between WISC-V tasks (0.663), and relatively modest between NIH Toolbox Pattern Comparison and other PS scales (0.14-0.27). Regression analyses revealed specific significant relationships between PS and reading and math disabilities, ADHD-inattentive type (ADHD-I), and ADHD-combined type (ADHD-C). Secondary analyses accounting for inattention dimensionally diminished associations with ADHD-C, but not ADHD-I or specific learning disability subtypes. The present study did not find a significant relationship with Autism Spectrum Disorder after accounting for inattentive symptoms. Consistent with prior work, demographic variables, including sex, socioeconomic status, and motor control exhibited independent relationships with PC1 as well. Discussion. This study provided a comprehensive examination of PS, mental health disorders, and learning disabilities through a transdiagnostic approach. Implications for understanding how PS interacts with a highly heterogeneous childhood sample, as well as the need for increased focus on detection of affected populations are discussed.

Selective Mutism (SM) is an anxiety disorder often diagnosed in early childhood and characterized by persistent failure to speak in certain social situations but not others. Diagnosing SM and monitoring treatment response can be quite complex, due in part to changing definitions of and scarcity of research about the disorder. Subjective self-reports and parent/teacher interviews can complicate SM diagnosis and therapy, given that similar speech problems of etiologically heterogeneous origin can be attributed to SM. The present perspective discusses the potential for passive audio capture to help overcome psychiatry's current lack of objective and quantifiable assessments in the context of SM. We present evidence from two pilot studies indicating the feasibility of using a digital wearable device to quantify child vocalization features affected by SM. We also highlight limitations in the design and implementation of this preliminary work that can help guide future efforts.