Examining the validity of the use of ratio IQs in psychological assessments | IQ tests are amongst the most used psychological assessments, both in research and clinical settings. For participants who cannot complete IQ tests normed for their age, ratio IQ scores (RIQ) are routinely computed and used as a proxy of IQ, especially in large research databases to avoid missing data points. However, because it has never been scientifically validated, this practice is questionable. In the era of big data, it is important to examine the validity of this widely used practice. In this paper, we use the case of autism to examine the differences between standard full-scale IQ (FSIQ) and RIQ. Data was extracted from four databases in which ages, FSIQ scores and subtests raw scores were available for autistic participants between 2 and 17 years old. The IQ tests included were the MSEL (N=12033), DAS-II early years (N=1270), DAS-II school age (N=2848), WISC-IV (N=471) and WISC-V (N=129). RIQs were computed for each participant as well as the discrepancy (DSC) between RIQ and FSIQ. We performed two linear regressions to respectively assess the effect of FSIQ and of age on the DSC for each IQ test, followed by additional analyses comparing age subgroups as well as FSIQ subgroups on DSC. Participants at the extremes of the FSIQ distribution tended to have a greater DSC than participants with average FSIQ. Furthermore, age significantly predicted the DSC, with RIQ superior to FSIQ for younger participants while the opposite was found for older participants. These results question the validity of this widely used alternative scoring method, especially for individuals at the extremes of the normal distribution, with whom RIQs are most often employed. | 35/17423 | Secondary Analysis | Shared |
Prognostic early snapshot stratification of autism based on adaptive functioning | A major goal of precision medicine is to predict prognosis based on individualized information at the earliest possible points in development. Using early snapshots of adaptive functioning and unsupervised data- driven discovery methods, we uncover highly stable early autism subtypes that yield information relevant to later prognosis. Data from the National Institute of Mental Health Data Archive (NDA) (n = 1,098) was used to uncover three early subtypes (<72 months) that generalize with 96% accuracy. Outcome data from NDA (n = 2,561; mean age, 13 years) also reproducibly clusters into three subtypes with 99% generalization accuracy. Early snapshot subtypes predict developmental trajectories in non-verbal cognitive, language and motor domains and are predictive of membership in different adaptive functioning outcome subtypes. Robust and prognosis- relevant subtyping of autism based on early snapshots of adaptive functioning may aid future research work via prediction of these subtypes with our reproducible stratification model. | 3/3517 | Secondary Analysis | Shared |
Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using C-PAC pipeline and ANTs | An automated pipeline was developed to reference Neuroimages hosted by the National Database for Autism Research (NDAR) and derive volumes for distinct brain structures using Advanced Normalization Tools (ANTs) and the Configurable-Pipeline for the Analysis of Connectomes (C-PAC) platform. This pipeline utilized the ANTs cortical thickness methodology discuessed in "Large-Scale Evaluation of ANTs and Freesurfer Cortical Tchickness Measurements" [http://www.ncbi.nlm.nih.gov/pubmed/24879923] to extract a cortical thickness volume from T1-weighted anatomical MRI data gathered from the NDAR database. This volume was then registered to an stereotaxic-space anatomical template (OASIS-30 Atropos Template) which was acquired from the Mindboggle Project webpage [http://mindboggle.info/data.html]. After registration, the mean cortical thickness was calculated at 31 ROIs on each hemisphere of the cortex and using the Desikan-Killiany-Tourville (DKT-31) cortical labelling protocol [http://mindboggle.info/faq/labels.html] over the OASIS-30 template.
**NOTE: This study is ongoing; additional data my be available in the future.**
As a result, each subject that was processed has a cortical thickness volume image and a text file with the mean thickness ROIs (in mm) stored in Amazon Web Services (AWS) Simple Storage Service (S3). Additionally, these results were tabulated in an AWS-hosted database (through NDAR) to enable simple, efficient querying and data access.
All of the code used to perform this analysis is publicly available on Github [https://github.com/FCP-INDI/ndar-dev]. Additionally, as a computing platform, we developed an Amazon Machine Image (AMI) that comes fully equipped to run this pipeline on any dataset. Using AWS Elastic Cloud Computing (EC2), users can launch our publicly available AMI ("C-PAC with benchmark", AMI ID: "ami-fee34296", N. Virginia region) and run the ANTs cortical thickness pipeline. The AMI is fully compatible with Sun Grid Engine as well; this enables users to perform many pipeline runs in parallel over a cluster-computing framework. | 3/1428 | Secondary Analysis | Shared |
Brain-based sex differences in autism spectrum disorder across the lifespan: A systematic review of structural MRI, fMRI, and DTI findings | Females with autism spectrum disorder (ASD) have been long overlooked in neuroscience research, but emerging evidence suggests they show distinct phenotypic trajectories and age-related brain differences. Sex-related biological factors (e.g., hormones, genes) may play a role in ASD etiology and have been shown to influence neurodevelopmental trajectories. Thus, a lifespan approach is warranted to understand brain-based sex differences in ASD. This systematic review on MRI-based sex differences in ASD was conducted to elucidate variations across the lifespan and inform biomarker discovery of ASD in females. We identified articles through two database searches. Fifty studies met criteria and underwent integrative review. We found that regions expressing replicable sex-by-diagnosis differences across studies overlapped with regions showing sex differences in neurotypical (NT) cohorts, in particular regions showing NT male>female volumes. Furthermore, studies investigating age-related brain differences across a broad age-span suggest distinct neurodevelopmental patterns in females with ASD. Qualitative comparison across youth and adult studies also supported this hypothesis. However, many studies collapsed across age, which may mask differences. Furthermore, accumulating evidence supports the female protective effect in ASD, although only one study examined brain circuits implicated in “protection.” When synthesized with the broader literature, brain-based sex differences in ASD may come from various sources, including genetic and endocrine processes involved in brain “masculinization” and “feminization” across early development, puberty, and other lifespan windows of hormonal transition. Furthermore, sex-related biology may interact with peripheral processes, in particular the stress axis and brain arousal system, to produce distinct neurodevelopmental patterns in males and females with ASD. Future research on neuroimaging-based sex differences in ASD would benefit from a lifespan approach in well-controlled and multivariate studies. Possible relationships between behavior, sex hormones, and brain development in ASD remain largely unexamined. | 1/759 | Secondary Analysis | Shared |
Derivation of Quality Measures for Structural Images by Neuroimaging Pipelines | Using the National Database for Autism Research cloud platform, MRI data were analyzed using neuroimaging pipelines that included packages available as part of the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Computational Environment to derive standardized measures of MR image quality. Structural QA was performed according to Haselgrove, et al (http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00052/abstract) to provide values for Signal to Noise (SNR) and Contrast to Noise (CNR) Ratios that can be compared between subjects within NDAR and between other public data releases. | 3/423 | Secondary Analysis | Shared |
Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using NITRC-CE | A draft publication is in progress.
GitHub repository with code for working with NDAR Data is available here: https://github.com/chaselgrove/ndar
**Note this study is ongoing; additional may be added.** | 3/356 | Secondary Analysis | Shared |
Derivation of Quality Measures for Time-Series Images by Neuroimaging Pipelines | Using the National Database for Autism Research cloud platform, MRI data were analyzed using neuroimaging pipelines that included packages available as part of the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Computational Environment to derive standardized measures of MR image quality. Time series QA was performed according to Friedman, et al. (http://www.ncbi.nlm.nih.gov/pubmed/16952468) providing values for Signal to Noise Ratio that can be compared to other subjects. | 3/356 | Secondary Analysis | Shared |