Identification of Infants at High-Risk for Autism Spectrum Disorder Using Multiparameter Multiscale White Matter Connectivity Networks
Contributors: Shen, Dinggang, Jin, Yan, Wee, Chong-Yaw, Shi, Feng, Thung, Kim-Han, Ni, Dong, Yap, Pew-Thian
... Abstract Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best singleparameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis.
Contributors: Eichler, E, Krumm, N, Turner, TN, Baker, C, Vives, L, Mohajeri, K, Witherspoon, K, Raja, A, Coe, BP, Stessman, HA
... Abstract In order to quantify the effect of private, inherited mutations on autism risk, we generated a callset of both inherited and de novo single nucleotide variants (SNVs) and copy number variants (CNVs) across 2,377 Simons Simplex Collection families. The publically deposited dataset includes 1,786 parents-child-unaffected sibling "quads" allowing us to compare burden of inherited and de novo mutations between affected and unaffected siblings in simplex autism families. We find that private, inherited truncating SNV mutations in conserved genes are significantly enriched in probands (odds ratio = 1.14, p = 0.0002) and more likely to be transmitted to children with autism when compared to their unaffected siblings (p < 0.0001). We find that this effect becomes more pronounced with increasing gene conservation (Residual Variation Intolerance Score, RVIS). Likewise, we observe a similar bias for inherited CNVs specifically for small (<100 kbp), maternally inherited events (p = 9.6x10^-3) that are enriched in CHD8 target genes (OR = 3.6, p = 2.0x10^-3). We quantified autism spectrum disorder (ASD) risk for de novo and inherited CNVs and SNVs by using a conditional logistic regression model. Independent from de novo mutations, private truncating SNVs and rare, inherited CNVs contribute an increase in risk with an odds ratio 1.11 (p = 0.0002) and 1.23 (p = 0.01), respectively. Our results indicate a statistically independent role for inherited mutations in ASD risk and identify additional high-impact risk candidate genes (e.g., RIMS1, CUL7, LZTR1 and CC2D2A) where transmitted mutations may create a sensitized background for autism but are unlikely to be necessary and sufficient for the disorder.
Contributors: Mizuno, A, Liu, Y, Williams, D, Keller, T, Minshew, N, Just, M
... Abstract Personal pronouns, such as 'I' and 'you', require a speaker/listener to continuously re-map their reciprocal relation to their referent, depending on who is saying the pronoun. This process, called 'deictic shifting', may underlie the incorrect production of these pronouns, or 'pronoun reversals', such as referring to oneself with the pronoun 'you', which has been reported in children with autism. The underlying neural basis of deictic shifting, however, is not understood, nor has the processing of pronouns been studied in adults with autism. The present study compared the brain activation pattern and functional connectivity (synchronization of activation across brain areas) of adults with high-functioning autism and control participants using functional magnetic resonance imaging in a linguistic perspective-taking task that required deictic shifting. The results revealed significantly diminished frontal (right anterior insula) to posterior (precuneus) functional connectivity during deictic shifting in the autism group, as well as reliably slower and less accurate behavioural responses. A comparison of two types of deictic shifting revealed that the functional connectivity between the right anterior insula and precuneus was lower in autism while answering a question that contained the pronoun 'you', querying something about the participant's view, but not when answering a query about someone else's view. In addition to the functional connectivity between the right anterior insula and precuneus being lower in autism, activation in each region was atypical, suggesting over reliance on individual regions as a potential compensation for the lower level of collaborative interregional processing. These findings indicate that deictic shifting constitutes a challenge for adults with high-functioning autism, particularly when reference to one's self is involved, and that the functional collaboration of two critical nodes, right anterior insula and precuneus, may play a critical role for deictic shifting by supporting an attention shift between oneself and others.
Contributors: Walsh, CA, D’Gama, I, Pochareddy, S, Li, M, Jamuar, S, Reiff, RE, Lam, A, Sestan, N
... Abstract Single nucleotide variants (SNVs), particularly loss-of-function mutations, are significant contributors to autism spectrum disorder (ASD) risk. Here we report the first systematic deep sequencing study of 55 postmortem ASD brains for SNVs in 78 known ASD candidate genes. Remarkably, even without parental samples, we find more ASD brains with mutations that are protein-altering (26/55 cases versus 12/50 controls, p = 0.015), deleterious (16/55 versus 5/50, p = 0.016), or loss-of-function (6/55 versus 0/50, p = 0.028) compared to controls, with recurrent deleterious mutations in ARID1B, SCN1A, SCN2A, and SETD2, suggesting these mutations contribute to ASD risk. In several cases, the identified mutations and medical records suggest syndromic ASD diagnoses. Two ASD and one Fragile X premutation case showed deleterious somatic mutations, providing evidence that somatic mutations occur in ASD cases, and supporting a model in which a combination of germline and/or somatic mutations may contribute to ASD risk on a case-by-case basis.
Contributors: Krumm, N, Eichler, E
... Abstract Whole Exome Sequencing has been completed for ~ 2500 families from the Simons Simplex Collection. Sequencing was performed at three individual sequencing centers with original data submitted to NDAR Collections 1878, 1895, and 1936; subsets of these data have been analyzed by various methods and published. This study represents an effort to call and annotate SNPs and Indels on data from all three collections in a uniform manner using the latest toolchains and algorithms available. Variant calls from this study were generated using FreeBayes, Famseq, and some custom scripts; annotation was provided by SnpEff, dbNSFP, and vcftools. Note that variants were called in batches with ~ 20 families per batch. Complete methods, including source code for pipeline and custom scripts can be found at: https://github.com/nkrumm/asd-jre-public The data package for this study includes the genomics_sample02, genomics_sample03 structures with annotated and un-annotated VCF files for each family. Another NDAR Study (348) is available with VCF files generated using GATK (https://ndar.nih.gov/study.html?id=348), and the complete set of BAM files used for variant calling are available in NDAR Study 334 (https://ndar.nih.gov/study.html?id=334)
Contributors: Turner, TN
... Abstract We performed whole-genome sequencing (WGS) of 160 genomes from 40 simplex autism families, the majority of which had no copy number variant (CNV) or candidate de novo gene-disruptive single nucleotide variant (SNV) by microarray or whole-exome sequencing (WES). SNV and CNV calling was achieved by a number of variant calling algorithms. This accession contains SNV (FreeBayes) and CNV (digital comparative genomic hybridization [dCGH], GenomeSTRiP, VariationHunter) calls from this study.
Revising the Social Communication Questionnaire scoring procedures for Autism Spectrum Disorder and potential Social Communication Disorder
Contributors: Barnard-Brak, L
... Abstract In analyzing data from the National Database for Autism Research, we examine revising the Social Communication Questionnaire (SCQ), a commonly used screening instrument for Autism Spectrum Disorder. A combination of Item Response Theory and Mokken scaling techniques were utilized to achieve this and abbreviated scoring of the SCQ is suggested. The psychometric sensitivity of this abbreviated SCQ was examined via bootstrapped Receiver Operator Characteristic (ROC) curve analyses. Additionally, we examined the sensitivity of the abbreviated and total scaled SCQ as it relates to a potential diagnosis of Social (Pragmatic) Communication Disorder (SCD). As SCD is a new disorder introduced with the fifth edition of the Diagnostic and Statistical Manual (DSM-5), we identified individuals with potential diagnosis of SCD among individuals with ASD via mixture modeling techniques using the same NDAR data. These analyses revealed two classes or clusters of individuals when considering the two core areas of impairment among individuals with ASD: social communication and restricted, repetitive patterns of behavior.
Contributors: Tilford, MJ, Payakachat, N, Kuhlthau, KA, van Exel, NJ, Kovacs, E, Bellando, J, Pyne, JM, Brouwer, WBF
... Abstract Comparative effectiveness of interventions for children with autism spectrum disorders (ASDs) that incorporates costs is lacking due to the scarcity of information on health utility scores or preference-weighted outcomes typically used for calculating quality-adjusted life years (QALYs). This study created algorithms for mapping clinical and behavioral measures for children with ASDs to health utility scores. The algorithms could be useful for estimating the value of different interventions and treatments used in the care of children with ASDs. Participants were recruited from two Autism Treatment Network sites. Health utility data based on the Health Utilities Index Mark 3 (HUI3) for the child were obtained from the primary caregiver (proxy-reported) through a survey (N = 224). During the initial clinic visit, proxy-reported measures of the Child Behavior Checklist, Vineland II Adaptive Behavior Scales, and the Pediatric Quality of Life Inventory 4.0 (start measures) were obtained and then merged with the survey data. Nine mapping algorithms were developed using the HUI3 scores as dependent variables in ordinary least squares regressions along with the start measures, the Autism Diagnostic Observation Schedule, to measure severity, child age, and cognitive ability as independent predictors. In-sample cross-validation was conducted to evaluate predictive accuracy. Multiple imputation techniques were used for missing data. The average age for children with ASDs in this study was 8.4 (standard deviation = 3.5) years. Almost half of the children (47%) had cognitive impairment (IQ < 70). Total scores for all of the outcome measures were significantly associated with the HUI3 score. The algorithms can be applied to clinical studies containing start measures of children with ASDs to predict QALYs gained from interventions.
Contributors: Charles, Schwartz, Boccuto, L, Chen, C, Pittman, A, Skinner, C, McCartney, H, Jones, K, Bochner, B, Stevenson, R.
... Abstract Background: Autism spectrum disorders (ASDs) are relatively common neurodevelopmental conditions whose biological basis has been incompletely determined. Several biochemical markers have been associated with ASDs, but there is still no laboratory test for these conditions. Methods: We analyzed the metabolic profile of lymphoblastoid cell lines from 137 patients with neurodevelopmental disorders with or without ASDs and 78 normal individuals, using Biolog Phenotype MicroArrays. Results: Metabolic profiling of lymphoblastoid cells revealed that the 87 patients with ASD as a clinical feature, as compared to the 78 controls, exhibited on average reduced generation of NADH when tryptophan was the sole energy source. The results correlated with the behavioral traits associated with either syndromal or non-syndromal autism, independent of the genetic background of the individual. The low level of NADH generation in the presence of tryptophan was not observed in cell lines from non-ASD patients with intellectual disability, schizophrenia or conditions exhibiting several similarities with syndromal autism except for the behavioral traits. Analysis of a previous small gene expression study found abnormal levels for some genes involved in tryptophan metabolic pathways in 10 patients. Conclusions: Tryptophan is a precursor of important compounds, such as serotonin, quinolinic acid, and kynurenic acid, which are involved in neurodevelopment and synaptogenesis. In addition, quinolinic acid is the structural precursor of NAD+, a critical energy carrier in mitochondria. Also, the serotonin branch of the tryptophan metabolic pathway generates NADH. Lastly, the levels of quinolinic and kynurenic acid are strongly influenced by the activity of the immune system. Therefore, decreased tryptophan metabolism may alter brain development, neuroimmune activity and mitochondrial function. Our finding of decreased tryptophan metabolism appears to provide a unifying biochemical basis for ASDs and perhaps an initial step in the development of a diagnostic assay for ASDs. Keywords: Autism, Biomarker, Tryptophan, Metabolism, Screening
Contributors: Benson, Mwangi PhD, Cao, Bo, Hasan, Khader, Selvaraj, Sudhakar, Zeni, Cristian, Zunta-Soares, Giovan, Soares, Jair.
... Abstract Background Major psychiatric disorders are increasingly being conceptualized as ‘neurodevelopmental’, because they are associated with aberrant brain maturation. Several studies have hypothesized that a brain maturation index integrating patterns of neuroanatomical measurements may reliably identify individual subjects deviating from a normative neurodevelopmental trajectory. However, while recent studies have shown great promise in developing accurate brain maturation indices using neuroimaging data and multivariate machine learning techniques, this approach has not been validated using a large sample of longitudinal data from children and adolescents. Methods T1-weighted scans from 303 healthy subjects aged 4.88 to 18.35 years were acquired from the National Institute of Health (NIH) pediatric repository (http://www.pediatricmri.nih.gov). Out of the 303 subjects, 115 subjects were re-scanned after 2 years. The least absolute shrinkage and selection operator algorithm (LASSO) was ‘trained’ to integrate neuroanatomical changes across chronological age and predict each individual's brain maturity. The resulting brain maturation index was developed using first-visit scans only, and was validated using second-visit scans. Results We report a high correlation between the first-visit chronological age and brain maturation index (r = 0.82, mean absolute error or MAE = 1.69 years), and a high correlation between the second-visit chronological age and brain maturation index (r = 0.83, MAE = 1.71 years). The brain maturation index captured neuroanatomical volume changes between the first and second visits with an MAE of 0.27 years. Conclusions The brain maturation index developed in this study accurately predicted individual subjects' brain maturation longitudinally. Due to its strong clinical potentials in identifying individuals with an abnormal brain maturation trajectory, the brain maturation index may allow timely clinical interventions for individuals at risk for psychiatric disorders.