Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification

Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L1-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD) from normal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods

Genetic implications of individual intervention and neuronal dysfunction in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) are a group of conditions appearing in childhood, with developmental deficits that produce impairments of functioning. Autism spectrum disorder (ASD) is a common NDD with a high heritability affected by complex genetic factors, including both common and rare variants. Behavior interventions such as social skills group training (SSGT) have been widely used in school-aged autistic individuals to relieve social communication difficulties in a group setting. Studies have confirmed that intervention outcomes can be influenced by sex and age, but how the genetic risk contributes to the outcome variability remains elusive. Furthermore, although large population cohorts have been well studied and have found numerous genes associated with ASD and NDDs, the molecular and neuronal outcomes of risk variants and genes are unclear. Therefore, this thesis included four studies in which the effects of genetic factors on intervention outcomes and cellular level neuronal functions were investigated. Results from this thesis may provide a genetic perspective for further studies to explore potential individualized treatments for ASD and other NDDs. Specifically, In STUDY 1-3, exome sequencing and microarray were performed on individuals from a randomized controlled trial of SSGT (KONTAKT®). Common and rare variants, including copy number variations (CNVs) and exome variants, were tested for association effects with SSGT and standard care intervention outcomes. Polygenic risk scores (PRSs) were calculated from common variants, and clinically significant rare CNVs and rare exome variants were prioritized. Molecular diagnoses were identified in 12.6% of the autistic participants. PRSs and carrier status of clinically significant rare variants were associated with intervention outcomes, although with varied effects on both SSGT and standard care. In addition, genetic scores representing variant loads in specific gene sets were obtained from rare and common variants in ASD-related pathways. Outcomes of interventions were differentially associated with genetic scores for ASD-related gene sets including synaptic transmission and transcription regulation from RNA polymerase II. After combining genetic information and behavior measures, a machine learning model was able to select important features and confirm that the intervention outcomes were predictable. In STUDY 4, genetic variants affecting Calcium/Calmodulin Dependent Serine Protein Kinase (CASK) gene, a risk gene for NDDs, were examined using human induced pluripotent stem cell-derived neuronal models to identify the cellular effects of these mutation consequences. CASK protein was reduced in maturing neurons from mutation carriers. Bulk RNA sequencing results revealed that the global expression of genes from presynaptic development and CASK network were downregulated in CASK-deficient neurons compared to controls. Neuronal cells influenced by CASK mutations showed a decrease of inhibitory presynapse size and changed excitatory-inhibitory (E/I) balance in developing neural circuitries. In summary, this is the first study to investigate the association of genome-wide rare and common variants with ASD intervention outcomes. Differential variant effects were found for individuals receiving SSGT or standard care. Future studies should include genetic information at different levels to improve molecular genetic testing for diagnoses and intervention plans. Presynapses and E/I imbalance could be an option to be developed for the treatment of CASK-related disorders

MULTI-OMIC DATA PROVIDE A MORE COMPLETE UNDERSTANDING OF THE AUTISTIC BRAIN

Autism is a complex neurodevelopmental disorder characterized by persistent social deficits and restricted or repetitive patterns of behavior. Despite an established genetic basis of the disorder, efforts to elucidate the genetic underpinnings of the disorder and our understanding of its etiology remains incomplete. As such, we set out to study the effects downstream of genetic variation by studying alterations in both gene expression and DNA methylation (DNAm) in post-mortem brain samples collected from individuals affected with autism and controls. This work highlights that even when there is no primary genetic lesion detected, the autistic brain shows a characteristic pattern of upregulation at M2-activation state microglia genes, a state potentially driven by Type I interferon responses. Additionally, by combining transcriptomic data across autism and two related neuropsychiatric disorders, schizophrenia and bipolar disorder, we have garnered a better understanding of the relationship between these disorders, where genes differentially expressed in autism are concordantly differentially expressed in schizophrenia, but not in bipolar disorder. Finally, as gene expression is regulated, at least in part, by DNAm, we have characterized DNAm at cytosines across the genome and have detected hypermethylation at cytosines outside the commonly-studied CpG context, suggesting that autistic brains have slight increases at many CpH sites (where H=A,T, or C) throughout their genome. These sites are enriched in repetitive regions of the genome and regions containing human-specific CpGs, offering an insight into how this hypermethylation may be functioning mechanistically. Taken together, by studying the downstream effects of genetic variation, at the levels of DNAm and gene expression, we have moved toward a more complete understanding of the autistic brain

Cerebral hemodynamic response to faces and emotions in infants at high risk for autism

Thesis (Ph. D.)--Harvard-MIT Program in Health Sciences and Technology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 109-144).The incidence of autism spectrum disorders (ASD) has risen alarmingly in the United States, and is now thought to affect approximately 1 in 110 live births. Early diagnosis and intervention is the only treatment proven effective in cases of autism, however the behavioral tests currently available cannot make this diagnosis until at least two years of age. A lack of normal attention to faces and abnormal face processing is a cognitive deficit common to nearly all individuals with autism spectrum disorder, and this deficit is likely present from a very early age. The primary goal of this dissertation is therefore to characterize the specific neural response of face processing in infants with near-infrared spectroscopy (NIRS), and to then apply these measures to the study of abnormal face processing in infants at high risk for autism. In order to achieve these objectives, the work described herein aims to: 1) characterize the hemodynamic response to faces in normal infants at six months of age as measured by the Hitachi ETG-4000 functional Near-Infrared Spectroscopy (fNIRS) system; 2) Simultaneously measure orbitofrontal hemodynamic responses to social/emotional engagement and the response to faces in infants at high risk for autism as compared to low risk controls; and 3) Utilize a novel method of condition-related component selection and classification to identify waveforms associated with face and emotion processing in 6-7-month-old infants at high risk for ASD, and matched low-risk controls. Our results indicate similarities of response waveforms, but differences in both the spatial distribution, magnitude, and timing of oxy-hemoglobin and deoxy-hemoglobin responses between groups. Our findings represent the first identification of neuroimaging markers of a functional endophenotype at six months of age that may be associated with high risk of ASD. These results support a model of altered frontal lobe structure through evidence of altered hemodynamic response and/or functional activity in the high risk infant group, and these changes may, in turn, contribute to the development of ASD in specific individuals.by Sharon Elizabeth Fox.Ph.D

Integrative functional genomic search for regulatory DNA sequence polymorphisms influencing DNA methylation and mRNA expression in hippocampal brain tissue

Neuropsychiatric disorders have a strong genetic predisposition, but their genetic basis remains elusive. Genome-wide association studies (GWASs) have mapped more than 2,000 susceptibility loci that were shown to increase the risk of common brain disorders. However, the majority of these susceptibility loci reside in non-coding regions and their functional consequences are unknown. The present study addresses the question whether regulatory sequence variants, affecting DNA methylation and gene expression, may be causal susceptibility alleles. I used an integrative functional genomics approach to investigate epigenetic regulation phenomena in human hippocampal brain of 115 European patients with pharmacoresistant mesial temporal lobe epilepsy. High-density SNP genotypes were correlated with genome-wide quantitative CpG methylation and mRNA expression levels using the Human Methylation450 array (HM450) and the Human HT-12 v3 array. Subsequently, a genome-wide map of methylation quantitative trait loci (meQTLs) and expression quantitative trait loci (eQTLs) was used to dissect regulatory SNPs (rSNPs) that confer susceptibility to common brain disorders at 488 known GWAS hits (P < 5.0 x 10-8). This is the first meQTL study of brain tissue applying the high-density HM450 array in specimens of fresh frozen human brain tissue obtained by epilepsy surgery at large scale. Linear regression analysis of this study implementing a correction for cell-type heterogeneity, identified 19,954 (8.5% of 362k CpGs) cis-acting meQTLs at a false-discovery rate (FDR) of 5%, which is a six-fold increase compared to previous meQTL studies that all investigated postmortem brain tissue. Specifically, cis-meQTLs were strongly enriched upstream of the gene promoter region (TSS201-1500; P = 7.7 x 10-61), highlighting the functional impact of this 5´-regulatory region that harbors binding sites of enhancers and insulators. Some of the most significant cis-meQTLs affected high-ranking candidate genes (ADARB2, HDAC4, NAPRT1, MAD1L1, PTPRN2 and RIMBP2) for neurodevelopmental disorders. To explore tissue specifity, the same approach was repeated in an additional meQTL analysis of whole blood cells originating from 496 German population controls without neuropsychiatric disorders. Results show that 65% of the meQTLs in brain tissues were also present in whole blood cells (Spearman’s Rank coefficient = 0.42). The present database of cis-meQTLs in brain and blood cells provides a key to select accessible epigenetic biomarkers for brain disorders in whole blood cells. The performed eQTL study identified 734 out of 31k mRNA probes at which expression levels were significantly influenced by cis-acting SNPs (FDR < 5%). Apart from meQTL and eQTL analyses, additionally a CpG methylation to gene expression correlation analysis was performed. This represents the first systematic delineation of methylation-driven genes in fresh frozen brain tissue. Both inverse correlations (73%) and positive correlations (27%) were observed, whereby the strongest inverse correlations were detected at NAPRT1, the gene encoding Nicotinate Phosphoribosyltransferase. Furthermore, the NAPRT1-associated meQTLs and eQTL were both genetically regulated by SNP rs9657360. The minor C allele of that very SNP was significantly associated with high methylation levels in the NAPRT1 promoter region and simultaneously associated with low gene expression of NAPRT1. Both, the tumor-specific hypermethylation of a promoter CpG island as well as loss of NAPRT1 expression have been previously proposed as predictive biomarkers for the therapy of carcinomas using NAMPT inhibitors. The additionally genetic risk constellation which has been identified by my approach – combining meQTLs and eQTLs to unravel the translational impact of epigenetic regulation of gene expression – is of high clinical relevance. It enables a diagnostically driven clinical strategy in tumorigenesis including the selection of patients which likely benefit from the administration of NAMPT inhibitors. To dissect imprinted meQTLs (imeQTLs) exhibiting differential methylation in a Parent-of-Origin (PofO) dependent manner, the CpG methylation states of blood cells in groups of 269 individuals stratified by parentally inverse heterozygous genotypes of nearby SNPs were compared. The imeQTL analysis revealed 177 CpGs at 31 genomic loci of which 22 were previously unknown. The strongest PofO effects were observed at loci harboring neurodevelopmental genes and on chromosome 3p21.1, which is a GWAS candidate region for mood disorders. Genes at genomic loci that show imprinting effects are promising candidate genes because of their potentially monoallelic gene expression which may unmask recessive susceptibility alleles. Enrichment analyses of genes associated with cis-meQTLs revealed an overrepresentation of genes implicated in GWAS hits of brain disorders (P = 5.8 x 10-4). Potential rSNPs at the GWAS candidate loci 1q31.2 (RGS1 gene locus) and 3p21.1 (PRBM1 gene locus) were identified. The allelic alteration of transcription factor binding sites by potential rSNPs is likely to result in changes of gene transcription or splicing processes which could contribute to pathogenic pathways underlying neuropsychiatric disorders. As exemplified in this thesis, the created database of autosomal meQTLs, imeQTLs and eQTLs in brain tissue provides a valuable resource to dissect rSNPs at GWAS hits and to decipher their functional effects

Modeling neuropsychiatric phenotypes in mice in the frame of translational neuroscience

Neuropsychiatric disorders, such as autism spectrum disorders (ASD), schizophrenia, anxiety disorders, major depression and substance use disorders are highly prevalent and contribute to disease burden worldwide. Accumulating evidence consistently suggest neuropsychiatric conditions as results of complex interactions between multiple genetic factors and environmental factors. However, despite large research efforts, the exact neurobiological mechanisms of neuropsychiatric disorders remain largely unknown. The present cumulative thesis has been prepared on the basis of a translational neuroscience approach based on schizophrenic patients of the GRAS (Göttingen Research Association for Schizophrenia) data collection (Ribbe, Friedrichs et al. 2010): translating the findings from basic research using animal models back to human, and vice versa to reveal the relevance of behavioral characterization of genetic mouse models of complex human behavioral disorders, such as psychiatric diseases. Using behavioral genetics to characterize mouse models of complex human behavioral disorders, the role of MECP2 and BAIAP3 genotypes and their common variants in modulating behavioral phenotypes was identified. Within the first paper, BAIAP3/Baiap3 was identified as a genetic factor that modulates anxiety and the response to benzodiazepines in mouse and man (Wojcik, Tantra, Stepniak et al. 2013). The second paper focuses on an elevated seizure propensity as the phenotypical consequences of mildly overexpressing the transcriptional regulator Mecp2 at ~1.5fold wild-type level (Bodda, Tantra et al. 2013). The third publication expanding the results published in the second paper, reveals that subtle alterations in the expression level of Mecp2/MECP2 influence social, particularly aggressive behavior (Tantra, Hammer, Kästner et al. submitted). Overall, this thesis shows the practical implications of behavioral studies of transgenic mice with an elaborate behavioral repertoire that can be used to model phenotypes of complex human behavioral disorders. Behavioral characterizations of these mice gave aid to identify behavioral domains targeted by the genetic variations of the genes of interest, revealing the probable functional role of the genes within the frame of complex human behavioral disorders. The validity of the genetic mouse models described here was further increased by addressing the relevance of the findings in a schizophrenic (GRAS) population

IN UTERO EXPOSURE TO MATERNAL IMMUNE ACTIVATION AND AUTISM SPECTRUM DISORDER

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication, and repetitive behavior and stereotypical interests. First we describe what is known about ASD risk factors, including genetic variants and environmental exposures, particularly during gestation (Chapter 1). Then we tested the hypothesis that prenatal exposure to maternal immune activation (MIA) increases the risk of ASD. In a prospective birth cohort (Boston Birth Cohort, BBC), we found that prenatal exposure to maternal fever, and not maternal genitourinary infections or influenza, is associated with an increased risk of ASD (Chapter 2). Electronic medical records (EMR) were used to identify children in the BBC with ASD or typical development. While reliance on EMR enables us to increase sample size compared to a traditional study that requires extensive research contact, it could lead to outcome misclassification. Here, we explored using Random Forests, a data mining and machine learning technique, and Latent Class Analysis, a probabilistic clustering method, to identify other EMR diagnosis codes that help predict a child’s ASD status (Chapter 3). These techniques were able to identify children with typical and atypical development in the BBC. Finally, to further explore the potential biological consequences of prenatal exposure to MIA, we analyzed DNA methylation in the whole blood of 2-5 year old children in the Study to Explore Early Development (Chapter 4). We found one site in an intergenic region that was differentially methylated in children whose mothers contracted an infection shortly before they were conceived, and two sites in the genome (IQSEC1, EPS8L3) that were differentially methylated in children whose mother had an infection during her third trimester. While the differences in percent methylation were small in magnitude (<1% mean or median absolute difference), they were statistically significant after accounting for technical and biological sources of variation, including ancestry and ASD case status. This dissertation contributes to our understanding of the role of MIA exposure during pregnancy in ASD risk, biological changes identified in early childhood associated with prenatal exposure to MIA, and suitable methods for conducting EMR-based epidemiological research of ASD

Temporality of Risk Factors and the Gender Differential Related to Autism Spectrum Disorder Diagnosis

Autism spectrum disorders (ASD) constitute life-long neurodevelopmental conditions. Globally, ASD risk for males remains 2 to 4 times greater than for females. Critical exposure mechanisms, their timing on ASD risk, and associations with the ASD gender differential remain elusive. The purpose of this study was to describe the relationship between preconception, pregnancy, recalled lactation practice, and infant traits, on ASD risk and the gender differential of ASD. A recently published temporal framework was adapted to study effects of maternal smoking and vitamin use, and recalled lactation practice on offspring ASD diagnosis with adjustment for preconception health and infant breathing traits. A retrospective case-control analysis using 733 child data records from U.S. autism registry characterized child gender-stratified relationships of 9 study variables. Logistic regression results showed prior maternal smoking, male gender, and maternal recollection of lactation practices were associated with offspring ASD diagnosis. Exposure factors associated with ASD did not differ by child gender or maternal vitamin use. Infant respiratory distress at birth was a covariate and collinearly related to obstetric risks. Maternal smoking was antecedent to respiratory distress and lactation practice. Study limitations included incomplete responses without repeated measures for recalled lactation practice and maternal diet variables. The implications for positive social change include a better understanding of reproductive, preconception, and prenatal risk factors of ASD. The study results have implications for reproductive health, smoking cessation programs, family planning, and prenatal care for women of reproductive age