Topological Learning for Brain Networks

This paper proposes a novel topological learning framework that can integrate networks of different sizes and topology through persistent homology. This is possible through the introduction of a new topological loss function that enables such challenging task. The use of the proposed loss function bypasses the intrinsic computational bottleneck associated with matching networks. We validate the method in extensive statistical simulations with ground truth to assess the effectiveness of the topological loss in discriminating networks with different topology. The method is further applied to a twin brain imaging study in determining if the brain network is genetically heritable. The challenge is in overlaying the topologically different functional brain networks obtained from the resting-state functional MRI (fMRI) onto the template structural brain network obtained through the diffusion MRI (dMRI)

Handedness in twins : meta-analyses

Funding: Open Access funding enabled and organized by Projekt DEAL. JS is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, 418445085). SP is funded by the Royal Society (UF150663).Background: In the general population, 10.6% of people favor their left hand over the right for motor tasks. Previous research suggests higher prevalence of atypical (left-, mixed-, or non-right-) handedness in (i) twins compared to singletons, and in (ii) monozygotic compared to dizygotic twins. Moreover, (iii) studies have shown a higher rate of handedness concordance in monozygotic compared to dizygotic twins, in line with genetic factors playing a role for handedness. Methods: By means of a systematic review, we identified 59 studies from previous literature and performed three sets of random effects meta-analyses on (i) twin-to-singleton Odds Ratios (21 studies, n = 189,422 individuals) and (ii) monozygotic-to-dizygotic twin Odds Ratios (48 studies, n = 63,295 individuals), both times for prevalence of left-, mixed-, and non-right-handedness. For monozygotic and dizygotic twin pairs we compared (iii) handedness concordance Odds Ratios (44 studies, n = 36,217 twin pairs). We also tested for potential effects of moderating variables, such as sex, age, the method used to assess handedness, and the twins’ zygosity. Results: We found (i) evidence for higher prevalence of left- (Odds Ratio = 1.40, 95% Confidence Interval = [1.26, 1.57]) and non-right- (Odds Ratio = 1.36, 95% Confidence Interval = [1.22, 1.52]), but not mixed-handedness (Odds Ratio = 1.08, 95% Confidence Interval = [0.52, 2.27]) among twins compared to singletons. We further showed a decrease in Odds Ratios in more recent studies (post-1975: Odds Ratio = 1.30, 95% Confidence Interval = [1.17, 1.45]) compared to earlier studies (pre-1975: Odds Ratio = 1.90, 95% Confidence Interval = [1.59–2.27]). While there was (ii) no difference between monozygotic and dizygotic twins regarding prevalence of left- (Odds Ratio = 0.98, 95% Confidence Interval = [0.89, 1.07]), mixed- (Odds Ratio = 0.96, 95% Confidence Interval = [0.46, 1.99]), or non-right-handedness (Odds Ratio = 1.01, 95% Confidence Interval = [0.91, 1.12]), we found that (iii) handedness concordance was elevated among monozygotic compared to dizygotic twin pairs (Odds Ratio = 1.11, 95% Confidence Interval = [1.06, 1.18]). By means of moderator analyses, we did not find evidence for effects of potentially confounding variables. Conclusion: We provide the largest and most comprehensive meta-analysis on handedness in twins. Although a raw, unadjusted analysis found a higher prevalence of left- and non-right-, but not mixed-handedness among twins compared to singletons, left-handedness was substantially more prevalent in earlier than in more recent studies. The single large, recent study which included birth weight, Apgar score and gestational age as covariates found no twin-singleton difference in handedness rate, but these covariates could not be included in the present meta-analysis. Together, the secular shift and the influence of covariates probably make it unsafe to conclude that twinning has a genuine relationship to handedness.Publisher PDFPeer reviewe

White matter connectivity, cognition, symptoms and genetic risk factors in Schizophrenia

Schizophrenia is a highly heritable complex neuropsychiatric disorder with a lifetime prevalence of around 1%. It is often characterised by impaired white matter structural dysconnectivity. In vivo and post-mortem alterations in white matter microstructure have been reported, along with differences in the topology of the structural connectome; overall these suggest a reduced communication between distal brain regions. Schizophrenia is characterised by persistent cognitive impairments that predate the occurrence of symptoms and have been shown to have a neural foundation reflecting aberrant brain connectivity. So far, 179 independent genome-wide significant single nucleotide polymorphisms (SNPs) have been associated with a diagnosis of schizophrenia. The high heritability and polygenicity of schizophrenia, white matter parameters and cognitive functions provides a great opportunity to investigate the potential relationships between them due to the genetic overlap shared among these factors. This work investigates the psychopathology of schizophrenia from a neurobiological, psychological and genetic perspective. The datasets used here include data from the Scottish Family Mental Health (SFMH) study, the Lothian Birth Cohort 1936 (LBC1936) and UK Biobank. The main goal of this thesis was to study white matter microstructure in schizophrenia using diffusion MRI (dMRI) data. Our first aim was to examine whether processing speed mediated the association between white matter structure and general intelligence in patients diagnosed with schizophrenia in the SFMH study. Secondly, we investigated specific networks from the structural connectome and their topological properties in both healthy controls and patients diagnosed with schizophrenia in the SFMH study. These networks were studied alongside cognition, clinical symptoms and polygenic risk factor for schizophrenia (szPGRS). The third aim of this thesis was to study the effects of szPGRS on the longitudinal trajectories of white matter connectivity (measured using tractography and graph theory metrics) in the LBC1936 over a period of three-years. Finally, we derived the salience network which has been previously associated with schizophrenia and examined the effect of szPGRS on the grey matter nodes associated with this network and their connecting white matter tracts in UK Biobank. With regards to the first aim, we found that processing speed significantly mediates the association between a general factor of white matter structure and general intelligence in schizophrenia. These results suggest that, as in healthy controls, processing speed acts as a key cognitive resource facilitating higher order cognition by allowing multiple cognitive processes to be simultaneously available. Secondly, we found that several graph theory metrics were significantly impaired in patients diagnosed with schizophrenia compared with healthy controls. Moreover, these metrics were significantly associated with intelligence. There was a strong tendency towards significance for a correlation between intelligence and szPGRS that was significantly mediated by graph theory metrics in both healthy controls and schizophrenia patients of the SFMH study. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. In the LBC1936 we found that higher szPGRS showed significant associations with longitudinal increases in MD in several white matter tracts. Significant declines over time were observed in graph theory metrics. Overall these findings suggest that szPGRS confer risk for ageing-related degradation of some aspects of structural connectivity. Moreover, we found significant associations between higher szPGRS and decreases in cortical thickness, in particular, in a latent factor for cortical thickness of the salience network. Taken together, our findings suggest that white matter connectivity plays a significant role in the disorder and its psychopathology. The computation of the structural connectome has improved our understanding of the topological characteristics of the brain’s networks in schizophrenia and how it relates to the microstructural level. In particular, the data suggests that white matter structure provides a neuroanatomical substrate for cognition and that structural connectivity mediates the relationship between szPGRS and intelligence. Additionally, these results suggest that szPGRS may have a role in age-related changes in brain structural connectivity, even among individuals who are not diagnosed with schizophrenia. Further work will be required to validate these results and will hopefully examine additional risk factors and biomarkers, with the ultimate aims of improving scientific knowledge about schizophrenia and conceivably of improving clinical practice

Gene Association Mapping in the Era of Next-Generation Sequencing and Systems Biology

In the past decade, advancement of genotyping technology, first microarray then “next-generation” sequencing, has enabled scientists to examine the susceptible genes that contribute to the risk of complex disorders using a genome-wide, “hypothesis free” strategy. However, despite this “hypothesis free” label, these genome-wide approaches (including genome-wide association and whole genome sequencing studies) depend on two implicit assumptions. The first assumption is that the genetic risk of complex traits is contributed by independent genes/variants (assumption of independence).The second assumption is that different genes have equal potentiality to confer to the genetic predisposition of the complex traits (assumption of equality). Despite the huge success in susceptible gene association mapping in the last decade, more and more evidence has indicated that these two underlying assumptions of these genome-wide approaches may not be sound. Other than just studying one locus at a time, alternative methods which can carry out global analyses of biological molecules in populations have been developed to understand the influence of the whole biological system on complex traits. Network based approaches, in particular, have proven informative. This dissertation will cover a few important issues concerning sequencing based study design and its applications in chapter II, III and IV. Human protein-protein interaction network will be constructed and a few of human gene network related issues will be studied and discussed in chapter V and VI. Abstracts for each chapter were summarized as followed. Chapter 2: In this chapter, we proposed a two-stage, gene-based method for association mapping of rare variants by applying four different non-collapsing algorithms. Using the Genome Analysis Workshop 18 whole genome sequencing dataset of simulated blood pressure phenotypes, we studied and contrasted the false positive rate of each algorithm using receiver operating characteristic curves. The statistical power of these methods was also evaluated and compared through the analysis of 200 simulated replications in a smaller genotype data set. We showed that the Fisher’s method was superior to the other three 3 non-collapsing methods, but was no better than the standard method implemented with famSKAT. Chapter 3: In this chapter, we aimed to identify potential susceptibility variants for bipolar disorder via the combination of exome sequencing and linkage analysis on 6 related subjects from a four-generation family. Our study identified a list of five potential candidate genes for bipolar disorder. Among these five genes, GRID1 (Glutamate Receptor Delta-1 Subunit), which was previously reported to be associated with several psychiatric disorders and brain related traits, is of particular interest. Our findings suggest a potential role for these genes and the related rare variants in the onset and development of bipolar disorder in this one family. Chapter 4: In this chapter, we investigated the potential of FMO genes to confer risk of nicotine dependence via deep targeted sequencing in 2,820 study subjects comprising of nicotine 1,583 dependents and 1,237 controls from European and African Americans. Specifically, we focused on the two genomic segments including FMO1, FMO3 and the pseudo gene FMO6P, and aimed to investigate the potential association between FMO genes and nicotine dependence. We identified different clusters of significant common variants in European (with most significant SNP rs6674596, P=0.0004, OR=0.67, MAF_EA=0.14) and African Americans (with the most significant SNP rs6608453, P=0.001, OR=0.64, MAF_AA=0.1). Most of the significant variants identified were SNPs located within intronic regions or with unknown functional significance. Chapter 5: In this chapter, we aimed to investigate the followed three scientific questions: 1) Can centrality reflect the biological significance of genes in a general human gene network? 2) Among these four commonly used centrality measures, does any of them outperform others? 3) Will they do better if we combine several centrality measures together using machine learning algorithms? To answer these scientific questions, we constructed a comprehensive human gene-gene network using protein-protein interaction data. Four essential gene sets were extracted from a variety of data sources serving as true answers in the evaluation and optimization process. Our analytic results indicated that there is a connection between the essentiality and centrality of human genes. A pattern of strong correlations was identified among the four commonly used centrality measures for a general human PPI network and the performance of each centrality measure was similar to others serving as predictors of the essentiality of genes. The improvement of the prediction models was limited when we combined several different centrality measures. Chapter 6: In this chapter, we aimed to investigate the potential enrichment pattern in centrality of susceptible genes for certain complex disorders in a functional specific sub-network. Gene expression data of human brain tissue recorded in the Human Protein Atlas were extracted and utilized to construct a series of brain function specific sub-networks. Susceptible genes from three categories of complex disorders, including neurodegenerative disorder, psychiatric disorder and non-brain related disorder, were extracted from the GWAS catalogue. We identified a significant enrichment pattern of high centrality of susceptibility genes contributing to neurodegenerative and psychiatric disorders in these sub-networks. Our findings indicate that susceptibility genes of complex disorder might have higher centralities in functional specific sub-networks

Evaluating the role of social attention in the causal path to Autism Spectrum Disorder

This thesis evaluated the evidence for the hypothesis that early disruptions in social attention are involved in the causal pathway to Autism Spectrum Disorder (ASD). The sample included infants at high and low familial risk for neurodevelopmental disorders participating in a prospective longitudinal study, and their family members. Five studies were conducted to test whether social attention atypicalities precede the onset of behavioural symptoms and whether they are related to familial, genetic and epigenetic burden for ASD. Chapter 2 examined neural correlates of attention measured with multi-channel electroencephalography in 8-month-old infants attending to faces and non-social stimuli, in relation to outcomes at age 3. Chapter 3 used structural equation modelling to investigate whether disruptions in neural response have cascading effects on learning from the environment via looking behaviour. Next, to further understand whether disruptions in social attention lie between genetic risk and ASD phenotype, Chapter 4 examined the association between ability to detect eye-gaze direction in a familial sample, severity of ASD symptoms and polygenic risk for ASD. Chapter 5 explored these patterns earlier in development, looking at the relationship between social attention at 14 months of age and familial burden, polygenic risk and parentreport traits of ASD and ADHD. Finally, Chapter 6, leveraging DNA methylation data, explored whether epigenetic signals were associated with early neural and behavioural correlates of social attention as well as developmental change leading to atypical outcome. Taken together, this work examined in depth the multifaceted nature of social attention, pointing to neural and behavioural atypicalities at critical time points as promising targets for cognitive and affective interventions. Furthermore, it pioneers future work integrating genetics, epigenetics and early neurocognitive measures of social attention in large prospective longitudinal studies of individuals at increased vulnerability for neurodevelopmental disorders, to shed light on the developmental mechanisms underlying the emergence of ASD

Brain Structural Maturation and Cognitive Abilities in Early Life

The first two years of life mark the most dynamic period of postnatal brain maturation, during which time cortical expansion and myelination reach peak developmental rates. Cortical morphology and white matter (WM) microstructure have been linked to cognition in older adults and children, yet we know remarkably little about how the brain matures to support emergent cognition. This is a critical gap in knowledge, as the first years of life mark a sensitive period in child development when atypical brain and behavioral phenotypes may become apparent. In this report, we examined cortical thickness (CT), surface area (SA), and WM fiber integrity in 450 typically-developing children at birth, age 1, and age 2 in association with assessments of motor, language, and general cognitive abilities at ages 1 and 2. Results revealed that generally thicker, larger cortices and more mature WM tract properties in early life related to better performance on cognitive tasks, suggesting that increased synaptogenesis, elaborations in dendritic arborization, and myelination may confer benefits for infant cognitive development. We found several expected brain-cognition relationships, with CT in regions associated with motor planning and execution and regions associated with language processing and production related to motor and language scores, respectively. Results between cognition and WM integrity were less specific, with tract properties across many fibers spanning the brain relating to cognition across domains. This finding, along with the fact that the majority of significant WM results were of a predictive nature, prompted further study into the organization of WM at birth and future outcomes. Using a deep learning approach, we successfully predicted 2-year cognitive outcomes using WM connectivity patterns at birth. Taken together, these results suggest that cortical structure and the organization and microstructural integrity of WM pathways at birth serve as a foundation upon which subsequent fine-tuning of brain structure takes place to support emergent cognition in infancy and toddlerhood. These findings offer novel insight into how prenatal and postnatal brain structural maturation support infant and toddler cognitive abilities and fills important gaps in our current understanding of the neurobiology of emergent language, motor, and cognitive abilities in early life.Doctor of Philosoph

From Hydra to Humans: Insights into molecular mechanisms of aging and longevity

Human aging is characterized by progressive functional decline that coincides with both increased morbidity and mortality. Aging affects every human being and only few individuals achieve longevity, a very special phenotype marked by extraordinary healthy aging. This thesis consists of three chapters; each one is devoted to a separate project that contributes to the growing body of knowledge about aging and longevity. The work required the compilation, management and analysis of diverse big data sets and the application of cutting-edge statistical and computational methods. Chapter 1 - A functional genomics study was conducted in the potentially immortal freshwater polyp Hydra using body part-specific microarray and RNA sequencing data. The results revealed gene expression patterns that allow boundary maintenance during Hydra’s continuous cell proliferation and tissue self-renewal. Furthermore, this study provided evidence for de-acetylation as a key mechanism underlying compartmentalization. Surprisingly, FoxO, which is known to substantially drive developmental processes and stem cell renewal in Hydra, did not seem to be affected by the acetylation status. Chapter 2 - Long-lived individuals (LLI, >95 years of age) epitomize the healthy aging phenotype and are thought to carry beneficial genetic variants that predispose to human longevity. Despite extensive research efforts, only few of these genetic factors in LLI have been identified so far. In contrast to previous investigations which mainly focused on intronic variants, a genome-wide exome-based case-control study was performed. DNA samples of more than 1,200 German LLI, including 599 centenarians (≥100 years), and about 6,900 younger controls were used for single-variant and gene-based association analyses that yielded two new candidate longevity genes, fructosamine 3 kinase related protein (FN3KRP) and phosphoglycolate phosphatase (PGP). FN3KRP functions in the deglycation of proteins to restore their function, while PGP via controlling glycerol-3-phosphate levels affects both glucose and fat metabolism. Given the biological functions of the genes, their longevity-associations appear very plausible. Chapter 3 - In recent years, the intestinal microbiome (GM) has increasingly gained attention in aging and longevity research. A 16S rRNA microbiome study was conducted using 1301 stool samples of healthy individuals (age range: 19 - 104 years) that were drawn from three cohorts. The aim was to investigate potential associations among GM composition, host genetics and environmental factors during aging. The GM composition changed with age, showing an increase of opportunistic pathogens that may generate an inflammatory environment in the gut. Age explained only ~1% of the inter-individual variation, whereas anthropometric measures, genetic background and dietary patterns together explained 20%. Strikingly, clear GM population stratification in terms of four enterotype-like clusters was observed, which were predominantly associated with dietary patterns. The correction for these clusters was shown to increase the comparability of findings from the different cohorts. In addition, the LLI showed a specific gut microbial pattern, which is in line with previously published reports. The present work shows that a thorough bioinformatics expertise helps to address the complexity of the two phenotypes aging and longevity. One highlight of the thesis is the discovery of two new candidate longevity loci that, in view of the limited output of previous study approaches, enlarge the existing database

Cracking meritocracy from the starting gate : social inequality in skill formation and school choice

Defence date: 29 October 2020Examining Board: Professor Fabrizio Bernardi (European University Institute); Professor Juho Härkönen (European University Institute); Professor Jonas Radl (Carlos III University / WZB Berlin Social Science Center); Professor Leire Salazar (Universidad Nacional de Educación a Distancia)In post-industrial societies, a college education is the main channel for upper classes to prevent their children falling down the social ladder, while, for working classes, it is the best bet for upward mobility. Despite attaining post-compulsory education was equalised and a driver of social mobility in the last decades, inequalities by socioeconomic status (SES) in college graduation, the main social lift, remained relatively unchanged. We are only starting to understand the complex interplay between biological and environmental factors explaining why educational inequalities gestate before birth and persist over generations. Besides, further research is needed to unravel why advantaged students are more likely to get ahead in education than equally-skilled, but disadvantaged peers. This thesis bridges interdisciplinary literature to study how parental SES affects educational attainment during childhood in Germany, evaluating the implications for social justice. It contributes to the literature by (1) analysing the consequences of prenatal health shocks on skill formation; (2) examining the effect of cognitive and non-cognitive skills on the transition to secondary education; and (3) assessing SES-heterogeneity in these associations. Drawing from compensatory theories, I demonstrate how negative traits for educational attainment—low birth weight and cognitive ability—are less detrimental for high-SES children from the early stages of the status-attainment process due to mechanisms like parental investments and aspirations, and teachers’ bias in assessments. The German educational system enforces early tracking into academic or vocational pathways from age 10, supposedly according to ability. Thus, the case of Germany represents an institutional starting gate to evaluate equal opportunity, where compensating for negative traits might be difficult. To test compensatory theories, I utilise the Twin Life Study and the National Educational Panel Study applying quasi-causal empirical designs. The findings challenge the liberal conception of merit as the sum of ability plus effort in evaluating equal opportunity