Why study movement variability in autism?

Autism has been defined as a disorder of social cognition, interaction and communication where ritualistic, repetitive behaviors are commonly observed. But how should we understand the behavioral and cognitive differences that have been the main focus of so much autism research? Can high-level cognitive processes and behaviors be identified as the core issues people with autism face, or do these characteristics perhaps often rather reflect individual attempts to cope with underlying physiological issues? Much research presented in this volume will point to the latter possibility, i.e. that people on the autism spectrum cope with issues at much lower physiological levels pertaining not only to Central Nervous Systems (CNS) function, but also to peripheral and autonomic systems (PNS, ANS) (Torres, Brincker, et al. 2013). The question that we pursue in this chapter is what might be fruitful ways of gaining objective measures of the large-scale systemic and heterogeneous effects of early atypical neurodevelopment; how to track their evolution over time and how to identify critical changes along the continuum of human development and aging. We suggest that the study of movement variability—very broadly conceived as including all minute fluctuations in bodily rhythms and their rates of change over time (coined micro-movements (Figure 1A-B) (Torres, Brincker, et al. 2013))—offers a uniquely valuable and entirely objectively quantifiable lens to better assess, understand and track not only autism but cognitive development and degeneration in general. This chapter presents the rationale firstly behind this focus on micro-movements and secondly behind the choice of specific kinds of data collection and statistical metrics as tools of analysis (Figure 1C). In brief the proposal is that the micro-movements (defined in Part I – Chapter 1), obtained using various time scales applied to different physiological data-types (Figure 1), contain information about layered influences and temporal adaptations, transformations and integrations across anatomically semi-independent subsystems that crosstalk and interact. Further, the notion of sensorimotor re-afference is used to highlight the fact that these layered micro-motions are sensed and that this sensory feedback plays a crucial role in the generation and control of movements in the first place. In other words, the measurements of various motoric and rhythmic variations provide an access point not only to the “motor systems”, but also access to much broader central and peripheral sensorimotor and regulatory systems. Lastly, we posit that this new lens can also be used to capture influences from systems of multiple entry points or collaborative control and regulation, such as those that emerge during dyadic social interactions

Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers

Echocardiographic deformation curves provide detailed information on myocardial function. Deep neural networks (DNNs) may enable automated detection of disease features in deformation curves, and improve the clinical assessment of these curves. We aimed to investigate whether an explainable DNN-based pipeline can be used to detect and visualize disease features in echocardiographic deformation curves of phospholamban (PLN) p.Arg14del variant carriers. A DNN was trained to discriminate PLN variant carriers (n = 278) from control subjects (n = 621) using raw deformation curves obtained by 2D-speckle tracking in the longitudinal axis. A visualization technique was used to identify the parts of these curves that were used by the DNN for classification. The PLN variant carriers were clustered according to the output of the visualization technique. The DNN showed excellent discriminatory performance (C-statistic 0.93 [95% CI 0.87–0.97]). We identified four clusters with PLN-associated disease features in the deformation curves. Two clusters showed previously described features: apical post-systolic shortening and reduced systolic strain. The two other clusters revealed novel features, both reflecting delayed relaxation. Additionally, a fifth cluster was identified containing variant carriers without disease features in the deformation curves, who were classified as controls by the DNN. This latter cluster had a very benign disease course regarding development of ventricular arrhythmias. Applying an explainable DNN-based pipeline to myocardial deformation curves enables automated detection and visualization of disease features. In PLN variant carriers, we discovered novel disease features which may improve individual risk stratification. Applying this approach to other diseases will further expand our knowledge on disease-specific deformation patterns. Graphical abstract: [Figure not available: see fulltext.] Overview of the deep neural network-based pipeline for feature detection in myocardial deformation curves. Firstly, phospholamban (PLN) p.Arg14del variant carriers and controls were selected and a deep neural network (DNN) was trained to detect the PLN variant carriers. Subsequently, a clustering-based approach was performed on the attention maps of the DNN, which revealed 4 distinct phenotypes of PLN variant carriers with different prognoses. Moreover, a cluster without features and a benign prognosis was detected

Phylogenetic surveillance of viral genetic diversity and the evolving molecular epidemiology of human immunodeficiency virus type 1

With ongoing generation of viral genetic diversity and increasing levels of migration, the global human immunodeficiency virus type 1 (HIV-1) epidemic is becoming increasingly heterogeneous. In this study, we investigate the epidemiological characteristics of 5,675 HIV-1 pol gene sequences sampled from distinct infections in the United Kingdom. These sequences were phylogenetically analyzed in conjunction with 976 complete-genome and 3,201 pol gene reference sequences sampled globally and representing the broad range of HIV-1 genetic diversity, allowing us to estimate the probable geographic origins of the various strains present in the United Kingdom. A statistical analysis of phylogenetic clustering in this data set identified several independent transmission chains within the United Kingdom involving recently introduced strains and indicated that strains more commonly associated with infections acquired heterosexually in East Africa are spreading among men who have sex with men. Coalescent approaches were also used and indicated that the transmission chains that we identify originated in the late 1980s to early 1990s. Similar changes in the epidemiological structuring of HIV epidemics are likely to be taking in place in other industrialized nations with large immigrant populations. The framework implemented here takes advantage of the vast amount of routinely generated HIV-1 sequence data and can provide epidemiological insights not readily obtainable through standard surveillance methods

Exploring Patterns of Epigenetic Information With Data Mining Techniques

[Abstract] Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT-0366Galicia. Consellería de Economía e Industria; 10SIN105004PRInstituto de Salud Carlos III; RD07/0067/000

Informatics for EEG biomarker discovery in clinical neuroscience

Neurological and developmental disorders (NDDs) impose an enormous burden of disease on children throughout the world. Two of the most common are autism spectrum disorder (ASD) and epilepsy. ASD has recently been estimated to affect 1 in 68 children, making it the most common neurodevelopmental disorder in children. Epilepsy is also a spectrum disorder that follows a developmental trajectory, with an estimated prevalence of 1%, nearly as common as autism. ASD and epilepsy co-occur in approximately 30% of individuals with a primary diagnosis of either disorder. Although considered to be different disorders, the relatively high comorbidity suggests the possibility of common neuropathological mechanisms. Early interventions for NDDs lead to better long-term outcomes. But early intervention is predicated on early detection. Behavioral measures have thus far proven ineffective in detecting autism before about 18 months of age, in part because the behavioral repertoire of infants is so limited. Similarly, no methods for detecting emerging epilepsy before seizures begin are currently known. Because atypical brain development is likely to precede overt behavioral manifestations by months or even years, a critical developmental window for early intervention may be opened by the discovery of brain based biomarkers. Analysis of brain activity with EEG may be under-utilized for clinical applications, especially for neurodevelopment. The hypothesis investigated in this dissertation is that new methods of nonlinear signal analysis, together with methods from biomedical informatics, can extract information from EEG data that enables detection of atypical neurodevelopment. This is tested using data collected at Boston Children’s Hospital. Several results are presented. First, infants with a family history of ASD were found to have EEG features that may enable autism to be detected as early as 9 months. Second, significant EEG-based differences were found between children with absence epilepsy, ASD and control groups using short 30-second EEG segments. Comparison of control groups using different EEG equipment supported the claim that EEG features could be computed that were independent of equipment and lab conditions. Finally, the potential for this technology to help meet the clinical need for neurodevelopmental screening and monitoring in low-income regions of the world is discussed

Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression

We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimer's disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene-gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 464 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathways database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24 months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing chemokine, Jak-stat and insulin signalling pathways, and tight junction interactions. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection, and identify a number of previously validated AD genes including CR1, APOE and TOMM40

PREDICTING COMPLEX PHENOTYPE-GENOTYPE RELATIONSHIPS IN GRASSES: A SYSTEMS GENETICS APPROACH

It is becoming increasingly urgent to identify and understand the mechanisms underlying complex traits. Expected increases in the human population coupled with climate change make this especially urgent for grasses in the Poaceae family because these serve as major staples of the human and livestock diets worldwide. In particular, Oryza sativa (rice), Triticum spp. (wheat), Zea mays (maize), and Saccharum spp. (sugarcane) are among the top agricultural commodities. Molecular marker tools such as linkage-based Quantitative Trait Loci (QTL) mapping, Genome-Wide Association Studies (GWAS), Multiple Marker Assisted Selection (MMAS), and Genome Selection (GS) techniques offer promise for understanding the mechanisms behind complex traits and to improve breeding programs. These methods have shown some success. Often, however, they cannot identify the causal genes underlying traits nor the biological context in which those genes function. To improve our understanding of complex traits as well improve breeding techniques, additional tools are needed to augment existing methods. This work proposes a knowledge-independent systems-genetic paradigm that integrates results from genetic studies such as QTL mapping, GWAS and mutational insertion lines such as Tos17 with gene co-expression networks for grasses--in particular for rice. The techniques described herein attempt to overcome the bias of limited human knowledge by relying solely on the underlying signals within the data to capture a holistic representation of gene interactions for a species. Through integration of gene co-expression networks with genetic signal, modules of genes can be identified with potential effect for a given trait, and the biological function of those interacting genes can be determined

Application of Metabolomics to Cardiovascular Biomarker and Pathway Discovery

Emerging technologies based on mass spectrometry and nuclear magnetic resonance enable the monitoring of hundreds of metabolites from tissues or body fluids, that is, “metabolomics.” Because metabolites change rapidly in response to physiologic perturbations, they represent proximal reporters of disease phenotypes. The profiling of low molecular weight biochemicals, including lipids, sugars, nucleotides, organic acids, and amino acids, that serve as substrates and products in metabolic pathways is particularly relevant to cardiovascular diseases. In addition to serving as disease biomarkers, circulating metabolites may participate in previously unanticipated roles as regulatory signals with hormone-like functions. Cellular metabolic pathways are highly conserved among species, facilitating complementary functional studies in model organisms to provide insight into metabolic changes identified in humans. Although metabolic profiling technologies and methods of pattern recognition and data reduction remain under development, the coupling of metabolomics with other functional genomic approaches promises to extend our ability to elucidate biological pathways and discover biomarkers of human disease