Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

The spectrum nature and heterogeneity within autism spectrum disorders (ASD) pose as a challenge for treatment. Personalisation of syllabus for children with ASD can improve the efficacy of learning by adjusting the number of opportunities and deciding the course of syllabus. We research the data-motivated approach in an attempt to disentangle this heterogeneity for personalisation of syllabus. With the help of technology and a structured syllabus, collecting data while a child with ASD masters the skills is made possible. The performance data collected are, however, growing and contain missing elements based on the pace and the course each child takes while navigating through the syllabus. Bayesian nonparametric methods are known for automatically discovering the number of latent components and their parameters when the model involves higher complexity. We propose a nonparametric Bayesian matrix factorisation model that discovers learning patterns and the way participants associate with them. Our model is built upon the linear Poisson gamma model (LPGM) with an Indian buffet process prior and extended to incorporate data with missing elements. In this paper, for the first time we have presented learning patterns deduced automatically from data mining and machine learning methods using intervention data recorded for over 500 children with ASD. We compare the results with non-negative matrix factorisation and K-means, which being parametric, not only require us to specify the number of learning patterns in advance, but also do not have a principle approach to deal with missing data. The F1 score observed over varying degree of similarity measure (Jaccard Index) suggests that LPGM yields the best outcome. By observing these patterns with additional knowledge regarding the syllabus it may be possible to observe the progress and dynamically modify the syllabus for improved learning

Applied machine learning for personalised early intervention in autism

This thesis is the first to address the problems of early intervention in Autism Spectrum Disorder through the lens of machine learning and data analytics. The key contribution is the establishment of large datasets in this domain for the first time together with a systematic data-based approach to extract knowledge relevant to Autism

The meaning of significant mean-group differences for biomarker discovery

Over the past decade, biomarker discovery has become a key goal in psychiatry to aid in the more reliable diagnosis and prognosis of heterogeneous psychiatric conditions and the development of tailored therapies. Nevertheless, the prevailing statistical approach is still the mean group comparison between “cases” and “controls,” which tends to ignore within-group variability. In this educational article, we used empirical data simulations to investigate how effect size, sample size, and the shape of distributions impact the interpretation of mean group differences for biomarker discovery. We then applied these statistical criteria to evaluate biomarker discovery in one area of psychiatric research—autism research. Across the most influential areas of autism research, effect size estimates ranged from small (d = 0.21, anatomical structure) to medium (d = 0.36 electrophysiology, d = 0.5, eye-tracking) to large (d = 1.1 theory of mind). We show that in normal distributions, this translates to approximately 45% to 63% of cases performing within 1 standard deviation (SD) of the typical range, i.e., they do not have a deficit/atypicality in a statistical sense. For a measure to have diagnostic utility as defined by 80% sensitivity and 80% specificity, Cohen’s d of 1.66 is required, with still 40% of cases falling within 1 SD. However, in both normal and nonnormal distributions, 1 (skewness) or 2 (platykurtic, bimodal) biologically plausible subgroups may exist despite small or even nonsignificant mean group differences. This conclusion drastically contrasts the way mean group differences are frequently reported. Over 95% of studies omitted the “on average” when summarising their findings in their abstracts (“autistic people have deficits in X”), which can be misleading as it implies that the group-level difference applies to all individuals in that group. We outline practical approaches and steps for researchers to explore mean group comparisons for the discovery of stratification biomarkers

Analyzing Heterogeneity In Neuroimaging With Probabilistic Multivariate Clustering Approaches

Automated quantitative neuroimaging analysis methods have been crucial in elucidating normal and pathological brain structure and function, and in building in vivo markers of disease and its progression. Commonly used methods can identify and precisely quantify subtle and spatially complex imaging patterns of brain change associated with brain diseases. However, the overarching premise of these methods is that the disease group is a homogeneous entity resulting from a single, unifying pathophysiological process that has a single imaging signature. This assumption ignores ample evidence for the heterogeneous nature of neurodegenerative diseases and neuropsychiatric disorders, resulting in incomplete or misleading descriptions. Accurate characterization of heterogeneity is important for deepening our understanding of neurobiological processes, thus leading to improved disease diagnosis and prognosis. In this thesis, we leveraged machine learning techniques to develop novel tools that can analyze the heterogeneity in both cross-sectional and longitudinal neuroimaging studies. Specifically, we developed a semi-supervised clustering method for characterizing heterogeneity in cross-sectional group comparison studies, where normal and patient populations are modeled as high-dimensional point distributions, and heterogeneous disease effects are captured by estimating multiple transformations that align the two distributions, while accounting for the effect of nuisance covariates. Moreover, toward dissecting the heterogeneity in longitudinal cohorts, we proposed a method which simultaneously fits multiple population longitudinal multivariate trajectories and clusters subjects into subgroups. Longitudinal trajectories are modeled using spatiotemporally regularized cubic splines, while clustering is performed by assigning subjects to the subgroup whose population trajectory best fits their data. The proposed tools were extensively validated using synthetic data. Importantly, they were applied to study the heterogeneity in large clinical neuroimaging cohorts. We identified four disease subtypes with distinct imaging signatures using data from Alzheimer’s Disease Neuroimaging Initiative, and revealed two subgroups with different longitudinal patterns using data from Baltimore Longitudinal Study on Aging. Critically, we were able to further characterize the subgroups in each of the studies by performing statistical analyses evaluating subgroup differences with additional information such as neurocognitive data. Our results demonstrate the strength of the developed methods, and may pave the road for a broader understanding of the complexity of brain aging and Alzheimer’s disease

Distinct DNA Methylation Patterns of Subependymal Giant Cell Astrocytomas in Tuberous Sclerosis Complex

Tuberous sclerosis complex (TSC) is a monogenic disorder caused by mutations in either the TSC1 or TSC2 gene, two key regulators of the mechanistic target of the rapamycin complex pathway. Phenotypically, this leads to growth and formation of hamartomas in several organs, including the brain. Subependymal giant cell astrocytomas (SEGAs) are low-grade brain tumors commonly associated with TSC. Recently, gene expression studies provided evidence that the immune system, the MAPK pathway and extracellular matrix organization play an important role in SEGA development. However, the precise mechanisms behind the gene expression changes in SEGA are still largely unknown, providing a potential role for DNA methylation. We investigated the methylation profile of SEGAs using the Illumina Infinium HumanMethylation450 BeadChip (SEGAs n = 42, periventricular control n = 8). The SEGA methylation profile was enriched for the adaptive immune system, T cell activation, leukocyte mediated immunity, extracellular structure organization and the ERK1 & ERK2 cascade. More interestingly, we identified two subgroups in the SEGA methylation data and show that the differentially expressed genes between the two subgroups are related to the MAPK cascade and adaptive immune response. Overall, this study shows that the immune system, the MAPK pathway and extracellular matrix organization are also affected on DNA methylation level, suggesting that therapeutic intervention on DNA level could be useful for these specific pathways in SEGA. Moreover, we identified two subgroups in SEGA that seem to be driven by changes in the adaptive immune response and MAPK pathway and could potentially hold predictive information on target treatment response

Effects of a Synbiotic on Plasma Immune Activity Markers and Short-Chain Fatty Acids in Children and Adults with ADHD—A Randomized Controlled Trial

Synbiotic 2000, a pre + probiotic, reduced comorbid autistic traits and emotion dysregulation in attention deficit hyperactivity disorder (ADHD) patients. Immune activity and bacteria-derived short-chain fatty acids (SCFAs) are microbiota–gut–brain axis mediators. The aim was to investigate Synbiotic 2000 effects on plasma levels of immune activity markers and SCFAs in children and adults with ADHD. ADHD patients (n = 182) completed the 9-week intervention with Synbiotic 2000 or placebo and 156 provided blood samples. Healthy adult controls (n = 57) provided baseline samples. At baseline, adults with ADHD had higher pro-inflammatory sICAM-1 and sVCAM-1 and lower SCFA levels than controls. Children with ADHD had higher baseline sICAM-1, sVCAM-1, IL-12/IL-23p40, IL-2Rα, and lower formic, acetic, and propionic acid levels than adults with ADHD. sICAM-1, sVCAM-1, and propionic acid levels were more abnormal in children on medication. Synbiotic 2000, compared to placebo, reduced IL-12/IL-23p40 and sICAM-1 and increased propionic acid levels in children on medication. SCFAs correlated negatively with sICAM-1 and sVCAM-1. Preliminary human aortic smooth-muscle-cell experiments indicated that SCFAs protected against IL-1β-induced ICAM-1 expression. These findings suggest that treatment with Synbiotic 2000 reduces IL12/IL-23p40 and sICAM-1 and increases propionic acid levels in children with ADHD. Propionic acid, together with formic and acetic acid, may contribute to the lowering of the higher-than-normal sICAM-1 levels