mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds

Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity data. Despite connectomes inhabiting a matrix manifold, most analytical frameworks ignore the underlying data geometry. This is largely because simple operations, such as mean estimation, do not have easily computable closed-form solutions. We propose a geometrically aware neural framework for connectomes, i.e., the mSPD-NN, designed to estimate the geodesic mean of a collections of symmetric positive definite (SPD) matrices. The mSPD-NN is comprised of bilinear fully connected layers with tied weights and utilizes a novel loss function to optimize the matrix-normal equation arising from Fr\'echet mean estimation. Via experiments on synthetic data, we demonstrate the efficacy of our mSPD-NN against common alternatives for SPD mean estimation, providing competitive performance in terms of scalability and robustness to noise. We illustrate the real-world flexibility of the mSPD-NN in multiple experiments on rs-fMRI data and demonstrate that it uncovers stable biomarkers associated with subtle network differences among patients with ADHD-ASD comorbidities and healthy controls.Comment: Accepted into IPMI 202

Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology

Cognitive deficits are known to be related to most forms of psychopathology. Here, we perform local genetic correlation analysis as a means of identifying independent segments of the genome that show biologically interpretable pleiotropic associations between cognitive dimensions and psychopathology. We identify collective segments of the genome, which we call “meta-loci”, showing differential pleiotropic patterns for psychopathology relative to either cognitive task performance (CTP) or performance on a non-cognitive factor (NCF) derived from educational attainment. We observe that neurodevelopmental gene sets expressed during the prenatal-early childhood period predominate in CTP-relevant meta-loci, while post-natal gene sets are more involved in NCF-relevant meta-loci. Further, we demonstrate that neurodevelopmental gene sets are dissociable across CTP meta-loci with respect to their spatial distribution across the brain. Additionally, we find that GABA-ergic, cholinergic, and glutamatergic genes drive pleiotropic relationships within dissociable meta-loci

Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology

Cognitive deficits are known to be related to most forms of psychopathology. Here, we perform local genetic correlation analysis as a means of identifying independent segments of the genome that show biologically interpretable pleiotropic associations between cognitive dimensions and psychopathology. We identify collective segments of the genome, which we call meta-loci , showing differential pleiotropic patterns for psychopathology relative to either cognitive task performance (CTP) or performance on a non-cognitive factor (NCF) derived from educational attainment. We observe that neurodevelopmental gene sets expressed during the prenatal-early childhood period predominate in CTP-relevant meta-loci, while post-natal gene sets are more involved in NCF-relevant meta-loci. Further, we demonstrate that neurodevelopmental gene sets are dissociable across CTP meta-loci with respect to their spatial distribution across the brain. Additionally, we find that GABA-ergic, cholinergic, and glutamatergic genes drive pleiotropic relationships within dissociable meta-loci

Biologically informed risk scoring in schizophrenia based on genome-wide omics data

Extensive efforts in characterizing the biological architecture of schizophrenia have moved psychiatric research closer towards clinical application. As our understanding of psychiatric illness is slowly shifting towards a conceptualization as dimensional constructs that cut across traditional diagnostic boundaries, opportunities for personalized medicine applications that are afforded by the application of advanced data science methods on the increasingly available, large-scale and multimodal data repositories are starting to be more broadly recognized. A particularly intriguing phenomenon is the discrepancy between the high heritability of schizophrenia and the difficulty in identifying predictive genetic signatures, for which polygenic risk scores of common variants that explain approximately 18% of illness-associated variance remain the gold standard. A substantial body of research points towards two lines of investigation that may lead to a significant advance, resolve at least in part the ‘missing heritability’ phenomenon, and potentially provide the basis for more predictive, personalized clinical tools. First, it is paramount to better understand the impact of environmental factors on illness risk and elucidate the biology underlying their impact on altered brain function in schizophrenia. This thesis aims to close a major gap in our understanding of the multivariate, epigenetic landscape associated with schizophrenia, its interaction with polygenic risk and its association with DLPFC-HC connectivity, a well-established and robust neural intermediate phenotype of schizophrenia. As a basis for this, we have developed a novel biologically-informed machine learning framework by incorporating systems-level biological domain knowledge, i.e., gene ontological pathways, entitled ‘BioMM’ using genome-wide DNA methylation data obtained from whole blood samples. An epigenetic poly-methylation score termed ‘PMS’ was estimated at the individual level using BioMM, trained and validated using a total of 2230 whole-blood samples and 244 post-mortem brain samples. The pathways contributing most to this PMS were strongly associated with synaptic, neural and immune system-related functions. The identified PMS could be successfully validated in two independent cohorts, demonstrating the robust generalizability of the identified model. Furthermore, the PMS could significantly differentiate patients with schizophrenia from healthy controls when predicted in DLPFC post-mortem brain samples, suggesting that the epigenetic landscape of schizophrenia is to a certain extent shared between the central and peripheral tissues. Importantly, the peripheral PMS was associated with an intermediate neuroimaging phenotype (i.e., DLPFC-HC functional connectivity) in two independent imaging samples under the working memory paradigm. However, we did not find sufficient evidence for a combined genetic and epigenetic effect on brain function by integrating PRS derived from GWAS data, which suggested that DLPFC-HC coupling was predominantly impacted by environmental risk components, rather than polygenic risk of common variants. The epigenetic signature was further not associated with GWAS-derived risk scores implying the observed epigenetic effect did likely not depend on the underlying genetics, and this was further substantiated by investigation of data from unaffected first-degree relatives of patients with SCZ, BD, MDD and autism. In summary, the characterization of PMS through the systems-level integration of multimodal data elucidates the multivariate impact of epigenetic effects on schizophrenia-relevant brain function and its interdependence with genetic illness risk. Second, the limited predictive value of polygenic risk scores and the difficulty in identifying associations with heritable neural differences found in schizophrenia may be due to the possibility that the manifestation of the functional consequences of genetic risk is modulated by spatio-temporal as well as sex-specific effects. To address this, this thesis identifies sex-differences in the spatio-temporal expression trajectories during human development of genes that showed significant prefrontal co-expression with schizophrenia risk genes during the fetal phase and adolescence, consistent with a core developmental hypothesis of schizophrenia. More specifically, it was found that during these two time-periods, prefrontal expression was significantly more variable in males compared to females, a finding that could be validated in an independent data source and that was specific for schizophrenia compared to other psychiatric as well as somatic illnesses. Similar to the epigenetic differences described above, the genes underlying the risk-associated gene expression differences were significantly linked to synaptic function. Notably, individual genes with male-specific variability increases were distinct between the fetal phase and adolescence, potentially suggesting different risk associated mechanisms that converge on the shared synaptic involvement of these genes. These results provide substantial support to the hypothesis that the functional consequences of genetic risk show spatiotemporal specificity. Importantly, the temporal specificity was linked to the fetal phase and adolescence, time-periods that are thought to be of predominant importance for the brain-functional consequences of environmental risk exposure. Therefore, the presented results provide the basis for future studies exploring the polygenic risk architecture and its interaction with environmental effects in a multivariate and spatiotemporally stratified manner. In summary, the work presented in this thesis describes multivariate, multimodal approaches to characterize the (epi-)genetic basis of schizophrenia, explores its association with a well-established neural intermediate phenotype of the illness and investigates the spatio-temporal specificity of schizophrenia-relevant gene expression effects. This work expands our knowledge of the complex biology underlying schizophrenia and provides the basis for the future development of more predictive biological algorithms that may aid in advancing personalized medicine in psychiatry

Recurrent, Robust and Scalable Patterns Underlie Human Approach and Avoidance

BACKGROUND. Approach and avoidance behavior provide a means for assessing the rewarding or aversive value of stimuli, and can be quantified by a keypress procedure whereby subjects work to increase (approach), decrease (avoid), or do nothing about time of exposure to a rewarding/aversive stimulus. To investigate whether approach/avoidance behavior might be governed by quantitative principles that meet engineering criteria for lawfulness and that encode known features of reward/aversion function, we evaluated whether keypress responses toward pictures with potential motivational value produced any regular patterns, such as a trade-off between approach and avoidance, or recurrent lawful patterns as observed with prospect theory. METHODOLOGY/PRINCIPAL FINDINGS. Three sets of experiments employed this task with beautiful face images, a standardized set of affective photographs, and pictures of food during controlled states of hunger and satiety. An iterative modeling approach to data identified multiple law-like patterns, based on variables grounded in the individual. These patterns were consistent across stimulus types, robust to noise, describable by a simple power law, and scalable between individuals and groups. Patterns included: (i) a preference trade-off counterbalancing approach and avoidance, (ii) a value function linking preference intensity to uncertainty about preference, and (iii) a saturation function linking preference intensity to its standard deviation, thereby setting limits to both. CONCLUSIONS/SIGNIFICANCE. These law-like patterns were compatible with critical features of prospect theory, the matching law, and alliesthesia. Furthermore, they appeared consistent with both mean-variance and expected utility approaches to the assessment of risk. Ordering of responses across categories of stimuli demonstrated three properties thought to be relevant for preference-based choice, suggesting these patterns might be grouped together as a relative preference theory. Since variables in these patterns have been associated with reward circuitry structure and function, they may provide a method for quantitative phenotyping of normative and pathological function (e.g., psychiatric illness).National Institute on Drug Abuse (14118, 026002, 026104, DABK39-03-0098, DABK39-03-C-0098); The MGH Phenotype Genotype Project in Addiction and Mood Disorder from the Office of National Drug Control Policy - Counterdrug Technology Assessment Center; MGH Department of Radiology; the National Center for Research Resources (P41RR14075); National Institute of Neurological Disorders and Stroke (34189, 05236

Development of High Angular Resolution Diffusion Imaging Analysis Paradigms for the Investigation of Neuropathology

Diffusion weighted magnetic resonance imaging (DW-MRI), provides unique insight into the microstructure of neural white matter tissue, allowing researchers to more fully investigate white matter disorders. The abundance of clinical research projects incorporating DW-MRI into their acquisition protocols speaks to the value this information lends to the study of neurological disease. However, the most widespread DW-MRI technique, diffusion tensor imaging (DTI), possesses serious limitations which restrict its utility in regions of complex white matter. Fueled by advances in DW-MRI acquisition protocols and technologies, a group of exciting new DW-MRI models, developed to address these concerns, are now becoming available to clinical researchers. The emergence of these new imaging techniques, categorized as high angular resolution diffusion imaging (HARDI), has generated the need for sophisticated computational neuroanatomic techniques able to account for the high dimensionality and structure of HARDI data. The goal of this thesis is the development of such techniques utilizing prominent HARDI data models. Specifically, methodologies for spatial normalization, population atlas building and structural connectivity have been developed and validated. These methods form the core of a comprehensive analysis paradigm allowing the investigation of local white matter microarcitecture, as well as, systemic properties of neuronal connectivity. The application of this framework to the study of schizophrenia and the autism spectrum disorders demonstrate its sensitivity sublte differences in white matter organization, as well as, its applicability to large population DW-MRI studies

Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches

Introduction: Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design. Methods: Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year follow-up, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay. Results: i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”. ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively. iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features. Conclusions: Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people

Multi-atlas segmentation using clustering, local non-linear manifold embeddings and target-specific templates

Multi-atlas segmentation (MAS) has become an established technique for the automated delineation of anatomical structures. The often manually annotated labels from each of multiple pre-segmented images (atlases) are typically transferred to a target through the spatial mapping of corresponding structures of interest. The mapping can be estimated by pairwise registration between each atlas and the target or by creating an intermediate population template for spatial normalisation of atlases and targets. The former is done at runtime which is computationally expensive but provides high accuracy. In the latter approach the template can be constructed from the atlases offline requiring only one registration to the target at runtime. Although this is computationally more efficient, the composition of deformation fields can lead to decreased accuracy. Our goal was to develop a MAS method which was both efficient and accurate. In our approach we create a target-specific template (TST) which has a high similarity to the target and serves as intermediate step to increase registration accuracy. The TST is constructed from the atlas images that are most similar to the target. These images are determined in low-dimensional manifold spaces on the basis of deformation fields in local regions of interest. We also introduce a clustering approach to divide atlas labels into meaningful sub-regions of interest and increase local specificity for TST construction and label fusion. Our approach was tested on a variety of MR brain datasets and applied to an in-house dataset. We achieve state-of-the-art accuracy while being computationally much more efficient than competing methods. This efficiency opens the door to the use of larger sets of atlases which could lead to further improvement in segmentation accuracy