Multimodal population brain imaging in the UK Biobank prospective epidemiological study

Medical imaging has enormous potential for early disease prediction, but is impeded by the difficulty and expense of acquiring data sets before symptom onset. UK Biobank aims to address this problem directly by acquiring high-quality, consistently acquired imaging data from 100,000 predominantly healthy participants, with health outcomes being tracked over the coming decades. The brain imaging includes structural, diffusion and functional modalities. Along with body and cardiac imaging, genetics, lifestyle measures, biological phenotyping and health records, this imaging is expected to enable discovery of imaging markers of a broad range of diseases at their earliest stages, as well as provide unique insight into disease mechanisms. We describe UK Biobank brain imaging and present results derived from the first 5,000 participants' data release. Although this covers just 5% of the ultimate cohort, it has already yielded a rich range of associations between brain imaging and other measures collected by UK Biobank

Imaging Genetics and Biomarker Variations of Clinically Diagnosed Alzheimer's Disease

Indiana University-Purdue University Indianapolis (IUPUI)Neuroimaging biomarkers play a crucial role in our understanding of Alzheimer’s disease. Beyond providing a fast and accurate in vivo picture of the neuronal structure and biochemistry, these biomarkers make up a research framework, defined in a 2018 as the A(amyloid)/T(tau)/N(neurodegeneration) framework after three of the hallmarks of Alzheimer’s disease. I first used imaging measures of amyloid, tau and neurodegeneration to study clinically diagnosed Alzheimer’s disease. After dividing subjects into early (onset younger than 65) and late-onset (onset of 65 and older) amyloid-positive (AD) and amyloid-negative (nonAD) groups, I saw radically differing topographical distribution of tau and neurodegeneration. AD subjects with an early disease onset had a much more severe amyloid, tau and neurodegeneration than lateonset AD. In the nonAD group, neurodegeneration was found only in early-onset FDG PET data and in a nonAlzheimer’s-like MRI and FDG pattern for late-onset. The late-onset nonAD resembled that of limbic-predominant age-related TDP-43 encephalopathy. I next utilized an imaging genetics approach to associate genome-wide significant Alzheimer’s risk variants to structural (MRI), metabolic (FDG PET) and tau (tau PET) imaging biomarkers. Linear regression was used to select variants for each of the models and included a pooled sample, cognitively normal, mild cognitive impairment and dementia groups in order to fully capture the cognitive spectrum from normal cognition to the most severely impaired. Model selected variants were replicated using voxelwise regression in an exploratory analysis of spatial associations for each modality. For each imaging type, I replicated some associations to the biomarkers previously seen, as well as identified several novel associations. Several variants identified with crucial Alzheimer’s biomarkers may be potential future targets for drug interventions

PARALLEL INDEPENDENT COMPONENT ANALYSIS WITH REFERENCE FOR IMAGING GENETICS: A SEMI-BLIND MULTIVARIATE APPROACH

Imaging genetics is an emerging field dedicated to the study of genetic underpinnings of brain structure and function. Over the last decade, brain imaging techniques such as magnetic resonance imaging (MRI) have been increasingly applied to measure morphometry, task-based function and connectivity in living brains. Meanwhile, high-throughput genotyping employing genome-wide techniques has made it feasible to sample the entire genome of a substantial number of individuals. While there is growing interest in image-wide and genome-wide approaches which allow unbiased searches over a large range of variants, one of the most challenging problems is the correction for the huge number of statistical tests used in univariate models. In contrast, a reference-guided multivariate approach shows specific advantage for simultaneously assessing many variables for aggregate effects while leveraging prior information. It can improve the robustness of the results compared to a fully blind approach. In this dissertation we present a semi-blind multivariate approach, parallel independent component analysis with reference (pICA-R), to better reveal relationships between hidden factors of particular attributes. First, a consistency-based order estimation approach is introduced to advance the application of ICA to genotype data. The pICA-R approach is then presented, where independent components are extracted from two modalities in parallel and inter-modality associations are subsequently optimized for pairs of components. In particular, prior information is incorporated to elicit components of particular interests, which helps identify factors carrying small amounts of variance in large complex datasets. The pICA-R approach is further extended to accommodate multiple references whose interrelationships are unknown, allowing the investigation of functional influence on neurobiological traits of potentially related genetic variants implicated in biology. Applied to a schizophrenia study, pICA-R reveals that a complex genetic factor involving multiple pathways underlies schizophrenia-related gray matter deficits in prefrontal and temporal regions. The extended multi-reference approach, when employed to study alcohol dependence, delineates a complex genetic architecture, where the CREB-BDNF pathway plays a key role in the genetic factor underlying a proportion of variation in cue-elicited brain activations, which plays a role in phenotypic symptoms of alcohol dependence. In summary, our work makes several important contributions to advance the application of ICA to imaging genetics studies, which holds the promise to improve our understating of genetics underlying brain structure and function in healthy and disease

Multimodal phenotyping of synaptic damage in Alzheimer’s disease : translational perspective with focus on quantitative EEG

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common form of dementia. Accumulation of AD-associated pathology in the brain may begin a decade or more before the appearance of the first symptoms of the disease. The pathological-clinical “continuum of AD” therefore encompasses time between the initial neuropathological changes and symptoms of advanced disease. Besides cognitively healthy individuals at risk, it includes subjects with subjective cognitive decline (SCD), mild cognitive impairment (MCI) and eventually dementia when the severity of cognitive impairment affects patients’ ability to carry out everyday activities. Timely detection of the disease would therefore recognize patients that are at risk for future cognitive deterioration and provide time window for the prevention and novel therapeutical interventions. Accumulating evidence suggests that degeneration and dysfunction of brain neuronal connections, i.e. synapses, is one of the earliest and best proxies of cognitive deficits in patients along AD continuum. Human electroencephalography (EEG) is a non-invasive and widely available diagnostic method that records real-time large-scale synaptic activity. The commonly used method in research settings is quantitative EEG (qEEG) analysis that provides objective information on EEG recorded at the level of the scalp. Quantitative EEG analysis unravels complex EEG signal and adds relevant information on its spectral components (frequency domain), temporal dynamics (time domain) and topographic estimates (space domain) of brain cortical activity. The general aim of the present thesis was to characterize different aspects of synaptic degeneration in AD, with the focus on qEEG and its relationship to both conventional and novel synaptic markers. In study I, global qEEG measures of power and synchronization were found to correlate with conventional cerebrospinal fluid (CSF) biomarkers of Aβ and tau pathology in patients diagnosed with SCD, MCI and AD, linking the markers of AD pathology to the generalized EEG slowing and reduced brain connectivity in fast frequency bands. In study II, qEEG analysis in the time domain (EEG microstates) revealed alterations in the organization and dynamics of large-scale brain networks in memory clinic patients compared to healthy elderly controls. In study III, topographical qEEG analysis of brain functional connectivity was associated with regionspecific cortical glucose hypometabolism ([18F]Fluorodeoxyglucose positron-emission tomography) in MCI and AD patients. Study IV provided evidence that qEEG measures of global power and synchronization correlate with CSF levels of synaptic marker neurogranin, both modalities being in combination independent predictors of progression to AD dementia in MCI patients. Study V and associated preliminary study introduced in the thesis assessed the translational potential of CSF neurogranin and qEEG as well as their direct relationship to AD neuropathology in App knock-in mouse models of AD. In study V, changes in CSF neurogranin levels and their relationship to conventional CSF markers in App knock-in mice corresponded to the pattern observed in clinical AD cohorts. These findings highlighted the potential use of mouse CSF biomarkers as well as App knock-in mouse models for translational investigation of synaptic dysfunction due to AD. In general, the results of the thesis invite for further clinical validation of multimodal synaptic markers in the context of early AD diagnosis, prognosis, and treatment monitoring in individual patients

The anthropometric, environmental and genetic determinants of right ventricular structure and function

BACKGROUND Measures of right ventricular (RV) structure and function have significant prognostic value. The right ventricle is currently assessed by global measures, or point surrogates, which are insensitive to regional and directional changes. We aim to create a high-resolution three-dimensional RV model to improve understanding of its structural and functional determinants. These may be particularly of interest in pulmonary hypertension (PH), a condition in which RV function and outcome are strongly linked. PURPOSE To investigate the feasibility and additional benefit of applying three-dimensional phenotyping and contemporary statistical and genetic approaches to large patient populations. METHODS Healthy subjects and incident PH patients were prospectively recruited. Using a semi-automated atlas-based segmentation algorithm, 3D models characterising RV wall position and displacement were developed, validated and compared with anthropometric, physiological and genetic influences. Statistical techniques were adapted from other high-dimensional approaches to deal with the problems of multiple testing, contiguity, sparsity and computational burden. RESULTS 1527 healthy subjects successfully completed high-resolution 3D CMR and automated segmentation. Of these, 927 subjects underwent next-generation sequencing of the sarcomeric gene titin and 947 subjects completed genotyping of common variants for genome-wide association study. 405 incident PH patients were recruited, of whom 256 completed phenotyping. 3D modelling demonstrated significant reductions in sample size compared to two-dimensional approaches. 3D analysis demonstrated that RV basal-freewall function reflects global functional changes most accurately and that a similar region in PH patients provides stronger survival prediction than all anthropometric, haemodynamic and functional markers. Vascular stiffness, titin truncating variants and common variants may also contribute to changes in RV structure and function. CONCLUSIONS High-resolution phenotyping coupled with computational analysis methods can improve insights into the determinants of RV structure and function in both healthy subjects and PH patients. Large, population-based approaches offer physiological insights relevant to clinical care in selected patient groups.Open Acces

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Identification of genetic factors underpinning phenotypic heterogeneity in Huntington's disease and other neurodegenerative disorders

Neurodegenerative diseases including Huntington’s disease (HD), the spinocerebellar ataxias and C9orf72 associated Amyotrophic Lateral Sclerosis / Frontotemporal dementia (ALS/FTD) do not present and progress in the same way in all patients. Instead there is phenotypic variability in age at onset, progression and symptoms. Understanding this variability is not only clinically valuable, but identification of the genetic factors underpinning this variability has the potential to highlight genes and pathways which may be amenable to therapeutic manipulation, hence help find drugs for these devastating and currently incurable diseases. Identification of genetic modifiers of neurodegenerative diseases is the overarching aim of this thesis. To identify genetic variants which modify disease progression it is first necessary to have a detailed characterization of the disease and its trajectory over time. In this thesis clinical data from the TRACK-HD studies, for which I collected data as a clinical fellow, was used to study disease progression over time in HD, and give subjects a progression score for subsequent analysis. In this thesis I show blood transcriptomic signatures of HD status and stage which parallel HD brain and overlap with Alzheimer’s disease brain. Using the Huntington’s disease progression score in a genome wide association study, both a locus on chromosome 5 tagging MSH3, and DNA handling pathways more broadly, are shown to modify HD progression: these results are explored. Transcriptomic signatures associated with HD progression rate are also investigated. In this thesis I show that DNA repair variants also modify age at onset in spinocerebellar ataxias (1, 2, 3, 6, 7 and 17), which are, like HD, caused by triplet repeat expansions, suggesting a common mechanism. Extending this thesis’ examination of the relationship between phenotype and genotype I show that the C9orf72 expansion, normally associated with ALS/FTD, is also the commonest cause of HD phenocopy presentations

Cognitive impairment in Parkinson’s disease: Impact and identification of comorbid disease mechanisms

Cognitive impairment is a common and debilitating feature of Parkinson's disease (PD). While it is primarily caused by cerebral propagation of α-synuclein protein, evidence of comorbid diseases is frequently found in autopsy samples. This includes tau and amyloid-β pathologies – the hallmarks of Alzheimer's disease (AD) – and cerebrovascular damage. Comorbid diseases may influence cognition in PD over and above the effects of α-synuclein alone, and this influence may interfere with the results of clinical trials of next-generation medical treatments that target α-synuclein. The primary aims of this thesis were to define the extent and the effects of comorbid disease mechanisms in PD, and to identify viable clinical strategies for detecting coexistent disorders in vivo. Methods included a systematic review of autopsy studies; a factor analysis of the Montreal Cognitive Assessment (MoCA); a regression analysis of two genes; and a cross-sectional neuropsychological study of 45 patients. The systematic review found significant tau pathology in around one-third of PD patients at death. Significant amyloid-β pathology affected over half, and conferred a worse prognosis. Other pathologies (e.g. cerebrovascular disease) were less common, and did not contribute to dementia in PD. The factor analysis showed that the MoCA has limited value for distinguishing cognitive profiles in PD, suggesting that it should be used only for screening. The genetic project found that variation in the APOE gene influenced cognitive decline in early PD; the effect varied between men and women. Variation in MAPT did not affect cognitive decline. Finally, the neuropsychological study found that over half of cognitively impaired PD patients could be clinically diagnosed with a coexistent cognitive disorder, with AD being the most common. Collectively, the results of this thesis show that comorbid diseases, particularly AD, are common in PD, and these contribute to the cognitive phenotype. Consequently, a clinical assessment incorporating selected neuropsychological tests can be used to identify comorbid diseases in PD patients. It is important to consider the potentially confounding impact of multimorbidity in the design and analysis of clinical trials that aim to modulate neurodegeneration in PD by targeting α-synuclein

Any-way and Sparse Analyses for Multimodal Fusion and Imaging Genomics

This dissertation aims to develop new algorithms that leverage sparsity and mutual information across data modalities built upon the independent component analysis (ICA) framework to improve the performance of current ICA-based multimodal fusion approaches. These algorithms are further applied to both simulated data and real neuroimaging and genomic data to examine their performance. The identified neuroimaging and genomic patterns can help better delineate the pathology of mental disorders or brain development. To alleviate the signal-background separation difficulties in infomax-decomposed sources for genomic data, we propose a sparse infomax by enhancing a robust sparsity measure, the Hoyer index. Hoyer index is scale-invariant and well suited for ICA frameworks since the scale of decomposed sources is arbitrary. Simulation results demonstrate that sparse infomax increases the component detection accuracy for situations where the source signal-to-background (SBR) ratio is low, particularly for single nucleotide polymorphism (SNP) data. The proposed sparse infomax is further extended into two data modalities as a sparse parallel ICA for applications to imaging genomics in order to investigate the associations between brain imaging and genomics. Simulation results show that sparse parallel ICA outperforms parallel ICA with improved accuracy for structural magnetic resonance imaging (sMRI)-SNP association detection and component spatial map recovery, as well as with enhanced sparsity for sMRI and SNP components under noisy cases. Applying the proposed sparse parallel ICA to fuse the whole-brain sMRI and whole-genome SNP data of 24985 participants in the UK biobank, we identify three stable and replicable sMRI-SNP pairs. The identified sMRI components highlight frontal, parietal, and temporal regions and associate with multiple cognitive measures (with different association strengths in different age groups for the temporal component). Top SNPs in the identified SNP factor are enriched in inflammatory disease and inflammatory response pathways, which also regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region, and the regulation effects are significantly enriched. Applying the proposed sparse parallel ICA to imaging genomics in attention-deficit/hyperactivity disorder (ADHD), we identify and replicate one SNP component related to gray matter volume (GMV) alterations in superior and middle frontal gyri underlying working memory deficit in adults and adolescents with ADHD. The association is more significant in ADHD families than controls and stronger in adults and older adolescents than younger ones. The identified SNP component highlights SNPs in long non-coding RNAs (lncRNAs) in chromosome 5 and in several protein-coding genes that are involved in ADHD, such as MEF2C, CADM2, and CADPS2. Top SNPs are enriched in human brain neuron cells and regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region. Moreover, to increase the flexibility and robustness in mining multimodal data, we propose aNy-way ICA, which optimizes the entire correlation structure of linked components across any number of modalities via the Gaussian independent vector analysis and simultaneously optimizes independence via separate (parallel) ICAs. Simulation results demonstrate that aNy-way ICA recover sources and loadings, as well as the true covariance patterns with improved accuracy compared to existing multimodal fusion approaches, especially under noisy conditions. Applying the proposed aNy-way ICA to integrate structural MRI, fractal n-back, and emotion identification task functional MRIs collected in the Philadelphia Neurodevelopmental Cohort (PNC), we identify and replicate one linked GMV-threat-2-back component, and the threat and 2-back components are related to intelligence quotient (IQ) score in both discovery and replication samples. Lastly, we extend the proposed aNy-way ICA with a reference constraint to enable prior-guided multimodal fusion. Simulation results show that aNy-way ICA with reference recovers the designed linkages between reference and modalities, cross-modality correlations, as well as loading and component matrices with improved accuracy compared to multi-site canonical correlation analysis with reference (MCCAR)+joint ICA under noisy conditions. Applying aNy-way ICA with reference to supervise structural MRI, fractal n-back, and emotion identification task functional MRIs fusion in PNC with IQ as the reference, we identify and replicate one IQ-related GMV-threat-2-back component, and this component is significantly correlated across modalities in both discovery and replication samples.Ph.D