Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imagi...
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Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.
Background: Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data ma...
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Background: Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable dataintegration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes. Solution: To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. Performance: We outline research and clinical applications for the platform, including dataintegration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on "flattening" complex data structures, and runs efficiently when alternative approaches are unable to perform at all.
Context The progressive neurodegenerative disorder Parkinson’s disease (PD) features diverse symptom presentation that progresses at different speeds and demands effective disease classification with precise patient ...
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Context The progressive neurodegenerative disorder Parkinson’s disease (PD) features diverse symptom presentation that progresses at different speeds and demands effective disease classification with precise patient management. The traditional methods demonstrate insufficient ability to detect the complex spatial-temporal connections between various clinical data types which highlights the need for advanced computational approaches. Objective The proposed study pioneered a state-of-the-art Spatio-temporal fusion and prediction (STFP) algorithm model approach that utilizes Graph neural network (GNN) and Temporal convolutional network (TCN) for multi-task classification of PD covering three significant dimensions: disease stage, progression speed, symptom subtype classification. Methods By employing the fusing approach for structural medical resonance imaging (MRI) and dopamine transporter scan (DaTSCAN) along with clinical biomarker data to examine spatial and temporal relationships a fused feature representation was developed. PPMI dataset was employed for training and evaluation of the multi-task classification model with Cohen’s Kappa, cumulative link model (CLM), the area under the precision-recall curve (AUC-PR), and Brier score prominent evaluation metrics for analyzing the robustness of the model. Key Findings A Cohen’s Kappa of 0.89, a CLM score of 0.91, and an AUC-PR of 0.94 have been recorded for early-stage classification, slow progression, and motor symptom classification respectively demonstrating the outperformance of the proposed model. The superiority of the model has been established with p-values (<0.05) for ANOVA and Wilcoxon tests. Conclusion The STFP-based model advances the modeling of PD progression by improving classification accuracy and diagnostic clarity. Subsequent research will investigate more datamodalities and their practical application in healthcare.
UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart an...
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UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.
The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. ...
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The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. (C) 2013 Elsevier Inc. All rights reserved.
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