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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Sha, Jiahang Bao, Jingxuan Liu, Kefei Yang, Shu Wen, Zixuan Cui, Yuhan Wen, Junhao Davatzikos, Christos Moore, Jason H. Saykin, Andrew J. Long, Qi Shen, Li University of Pennsylvania Epidemiology and Informatics Department of Biostatistics Philadelphia United States Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China University of Pennsylvania Center for Biomedical Image Computing and Analytics Philadelphia United States Cedars-Sinai Medical Center Department of Computational Biomedicine West Hollywood United States Indiana University Department of Radiology and Imaging Sciences Indianapolis United States

ISBN: (纸本)9781665468190

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively. © 2022 IEEE.

关键词： Matrix algebra

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Mutually-Constrained Cross-Sectional and Longitudinal Non-Negative Matrix Factorization: Application to Modeling Brain Aging Trajectories

Mutually-Constrained Cross-Sectional and Longitudinal Non-Ne...

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IEEE International Symposium on biomedical Imaging

作者： Ioanna Skampardoni Junhao Wen Erus Guray Haochang Shou Konstantina Nikita Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Laboratory of AI and Biomedical Science (LABS) Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.

关键词： Pathology Magnetic resonance imaging Precision medicine Sociology Aging Hazards Trajectory

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Transcriptional and Neurochemical Signatures of Cerebral Blood Flow Alterations in Individuals With Schizophrenia or at Clinical High Risk for Psychosis

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Biological Psychiatry 2025年第2期98卷 144-155页

作者： Knight, Samuel R. Abbasova, Leyla Zeighami, Yashar Hansen, Justine Y. Martins, Daniel Zelaya, Fernando Dipasquale, Ottavia Liu, Thomas Shin, David Bossong, Matthijs Azis, Matilda Antoniades, Mathilde Howes, Oliver D. Bonoldi, Ilaria Egerton, Alice Allen, Paul McGuire, Philip Modinos, Gemma Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Centre for Developmental Neurobiology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Medical Research Council Centre for Neurodevelopmental Disorders King's College London London United Kingdom Douglas Research Centre Department of Psychiatry McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Olea Medical La Ciotat France Centre for Functional MRI University of California San Diego San Diego CA United States Global MR Applications and Workflow GE Healthcare Menlo Park CA United States Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Department of Psychiatry Brain Center Rudoph Magnus University Medical Center Utrecht Utrecht Netherlands Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States Department of Psychiatry Oxford University Oxford United Kingdom

Background: The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear. Methods: Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers. Results: We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D₁, D₂, dopamine transporter), acetylcholine (VAChT, M₁), gamma-aminobutyric acid A (GABA_A), and glutamate (NMDA) receptors as key predictors for SSD (R²_adjusted = 0.58, false discovery rate [FDR]–corrected p <.05) and CHR-P (R²_adjusted = 0.6, p_FDR <.05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychos

关键词： Arterial spin labeling Biomarkers Gene expression Mental health Neuroimaging Neurotransmitters

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Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer’s Disease

Preference Matrix Guided Sparse Canonical Correlation Analys...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiahang Sha Jingxuan Bao Kefei Liu Shu Yang Zixuan Wen Yuhan Cui Junhao Wen Christos Davatzikos Jason H. Moore Andrew J. Saykin Qi Long Li Shen Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia USA Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia USA Department of Computational Biomedicine Cedars-Sinai Medical Center West Hollywood USA Department of Radiology and Imaging Sciences Indiana University Indianapolis USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer’s disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

关键词： Analytical models Correlation Biological system modeling Computational modeling Genetics Feature extraction Data models

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Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies

arXiv

引用

arXiv 2022年

作者： Wang, Rongguang Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multi-study magnetic resonance imaging consortia for diagnosing Alzheimer's disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attributes such as gender, age, racial groups, and different clinical studies and are unbiased under multiple fairness metrics such as demographic parity difference, equalized odds difference, equal opportunity difference etc. We find that models that incorporate multi-source data from demographic, clinical, genetic factors and cognitive scores are also unbiased. These models have better predictive AUC across subgroups than those trained only with imaging features but there are also situations when these additional features do not help. © 2022, CC BY.

关键词： Magnetic resonance imaging

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Subtyping brain diseases from imaging data

arXiv

引用

arXiv 2022年

作者： Wen, Junhao Varol, Erdem Yang, Zhijian Hwang, Gyujoon Dwyer, Dominique Kazerooni, Anahita Fathi Lalousis, Paris Alexandros Davatzikos, Christos Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia United States Department of Statistics Center for Theoretical Neuroscience Zuckerman Institute Columbia University New York United States Department of Psychiatry and Psychotherapy Ludwig-Maximilian University Munich Germany Institute for Mental Health and Centre for Human Brain Health School of Psychology University of Birmingham Birmingham United Kingdom

The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment. Clinical neuroscience and cancer imaging have been two areas in which ML has offered particular promise. However, many neurologic and neuropsychiatric diseases, as well as cancer, are often heterogeneous in terms of their clinical manifestations, neuroanatomical patterns or genetic underpinnings. Therefore, in such cases, seeking a single disease signature might be ineffectual in delivering individualized precision diagnostics. The current chapter focuses on ML methods, especially semi-supervised clustering, that seek disease subtypes using imaging data. Work from Alzheimer's Disease and its prodromal stages, psychosis, depression, autism, and brain cancer are discussed. Our goal is to provide the readers with a broad overview in terms of methodology and clinical applications. © 2022, CC BY.

关键词： Neuroimaging

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Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience

arXiv

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arXiv 2022年

作者： Wang, Rongguang Bashyam, Vishnu Yang, Zhijian Yu, Fanyang Tassopoulou, Vasiliki Chintapalli, Sai Spandana Skampardoni, Ioanna Sreepada, Lasya P. Sahoo, Dushyant Nikita, Konstantina Abdulkadir, Ahmed Wen, Junhao Davatzikos, Christos Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania Philadelphia United States Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia United States School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Clinical Neurosciences Lausanne University Hospital University of Lausanne Lausanne Switzerland Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia United States

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer’s disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases. © 2022, CC BY.

关键词： Generative adversarial networks

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ACEnet: Anatomical context-encoding network for neuroanatomy segmentation

arXiv

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arXiv 2020年

作者： Li, Yuemeng Li, Hongming Fan, Yong Center for Biomedical Image Computing and Analytics The Department of Radiology The Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA19104 United States

Segmentation of brain structures from magnetic resonance (MR) scans plays an important role in the quantification of brain morphology. Since 3D deep learning models suffer from high computational cost, 2D deep learning methods are favored for their computational efficiency. However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation. In order to overcome this limitation, we develop an Anatomical Context-Encoding Network (ACEnet) to incorporate 3D spatial and anatomical contexts in 2D convolutional neural networks (CNNs) for efficient and accurate segmentation of brain structures from MR scans, consisting of 1) an anatomical context encoding module to incorporate anatomical information in 2D CNNs and 2) a spatial context encoding module to integrate 3D image information in 2D CNNs. In addition, a skull stripping module is adopted to guide the 2D CNNs to attend to the brain. Extensive experiments on three benchmark datasets have demonstrated that our method achieves promising performance compared with state-of-the-art alternative methods for brain structure segmentation in terms of both computational efficiency and segmentation accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： image segmentation

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Questionnaire-Based Prediction of Incident Coronary Artery Disease: Developing and Validating Machine Learning Models for Multiple Populations

SSRN

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SSRN 2024年

作者： Kokkorakis, Michail Folkertsma, Pytrik Anagnostakis, Filippos Sirotin, Nicole Agarwal, Manyoo Shantouf, Ronney Henning, Robert H. Pijl, Hanno Wolffenbuttel, Bruce H.R. Bax, Jeroen J. Atsma, Douwe E. Forte, José Castela Mantzoros, Christos S. van Dam, Sipko Department of Clinical Pharmacy and Pharmacology University of Groningen University Medical Center Groningen Groningen Netherlands Department of Medicine Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Endocrinology University of Groningen University Medical Center Groningen Groningen Netherlands Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Medical and Surgical Sciences Alma Mater Studiorum University of Bologna Bologna Italy Department of Preventive Medicine Cleveland Clinic Abu Dhabi Abu Dhabi United Arab Emirates Vascular and Thoracic Institute Cleveland Clinic Abu Dhabi Abu Dhabi United Arab Emirates Department of Endocrinology Leiden University Medical Center Leiden Netherlands Department of Cardiology Leiden University Medical Center Leiden Netherlands National eHealth Living Lab Leiden Netherlands Department of Design Organization and Strategy Faculty of Industrial Design Engineering Delft University of Technology Delft Netherlands Boston VA Healthcare System Harvard Medical School BostonMA United States

Background: Coronary artery disease (CAD) comprises one of the leading causes of morbidity and mortality both in the European population and globally. All established clinical risk prediction scores and models require blood lipids and physical measurements. The latest reports of the European Commission suggest that attracting health professionals to collect these data can be challenging, both from a logistic and cost perspective, which limits the usefulness of these models and makes them unsuitable for population-wide screening in resource-limited settings, i.e., rural areas. Therefore, the aim of this study was to develop and externally validate a questionnaire-based prediction model on a population scale at minimal cost, i.e., the Questionnaire-Based Evaluation for Estimating Coronary Artery Disease (QUES-CAD) to predict the ten-year incidence of coronary artery disease. Methods: Logistic regression (LR) and gradient boosting (GBT) models were trained using combinations of ten questionnaire variables, physical measurement data, and biomarkers on the White population of the UK Biobank (n=444,008) and internally validated the models in all ethnic minorities (n=27,118). The Lifelines cohort was employed as an independent external validation population (n=20,482). Additionally, we compared QUES-CAD’s performance, containing only questionnaire variables, to clinically established risk prediction tools, i.e., Framingham Coronary Heart Disease Risk Score, American College of Cardiology/American Heart Association pooled cohort equation, World Health Organization cardiovascular disease risk charts, and Systematic Coronary Risk Estimation 2, and conducted risk stratification and reclassification analyses. Findings: In the external validation set, QUES-CAD exhibited the highest area under the receiver operating characteristics curve (AUC) values of LR: 0.808 (95% Confidence Interval [CI]: 0.794¬–0.823) and GBT: 0.8 (95% CI: 0.785–0.815) for the male population and LR: 0.845

关键词： Forecasting

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Questionnaire-Based Prediction of Incident Coronary Artery Disease: Developing and Validating Machine Learning Models for Multiple Populations

SSRN

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SSRN 2024年

作者： Kokkorakis, Michail Folkertsma, Pytrik Anagnostakis, Filippos Sirotin, Nicole Agarwal, Manyoo Shantouf, Ronney Henning, Robert H. Pijl, Hanno Wolffenbuttel, Bruce H.R. Bax, Jeroen J. Atsma, Douwe E. Forte, José Castela Mantzoros, Christos S. van Dam, Sipko Department of Clinical Pharmacy and Pharmacology University of Groningen University Medical Center Groningen Groningen Netherlands Department of Medicine Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Endocrinology University of Groningen University Medical Center Groningen Groningen Netherlands Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Medical and Surgical Sciences Alma Mater Studiorum University of Bologna Bologna Italy Department of Preventive Medicine Cleveland Clinic Abu Dhabi Abu Dhabi United Arab Emirates Heart Vascular and Thoracic Institute Cleveland Clinic Abu Dhabi Abu Dhabi United Arab Emirates Department of Endocrinology Leiden University Medical Center Leiden Netherlands Department of Cardiology Leiden University Medical Center Leiden Netherlands National eHealth Living Lab Leiden Netherlands Department of Design Organization and Strategy Faculty of Industrial Design Engineering Delft University of Technology Delft Netherlands Boston VA Healthcare System Harvard Medical School BostonMA United States

关键词： Forecasting

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