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Wang Y, Yen PS, Ajilore OA, Bhaumik DK. A novel biomarker selection method using multimodal neuroimaging data. PLoS One 2024; 19:e0289401. [PMID: 38573979 PMCID: PMC10994318 DOI: 10.1371/journal.pone.0289401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 07/18/2023] [Indexed: 04/06/2024] Open
Abstract
Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.
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Affiliation(s)
- Yue Wang
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Pei-Shan Yen
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Olusola A. Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Dulal K. Bhaumik
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States of America
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Chén OY, Vũ DT, Diaz CS, Bodelet JS, Phan H, Allali G, Nguyen VD, Cao H, He X, Müller Y, Zhi B, Shou H, Zhang H, He W, Wang X, Munafò M, Trung NL, Nagels G, Ryvlin P, Pantaleo G. Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.11.584383. [PMID: 38559263 PMCID: PMC10979899 DOI: 10.1101/2024.03.11.584383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Alzheimer's Disease (AD) is the leading cause of dementia. It results in cortical thickness changes and is associated with a decline in cognition and behaviour. Such decline affects multiple important day-to-day functions, including memory, language, orientation, judgment and problem-solving. Recent research has made important progress in identifying brain regions associated with single outcomes, such as individual AD status and general cognitive decline. The complex projection from multiple brain areas to multiple AD outcomes, however, remains poorly understood. This makes the assessment and especially the prediction of multiple AD outcomes - each of which may unveil an integral yet different aspect of the disease - challenging, particularly when some are not strongly correlated. Here, uniting residual learning, partial least squares (PLS), and predictive modelling, we develop an explainable, generalisable, and reproducible method called the Residual Partial Least Squares Learning (the re-PLS Learning) to (1) chart the pathways between large-scale multivariate brain cortical thickness data (inputs) and multivariate disease and behaviour data (outcomes); (2) simultaneously predict multiple, non-pairwise-correlated outcomes; (3) control for confounding variables (e.g., age and gender) affecting both inputs and outcomes and the pathways in-between; (4) perform longitudinal AD disease status classification and disease severity prediction. We evaluate the performance of the proposed method against a variety of alternatives on data from AD patients, subjects with mild cognitive impairment (MCI), and cognitively normal individuals ( n = 1,196 ) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results unveil pockets of brain areas in the temporal, frontal, sensorimotor, and cingulate areas whose cortical thickness may be respectively associated with declines in different cognitive and behavioural subdomains in AD. Finally, we characterise re-PLS' geometric interpretation and mathematical support for delivering meaningful neurobiological insights and provide an open software package (re-PLS) available at https://github.com/thanhvd18/rePLS.
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Affiliation(s)
- Oliver Y Chén
- Département Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Faculté de Biologie et de Médecine, Université de Lausanne (UNIL), Lausanne, Switzerland
| | - Duy Thanh Vũ
- Département Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- The Advanced Institute of Engineering and Technology, Vietnam National University, Hanoi, Vietnam
| | - Christelle Schneuwly Diaz
- Département Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Faculté de Biologie et de Médecine, Université de Lausanne (UNIL), Lausanne, Switzerland
| | - Julien S Bodelet
- Département Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Huy Phan
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Gilles Allali
- Centre Leenaards de la Mémoire, CHUV, Lausanne, Switzerland
| | - Viet-Dung Nguyen
- Lab-STICC, École Nationale Supérieure de Techniques Avancées de Bretagne, Bretagne, France
- The Advanced Institute of Engineering and Technology, Vietnam National University, Hanoi, Vietnam
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Xingru He
- School of Public Health, He University, Shengyang, China
| | - Yannick Müller
- Département Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Bangdong Zhi
- Innovation and Healthcare Group, University of Bristol, Bristol, UK
| | - Haochang Shou
- Department of Biostatistics, University of Pennsylvania, Philadelphia, PA, USA
| | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Institutes of Health, Bethesda, MD, USA
| | - Wei He
- School of Public Health, He University, Shengyang, China
| | - Xiaojun Wang
- Innovation and Healthcare Group, University of Bristol, Bristol, UK
| | - Marcus Munafò
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Nguyen Linh Trung
- The Advanced Institute of Engineering and Technology, Vietnam National University, Hanoi, Vietnam
| | - Guy Nagels
- Department of Neurology, Universitair Ziekenhuis Brussel, Jette, Belgium
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Philippe Ryvlin
- Département des Neurosciences Cliniques, CHUV, Lausanne, Switzerland
| | - Giuseppe Pantaleo
- Département Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
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Chén OY, Bodelet JS, Saraiva RG, Phan H, Di J, Nagels G, Schwantje T, Cao H, Gou J, Reinen JM, Xiong B, Zhi B, Wang X, de Vos M. The roles, challenges, and merits of the p value. PATTERNS (NEW YORK, N.Y.) 2023; 4:100878. [PMID: 38106615 PMCID: PMC10724370 DOI: 10.1016/j.patter.2023.100878] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Since the 18th century, the p value has been an important part of hypothesis-based scientific investigation. As statistical and data science engines accelerate, questions emerge: to what extent are scientific discoveries based on p values reliable and reproducible? Should one adjust the significance level or find alternatives for the p value? Inspired by these questions and everlasting attempts to address them, here, we provide a systematic examination of the p value from its roles and merits to its misuses and misinterpretations. For the latter, we summarize modest recommendations to handle them. In parallel, we present the Bayesian alternatives for seeking evidence and discuss the pooling of p values from multiple studies and datasets. Overall, we argue that the p value and hypothesis testing form a useful probabilistic decision-making mechanism, facilitating causal inference, feature selection, and predictive modeling, but that the interpretation of the p value must be contextual, considering the scientific question, experimental design, and statistical principles.
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Affiliation(s)
- Oliver Y. Chén
- Département Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Faculté de Biologie et de Médecine, Université de Lausanne, Lausanne, Switzerland
| | - Julien S. Bodelet
- Département Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Raúl G. Saraiva
- Department of Molecular Microbiology and Immunology, Johns Hopkins University, Baltimore, MD, USA
| | - Huy Phan
- Department of Computer Science, Queen Mary University of London, London, UK
| | - Junrui Di
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Guy Nagels
- St. Edmund Hall, University of Oxford, Oxford, UK
- Department of Neurology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Jette, Belgium
| | - Tom Schwantje
- Department of Economics, University of Oxford, Oxford, UK
| | - Hengyi Cao
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Jiangtao Gou
- Department of Mathematics and Statistics, Villanova University, Villanova, PA, USA
| | - Jenna M. Reinen
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA
| | - Bin Xiong
- Department of Statistics, Northwestern University, Evanston, IL, USA
| | - Bangdong Zhi
- School of Business, University of Bristol, Bristol, UK
| | - Xiaojun Wang
- Birmingham Business School, University of Birmingham, Birmingham, UK
| | - Maarten de Vos
- Faculty of Engineering Science, KU Leuven, Leuven, Belgium
- Faculty of Medicine, KU Leuven, Leuven, Belgium
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Chén OY. Erratum: Chén "The Roles of Statistics in Human Neuroscience", Brain Sci. 2019, 9 (8), 194. Brain Sci 2020; 10:E149. [PMID: 32143516 PMCID: PMC7139498 DOI: 10.3390/brainsci10030149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 03/04/2020] [Indexed: 11/25/2022] Open
Abstract
The author wishes to make an erratum to the published version of his paper [...].
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Affiliation(s)
- Oliver Y. Chén
- Institute of Biomedical Engineering, University of Oxford, Oxford OX1 3PJ, UK;
- Department of Psychology, Yale University, New Haven, CT 06510, USA
- Laboratory of Neurobiology, University College London, London WC1E 6BT, UK
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