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Herrgårdh T, Madai VI, Kelleher JD, Magnusson R, Gustafsson M, Milani L, Gennemark P, Cedersund G. Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios. Neuroimage Clin 2021; 31:102694. [PMID: 34000646 PMCID: PMC8141769 DOI: 10.1016/j.nicl.2021.102694] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/27/2021] [Accepted: 05/04/2021] [Indexed: 11/28/2022]
Abstract
Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.
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Affiliation(s)
- Tilda Herrgårdh
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, 58185 Linköping, Sweden
| | - Vince I Madai
- Charité Lab for Artificial Intelligence in Medicine - CLAIM, Charité University Medicine Berlin, Germany; School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, UK
| | - John D Kelleher
- ADAPT Research Centre, Technological University Dublin, Ireland
| | - Rasmus Magnusson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden
| | - Mika Gustafsson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Peter Gennemark
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, 58185 Linköping, Sweden; Drug Metabolism and Pharmacokinetics, Early Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Gunnar Cedersund
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, 58185 Linköping, Sweden.
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Wong HK, Tiffin PA, Chappell MJ, Nichols TE, Welsh PR, Doyle OM, Lopez-Kolkovska BC, Inglis SK, Coghill D, Shen Y, Tiño P. Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space. Front Physiol 2017; 8:199. [PMID: 28443027 PMCID: PMC5387107 DOI: 10.3389/fphys.2017.00199] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 03/17/2017] [Indexed: 12/04/2022] Open
Abstract
Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health disorders amongst school-aged children with an estimated prevalence of 5% in the global population (American Psychiatric Association, 2013). Stimulants, particularly methylphenidate (MPH), are the first-line option in the treatment of ADHD (Reeves and Schweitzer, 2004; Dopheide and Pliszka, 2009) and are prescribed to an increasing number of children and adolescents in the US and the UK every year (Safer et al., 1996; McCarthy et al., 2009), though recent studies suggest that this is tailing off, e.g., Holden et al. (2013). Around 70% of children demonstrate a clinically significant treatment response to stimulant medication (Spencer et al., 1996; Schachter et al., 2001; Swanson et al., 2001; Barbaresi et al., 2006). However, it is unclear which patient characteristics may moderate treatment effectiveness. As such, most existing research has focused on investigating univariate or multivariate correlations between a set of patient characteristics and the treatment outcome, with respect to dosage of one or several types of medication. The results of such studies are often contradictory and inconclusive due to a combination of small sample sizes, low-quality data, or a lack of available information on covariates. In this paper, feature extraction techniques such as latent trait analysis were applied to reduce the dimension of on a large dataset of patient characteristics, including the responses to symptom-based questionnaires, developmental health factors, demographic variables such as age and gender, and socioeconomic factors such as parental income. We introduce a Bayesian modeling approach in a "learning in the model space" framework that combines existing knowledge in the literature on factors that may potentially affect treatment response, with constraints imposed by a treatment response model. The model is personalized such that the variability among subjects is accounted for by a set of subject-specific parameters. For remission classification, this approach compares favorably with conventional methods such as support vector machines and mixed effect models on a range of performance measures. For instance, the proposed approach achieved an area under receiver operator characteristic curve of 82-84%, compared to 75-77% obtained from conventional regression or machine learning ("learning in the data space") methods.
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Affiliation(s)
- Hin K. Wong
- Warwick Manufacturing Group, Institute of Digital Healthcare, University of WarwickCoventry, UK
| | - Paul A. Tiffin
- Mental Health and Addiction Research Group, Department of Health Sciences, University of YorkYork, UK
| | | | - Thomas E. Nichols
- Warwick Manufacturing Group, Institute of Digital Healthcare, University of WarwickCoventry, UK
| | - Patrick R. Welsh
- School of Psychology, Newcastle UniversityNewcastle upon Tyne, UK
| | - Orla M. Doyle
- Centre for Neuroimaging Sciences, King's College LondonLondon, UK
| | | | - Sarah K. Inglis
- Division of Maternal and Child Health Sciences, Ninewells Hospital and Medical School, University of DundeeDundee, UK
| | - David Coghill
- Departments of Paediatrics and Psychiatry, University of MelbourneMelbourne, VIC, Australia
| | - Yuan Shen
- School of Computer Science, University of BirminghamBirmingham, UK
| | - Peter Tiño
- School of Computer Science, University of BirminghamBirmingham, UK
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Raman S, Deserno L, Schlagenhauf F, Stephan KE. A hierarchical model for integrating unsupervised generative embedding and empirical Bayes. J Neurosci Methods 2016; 269:6-20. [DOI: 10.1016/j.jneumeth.2016.04.022] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 04/23/2016] [Accepted: 04/24/2016] [Indexed: 11/25/2022]
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Stephan KE, Schlagenhauf F, Huys QJM, Raman S, Aponte EA, Brodersen KH, Rigoux L, Moran RJ, Daunizeau J, Dolan RJ, Friston KJ, Heinz A. Computational neuroimaging strategies for single patient predictions. Neuroimage 2016; 145:180-199. [PMID: 27346545 DOI: 10.1016/j.neuroimage.2016.06.038] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Revised: 05/21/2016] [Accepted: 06/20/2016] [Indexed: 10/21/2022] Open
Abstract
Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.
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Affiliation(s)
- K E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK; Max Planck Institute for Metabolism Research, 50931 Cologne, Germany
| | - F Schlagenhauf
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, 10115 Berlin, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, 04130 Leipzig, Germany
| | - Q J M Huys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Department of Psychiatry, Psychosomatics and Psychotherapy, Hospital of Psychiatry, University of Zurich, Switzerland
| | - S Raman
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - E A Aponte
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - K H Brodersen
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - L Rigoux
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Max Planck Institute for Metabolism Research, 50931 Cologne, Germany
| | - R J Moran
- Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK; Virgina Institute of Technology, USA
| | - J Daunizeau
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; ICM Paris, France
| | - R J Dolan
- Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK
| | - K J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK
| | - A Heinz
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, 10115 Berlin, Germany; Humboldt Universität zu Berlin, Berlin School of Mind and Brain, 10115 Berlin, Germany
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Doyle OM, Mehta MA, Brammer MJ. The role of machine learning in neuroimaging for drug discovery and development. Psychopharmacology (Berl) 2015; 232:4179-89. [PMID: 26014110 DOI: 10.1007/s00213-015-3968-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 05/11/2015] [Indexed: 12/30/2022]
Abstract
Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. In this review, we focus on machine learning approaches for neuroimaging which enable us to make predictions at the individual level based on the distributed effects across the whole brain. Crucially, these approaches can be trained on data from one study and applied to an independent study and, unlike group-level statistics, can be readily use to assess the generalisability to unseen data. In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study? (2) What should I measure and when should I measure it? (3) How does the pharmacological agent behave using an experimental medicine model?, and (4) How does a compound differ from and/or resemble existing compounds? Specifically, we present studies from the literature and we suggest areas for the focus of future development. Further refinement and tailoring of machine learning techniques may help realise their tremendous potential for drug discovery and drug validation.
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Affiliation(s)
- Orla M Doyle
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK.
| | - Mitul A Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK
| | - Michael J Brammer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK
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Grecchi E, Doyle OM, Bertoldo A, Pavese N, Turkheimer FE. Brain shaving: adaptive detection for brain PET data. Phys Med Biol 2014; 59:2517-34. [DOI: 10.1088/0031-9155/59/10/2517] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Ziegler G, Ridgway GR, Dahnke R, Gaser C. Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects. Neuroimage 2014; 97:333-48. [PMID: 24742919 PMCID: PMC4077633 DOI: 10.1016/j.neuroimage.2014.04.018] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Revised: 02/25/2014] [Accepted: 04/04/2014] [Indexed: 01/24/2023] Open
Abstract
Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18–94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. We propose an approach to support individualized clinical decisions in elderlies. Gaussian process models are used to build a normative generative model of aging. It affords probabilistic predictions of local gray matter volume in subjects. We validate the model using simulated and large real MRI data samples.
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Affiliation(s)
- G Ziegler
- Wellcome Trust Center for Neuroimaging, Institute of Neurology, London, UK; Department of Psychiatry, Jena University Hospital, Jena, Germany.
| | - G R Ridgway
- Wellcome Trust Center for Neuroimaging, Institute of Neurology, London, UK
| | - R Dahnke
- Department of Psychiatry, Jena University Hospital, Jena, Germany
| | - C Gaser
- Department of Psychiatry, Jena University Hospital, Jena, Germany; Department of Neurology, Jena University Hospital, Jena, Germany
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