Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how mu...

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Vydáno v:	Human brain mapping Ročník 44; číslo 5; s. 1913 - 1933
Hlavní autoři:	Yeung, Hon Wah, Stolicyn, Aleks, Buchanan, Colin R., Tucker‐Drob, Elliot M., Bastin, Mark E., Luz, Saturnino, McIntosh, Andrew M., Whalley, Heather C., Cox, Simon R., Smith, Keith
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hoboken, USA John Wiley & Sons, Inc 01.04.2023
Témata:	Accuracy Algorithms Brain Brain - diagnostic imaging Brain - pathology Cognition Cognitive ability Complexity Connectome - methods Decision making Deep learning diffusion tensor imaging general psychopathology Humans Learning algorithms Machine Learning Magnetic resonance imaging Mathematical models Medical imaging Mental disorders Mental Health Neural networks Neuroimaging Phenotypes Predictions Psychopathology Sex Statistical methods structural connectomes deep learning diffusion tensor imaging cognition structural connectomes general psychopathology
ISSN:	1065-9471, 1097-0193, 1097-0193
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Abstract	There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. We explore how multiple aspects of brain structural connectivity can predict general cognitive function and general psychopathology. To do this, we modelled six different structural network weightings obtained from diffusion MRI and tested different machine learning (ML) algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. DL did not improve on prediction accuracies from simpler linear models, indicating that most of the variation between phenotypes in weighted structural connectomes is linear rather than non‐linear in nature. This may have implied that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.
AbstractList	There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. We explore how multiple aspects of brain structural connectivity can predict general cognitive function and general psychopathology. To do this, we modelled six different structural network weightings obtained from diffusion MRI and tested different machine learning (ML) algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. DL did not improve on prediction accuracies from simpler linear models, indicating that most of the variation between phenotypes in weighted structural connectomes is linear rather than non‐linear in nature. This may have implied that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. We explore how multiple aspects of brain structural connectivity can predict general cognitive function and general psychopathology. To do this, we modelled six different structural network weightings obtained from diffusion MRI and tested different machine learning (ML) algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. DL did not improve on prediction accuracies from simpler linear models, indicating that most of the variation between phenotypes in weighted structural connectomes is linear rather than non‐linear in nature. This may have implied that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.
Author	Yeung, Hon Wah Tucker‐Drob, Elliot M. Bastin, Mark E. McIntosh, Andrew M. Whalley, Heather C. Stolicyn, Aleks Buchanan, Colin R. Cox, Simon R. Smith, Keith Luz, Saturnino
AuthorAffiliation	1 Department of Psychiatry University of Edinburgh Edinburgh UK 2 Department of Psychology University of Edinburgh Edinburgh UK 9 Centre for Genomic and Experimental Medicine Institute of Genetics and Molecular Medicine, University of Edinburgh Edinburgh UK 4 Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE) Edinburgh UK 5 Department of Psychology University of Texas Austin Texas USA 3 Lothian Birth Cohorts, University of Edinburgh Edinburgh UK 6 Population Research Center and Center on Aging and Population Sciences University of Texas at Austin Austin Texas USA 8 Edinburgh Medical School Usher Institute, The University of Edinburgh Edinburgh UK 10 Department of Physics and Mathematics Nottingham Trent University Nottingham UK 7 Centre for Clinical Brain Science University of Edinburgh Edinburgh UK
AuthorAffiliation_xml	– name: 4 Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE) Edinburgh UK – name: 1 Department of Psychiatry University of Edinburgh Edinburgh UK – name: 7 Centre for Clinical Brain Science University of Edinburgh Edinburgh UK – name: 3 Lothian Birth Cohorts, University of Edinburgh Edinburgh UK – name: 6 Population Research Center and Center on Aging and Population Sciences University of Texas at Austin Austin Texas USA – name: 2 Department of Psychology University of Edinburgh Edinburgh UK – name: 8 Edinburgh Medical School Usher Institute, The University of Edinburgh Edinburgh UK – name: 9 Centre for Genomic and Experimental Medicine Institute of Genetics and Molecular Medicine, University of Edinburgh Edinburgh UK – name: 5 Department of Psychology University of Texas Austin Texas USA – name: 10 Department of Physics and Mathematics Nottingham Trent University Nottingham UK
Author_xml	– sequence: 1 givenname: Hon Wah orcidid: 0000-0002-4993-9014 surname: Yeung fullname: Yeung, Hon Wah email: s1884975@ed.ac.uk organization: University of Edinburgh – sequence: 2 givenname: Aleks orcidid: 0000-0002-1147-7539 surname: Stolicyn fullname: Stolicyn, Aleks organization: University of Edinburgh – sequence: 3 givenname: Colin R. orcidid: 0000-0001-6501-628X surname: Buchanan fullname: Buchanan, Colin R. organization: Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE) – sequence: 4 givenname: Elliot M. orcidid: 0000-0001-5599-6237 surname: Tucker‐Drob fullname: Tucker‐Drob, Elliot M. organization: University of Texas at Austin – sequence: 5 givenname: Mark E. orcidid: 0000-0002-0490-0845 surname: Bastin fullname: Bastin, Mark E. organization: University of Edinburgh – sequence: 6 givenname: Saturnino orcidid: 0000-0001-8430-7875 surname: Luz fullname: Luz, Saturnino organization: Usher Institute, The University of Edinburgh – sequence: 7 givenname: Andrew M. orcidid: 0000-0002-0198-4588 surname: McIntosh fullname: McIntosh, Andrew M. organization: Institute of Genetics and Molecular Medicine, University of Edinburgh – sequence: 8 givenname: Heather C. orcidid: 0000-0002-4505-8869 surname: Whalley fullname: Whalley, Heather C. organization: University of Edinburgh – sequence: 9 givenname: Simon R. orcidid: 0000-0003-4036-3642 surname: Cox fullname: Cox, Simon R. organization: Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE) – sequence: 10 givenname: Keith orcidid: 0000-0002-4615-9020 surname: Smith fullname: Smith, Keith organization: Nottingham Trent University
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Copyright	2022 The Authors. published by Wiley Periodicals LLC. 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. 2022. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Keywords	deep learning diffusion tensor imaging cognition structural connectomes general psychopathology
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Notes	Funding information Heather C. Whalley, Simon R. Cox and Keith Smith contributed equally to senior authorship. Medical Research Council, Grant/Award Number: MR/R024065/1; National Institutes of Health, Grant/Award Numbers: P2CHD042849, P30AG066614, R01AG054628; UK Research and Innovation, Grant/Award Number: MC_PC_17209; Wellcome Trust, Grant/Award Numbers: 104036/Z/14/Z, 220857/Z/20/Z, 221890/Z/20/Z ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Funding information Medical Research Council, Grant/Award Number: MR/R024065/1; National Institutes of Health, Grant/Award Numbers: P2CHD042849, P30AG066614, R01AG054628; UK Research and Innovation, Grant/Award Number: MC_PC_17209; Wellcome Trust, Grant/Award Numbers: 104036/Z/14/Z, 220857/Z/20/Z, 221890/Z/20/Z
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ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_9980898 proquest_miscellaneous_2756670906 proquest_journals_2781282302 pubmed_primary_36541441 crossref_citationtrail_10_1002_hbm_26182 crossref_primary_10_1002_hbm_26182 wiley_primary_10_1002_hbm_26182_HBM26182
PublicationCentury	2000
PublicationDate	April 1, 2023
PublicationDateYYYYMMDD	2023-04-01
PublicationDate_xml	– month: 04 year: 2023 text: April 1, 2023 day: 01
PublicationDecade	2020
PublicationPlace	Hoboken, USA
PublicationPlace_xml	– name: Hoboken, USA – name: United States – name: San Antonio
PublicationTitle	Human brain mapping
PublicationTitleAlternate	Hum Brain Mapp
PublicationYear	2023
Publisher	John Wiley & Sons, Inc
Publisher_xml	– name: John Wiley & Sons, Inc
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Snippet	There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide... There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide...
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SubjectTerms	Accuracy Algorithms Brain Brain - diagnostic imaging Brain - pathology Cognition Cognitive ability Complexity Connectome - methods Decision making Deep learning diffusion tensor imaging general psychopathology Humans Learning algorithms Machine Learning Magnetic resonance imaging Mathematical models Medical imaging Mental disorders Mental Health Neural networks Neuroimaging Phenotypes Predictions Psychopathology Sex Statistical methods structural connectomes
Title	Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes
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