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Rosenblatt M, Tejavibulya L, Sun H, Camp CC, Khaitova M, Adkinson BD, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nat Hum Behav 2024; 8:2018-2033. [PMID: 39085406 DOI: 10.1038/s41562-024-01931-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/18/2024] [Indexed: 08/02/2024]
Abstract
Brain-phenotype predictive models seek to identify reproducible and generalizable brain-phenotype associations. External validation, or the evaluation of a model in external datasets, is the gold standard in evaluating the generalizability of models in neuroimaging. Unlike typical studies, external validation involves two sample sizes: the training and the external sample sizes. Thus, traditional power calculations may not be appropriate. Here we ran over 900 million resampling-based simulations in functional and structural connectivity data to investigate the relationship between training sample size, external sample size, phenotype effect size, theoretical power and simulated power. Our analysis included a wide range of datasets: the Healthy Brain Network, the Adolescent Brain Cognitive Development Study, the Human Connectome Project (Development and Young Adult), the Philadelphia Neurodevelopmental Cohort, the Queensland Twin Adolescent Brain Project, and the Chinese Human Connectome Project; and phenotypes: age, body mass index, matrix reasoning, working memory, attention problems, anxiety/depression symptoms and relational processing. High effect size predictions achieved adequate power with training and external sample sizes of a few hundred individuals, whereas low and medium effect size predictions required hundreds to thousands of training and external samples. In addition, most previous external validation studies used sample sizes prone to low power, and theoretical power curves should be adjusted for the training sample size. Furthermore, model performance in internal validation often informed subsequent external validation performance (Pearson's r difference <0.2), particularly for well-harmonized datasets. These results could help decide how to power future external validation studies.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chris C Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Milana Khaitova
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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2
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Xue T, Zhang F, Zekelman LR, Zhang C, Chen Y, Cetin-Karayumak S, Pieper S, Wells WM, Rathi Y, Makris N, Cai W, O'Donnell LJ. TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography. Front Neurosci 2024; 18:1411797. [PMID: 38988766 PMCID: PMC11233814 DOI: 10.3389/fnins.2024.1411797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 06/10/2024] [Indexed: 07/12/2024] Open
Abstract
Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.
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Affiliation(s)
- Tengfei Xue
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Leo R. Zekelman
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Chaoyi Zhang
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Yuqian Chen
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Steve Pieper
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - William M. Wells
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Weidong Cai
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Lauren J. O'Donnell
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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3
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Tetereva A, Knodt AR, Melzer TR, van der Vliet W, Gibson B, Hariri AR, Whitman ET, Li J, Deng J, Ireland D, Ramrakha S, Pat N. Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.589404. [PMID: 38746222 PMCID: PMC11092590 DOI: 10.1101/2024.05.03.589404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed "stacking" that combines brain magnetic resonance imaging of different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults and Aging and the Dunedin Multidisciplinary Health and Development Study. For predictability, stacked models led to out-of-sample r ∼.5-.6 when predicting cognitive abilities at the time of scanning and 36 years earlier. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.
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4
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Ruffle JK, Gray RJ, Mohinta S, Pombo G, Kaul C, Hyare H, Rees G, Nachev P. Computational limits to the legibility of the imaged human brain. Neuroimage 2024; 291:120600. [PMID: 38569979 DOI: 10.1016/j.neuroimage.2024.120600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/08/2024] [Accepted: 03/31/2024] [Indexed: 04/05/2024] Open
Abstract
Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.
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Affiliation(s)
- James K Ruffle
- Queen Square Institute of Neurology, University College London, London, United Kingdom.
| | - Robert J Gray
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Samia Mohinta
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Guilherme Pombo
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Chaitanya Kaul
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Harpreet Hyare
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Geraint Rees
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Parashkev Nachev
- Queen Square Institute of Neurology, University College London, London, United Kingdom.
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5
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Dafflon J, Moraczewski D, Earl E, Nielson DM, Loewinger G, McClure P, Thomas AG, Pereira F. Reliability and predictability of phenotype information from functional connectivity in large imaging datasets. ARXIV 2024:arXiv:2405.00255v1. [PMID: 38745697 PMCID: PMC11092871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as latent phenotypes from resting state functional connectivity. For this task, we predicted phenotypes in two large neuroimaging datasets: the Human Connectome Project (HCP) and the Philadelphia Neurodevelopmental Cohort (PNC). We illustrate the importance of regressing out confounds, which could significantly influence phenotype prediction. Our findings reveal that both phenotypes and their corresponding latent phenotypes yield similar predictive performance. Interestingly, only the first five latent phenotypes were reliably identified, and using just these reliable phenotypes for predicting phenotypes yielded a similar performance to using all latent phenotypes. This suggests that the predictable information is present in the first latent phenotypes, allowing the remainder to be filtered out without any harm in performance. This study sheds light on the intricate relationship between functional connectivity and the predictability and reliability of phenotypic information, with potential implications for enhancing predictive modeling in the realm of neuroimaging research.
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Affiliation(s)
- Jessica Dafflon
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
- Machine Learning Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Dustin Moraczewski
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Eric Earl
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Dylan M Nielson
- Machine Learning Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Gabriel Loewinger
- Machine Learning Team, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Adam G Thomas
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Francisco Pereira
- Machine Learning Team, National Institute of Mental Health, Bethesda, MD, USA
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6
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Chen P, An L, Wulan N, Zhang C, Zhang S, Ooi LQR, Kong R, Chen J, Wu J, Chopra S, Bzdok D, Eickhoff SB, Holmes AJ, Yeo BT. Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.569848. [PMID: 38106085 PMCID: PMC10723283 DOI: 10.1101/2023.12.05.569848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.
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Affiliation(s)
- Pansheng Chen
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - Lijun An
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - Naren Wulan
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - Chen Zhang
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - Shaoshi Zhang
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Leon Qi Rong Ooi
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - Jianxiao Wu
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, School of Computer Science, McGill University, Montreal QC, Canada
- Mila – Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Avram J Holmes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
| | - B.T. Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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7
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Rosenblatt M, Tejavibulya L, Camp CC, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.563971. [PMID: 37961654 PMCID: PMC10634903 DOI: 10.1101/2023.10.25.563971] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.
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Affiliation(s)
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Chris C. Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Margaret L. Westwater
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Department of Bioengineering, Northeastern University, Boston, MA
- Department of Psychology, Northeastern University, Boston, MA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Child Study Center, Yale School of Medicine, New Haven, CT
- Department of Statistics & Data Science, Yale University, New Haven, CT
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8
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Wu J, Li J, Eickhoff SB, Scheinost D, Genon S. The challenges and prospects of brain-based prediction of behaviour. Nat Hum Behav 2023; 7:1255-1264. [PMID: 37524932 DOI: 10.1038/s41562-023-01670-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/27/2023] [Indexed: 08/02/2023]
Abstract
Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.
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Affiliation(s)
- Jianxiao Wu
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Sciences, New Haven, CT, USA
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
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9
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Multivariate BWAS can be replicable with moderate sample sizes. Nature 2023; 615:E4-E7. [PMID: 36890392 PMCID: PMC9995263 DOI: 10.1038/s41586-023-05745-x] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 01/19/2023] [Indexed: 03/10/2023]
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