Bayesian multi-task learning for decoding multi-subject neuroimaging data

Marquand, Andre F.; Brammer, Michael; Williams, Steven C.R.; Doyle, Orla M.

doi:10.1016/j.neuroimage.2014.02.008

Cited by 59Functional Brain Connectivity StudiesGaussian Processes and Bayesian InferenceNeural and Behavioral Psychology Studies

Bayesian multi-task learning for decoding multi-subject neuroimaging data

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Marquand, Andre F., Brammer, Michael, Williams, Steven C.R., Doyle, Orla M.·2014·NeuroImage

DOI 10.1016/j.neuroimage.2014.02.008PMC PMC4010954

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Abstract

Decoding models based on pattern recognition (PR) are becoming increasingly important tools for neuroimaging data analysis. In contrast to alternative (mass-univariate) encoding approaches that use hierarchical models to capture inter-subject variability, inter-subject differences are not typically handled efficiently in PR. In this work, we propose to overcome this problem by recasting the decoding problem in a multi-task learning (MTL) framework. In MTL, a single PR model is used to learn different but related "tasks" simultaneously. The primary advantage of MTL is that it makes more efficient use of the data available and leads to more accurate models by making use of the relationships between tasks. In this work, we construct MTL models where each subject is modelled by a separate task. We use a flexible covariance structure to model the relationships between tasks and induce coupling between them using Gaussian process priors. We present an MTL method for classification problems and demonstrate a novel mapping method suitable for PR models. We apply these MTL approaches to classifying many different contrasts in a publicly available fMRI dataset and show that the proposed MTL methods produce higher decoding accuracy and more consistent discriminative activity patterns than currently used techniques. Our results demonstrate that MTL provides a promising method for multi-subject decoding studies by focusing on the commonalities between a group of subjects rather than the idiosyncratic properties of different subjects.

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Gaussian process regression can be extended to directly predict multivariate phenotypes.
“Alternatively, Gaussian process regression can be extended to directly predict multivariate phenotypes, as we demonstrated previously (43, 44).”
From Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies (2016) · cites this paper as [43]

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A survey on transfer learning
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works
STATISTICS FOR SPATIAL DATA
Cressie, N. (1992) · Terra Nova · doi:10.1111/j.1365-3121.1992.tb00605.x · Cited by 9,139 · see citing works
Multitask Learning
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636 · see citing works
Statistics for Spatial Data
Cressie, N. A. C. (1993) · Wiley Series in Probability and Statistics · doi:10.1002/9781119115151 · Cited by 6,507 · see citing works
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256 · see citing works
5. Statistics for Spatial Data
Rees, M., & Cressie, N. (1993) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982871 · Cited by 4,983 · see citing works
Pattern Recognition and Machine Learning
Neal, R. M. (2007) · Technometrics · doi:10.1198/tech.2007.s518 · Cited by 4,629 · see citing works
Beyond mind-reading: multi-voxel pattern analysis of fMRI data
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.07.005 · Cited by 2,271 · see citing works
Multilevel linear modelling for FMRI group analysis using Bayesian inference
Woolrich, M. W., Behrens, T. E. J., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.023 · Cited by 1,719 · see citing works
Machine learning classifiers and fMRI: A tutorial overview
Pereira, F., Mitchell, T., & Botvinick, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.11.007 · Cited by 1,624 · see citing works
Multi-Task Feature Learning
Argyriou, A., Evgeniou, T., & Pontil, M. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0010 · Cited by 1,376 · see citing works
The Variability of Human, BOLD Hemodynamic Responses
Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1998) · NeuroImage · doi:10.1006/nimg.1998.0369 · Cited by 1,224 · see citing works
Automatic classification of MR scans in Alzheimer’s disease.
Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2008) · Brain · doi:10.1093/brain/awm319 · Cited by 1,170 · see citing works
Multitask learning
Caruana, R. (1998) · Cited by 1,110 · see citing works
Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review
Orrù, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.01.004 · Cited by 1,012 · see citing works
Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses
Handwerker, D. A., Ollinger, J. M., & D’Esposito, M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.11.029 · Cited by 993 · see citing works
Multi-task Gaussian Process Prediction
Bonilla, E. V., Chai, K. M. A., & Williams, C. K. I. (2007) · Cited by 916 · see citing works
Generalisability, Random Effects & Population Inference
Holmes, A. P., & Friston, K. J. (1998) · NeuroImage · doi:10.1016/s1053-8119(18)31587-8 · Cited by 906 · see citing works
Encoding and decoding in fMRI
Naselaris, T., Kay, K., Nishimoto, S., & Gallant, J. L. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.073 · Cited by 894 · see citing works
A family of algorithms for approximate bayesian inference
Minka, T. P., & Picard, R. W. (2001) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 877 · see citing works
Penalized Discriminant Analysis
Hastie, T., Buja, A., & Tibshirani, R. J. (1995) · The Annals of Statistics · doi:10.1214/aos/1176324456 · Cited by 872 · see citing works
Flexible Discriminant Analysis by Optimal Scoring
Hastie, T., Tibshirani, R. J., & Buja, A. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476866 · Cited by 840 · see citing works
Learning Multiple Tasks with Kernel Methods
Evgeniou, T., Micchelli, C. A., & Pontil, M. (2005) · doi:10.5555/1046920.1088693 · Cited by 807 · see citing works
Human Primary Auditory Cortex: Cytoarchitectonic Subdivisions and Mapping into a Spatial Reference System
Morosan, P., Rademacher, J., Schleicher, A., Amunts, K., Schormann, T., & Zilles, K. (2001) · NeuroImage · doi:10.1006/nimg.2000.0715 · Cited by 761 · see citing works
Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data
Mourão-Miranda, J., Bokde, A. L. W., Born, C., Hampel, H. J., & Stetter, M. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.06.070 · Cited by 700 · see citing works
Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
Zhang, D., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.069 · Cited by 679 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Characterizing Dynamic Brain Responses with fMRI: A Multivariate Approach
Friston, K. J., Frith, C., Frackowiak, R. S. J., & Turner, R. (1995) · NeuroImage · doi:10.1006/nimg.1995.1019 · Cited by 568 · see citing works
Measuring structural–functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment
Frost, M. A., & Goebel, R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.035 · Cited by 305 · see citing works
Approximations for Binary Gaussian Process Classification
Nickisch, H., & Rasmussen, C. E. (2008) · Cited by 298 · see citing works
Task Clustering and Gating for Bayesian Multitask Learning
Bakker, B., & Heskes, T. (2003) · Radboud Repository (Radboud University) · Cited by 231 · see citing works
Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes
Marquand, A. F., Howard, M. A., Brammer, M. J., Chu, C., Coen, S. J., & Mourão-Miranda, J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.072 · Cited by 226 · see citing works
Dependent Gaussian Processes
Boyle, P., & Frean, M. R. (2004) · Cited by 225 · see citing works
Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty
Ryali, S., Chen, T., Supekar, K., & Menon, V. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.054 · Cited by 219 · see citing works
Modeling disease progression via multi-task learning
Zhou, J., Liu, J., Narayan, V. A., & Ye, J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.073 · Cited by 212 · see citing works
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
Abernethy, J., Bach, F., Evgeniou, T., & Vert, J.-P. (2008) · arXiv · doi:10.48550/arxiv.0802.1430 · Cited by 210 · see citing works
Mixed-effects and fMRI studies
Friston, K. J., Stephan, K. E., Lund, T. E., Morcom, A. M., & Kiebel, S. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.08.055 · Cited by 204 · see citing works
Decoding the perception of pain from fMRI using multivariate pattern analysis
Brodersen, K. H., Wiech, K., Lomakina, E. I., Lin, C.-S., Buhmann, J. M., Bingel, U., Ploner, M., Stephan, K. E., & Tracey, I. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.035 · Cited by 200 · see citing works
Interpretable whole-brain prediction analysis with GraphNet
Grosenick, L., Klingenberg, B., Katovich, K., Knutson, B. D., & Taylor, J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.12.062 · Cited by 197 · see citing works
Brain covariance selection: better individual functional connectivity models using population priorPreprint
Varoquaux, G. P., Gramfort, A., Poline, J., & Thirion, B. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1008.5071 · Cited by 197 · see citing works
Sparse Convolved Gaussian Processes for Multi-output Regression
Álvarez, M. A., & Lawrence, N. D. (2008) · Cited by 167 · see citing works
Dissociable Effects of Top-Down and Bottom-Up Attention during Episodic Encoding
Uncapher, M. R., Hutchinson, J. B., & Wagner, A. D. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.0152-11.2011 · Cited by 143 · see citing works
Support vector machine learning-based fMRI data group analysis
Wang, Z., Childress, A. R., Wang, J., & Detre, J. A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.03.072 · Cited by 138 · see citing works
Total Variation Regularization for fMRI-Based Prediction of Behavior
Michel, V., Gramfort, A., Varoquaux, G. P., Eger, E., & Thirion, B. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2113378 · Cited by 136 · see citing works
Optimizing the fMRI data-processing pipeline using prediction and reproducibility performance metrics: I. A preliminary group analysis
Strother, S. C., Conte, S. L., Hansen, L. K., Anderson, J., Zhang, J., Pulapura, S. K., & Rottenberg, D. A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.022 · Cited by 134 · see citing works
Model sparsity and brain pattern interpretation of classification models in neuroimaging
Rasmussen, P. M., Hansen, L. K., Madsen, K. H., Churchill, N. W., & Strother, S. C. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.09.011 · Cited by 120 · see citing works
Interpretable Classifiers for fMRI Improve Prediction of Purchases
Grosenick, L., Greer, S. M., & Knutson, B. D. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.926701 · Cited by 109 · see citing works
Pattern Classification of Working Memory Networks Reveals Differential Effects of Methylphenidate, Atomoxetine, and Placebo in Healthy Volunteers
Marquand, A. F., Simoni, S. D., O’Daly, O., Williams, S., Mourão-Miranda, J., & Mehta, M. A. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2011.9 · Cited by 94 · see citing works
Joint Estimation of Structured Sparsity and Output Structure in Multiple-Output Regression via Inverse-Covariance Regularization
Sohn, K.-A., & Kim, S. Y. (2012) · Cited by 75 · see citing works
Dissociable effects of methylphenidate, atomoxetine and placebo on regional cerebral blood flow in healthy volunteers at rest: A multi-class pattern recognition approach
Marquand, A. F., O’Daly, O., Simoni, S. D., Alsop, D. C., Maguire, R. P., Williams, S., Zelaya, F. O., & Mehta, M. A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.058 · Cited by 72 · see citing works
SCoRS—A Method Based on Stability for Feature Selection and Mapping in Neuroimaging
Rondina, J. M., Hahn, T., Oliveira, L., Marquand, A. F., Dresler, T., Leitner, T., Fallgatter, A. J., Shawe-Taylor, J. S., & Mourão-Miranda, J. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2281398 · Cited by 64 · see citing works
Bayesian Multitask Classification With Gaussian Process Priors
Skolidis, G., & Sanguinetti, G. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2168568 · Cited by 57 · see citing works
Automated, High Accuracy Classification of Parkinsonian Disorders: A Pattern Recognition Approach
Marquand, A. F., Filippone, M., Ashburner, J., Girolami, M., Mourão-Miranda, J., Barker, G. J., Williams, S., Leigh, P. N., & Blain, C. R. V. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069237 · Cited by 44 · see citing works
Combined permutation test and mixed‐effect model for group average analysis in fMRI
Mériaux, S., Roche, A., Dehaene‐Lambertz, G., Thirion, B., & Poline, J. (2006) · Human Brain Mapping · doi:10.1002/hbm.20251 · Cited by 44 · see citing works
New Methodological Tools for Multiple-Reader ROC Studies
Obuchowski, N. A. (2007) · Radiology · doi:10.1148/radiol.2432060387 · Cited by 37 · see citing works
Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities
Filippone, M., Marquand, A. F., Blain, C. R. V., Williams, S., Mourão-Miranda, J., & Girolami, M. (2012) · The Annals of Applied Statistics · doi:10.1214/12-aoas562 · Cited by 35 · see citing works
Graphical Multi-Task Learning
Sheldon, D. R. (2008) · eCommons (Cornell University) · Cited by 34 · see citing works
PROBABILISTIC PREDICTION OF NEUROLOGICAL DISORDERS WITH A STATISTICAL ASSESSMENT OF NEUROIMAGING DATA MODALITIES
Filippone, M., Marquand, A., Blain, C. R. V., Williams, S., Mourão-Miranda, J., Girolami, M., et al. (2011) · Europe PMC (PubMed Central) · Cited by 33 · see citing works
Multivariate decoding of brain images using ordinal regression
Doyle, O., Ashburner, J., Zelaya, F. O., Williams, S., Mehta, M. A., & Marquand, A. F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.036 · Cited by 29 · see citing works
Deriving Statistical Significance Maps for SVM Based Image Classification and Group Comparisons
Gaonkar, B., & Davatzikos, C. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33415-3_89 · Cited by 24 · see citing works
Focused Multi-task Learning Using Gaussian Processes
Leen, G., Peltonen, J., & Samuel Kaski, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23783-6_20 · Cited by 21 · see citing works
Multivariate linear regression of high‐dimensional fMRI data with multiple target variables
Valente, G., Lage‐Castellanos, A., Vanacore, G., & Formisano, E. (2013) · Human Brain Mapping · doi:10.1002/hbm.22318 · Cited by 16 · see citing works
Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Ayache, N., Delingette, H., Golland, P., & Mori, K. (2012) · Medical Image Computing and Computer-Assisted Intervention · Cited by 12 · see citing works
Beyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and Identify
Gramfort, A., Varoquaux, G. P., & Thirion, B. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34713-9_2 · Cited by 9 · see citing works
A case study on meta-generalising: a Gaussian processes approach
Skolidis, G., & Sanguinetti, G. (2012) · Cited by 6 · see citing works
Conditional Gaussian Graphical Models for Multi-output Regression of Neuroimaging Data
Marquand, A. F., Rosa, M. J., & Doyle, O. (2013) · Cited by 1 · see citing works

Flagged first, then by citations

2 of the 69 references couldn’t be matched to a known paper.

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Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies
Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.023 · Cited by 683 · see citing works
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives
Haynes, J.-D. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.025 · Cited by 402 · see citing works
From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics
Wolfers, T., Buitelaar, J. K., Beckmann, C. F., Franke, B., & Marquand, A. F. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.08.001 · Cited by 290 · see citing works
OpenfMRI: Open sharing of task fMRI data
Poldrack, R. A., & Gorgolewski, K. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.073 · Cited by 152 · see citing works
Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models
Bayer, J., Dinga, R., Kia, S. M., Kottaram, A., Wolfers, T., Lv, J., Zalesky, A., Schmaal, L., & Marquand, A. F. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119699 · Cited by 72 · see citing works
Association Between the Probability of Autism Spectrum Disorder and Normative Sex-Related Phenotypic Diversity in Brain Structure
Ecker, C., Andrews, D. S., Gudbrandsen, C. M., Marquand, A. F., Ginestet, C. E., Daly, E., Murphy, C. M., Lai, M., Lombardo, M. V., Ruigrok, A. N. V., et al. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.3990 · Cited by 65 · see citing works
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Group-regularized individual prediction: theory and application to pain
Lindquist, M. A., Krishnan, A., López‐Solà, M., Jepma, M., Woo, C., Koban, L., Roy, M., Atlas, L. Y., Schmidt, L., Chang, L. J., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.074 · Cited by 62 · see citing works
Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models
Schrouff, J., Monteiro, J. M., Portugal, L. C. L., Rosa, M. J., Phillips, C. L. M., & Mourão-Miranda, J. (2018) · Neuroinformatics · doi:10.1007/s12021-017-9347-8 · Cited by 62 · see citing works
Joint prediction of multiple scores captures better individual traits from brain images
Rahim, M., Thirion, B., Bzdok, D., Buvat, I., & Varoquaux, G. P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.072 · Cited by 46 · see citing works
MEG decoding across subjects
Olivetti, E., Kia, S. M., & Avesani, P. (2014) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2014.6858538 · Cited by 41 · see citing works
Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI
Huang, Z.-A., Liu, R., Zhu, Z., & Tan, K. C. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3225179 · Cited by 34 · see citing works
ℓ 2 , 1 − ℓ 1 regularized nonlinear multi-task representation learning based cognitive performance prediction of Alzheimer’s disease
Cao, P., Liu, X., Yang, J., Zhao, D., Huang, M., & Zai͏̈ane, O. R. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.01.028 · Cited by 34 · see citing works
Classification of multi-site MR images in the presence of heterogeneity using multi-task learning
Ma, Q., Zhang, T., Zanetti, M. V., Shen, H., Satterthwaite, T. D., Wolf, D. H., Gur, R. E., Yong Fan, A., Hu, D., Busatto, G. F., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.04.037 · Cited by 34 · see citing works
Mixed-effects Gaussian process modeling approach with application in injection molding processes
Luo, L., Yao, Y., Gao, F., & Zhao, C. (2017) · Journal of Process Control · doi:10.1016/j.jprocont.2017.12.003 · Cited by 33 · see citing works
A Bayesian spatial model for neuroimaging data based on biologically informed basis functions
Huertas, I., Oldehinkel, M., van Oort, E. S. B., García-Solís, D., Mir, P., Beckmann, C. F., & Marquand, A. F. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.009 · Cited by 31 · see citing works
Graph-Based Inter-Subject Pattern Analysis of fMRI Data
Takerkart, S., Auzias, G., Thirion, B., & Ralaivola, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104586 · Cited by 27 · see citing works
Decoding Behavior Tasks From Brain Activity Using Deep Transfer Learning
Gao, Y., Zhang, Y., Wang, H., Guo, X., & Zhang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2907040 · Cited by 26 · see citing works
Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian modelsPreprint
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Sparse semi-supervised heterogeneous interbattery bayesian analysis
Sevilla-Salcedo, C., Gómez-Verdejo, V., & Olmos, P. M. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108141 · Cited by 11 · see citing works
Kernel-Based Regularized EEGNet Using Centered Alignment and Gaussian Connectivity for Motor Imagery Discrimination
Tobón-Henao, M., Álvarez-Meza, A. M., & Castellanos-Dominguez, G. (2023) · Computers · doi:10.3390/computers12070145 · Cited by 11 · see citing works
Urban network travel time prediction via online multi-output Gaussian process regression
Rodríguez-Déniz, H., Jenelius, E., & Villani, M. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317796 · Cited by 9 · see citing works
Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance
Lin, Q., Li, L., Liu, J., Liu, W., Huang, G., & Zhang, Z. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00569 · Cited by 9 · see citing works
Toward a Multisubject Analysis of Neural Connectivity
Oates, C. J., da Costa, L. C. C., & Nichols, T. E. (2014) · Neural Computation · doi:10.1162/neco_a_00690 · Cited by 6 · see citing works
Supervised Phenotype Discovery from Multimodal Brain ImagingPreprint
Gong, W., Bai, S., Zheng, Y., Smith, S. M., & Beckmann, C. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.03.458926 · Cited by 6 · see citing works
Making use of longitudinal information in pattern recognition
Aksman, L., Lythgoe, D. J., Williams, S., Jokisch, M., Mönninghoff, C., Streffer, J. R., Jöckel, K., Weimar, C., & Marquand, A. F. (2016) · Human Brain Mapping · doi:10.1002/hbm.23317 · Cited by 5 · see citing works
Transfer Learning-Based Behavioural Task Decoding from Brain Activity
Gao, Y., Zhou, B., Zhou, Y., Shi, L., Tao, Y., & Zhang, J. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-6837-0_6 · Cited by 5 · see citing works
Full Bayesian multi-task learning for multi-output brain decoding and accommodating missing data
Marquand, A. F., Williams, S. C. R., Doyle, O. M., & Rosa, M. J. (2014) · 2014 International Workshop on Pattern Recognition in Neuroimaging · doi:10.1109/prni.2014.6858533 · Cited by 4 · see citing works
Multiple subject learning for inter-subject prediction
Takerkart, S., & Ralaivola, L. (2014) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2014.6858548 · Cited by 4 · see citing works
Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging
Fuchigami, T., Shikauchi, Y., Nakae, K., Shikauchi, M., Ogawa, T., & Ishii, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-30676-3 · Cited by 4 · see citing works
Multi-task Learning for Brain Network Analysis in the ABCD Study
Kan, X., Cui, H., Han, K., Guo, Y., & Yang, C. (2024) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi62660.2024.10913627 · Cited by 3 · see citing works
Multi-task Learning of Structural MRI for Multi-site Classification
Hu, D., & Zeng, L. (2019) · Springer eBooks · doi:10.1007/978-981-32-9523-0_11 · Cited by 3 · see citing works
Towards a Multi-Subject Analysis of Neural ConnectivityPreprint
Oates, C. J., da Costa, L. C., & Nichols, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.1239 · Cited by 3 · see citing works
Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients
Harvey, A., Moreau, C. A., Kumar, K., Huguet, G., Urchs, S. G. W., Sharmarke, H., Jizi, K., Martin, C.-O., Younis, N., Tamer, P., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00222 · Cited by 3 · see citing works
Inter-subject pattern analysis for multivariate group analysis of functional neuroimaging. A unifying formalization
Wang, Q., Artières, T., & Takerkart, S. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105730 · Cited by 2 · see citing works
Predicting Numerical Processing in Naturalistic Settings from Controlled Experimental Conditions
Schrouff, J., Phillips, C. L. M., Parvizi, J., & Mourão-Miranda, J. (2015) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2015.13 · Cited by 1 · see citing works
Monotonic Gaussian Process for spatio-temporal disease progression modeling in brain imaging data
Nader, C. A., Ayache, N., Robert, P. H., & Lorenzi, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116266 · Cited by 1 · see citing works
Linear methods for classification
Marquand, A. F., & Kia, S. M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00005-5 · Cited by 1 · see citing works
Using Regularized Multi-Task Learning for Schizophrenia MRI Data Classification
Wang, Y., Shi, J., & Xiao, H. (2022) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2104119 · Cited by 1 · see citing works
MATTE: Multi-task multi-scale attention
Strezoski, G., van Noord, N., & Worring, M. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103622 · Cited by 1 · see citing works
Individualized Gaussian Process-based Prediction of Memory Performance and Biomarker Status in Ageing and Alzheimer’s diseasePreprint
Nemali, A., Vockert, N., Berron, D., Maaß, A., Yakupov, R., Peters, O., Gref, D., Cosma, N. C., Preis, L., Priller, J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.14.484226 · Cited by 1 · see citing works
A Unified Form of Batch Harmonization Equation for Normative Modeling: A Location–Scale FrameworkPreprint
Li, M., Wang, Y., Shen, Y., An, L., Jia, G., Bringas-Vega, M. L., & Valdes-Sosa, P. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.17.725713 · Cited by 1 · see citing works
Scalable Machine Learning Methods for Massive Biomedical Data Analysis.
Watanabe, T. (2015) · Deep Blue (University of Michigan)
Methods and Applications of Multivariate Pattern Analysis in Functional MRI Data Analysis.
Shah, Y. (2015) · Deep Blue (University of Michigan)
MEG Decoding Across SubjectsPreprint
Olivetti, E., Kia, S. M., & Avesani, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.4175
Advancing translational research in neuroscience through multi-task learning
Cao, H., Hong, X., Tost, H., Meyer‐Lindenberg, A., & Schwarz, E. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.993289
Challenges in Multi-Task Learning for fMRI-Based Diagnosis: Benefits for Psychiatric Conditions and CNVs Would Likely Require Thousands of PatientsPreprint
Harvey, A., Moreau, C. A., Kumar, K., Guillaumeaf, Urchs, S. G. W., Sharmarke, H., Jizi, K., Martin, C.-O., Younis, N., Tamer, P., et al. (2023) · doi:10.31234/osf.io/srmx7
Supervised Phenotype Discovery from Multimodal Brain ImagingPreprint
Gong, W., Bai, S., Zheng, Y., Smith, S. M., & Beckmann, C. F. (2024) · Research Square · doi:10.21203/rs.3.rs-880321/v1
Multivariate group analyses for functional neuroimaging : conceptual and experimental advances
Wang, Q. (2020) · doi:10.70675/f693b631z573fz4c70z80dfz789dc1fec37e
Modelling and simulating the progression of Alzheimer's disease through the analysis of multi-modal neuroimages and clinical data
Nader, C. A. (2021) · doi:10.70675/4533532ezc434z4c80zae91z3bf82d5b852b

Full Bayesian multi-task learning for multi-output brain decoding and accommodating missing data
Marquand, A. F., Williams, S. C. R., Doyle, O. M., & Rosa, M. J. (2014) · 2014 International Workshop on Pattern Recognition in Neuroimaging · doi:10.1109/prni.2014.6858533 · Cited by 4 · see citing works
Multi-subject brain decoding with multi-task feature selection
Wang, L., Tang, X., Liu, W., Peng, Y., Gao, T., & Xu, Y. J. (2014) · Bio-Medical Materials and Engineering · doi:10.3233/bme-141119 · Cited by 1 · see citing works
Concept Classification with Bayesian Multi-task Learning Marcel
Gerven, V., & Simanova, I. (2013)
Decoding brain cognitive activity across subjects using multimodal M/EEG neuroimaging
Fatima, S., & Kamboh, A. M. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037543 · Cited by 7 · see citing works
Concept Classification with Bayesian Multi-task Learning
van Gerven, M. A. J., & Simanova, I. (2010) · MPG.PuRe (Max Planck Society) · Cited by 3 · see citing works

Authors: Marquand, A. F., Brammer, M., Williams, S. C. R., & Doyle, O. M.
ORCID: Brammer, Michael
Williams, Steven C.R.
Doyle, Orla M.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2014-05
Volume: 92
Pages: 298-311
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2014.02.008
PMC: PMC4010954
OpenAlex: W2110185148
Cited by: 59
References: 69
Citations by year: 2026: 1 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 4 · 2020: 3 · 2019: 9 · 2018: 6 · 2017: 7 · 2016: 2 · 2015: 7 · 2014: 7
FWCI: 4.28
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
creativecommons.org/licenses/by/3.0/ (vor)
Locations: NeuroImage (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Research Portal (King's College London) (publishedVersion, other-oa)
Radboud Repository (Radboud University) (submittedVersion)
Funders: King's College London Centre of Excellence in Medical Engineering
Wellcome Trust
EPSRC (WT088641/Z/09/Z)
Innovative Medicines Initiative Joint Undertaking (115008 (NEWMEDS))
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords: multi-task learning, Bayesian decoding, pattern recognition, interindividual variability, Gaussian process priors, covariance structure, fMRI classification, decoding accuracy
MeSH terms: Adolescent, Adult, Algorithms, Artificial Intelligence, Bayes Theorem, Brain Mapping, Female, Humans, Image Interpretation, Computer-Assisted, Male, Pattern Recognition, Automated, Pattern Recognition, Visual, Sensitivity and Specificity, Visual Cortex, Reproducibility of Results, Young Adult, Memory, Episodic

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