publication . Thesis . 2017

Exploring the latent space between brain and behaviour using eigen-decomposition methods

De Matos Monteiro, João André;
Open Access English
  • Published: 28 Oct 2017
  • Publisher: UCL (University College London)
  • Country: United Kingdom
Abstract
Machine learning methods have been successfully used to analyse neuroimaging data for a variety of applications, including the classification of subjects with different brain disorders. However, most studies still rely on the labelling of the subjects, constraining the study of several brain diseases within a paradigm of pre-defined clinical labels, which have shown to be unreliable in some cases. The lack of understanding regarding the association between brain and behaviour presents itself as an interesting challenge for more exploratory machine learning approaches, which could potentially help in the study of diseases whose clinical labels have shown limitati...
Subjects
free text keywords: Machine Learning, Neuroimaging
Related Organizations
Funded by
FCT| SFRH/BD/88345/2012
Project
SFRH/BD/88345/2012
MACHINE LEARNING MODELS FOR THE ANALYSIS OF PSYCHIATRIC NEUROIMAGING DATA
  • Funder: Fundação para a Ciência e a Tecnologia, I.P. (FCT)
  • Project Code: SFRH/BD/88345/2012
  • Funding stream: SFRH | Doutoramento
Download from
UCL Discovery
Thesis . 2017
96 references, page 1 of 7

2017 • A. Rao, J. M. Monteiro, and J. Mourão-Miranda. Predictive modelling using neuroimaging data in the presence of confounds. NeuroImage, 2017

2016 • J. M. Monteiro, A. Rao, J. Shawe-Taylor, and J. Mourão-Miranda. A multiple hold-out framework for sparse partial least squares. Journal of Neuroscience Methods, 271:182-194, 2016 [OpenAIRE]

• M. Donini, J. M. Monteiro, M. Pontil, J. Shawe-Taylor, and J. Mourao-Miranda. A multimodal multiple kernel learning approach to Alzheimer's disease detection. In Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on, pages 1-6. IEEE, 2016

• A. Rao, J. Monteiro, and J. Mourao-Miranda. Prediction of clinical scores from neuroimaging data with censored likelihood Gaussian processes. In Pattern Recognition in Neuroimaging (PRNI), 2016 International Workshop on, pages 1-4. IEEE, 2016

2015 • J. M. Monteiro, A. Rao, J. Ashburner, J. Shawe-Taylor, and J. Mourão Miranda. Multivariate effect ranking via adaptive sparse PLS. In Pattern Recognition in NeuroImaging (PRNI), 2015 International Workshop on, pages 25-28. IEEE, 2015 [OpenAIRE]

• A. Rao, J. M. Monteiro, J. Ashburner, L. Portugal, O. Fernandes, L. De Oliveira, M. Pereira, and J. Mourão-Miranda. A comparison of strategies for incorporating nuisance variables into predictive neuroimaging models. In Pattern Recognition in NeuroImaging (PRNI), 2015 International Workshop on, pages 61-64. IEEE, 2015

2014 • J. M. Monteiro, A. Rao, J. Ashburner, J. Shawe-Taylor, and J. Mourão-Miranda. Leveraging clinical data to enhance localization of brain atrophy. In International Workshop on Machine Learning and Interpretation in Neuroimaging, pages 60-68. Springer, 2014

3.1 Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . 55

5.1 Clinical weight vectors. . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.2 Image weight vectors. . . . . . . . . . . . . . . . . . . . . . . . . . . 90

6.1 Hyper-parameter optimisation framework. . . . . . . . . . . . . . . . 98 6.2 Permutation framework. . . . . . . . . . . . . . . . . . . . . . . . . . 100 6.3 Average absolute correlation on the hold-out datasets. . . . . . . . . 106 6.4 SPLS clinical weight vectors. . . . . . . . . . . . . . . . . . . . . . . 107 6.5 SPLS image weight vectors. . . . . . . . . . . . . . . . . . . . . . . . 108 6.6 Projection of the data onto the SPLS weight vector pairs. . . . . . . 110 J. Ashburner, G. Barnes, C.-c. Chen, J. Daunizeau, R. Moran, R. Henson, V. Glauche, and C. Phillips. SPM12 Manual. Functional Imaging Laboratory, pages 475-1, 2013. ISSN 09621083. doi: 10.1111/j.1365-294X.2006.02813.x.

B. B. Avants, P. A. Cook, L. Ungar, J. C. Gee, and M. Grossman. Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis. NeuroImage, 50(3):1004-1016, 2010.

B. B. Avants, D. J. Libon, K. Rascovsky, A. Boller, C. T. McMillan, L. Massimo, H. B. Coslett, A. Chatterjee, R. G. Gross, and M. Grossman. Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population. NeuroImage, 84(0):698-711, 2014.

F. R. Bach and M. I. Jordan. Kernel independent component analysis. Journal of machine learning research, 3(Jul):1-48, 2002.

r. Björck and G. H. Golub. Numerical methods for computing angles between linear subspaces. Mathematics of computation, 27(123):579-594, 1973. [OpenAIRE]

M. B. Blaschko, J. A. Shelton, A. Bartels, C. H. Lampert, and A. Gretton. Semisupervised kernel canonical correlation analysis with application to human fmri. Pattern Recognition Letters, 32(11):1572-1583, 2011.

96 references, page 1 of 7
Abstract
Machine learning methods have been successfully used to analyse neuroimaging data for a variety of applications, including the classification of subjects with different brain disorders. However, most studies still rely on the labelling of the subjects, constraining the study of several brain diseases within a paradigm of pre-defined clinical labels, which have shown to be unreliable in some cases. The lack of understanding regarding the association between brain and behaviour presents itself as an interesting challenge for more exploratory machine learning approaches, which could potentially help in the study of diseases whose clinical labels have shown limitati...
Subjects
free text keywords: Machine Learning, Neuroimaging
Related Organizations
Funded by
FCT| SFRH/BD/88345/2012
Project
SFRH/BD/88345/2012
MACHINE LEARNING MODELS FOR THE ANALYSIS OF PSYCHIATRIC NEUROIMAGING DATA
  • Funder: Fundação para a Ciência e a Tecnologia, I.P. (FCT)
  • Project Code: SFRH/BD/88345/2012
  • Funding stream: SFRH | Doutoramento
Download from
UCL Discovery
Thesis . 2017
96 references, page 1 of 7

2017 • A. Rao, J. M. Monteiro, and J. Mourão-Miranda. Predictive modelling using neuroimaging data in the presence of confounds. NeuroImage, 2017

2016 • J. M. Monteiro, A. Rao, J. Shawe-Taylor, and J. Mourão-Miranda. A multiple hold-out framework for sparse partial least squares. Journal of Neuroscience Methods, 271:182-194, 2016 [OpenAIRE]

• M. Donini, J. M. Monteiro, M. Pontil, J. Shawe-Taylor, and J. Mourao-Miranda. A multimodal multiple kernel learning approach to Alzheimer's disease detection. In Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on, pages 1-6. IEEE, 2016

• A. Rao, J. Monteiro, and J. Mourao-Miranda. Prediction of clinical scores from neuroimaging data with censored likelihood Gaussian processes. In Pattern Recognition in Neuroimaging (PRNI), 2016 International Workshop on, pages 1-4. IEEE, 2016

2015 • J. M. Monteiro, A. Rao, J. Ashburner, J. Shawe-Taylor, and J. Mourão Miranda. Multivariate effect ranking via adaptive sparse PLS. In Pattern Recognition in NeuroImaging (PRNI), 2015 International Workshop on, pages 25-28. IEEE, 2015 [OpenAIRE]

• A. Rao, J. M. Monteiro, J. Ashburner, L. Portugal, O. Fernandes, L. De Oliveira, M. Pereira, and J. Mourão-Miranda. A comparison of strategies for incorporating nuisance variables into predictive neuroimaging models. In Pattern Recognition in NeuroImaging (PRNI), 2015 International Workshop on, pages 61-64. IEEE, 2015

2014 • J. M. Monteiro, A. Rao, J. Ashburner, J. Shawe-Taylor, and J. Mourão-Miranda. Leveraging clinical data to enhance localization of brain atrophy. In International Workshop on Machine Learning and Interpretation in Neuroimaging, pages 60-68. Springer, 2014

3.1 Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . 55

5.1 Clinical weight vectors. . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.2 Image weight vectors. . . . . . . . . . . . . . . . . . . . . . . . . . . 90

6.1 Hyper-parameter optimisation framework. . . . . . . . . . . . . . . . 98 6.2 Permutation framework. . . . . . . . . . . . . . . . . . . . . . . . . . 100 6.3 Average absolute correlation on the hold-out datasets. . . . . . . . . 106 6.4 SPLS clinical weight vectors. . . . . . . . . . . . . . . . . . . . . . . 107 6.5 SPLS image weight vectors. . . . . . . . . . . . . . . . . . . . . . . . 108 6.6 Projection of the data onto the SPLS weight vector pairs. . . . . . . 110 J. Ashburner, G. Barnes, C.-c. Chen, J. Daunizeau, R. Moran, R. Henson, V. Glauche, and C. Phillips. SPM12 Manual. Functional Imaging Laboratory, pages 475-1, 2013. ISSN 09621083. doi: 10.1111/j.1365-294X.2006.02813.x.

B. B. Avants, P. A. Cook, L. Ungar, J. C. Gee, and M. Grossman. Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis. NeuroImage, 50(3):1004-1016, 2010.

B. B. Avants, D. J. Libon, K. Rascovsky, A. Boller, C. T. McMillan, L. Massimo, H. B. Coslett, A. Chatterjee, R. G. Gross, and M. Grossman. Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population. NeuroImage, 84(0):698-711, 2014.

F. R. Bach and M. I. Jordan. Kernel independent component analysis. Journal of machine learning research, 3(Jul):1-48, 2002.

r. Björck and G. H. Golub. Numerical methods for computing angles between linear subspaces. Mathematics of computation, 27(123):579-594, 1973. [OpenAIRE]

M. B. Blaschko, J. A. Shelton, A. Bartels, C. H. Lampert, and A. Gretton. Semisupervised kernel canonical correlation analysis with application to human fmri. Pattern Recognition Letters, 32(11):1572-1583, 2011.

96 references, page 1 of 7
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