A generative-discriminative basis learning framework to predict clinical severity from resting state functional MRI data

Niharika Shimona D’Souza, Mary Beth Nebel, Nicholas Wymbs, Stewart H Mostofsky, Archana Venkataraman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a matrix factorization technique that decomposes the resting state fMRI (rs-fMRI) correlation matrices for a patient population into a sparse set of representative subnetworks, as modeled by rank one outer products. The subnetworks are combined using patient specific non-negative coefficients; these coefficients are also used to model, and subsequently predict the clinical severity of a given patient via a linear regression. Our generative-discriminative framework is able to exploit the structure of rs-fMRI correlation matrices to capture group level effects, while simultaneously accounting for patient variability. We employ ten fold cross validation to demonstrate the predictive power of our model on a cohort of fifty eight patients diagnosed with Autism Spectrum Disorder. Our method outperforms classical semi-supervised frameworks, which perform dimensionality reduction on the correlation features followed by non-linear regression to predict the clinical scores.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsAlejandro F. Frangi, Christos Davatzikos, Gabor Fichtinger, Carlos Alberola-López, Julia A. Schnabel
PublisherSpringer Verlag
Pages163-171
Number of pages9
ISBN (Print)9783030009304
DOIs
StatePublished - Jan 1 2018
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 16 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11072 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period9/16/189/20/18

Fingerprint

Functional Magnetic Resonance Imaging
Correlation Matrix
Predict
Matrix Factorization
Nonlinear Regression
Dimensionality Reduction
Coefficient
Linear regression
Cross-validation
Disorder
Fold
Non-negative
Factorization
Decompose
Model
Demonstrate
Learning
Framework
Magnetic Resonance Imaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

D’Souza, N. S., Nebel, M. B., Wymbs, N., Mostofsky, S. H., & Venkataraman, A. (2018). A generative-discriminative basis learning framework to predict clinical severity from resting state functional MRI data. In A. F. Frangi, C. Davatzikos, G. Fichtinger, C. Alberola-López, & J. A. Schnabel (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 163-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11072 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00931-1_19

A generative-discriminative basis learning framework to predict clinical severity from resting state functional MRI data. / D’Souza, Niharika Shimona; Nebel, Mary Beth; Wymbs, Nicholas; Mostofsky, Stewart H; Venkataraman, Archana.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. ed. / Alejandro F. Frangi; Christos Davatzikos; Gabor Fichtinger; Carlos Alberola-López; Julia A. Schnabel. Springer Verlag, 2018. p. 163-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11072 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

D’Souza, NS, Nebel, MB, Wymbs, N, Mostofsky, SH & Venkataraman, A 2018, A generative-discriminative basis learning framework to predict clinical severity from resting state functional MRI data. in AF Frangi, C Davatzikos, G Fichtinger, C Alberola-López & JA Schnabel (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11072 LNCS, Springer Verlag, pp. 163-171, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 9/16/18. https://doi.org/10.1007/978-3-030-00931-1_19
D’Souza NS, Nebel MB, Wymbs N, Mostofsky SH, Venkataraman A. A generative-discriminative basis learning framework to predict clinical severity from resting state functional MRI data. In Frangi AF, Davatzikos C, Fichtinger G, Alberola-López C, Schnabel JA, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Springer Verlag. 2018. p. 163-171. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-00931-1_19
D’Souza, Niharika Shimona ; Nebel, Mary Beth ; Wymbs, Nicholas ; Mostofsky, Stewart H ; Venkataraman, Archana. / A generative-discriminative basis learning framework to predict clinical severity from resting state functional MRI data. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. editor / Alejandro F. Frangi ; Christos Davatzikos ; Gabor Fichtinger ; Carlos Alberola-López ; Julia A. Schnabel. Springer Verlag, 2018. pp. 163-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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