Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer's Disease Progression

Anees Abrol, Zening Fu, Yuhui Du, Vince D. Calhoun

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

Abstract

Early prediction of diseased brain conditions is critical for curing illness and preventing irreversible neuronal dysfunction and loss. Generically regarding the different neuroimaging modalities as filtered, complementary insights of brain's anatomical and functional organization, multimodal data fusion could be hypothesized to enhance the predictive power as compared to a unimodal prediction of disease progression. More recently, deep learning (DL) based methods on structural MRI (sMRI) data have outperformed classical machine learning approaches in several neuroimaging applications including diagnostic classification and prediction. Similarly, functional MRI (fMRI) features estimated using a dynamic (i.e. time-varying) functional connectivity (FC) approach have been found to be more discriminative and predictive of the clinical diagnosis than those based on the static FC approach. Motivated by this, we introduce a novel multimodal data fusion framework featuring deep residual learning of non-linear sMRI features and dynamic FC (dFC) based extraction of fMRI features to predict the subset of individuals with mild cognitive impairments who would progress to Alzheimer's disease within a time-period of three years from the baseline scanning sessions. Our cross-validated results from the developed multimodal (sMRI-fMRI) data fusion framework demonstrate a significant improvement in performance over the unimodal prediction analyses with the fMRI (p = 7.03 x 10-7) and sMRI (p = 6.72 x 10-4) modalities. As such, the findings in this work highlight the benefits of combining multiple neuroimaging data modalities via data fusion, corroborate the predictive value of the tested DL and dFC features and argue in favor of exploration of similar approaches to learn neuroanatomical and functional alterations in the neuroimaging data.

Original languageEnglish (US)
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4409-4413
Number of pages5
ISBN (Electronic)9781538613115
DOIs
StatePublished - Jul 2019
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: Jul 23 2019Jul 27 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period7/23/197/27/19

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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