Cited by 968Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsFunctional Brain Connectivity Studies

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Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis

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Suk, Heung-Il, Lee, Seong-Whan, Shen, Dinggang·2014·NeuroImage

DOI 10.1016/j.neuroimage.2014.06.077PMC PMC4165842

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Abstract

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)(2), a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET.

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Authors: Suk, H.-I., Lee, S.-W., & Shen, D.
Affiliations: University of North Carolina at Chapel Hill (US)
Korea University (KR)
Corresponding author: Shen, Dinggang
ORCID: Suk, Heung-Il
Lee, Seong-Whan
Shen, Dinggang
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2014-11
Volume: 101
Pages: 569-582
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2014.06.077
PMC: PMC4165842
OpenAlex: W2126598020
Cited by: 968
References: 92
Citations by year: 2026: 62 · 2025: 74 · 2024: 81 · 2023: 93 · 2022: 104 · 2021: 128 · 2020: 117 · 2019: 107 · 2018: 85 · 2017: 57 · 2016: 41 · 2015: 17 · 2014: 1
FWCI: 29.50
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
UNC Libraries
Funders: NIH (EB006733, EB008374, EB009634, AG041721, MH100217, AG042599)
National Research Foundation (2012-005741)
Korean Government
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: Alzheimer's disease, mild cognitive impairment, multimodal fusion, MRI-PET fusion, hierarchical feature representation, deep Boltzmann machine, neuroimaging diagnosis, MCI conversion, ADNI
MeSH terms: Aged, Aged, 80 and over, Alzheimer Disease, Artificial Intelligence, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Neural Networks, Computer, Positron-Emission Tomography, Cognitive Dysfunction, Multimodal Imaging

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Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis

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