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Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer’s Disease Classification

Xinyang Feng, Jie Yang, Zachary C. Lipton, Scott A. Small, Frank A. Provenzano, Alzheimer’s Disease Neuroimaging Initiative
doi: https://doi.org/10.1101/456277
Xinyang Feng
1Department of Biomedical Engineering, Columbia University, New York, NY, 10027, United States
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Jie Yang
1Department of Biomedical Engineering, Columbia University, New York, NY, 10027, United States
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Zachary C. Lipton
2Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, 15213, United States
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Scott A. Small
3Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, United States
4Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, 10032, United States
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  • For correspondence: fap2005@cumc.columbia.edu sas68@cumc.columbia.edu
Frank A. Provenzano
3Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, United States
4Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, 10032, United States
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  • For correspondence: fap2005@cumc.columbia.edu sas68@cumc.columbia.edu
Alzheimer’s Disease Neuroimaging Initiative
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ABSTRACT

Deep learning techniques on MRI scans have demonstrated great potential to improve the diagnosis of neurological diseases. Here, we investigate the application of 3D deep convolutional neural networks (CNNs) for classifying Alzheimer’s disease (AD) based on structural MRI data. In particular, we take on two challenges that are under-explored in the literature on deep learning for neuroimaging. First deep neural networks typically require large-scale data that is not always available in medical studies. Therefore, we explore the use of including longitudinal scans in classification studies, greatly increasing the amount of data for training and improving the generalization performance of our classifiers. Moreover, previous studies applying deep learning to classifying Alzheimer’s disease from neuroimaging have typically addressed classification based on whole brain volumes but stopped short of performing in-depth regional analyses to localize the most predictive areas. Additionally, we show a deep net trained to distinguish between AD and cognitively normal subjects can be applied to classify mild cognitive impairment patients, with classification scores aligning empirically with the likelihood of progression to AD. Our initial results demonstrate both that we can classify AD with an area under the receiver operator characteristic curve (AUROC) of .990 and that we can predict conversion to AD among patients in the MCI subgroup with an AURUC of 0.787. We then localize the predictive regions, by performing both saliency-based interpretation and rigorous slice and lobar level ablation studies. Interestingly, our regional analyses identified the hippocampal formation, including the entorhinal cortex, to be the most predictive region for our models. This finding adds evidence that the hippocampal formation is an anatomical seat of AD and a prominent feature in its diagnosis. Together, the results of this study further demonstrate the potential of deep learning to impact AD classification and to identify AD’s structural neuroimaging signatures. The proposed classification and regional analyses methods constitute a general framework that can easily be applied to other disorders and imaging modalities.

MRI
magnetic resonance imaging
AD
Alzheimer’s disease
CN
cognitively normal
MCI
mild cognitive impairment
MCIs
mild cognitive impairment stable
MCIp
mild cognitive impairment progression
HP
hippocampal formation
ADNI
Alzheimer’s Disease Neuroimaging Initiative
HCP
Human Connectome Project
CNNs
convolutional neural networks
CAM
class activation mapping
ReLU
rectified linear unit
BN
batch normalization
AUC
area under the curve
ROC
receiver operating characteristic
AUROC
area under the receiver operating characteristic curve
  • MRI
    magnetic resonance imaging
    AD
    Alzheimer’s disease
    CN
    cognitively normal
    MCI
    mild cognitive impairment
    MCIs
    mild cognitive impairment stable
    MCIp
    mild cognitive impairment progression
    HP
    hippocampal formation
    ADNI
    Alzheimer’s Disease Neuroimaging Initiative
    HCP
    Human Connectome Project
    CNNs
    convolutional neural networks
    CAM
    class activation mapping
    ReLU
    rectified linear unit
    BN
    batch normalization
    AUC
    area under the curve
    ROC
    receiver operating characteristic
    AUROC
    area under the receiver operating characteristic curve
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted October 31, 2018.
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    Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer’s Disease Classification
    Xinyang Feng, Jie Yang, Zachary C. Lipton, Scott A. Small, Frank A. Provenzano, Alzheimer’s Disease Neuroimaging Initiative
    bioRxiv 456277; doi: https://doi.org/10.1101/456277
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    Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer’s Disease Classification
    Xinyang Feng, Jie Yang, Zachary C. Lipton, Scott A. Small, Frank A. Provenzano, Alzheimer’s Disease Neuroimaging Initiative
    bioRxiv 456277; doi: https://doi.org/10.1101/456277

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