TY - JOUR
T1 - Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection
AU - Kam, Tae Eui
AU - Zhang, Han
AU - Jiao, Zhicheng
AU - Shen, DInggang
N1 - Funding Information:
Manuscript received April 24, 2019; accepted July 6, 2019. Date of publication July 17, 2019; date of current version January 31, 2020. This work was supported in part by NIH Grant EB022880 and Grant AG041721. (Corresponding authors: Han Zhang; Dinggang Shen.) T.-E. Kam, H. Zhang, and Z. Jiao are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA (e-mail: tekam@med.unc.edu; hanzhang@med. unc.edu; zhicheng_jiao@med.unc.edu).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is still difficult to apply it directly to resting-state functional MRI (rs-fMRI) and the derived brain functional networks (BFNs). We propose a novel CNN framework to simultaneously learn embedded features from BFNs for brain disease diagnosis. Since BFNs can be built by considering both static and dynamic functional connectivity (FC), we first decompose rs-fMRI into multiple static BFNs with modified independent component analysis. Then, the voxel-wise variability in dynamic FC is used to quantify BFN dynamics. A set of paired 3D images representing static/dynamic BFNs can be fed into 3D CNNs, from which we can hierarchically and simultaneously learn static/dynamic BFN features. As a result, the dynamic BFN features can complement static BFN features and, at the meantime, different BFNs can help each other toward a joint and better classification. We validate our method with a publicly accessible, large cohort of rs-fMRI dataset in early-stage mild cognitive impairment (eMCI) diagnosis, which is one of the most challenging problems to the clinicians. By comparing with a conventional method, our method shows significant diagnostic performance improvement by almost 10%. This result demonstrates the effectiveness of deep learning in preclinical Alzheimer's disease diagnosis, based on the complex and high-dimensional voxel-wise spatiotemporal patterns of the resting-state brain functional connectomics. The framework provides a new but intuitive way to fully exploit deeply embedded diagnostic features from rs-fMRI for a better-individualized diagnosis of various neurological diseases.
AB - While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is still difficult to apply it directly to resting-state functional MRI (rs-fMRI) and the derived brain functional networks (BFNs). We propose a novel CNN framework to simultaneously learn embedded features from BFNs for brain disease diagnosis. Since BFNs can be built by considering both static and dynamic functional connectivity (FC), we first decompose rs-fMRI into multiple static BFNs with modified independent component analysis. Then, the voxel-wise variability in dynamic FC is used to quantify BFN dynamics. A set of paired 3D images representing static/dynamic BFNs can be fed into 3D CNNs, from which we can hierarchically and simultaneously learn static/dynamic BFN features. As a result, the dynamic BFN features can complement static BFN features and, at the meantime, different BFNs can help each other toward a joint and better classification. We validate our method with a publicly accessible, large cohort of rs-fMRI dataset in early-stage mild cognitive impairment (eMCI) diagnosis, which is one of the most challenging problems to the clinicians. By comparing with a conventional method, our method shows significant diagnostic performance improvement by almost 10%. This result demonstrates the effectiveness of deep learning in preclinical Alzheimer's disease diagnosis, based on the complex and high-dimensional voxel-wise spatiotemporal patterns of the resting-state brain functional connectomics. The framework provides a new but intuitive way to fully exploit deeply embedded diagnostic features from rs-fMRI for a better-individualized diagnosis of various neurological diseases.
KW - Diagnosis
KW - brain network
KW - convolutional neural networks
KW - deep learning
KW - functional MRI
KW - independent component analysis
KW - mild cognitive impairment
KW - resting state
UR - http://www.scopus.com/inward/record.url?scp=85079021255&partnerID=8YFLogxK
U2 - 10.1109/TMI.2019.2928790
DO - 10.1109/TMI.2019.2928790
M3 - Article
C2 - 31329111
AN - SCOPUS:85079021255
SN - 0278-0062
VL - 39
SP - 478
EP - 487
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 2
M1 - 8765628
ER -