TY - JOUR
T1 - High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis
AU - Dong, Aimei
AU - Li, Zhigang
AU - Wang, Mingliang
AU - Shen, Dinggang
AU - Liu, Mingxia
N1 - Funding Information:
Data used in preparation of this article were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. The investigators within the ADNI contributed to the design and implementation of ADNI and provided data but did not participate in analysis or writing of this article. Funding. AD and ZL were supported by the National Natural Science Foundation of China under Grant (Nos. 61703219 and 61702292). ML was supported in part by United States National Institutes of Health (NIH) grant (No. AG041721).
Funding Information:
AD and ZL were supported by the National Natural Science Foundation of China under Grant (Nos. 61703219 and 61702292). ML was supported in part by United States National Institutes of Health (NIH) grant (No. AG041721).
Publisher Copyright:
© Copyright © 2021 Dong, Li, Wang, Shen and Liu.
PY - 2021/3/12
Y1 - 2021/3/12
N2 - Multimodal heterogeneous data, such as structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF), are effective in improving the performance of automated dementia diagnosis by providing complementary information on degenerated brain disorders, such as Alzheimer's prodromal stage, i.e., mild cognitive impairment. Effectively integrating multimodal data has remained a challenging problem, especially when these heterogeneous data are incomplete due to poor data quality and patient dropout. Besides, multimodal data usually contain noise information caused by different scanners or imaging protocols. The existing methods usually fail to well handle these heterogeneous and noisy multimodal data for automated brain dementia diagnosis. To this end, we propose a high-order Laplacian regularized low-rank representation method for dementia diagnosis using block-wise missing multimodal data. The proposed method was evaluated on 805 subjects (with incomplete MRI, PET, and CSF data) from the real Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Experimental results suggest the effectiveness of our method in three tasks of brain disease classification, compared with the state-of-the-art methods.
AB - Multimodal heterogeneous data, such as structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF), are effective in improving the performance of automated dementia diagnosis by providing complementary information on degenerated brain disorders, such as Alzheimer's prodromal stage, i.e., mild cognitive impairment. Effectively integrating multimodal data has remained a challenging problem, especially when these heterogeneous data are incomplete due to poor data quality and patient dropout. Besides, multimodal data usually contain noise information caused by different scanners or imaging protocols. The existing methods usually fail to well handle these heterogeneous and noisy multimodal data for automated brain dementia diagnosis. To this end, we propose a high-order Laplacian regularized low-rank representation method for dementia diagnosis using block-wise missing multimodal data. The proposed method was evaluated on 805 subjects (with incomplete MRI, PET, and CSF data) from the real Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Experimental results suggest the effectiveness of our method in three tasks of brain disease classification, compared with the state-of-the-art methods.
KW - classification
KW - dementia
KW - high-order
KW - incomplete heterogeneous data
KW - low-rank representation
UR - http://www.scopus.com/inward/record.url?scp=85103307948&partnerID=8YFLogxK
U2 - 10.3389/fnins.2021.634124
DO - 10.3389/fnins.2021.634124
M3 - Article
AN - SCOPUS:85103307948
SN - 1662-4548
VL - 15
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 634124
ER -