Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders

被引:4
作者
Liu, Liangliang [1 ]
Chang, Jing [1 ]
Wang, Ying [1 ]
Liang, Gongbo [2 ]
Wang, Yu-Ping [3 ]
Zhang, Hui [1 ]
机构
[1] Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou, Peoples R China
[2] Eastern Kentucky Univ, Dept Comp Sci, Richmond, KY 40475 USA
[3] Tulane Univ, Biomed Engn Dept, New Orleans, LA 70118 USA
基金
中国国家自然科学基金;
关键词
multi-modal; decomposition-based; matrix decomposition; canonical correlation analysis; neuropsychiatric disorders; INDEPENDENT COMPONENT ANALYSIS; FUNCTIONAL CONNECTIVITY; ALZHEIMERS-DISEASE; BIPOLAR DISORDER; IMAGING DATA; SCHIZOPHRENIA; DIFFUSION; FMRI; FUSION; MATTER;
D O I
10.3389/fnins.2022.832276
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Multi-modal magnetic resonance imaging (MRI) is widely used for diagnosing brain disease in clinical practice. However, the high-dimensionality of MRI images is challenging when training a convolution neural network. In addition, utilizing multiple MRI modalities jointly is even more challenging. We developed a method using decomposition-based correlation learning (DCL). To overcome the above challenges, we used a strategy to capture the complex relationship between structural MRI and functional MRI data. Under the guidance of matrix decomposition, DCL takes into account the spike magnitude of leading eigenvalues, the number of samples, and the dimensionality of the matrix. A canonical correlation analysis (CCA) was used to analyze the correlation and construct matrices. We evaluated DCL in the classification of multiple neuropsychiatric disorders listed in the Consortium for Neuropsychiatric Phenomics (CNP) dataset. In experiments, our method had a higher accuracy than several existing methods. Moreover, we found interesting feature connections from brain matrices based on DCL that can differentiate disease and normal cases and different subtypes of the disease. Furthermore, we extended experiments on a large sample size dataset and a small sample size dataset, compared with several other well-established methods that were designed for the multi neuropsychiatric disorder classification; our proposed method achieved state-of-the-art performance on all three datasets.
引用
收藏
页数:15
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