Multi-modality Low-Rank Learning Fused First-Order and Second-Order Information for Computer-Aided Diagnosis of Schizophrenia
The brain functional connectivity network (BFCN) based methods for diagnosing brain diseases have shown great advantages. At present, most BFCN construction strategies only calculate the first-order correlation between brain areas, such as the Pearson correlation coefficient method. Although the work of the low-order and high-order BFCN construction methods exists, there is very little work to integrate them, that is, to design a multi-modal BFCN feature selection and classification method to combine low-order and high-order information. This may affect the performance of brain disease diagnosis. To this end, we propose a multi-modality low-rank learning framework jointly learning first-order and second-order BFCN information and apply it to the diagnosis of schizophrenia. The proposed method not only embeds the correlation information of multi-modality data in the learning model, but also encourages the cooperation between the first-order and the second-order BFCN by combining the ideal representation term. The experimental results of the three schizophrenia datasets (totally including 168 patients and 163 normal controls) show that our proposed method achieves promising classification results in the diagnosis of schizophrenia.
KeywordsMulti-modality learning Low rank representation Brain functional connection network
This work was supported in part by National Natural Science Foundation of China (Nos. 61501230, 61732006, 61876082 and 81771444), National Science and Technology Major Project (No. 2018ZX10201002), and the Fundamental Research Funds for the Central Universities (No. NJ2019010).
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