{"doi":"10.5121/csit.2022.121812","title":"Tensor-based Multi-Modality Feature Selection and Regression for Alzheimer’s Disease Diagnosis","abstract":"The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.","journal":"PubMed","year":2022,"id":300558,"datarank":0.3098804426918033,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.1019362885238197,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.1019362885238197,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":3,"citers_with_citation_signal":3,"citers_with_endowment":3,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9592,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":817750,"name":"Zhaoming Kong","orcid":"0000-0001-9252-911X","position":1,"is_corresponding":false},{"id":333873,"name":"Liang Zhan","orcid":"0000-0002-7920-4828","position":2,"is_corresponding":false},{"id":49224,"name":"Li Shen","orcid":"0000-0002-5443-0503","position":3,"is_corresponding":false},{"id":443321,"name":"Lifang He","orcid":"0000-0001-7810-9071","position":4,"is_corresponding":false},{"id":991083,"name":"Jun Ye Yu","orcid":"0000-0003-4533-2693","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:31:53.559757Z","pmid":"36880061","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}