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Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment.
Zhao, Kun; Zheng, Qiang; Dyrba, Martin; Rittman, Timothy; Li, Ang; Che, Tongtong; Chen, Pindong; Sun, Yuqing; Kang, Xiaopeng; Li, Qiongling; Liu, Bing; Liu, Yong; Li, Shuyu.
Afiliação
  • Zhao K; Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
  • Zheng Q; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
  • Dyrba M; School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.
  • Rittman T; German Center for Neurodegenerative Diseases (DZNE), Rostock, 18147, Germany.
  • Li A; Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0SZ, UK.
  • Che T; State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
  • Chen P; Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
  • Sun Y; Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
  • Kang X; School of Artificial Intelligence, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100049, China.
  • Li Q; Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
  • Liu B; School of Artificial Intelligence, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100049, China.
  • Liu Y; Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
  • Li S; School of Artificial Intelligence, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100049, China.
Adv Sci (Weinh) ; 9(12): e2104538, 2022 04.
Article em En | MEDLINE | ID: mdl-35098696
ABSTRACT
Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual-level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients' R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into "similar to the pattern of NCs" (N-CI, N = 252) and "similar to the pattern of AD" (A-CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A-CI and 21.77% for N-CI) within three years; 4) enriched genes for potassium-ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença de Alzheimer / Disfunção Cognitiva Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Adv Sci (Weinh) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença de Alzheimer / Disfunção Cognitiva Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Adv Sci (Weinh) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China