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Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD.
Gonuguntla, Venkateswarlu; Yang, Ehwa; Guan, Yi; Koo, Bang-Bon; Kim, Jae-Hun.
Afiliação
  • Gonuguntla V; Medical Science Research Institute, Samsung Medical Center, Seoul, South Korea.
  • Yang E; Medical Science Research Institute, Samsung Medical Center, Seoul, South Korea.
  • Guan Y; Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA.
  • Koo BB; Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA.
  • Kim JH; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Hum Brain Mapp ; 43(9): 2845-2860, 2022 06 15.
Article em En | MEDLINE | ID: mdl-35289025
Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data-sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network-based applications.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença de Alzheimer / Disfunção Cognitiva Limite: Humans Idioma: En Revista: Hum Brain Mapp Assunto da revista: CEREBRO Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Coréia do Sul

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença de Alzheimer / Disfunção Cognitiva Limite: Humans Idioma: En Revista: Hum Brain Mapp Assunto da revista: CEREBRO Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Coréia do Sul