Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD

• Published in: Human brain mapping Vol. 43; no. 9; pp. 2845 - 2860
• Main Authors: Gonuguntla, Venkateswarlu, Yang, Ehwa, Guan, Yi, Koo, Bang‐Bon, Kim, Jae‐Hun
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.06.2022
• Subjects: Alzheimer's disease; Alzheimer's disease (AD); Atrophy; Brain; Brain mapping; brain signatures; Cognitive ability; Dementia; Disease control; gray matter; Investigations; Magnetic resonance imaging; Medical imaging; Networks; Neurodegenerative diseases; Neuroimaging; Signatures; structural MRI (sMRI); Substantia grisea; tissue probability maps; Tomography; brain signatures; Alzheimer's disease (AD); tissue probability maps; structural MRI (sMRI); gray matter
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25820

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.