Accumulation of network redundancy marks the early stage of Alzheimer's disease

• Published in: Human brain mapping Vol. 44; no. 8; pp. 2993 - 3006
• Main Authors: Ghanbari, Maryam, Li, Guoshi, Hsu, Li‐Ming, Yap, Pew‐Thian
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2023
• Subjects: Age; Alzheimer Disease; Alzheimer's disease; Brain; Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; Cognitive ability; Cognitive Dysfunction; Dementia; dynamic functional connectivity; Functional magnetic resonance imaging; graph theory; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Memory; Neural networks; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Neuroprotection; Pathology; Redundancy; Support vector machines; Variance analysis; Wiring; dynamic functional connectivity; graph theory; Alzheimer's disease
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.26257

Brain wiring redundancy counteracts aging‐related cognitive decline by reserving additional communication channels as a neuroprotective mechanism. Such a mechanism plays a potentially important role in maintaining cognitive function during the early stages of neurodegenerative disorders such as Alzheimer's disease (AD). AD is characterized by severe cognitive decline and involves a long prodromal stage of mild cognitive impairment (MCI). Since MCI subjects are at high risk of converting to AD, identifying MCI individuals is essential for early intervention. To delineate the redundancy profile during AD progression and enable better MCI diagnosis, we define a metric that reflects redundant disjoint connections between brain regions and extract redundancy features in three high‐order brain networks—medial frontal, frontoparietal, and default mode networks—based on dynamic functional connectivity (dFC) captured by resting‐state functional magnetic resonance imaging (rs‐fMRI). We show that redundancy increases significantly from normal control (NC) to MCI individuals and decreases slightly from MCI to AD individuals. We further demonstrate that statistical features of redundancy are highly discriminative and yield state‐of‐the‐art accuracy of up to 96.8 ± 1.0% in support vector machine (SVM) classification between NC and MCI individuals. This study provides evidence supporting the notion that redundancy serves as a crucial neuroprotective mechanism in MCI. We demonstrated the utility of our redundancy metric in differentiating the stages of AD progression. We reported a pattern of accrual of redundant connections in MCI to counter AD pathology. Our results provide evidence supporting redundancy as a neuroprotective mechanism in cognitive aging. By taking advantage of the high sensitivity of our metric, we showed that MCI individuals can be detected with high accuracy.