Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Subjects Based on Individual Default Mode Network Metabolic Connectivity Strength

Qi Zhang; Fangjie Li; Min Wei; Min Wang; Luyao Wang; Ying Han; Jiehui Jiang; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1016/j.bpsc.2024.04.004

Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Subjects Based on Individual Default Mode Network Metabolic Connectivity Strength

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Apr 15:S2451-9022(24)00104-6. doi: 10.1016/j.bpsc.2024.04.004. Online ahead of print.

Authors

Qi Zhang¹, Fangjie Li², Min Wei³, Min Wang⁴, Luyao Wang⁴, Ying Han⁵, Jiehui Jiang⁶; Alzheimer’s Disease Neuroimaging Initiative

Affiliations

¹ School of Communication & Information Engineering, Shanghai University, Shanghai, China, 200444; School of Life Sciences, Shanghai University, Shanghai, China, 200444.
² Shanghai University of Traditional Chinese Medicine, Shanghai, China, 201203.
³ Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China, 100053.
⁴ School of Life Sciences, Shanghai University, Shanghai, China, 200444.
⁵ Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China, 100053; School of Biomedical Engineering, Hainan University, Haikou, China, 570228; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China, 100053; National Clinical Research Center for Geriatric Diseases, Beijing, China, 100053. Electronic address: hanying@xwh.ccmu.edu.cn.
⁶ School of Life Sciences, Shanghai University, Shanghai, China, 200444. Electronic address: jiangjiehui@shu.edu.cn.

PMID: 38631552
DOI: 10.1016/j.bpsc.2024.04.004

Abstract

Background: Predicting cognitive decline in those already Aβ positive or Tau positive among the aging population poses clinical challenges. In Alzheimer's disease (AD) research, intra-default mode network (DMN) connections play a pivotal role in diagnosis. This paper proposes metabolic connectivity within the DMN as a supplementary biomarker to the AT(N) framework.

Methods: Extracting data from 1292 subjects in the Alzheimer's Disease Neuroimaging Initiative, we collected paired T1-weighted structural MRI and ¹⁸F-labeled-fluorodeoxyglucose positron emission computed tomography (PET) scans. Individual metabolic DMN networks were constructed, and metabolic connectivity (MC) strength in DMN was assessed. In the cognitively unimpaired (CU) group, the Cox model identified CU(MC+), high-risk subjects, with Kaplan-Meier survival analyses and hazard ratio (HR) revealing MC strength's predictive performance. Spearman correlation analyses explored relationships between MC strength, AT(N) biomarkers, and clinical scales. DMN standard uptake value ratio (SUVR) provided comparative insights in the analyses.

Results: Both MC strength and SUVR exhibit gradual declines with cognitive deterioration, displaying significant intergroup differences. Survival analyses indicate enhanced Aβ and Tau prediction with both metrics, with MC strength outperforming SUVR. Combined MC strength and Aβ yield optimal predictive performance (HR = 9.29), followed by MC strength and Tau (HR = 8.92). In CU(MC+), MC strength correlates significantly with CSF Aβ₄₂ and AV₄₅ PET SUVR (r = 0.22, -0.19). Generally, MC strength's correlation with AT(N) biomarkers exceeded SUVR.

Conclusions: Individuals with normal cognition and disrupted DMN metabolic connectivity face an elevated cognitive decline risk linked to Aβ, preceding metabolic issues.

Keywords: Alzheimer's disease; Cognitive decline; Cognitively unimpaired; Default mode network; FDG PET; Metabolic connectivity.