Alterations of Structural Network Efficiency in Early-Onset and Late-Onset Alzheimer's Disease

Suyeon Heo; Cindy W Yoon; Sang-Young Kim; Woo-Ram Kim; Duk L Na; Young Noh

doi:10.3988/jcn.2023.0092

Alterations of Structural Network Efficiency in Early-Onset and Late-Onset Alzheimer's Disease

J Clin Neurol. 2024 May;20(3):265-275. doi: 10.3988/jcn.2023.0092. Epub 2024 Feb 5.

Authors

Suyeon Heo^#¹, Cindy W Yoon^#², Sang-Young Kim^{3

4}, Woo-Ram Kim³, Duk L Na^{5

6}, Young Noh^{3

7}

Affiliations

¹ Gachon University, College of Medicine, Incheon, Korea.
² Department of Neurology, Inha University School of Medicine, Incheon, Korea.
³ Neuroscience Research Institute, Gachon University, Incheon, Korea.
⁴ MR Clinical Science, Health Systems, Philips Healthcare, Seoul, Korea.
⁵ Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
⁶ Happymind Clinic, Seoul, Korea.
⁷ Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea. ynoh@gilhospital.com.

^# Contributed equally.

Abstract

Background and purpose: Early- and late-onset Alzheimer's disease (EOAD and LOAD, respectively) share the same neuropathological hallmarks of amyloid and neurofibrillary tangles but have distinct cognitive features. We compared structural brain connectivity between the EOAD and LOAD groups using structural network efficiency and evaluated the association of structural network efficiency with the cognitive profile and pathological markers of Alzheimer's disease (AD).

Methods: The structural brain connectivity networks of 80 AD patients (47 with EOAD and 33 with LOAD) and 57 healthy controls were reconstructed using diffusion-tensor imaging. Graph-theoretic indices were calculated and intergroup differences were evaluated. Correlations between network parameters and neuropsychological test results were analyzed. The correlations of the amyloid and tau burdens with network parameters were evaluated for the patients and controls.

Results: Compared with the age-matched control group, the EOAD patients had increased global path length and decreased global efficiency, averaged local efficiency, and averaged clustering coefficient. In contrast, no significant differences were found in the LOAD patients. Locally, the EOAD patients showed decreases in local efficiency and the clustering coefficient over a wide area compared with the control group, whereas LOAD patients showed such decreases only within a limited area. Changes in network parameters were significantly correlated with multiple cognitive domains in EOAD patients, but only with Clinical Dementia Rating Sum-of-Boxes scores in LOAD patients. Finally, the tau burden was correlated with changes in network parameters in AD signature areas in both patient groups, while there was no correlation with the amyloid burden.

Conclusions: The impairment of structural network efficiency and its effects on cognition may differ between EOAD and LOAD.

Keywords: Alzheimer disease; diffusion-tensor imaging; early-onset Alzheimer's disease; late-onset Alzheimer's disease; white-matter connectivity.

Abstract

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