Hee Jin Kim,^1,2 Jin Ju Yang,³ Hunki Kwon,³ Changsoo Kim,⁴ Jong Min Lee,³ Phillip Chun,^5,6 Yeo Jin Kim,^1,2,7 Na-Yeon Jung,^1,2,8 Juhee Chin,^1,2 Seonwoo Kim,⁹ Sook-young Woo,⁹ Yearn Seong Choe,¹⁰ Kyung-Han Lee,¹⁰ Sung Tae Kim,¹¹ Jae Seung Kim,¹² Jae Hong Lee,¹³ Michael W. Weiner,¹⁴ Duk L. Na^1,2,15 and Sang Won Seo^1,2,16,*

¹ Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
² Neuroscience Center, Samsung Medical Center, Seoul, Korea
³ Department of Biomedical Engineering, Hanyang University, Seoul, Korea
⁴ Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea
⁵ Department of Emergency Medicine Behavioral Emergencies Research Lab, San Diego, CA, USA
⁶ Department of Biology, University of California San Diego, CA, USA
⁷ Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Korea
⁸ Department of Neurology, Pusan National University Hospital, Pusan National University School of Medicine and Medical
Research Institute, Busan, Republic of Korea
⁹ Biostatistics team, Samsung Biomedical Research Institute
¹⁰ Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
¹¹ Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
¹² Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
¹³ Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
¹⁴ Center for Imaging of Neurodegenerative Diseases, University of California, San Francisco, CA, USA
¹⁵ Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
¹⁶ Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea

^*Correspondence to: Sang Won Seo, MD, PhD
Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 50 Ilwon-dong, Gangnam-gu, Seoul 135-710, Republic of Korea

Summary
Amyloid-β and cerebral small vessel disease are the two major causes of cognitive impairment in the elderly. However, the underlying mechanisms responsible for precisely how amyloid-β and cerebral small vessel disease affect cognitive impairment remain unclear. We investigated the effects of amyloid-β and lacunes on downstream imaging markers including structural network and cortical thickness, further analyzing their relative impact on cognitive trajectories. We prospectively recruited a pool of 117 mild cognitive impairment patients (45 amnestic type and 72 subcortical vascular type), from which 83 patients received annual follow-up with neuropsychological tests and brain magnetic resonance imaging for 3 years, and 87 patients received a second Pittsburgh compound B positron emission tomography analysis. Structural networks based on diffusion tensor imaging and cortical thickness were analyzed. We used linear mixed effect regression models to evaluate the effects of imaging markers on cognitive decline. Time-varying Pittsburgh compound B uptake was associated with temporoparietal thinning, which correlated with memory decline (verbal memory test, unstandardized β = - 0.79, P < 0.001; visual memory test, unstandardized β = - 2.84, P = 0.009). Time-varying lacune number was associated with the degree of frontoparietal network disruption or thinning, which further affected frontal-executive function decline (Digit span backward test, unstandardized β =  - 0.05, P = 0.002; Stroop colour test, unstandardized β = - 0.94, P = 0.008). Of the multiple imaging markers analyzed, Pittsburgh compound B uptake and the number of lacunes had the greatest association with memory decline and frontal-executive function decline, respectively: Time-varying Pittsburgh compound B uptake (standardized β = - 0.25, P = 0.010) showed the strongest effect on visual memory test, followed by time-varying temporoparietal thickness (standardized β = 0.21, P = 0.010) and time-varying nodal efficiency (standardized β = 0.17, P = 0.024). Time-varying lacune number (standardized β = - 0.25, P = 0.014) showed the strongest effect on time-varying digit span backward test followed by time-varying nodal efficiency (standardized β = 0.17, P = 0.021). Finally, time-varying lacune number (β = - 0.22, P = 0.034) showed the strongest effect on time-varying Stroop colour test followed by time-varying frontal thickness (standardized β = 0.19, P = 0.026). Our multimodal imaging analyses suggest that cognitive trajectories related to amyloid-β and lacunes have distinct paths, and that amyloid-β or lacunes have greatest impact on cognitive decline. Our results provide rationale for the targeting of amyloid-β and lacunes in therapeutic strategies aimed at ameliorating cognitive decline.

mild cognitive impairment, amyloid-β, cerebral small vessel disease, downstream imaging markers, cognitive trajectory