Effect of education on functional network edge efficiency in Alzheimer’s disease

Yeshin Kim, Sung Woo Kim, Sang Won Seo, Hyemin Jang, Ko Woon Kim, Soo Hyun Cho, Si Eun Kim, Seung Joo Kim, Jin San Lee, Sung Tae Kim, Duk L. Na, Joon Kyung Seong, Hee Jin Kim

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2 Citations (Scopus)


We investigated the effect of education on the edge efficiency in resting state functional networks (RSFNs) in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease dementia (ADD). We collected the data of 57 early aMCI, 141 late aMCI, 173 mild ADD, and 39 moderate-to-severe ADD patients. We used years of education as a proxy for cognitive reserve. We measured edge efficiency for each edge in RSFNs, and performed simple slope analyses to discover their associations with education level among the four groups. In the late aMCI, a sub-network that had hub nodes in the right middle frontal gyrus and the right posterior cingulate gyrus, showed a positive association between RSFN edge efficiency and education (threshold = 2.5, p = 0.0478). There was no negative effect of education on the RSFN edge efficiency. In the early aMCI, mild ADD, and moderate-to-severe ADD, there were no sub-networks showing positive or negative correlation between education and RSFN edge efficiency. There was a positive effect of higher education on RSFN edge efficiency in the late aMCI, but not in the early aMCI or ADD. This indicates that in late aMCI, those who have higher education level have greater ability to resist collapsed functional network.

Original languageEnglish
Article number17255
JournalScientific reports
Issue number1
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the ICT Creative Consilience program (IITP-2021-2020-0-01821) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP); a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare and Ministry of science and ICT, Republic of Korea (HU21C0111, HI19C1132); IITP grant funded by the Korea government (MSIT) (2019-0-00079, Department of Artificial Intelligence (Korea University)); the Brain Korea 21 Plus (BK21) grant funded by the Korea government (MSIT) (Interdisciplinary Program in Precision Public Health (Korea University)); and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2019R1A2C109021211). The study sponsors had no role in the design, collection, analysis, or interpretation of data, and had no role in the writing of the manuscript.

Publisher Copyright:
© 2021, The Author(s).

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