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Altered spontaneous activity in Alzheimer's disease and mild cognitive impairment revealed by Regional Homogeneity
Abstract
Alzheimer's disease (AD), the most prevalent cause of dementia in the elderly, is characterized by progressive cognitive and intellectual deficits. Most patients with mild cognitive impairment (MCI) are thought to be in a very early stage of AD. Resting-state functional magnetic resonance imaging reflects spontaneous brain activities and/or the endogenous/background neurophysiological process of the human brain. Regional Homogeneity (ReHo) can provide a fast method for mapping regional activity across the whole brain. Little has been previously published about where or how spontaneous activity differs between MCI and AD, although many previous fMRI studies have shown that the activity pattern is altered in MCI/AD. In the present study, we first used the ReHo method to explore differences in regional spontaneous activities throughout the whole brain between normal controls (NC) and people with MCI and with AD. A one-way ANOVA was performed to determine the regions in which the ReHo differs between the three groups, and then a post hoc analysis was performed to evaluate differences in the pattern among the three groups. Finally a correlation analysis was done between the ReHo index of these regions and clinical variables in order to evaluate the relationship between ReHo and cognitive measures in the AD and MCI groups. An exploratory classification analysis also demonstrated that ReHo measures were able to correctly separate subjects in 71.4% of the cases. Altered brain spontaneous activations were found in the medial prefrontal cortex, the bilateral posterior cingulate gyrus/precuneus and the left inferior parietal lobule (IPL) in both MCI and AD. In MCI, the ReHo index in the left IPL was higher than that of the NC, which could indicate the presence of a compensatory mechanism in MCI. More obviously, the correlation analysis indicated that the lower the memory and other cognitive abilities, the lower the ReHo in patients with MCI and AD. Combining our findings with the results in earlier studies, we propose that the spontaneous activity pattern in the resting state could potentially be used as a clinical marker for MCI/AD.
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From State and training effects of mindfulness meditation on brain networks reflect neuronal mechanisms of its antidepressant effect (2016) · cites this paper as [77]
Citations
Cites
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · cited 2×
“…and profoundly involved area of amyloid deposits and neurofibrillary tangles, so a considerable amount of work on examining the anatomic basis for memory impairment in AD has focused on the hippocampus and other MTL structures (Arnold et al., 1991; Braak and Braak, 1991; Wolk and Dickerson, 2011).”
Development and validation of a geriatric depression screening scale: a preliminary report
Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1982) · Journal of Psychiatric Research · doi:10.1016/0022-3956(82)90033-4 · Cited by 15,025
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Current concepts in mild cognitive impairment
Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001) · Archives of Neurology · doi:10.1001/archneur.58.12.1985 · Cited by 4,781
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Wang, D.-Y., Han, X., Li, S., Liu, D., Yan, C., Zuo, X., Zhu, C.-Z., Yong He, A., Kiviniemi, V. J., & Zang, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051617 · Cited by 6
Toural, J. E. S., Pedrón, A. M., & Reyes, E. J. M. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100537 · Cited by 6
Wang, J., Tian, Y., Shao, Y., Feng, H., Qin, L., Xu, W., Liu, H., Xu, Q., Wei, S., & Ma, L. (2017) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s145183 · Cited by 6
Sun, Z., Cao, Z., Ge, L., Li, Y., Huang, H., Li, M., & Qiu, S. (2025) · Meta-Radiology · doi:10.1016/j.metrad.2025.100136 · Cited by 7
Cognition–Eye–Brain Connection in Alzheimer's Disease Spectrum Revealed by Multimodal Imaging
Shi, Y., Shen, T., Yan, S., Liang, J., Wei, T., Huang, Y., Gao, R., Zheng, N., Ci, R., Zhang, M., et al. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70003 · Cited by 5
Liu, Q., Xiao, K., Wan, P., & Zou, Q. (2025) · Clinical Interventions in Aging · doi:10.2147/cia.s529416 · Cited by 5
Zhou, J., Zhu, H., Wang, J., & Sun, J. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3276896 · Cited by 5
Ren, R., Zhang, G., Ma, J., Zheng, Y., Zhao, Y., Zhang, Y., & Zhao, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-89747-x · Cited by 5
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Nobili, F. M., Arnaldi, D., Roccatagliata, L., Chincarini, A., Accardo, J., Picco, A., Ferrara, M., Buschiazzo, A., & Morbelli, S. D. (2014) · Springer eBooks · doi:10.1007/978-3-642-54307-4_12 · Cited by 5
Li, H., Lin, Y., Ji, C., Wang, X., Wu, Q., Li, Q., & Chen, Z. (2017) · Japanese Journal of Radiology · doi:10.1007/s11604-017-0614-2 · Cited by 5
Zhu, H., Zhu, H., Liu, X., Zhou, Y., Wu, S., Wei, F., & Guo, Z. (2022) · Cureus · doi:10.7759/cureus.26797 · Cited by 5
Zhang, D., Yue, J., Niu, H., Wei, Z., Huang, D., Wang, P., Li, X., Zhao, Y., & Zhang, Q. (2025) · Current Alzheimer Research · doi:10.2174/0115672050352061250328055829 · Cited by 4
Self-citationWang, D., Xu, K., Dang, M., Sang, F., Chen, K., Zhang, Z., & Li, X. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad369 · Cited by 4
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Liu, T., Wang, Y., Yan, T., Liu, Y., Xu, R., Li, J., & Xie, Y. (2018) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2018.8633094 · Cited by 4
Self-citationLi, J., Rong, D., Shan, Y., Zhang, M., Zhao, C., & Lu, J. (2021) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2021.106205 · Cited by 5
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Early classification of Alzheimer's disease using hippocampal texture from structural MRI
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Sheng, J., Wang, J., Zhang, Q., Huang, R., Lu, Y., & Li, T. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.08.059 · Cited by 3
Bao, Y., Shea, Y. F., Chiu, P. K., Kwan, J. S. K., Chan, F. H.-W., Chow, W.-S., Chan, K., & Mak, H. K. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.956222 · Cited by 3
Xiao, X., Wei, J., Zhang, W., Jiao, B., Liao, X., Pan, C., Liu, X., Yan, X., Tang, B., Zhang, Y., et al. (2019) · Neuroreport · doi:10.1097/wnr.0000000000001297 · Cited by 3
Qin, Q., Liu, J., Fan, W., Zhang, X., Lu, J., Guo, X., Lei, Z., & Wang, J. (2025) · Biomedicines · doi:10.3390/biomedicines13040873 · Cited by 2
Self-citationJoshy, M. E., Liu, L., Dassanayake, P., Aiello, M., Cecca, A. D., Cavaliere, C., Anazodo, U., Finger, E. C., & Lawrence, K. S. (2025) · Human Brain Mapping · doi:10.1002/hbm.70388 · Cited by 2
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Li, Z., Yan, S. Q., Zhao, K., Wang, D., Yao, H., Zhou, B., Zhang, Z., Wang, P., Liao, Z., Chen, Y., et al. (2026) · BMC Medicine · doi:10.1186/s12916-026-04674-6 · Cited by 2
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Li, G., Song, Y., Liang, H., Sun, X., Yu, J., Zhai, R., & Liang, M. (2024) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-024-00912-5 · Cited by 2
Grazia, A., Dyrba, M., Pomara, N., Temp, A. G. M., Grothe, M. J., & Teipel, S. J. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2024.100030 · Cited by 2
Dou, X., Yao, H., Jin, D., Feng, F., Wang, P., Zhou, B., Liu, B., Yang, Z., An, N., Zhang, X., et al. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759211 · Cited by 2
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Fan, Z., Chen, X., Qi, Z., Li, L., Lu, B., Jiang, C., Zhu, R., Chen, L., & Yan, C. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/281352 · Cited by 2
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Self-citationvon Gal, A., Papa, D., D’Auria, M., & Piccardi, L. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251337770 · Cited by 1
Nguyen, J. Q., Riddle, T., Nguyen, N. D., Chao, S., Dutta, D., Hill, A., Yu, R., Rowland, K. M., & Shi, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-15547-y · Cited by 3
Yang, H., Lin, H., Lin, X.-F., Zhang, X., Xiong, H., & Zheng, Y. (2020) · Journal of Bio-X Research · doi:10.1097/jbr.0000000000000047 · Cited by 1
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Chen, S., Li, R., Wang, P., & Li, J. (2022) · PsyCh Journal · doi:10.1002/pchj.550 · Cited by 1
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Chen, C., Wang, J., Liu, Y., & Chen, X. (2017) · Journal of the Society for Information Display · doi:10.1002/jsid.601 · Cited by 1
Self-citationWang, Y., Wen, J., Kong, C., Xu, Z., Hu, S., Li, M., Wang, X., Zhang, H., Jia, X., Ding, Q., et al. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-229 · Cited by 1
Koike, M., Noguchi‐Shinohara, M., Morise, H., Kudo, K., Tsuchimine, S., Misaka, Y., Komatsu, J., Abe, C., Horimoto, M., Kitagawa, S., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1380609/v1 · Cited by 1
The Protective Impact of Education on Brain Structure and Function in Alzheimer’s DiseasePreprint
Zhu, W., Li, X., Li, X., Wang, H., Li, M., Gao, Z., Wu, X., Tian, Y., Zhou, S., Wang, K., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-73112/v1 · Cited by 1
The protective impact of education on brain structure and function in Alzheimer’s DiseasePreprint
Zhu, W., Li, X., Li, X., Wang, H., Li, M., Gao, Z., Wu, X., Tian, Y., Zhou, S., Wang, K., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-73112/v2 · Cited by 1
Choi, J., Go, H.-J., Woo, Y.-S., Song, S.-H., Yang, P.-S., & Jeong, B. (2012) · Journal of korean Academy of Child and Adolescent Psychiatry · doi:10.5765/jkacap.2012.23.2.57
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Howlett, R. D. M. (2020) · Brock University Digital Repository (Brock University)
Structural and functional brain changes after a 40-days short-term mindfulness meditation training
Yang, C.-C. (2020) · Digitalen Hochschulbibliothek Sachsen-Anhalt (Universitäts- und Landesbibliothek Sachsen-Anhalt) · doi:10.25673/35073
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Pilipenko, T., Gnutti, A., Silvestri, A., Serina, I., & Leonardi, R. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.187
Taoka, T. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70005
Chen, X., Zhou, S., Wang, W., Gao, Z., Ye, W., Zhu, W., Lu, Y., Ma, J., Li, X., Yu, Y., et al. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251364722
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Ke, H., Adhikari, B. M., Pan, Y., Keator, D. B., Amen, D., Gao, S., Ma, Y., Thompson, P. M., Jahanshad, N., Turner, J. A., et al. (2025) · Brain Sciences · doi:10.3390/brainsci15090908
Zhu, W., Li, X., Li, X., Wang, H., Li, M., Gao, Z., Wu, X., Tian, Y., Zhou, S., Wang, K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16909719
Zhu, W., Li, X., Li, X., Wang, H., Li, M., Gao, Z., Wu, X., Tian, Y., Zhou, S., Wang, K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16909725
Zhu, W., Li, X., Li, X., Wang, H., Li, M., Gao, Z., Wu, X., Tian, Y., Zhou, S., Wang, K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16909728
Zhu, W., Li, X., Li, X., Wang, H., Li, M., Gao, Z., Wu, X., Tian, Y., Zhou, S., Wang, K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16909722
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Duan, G., Ding, Z., Liu, Y., Zeng, X., Wang, Z., Xu, Y., & Wang, L. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462187
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Cheung, E. Y., Shea, Y. F., Chiu, P. K. C., Chan, K. H., Zhang, H., Yam, K. K., Bae, K. T., & Mak, H. K. F. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1903239
Zeng, P., Luo, D., Zeng, B., Li, B., Peng, Y., Xiang, Y., Nie, L., & Li, Y. (2026) · Brain Imaging and Behavior · doi:10.1007/s11682-026-01200-3
Metadata
- Authors
- Zhang, Z., Liu, Y., Jiang, T., Zhou, B., An, N., Dai, H., Wang, P., Niu, Y., Wang, L., & Zhang, X.
- Affiliations
- Chinese PLA General Hospital (CN)
- Chinese Academy of Sciences (CN)
- University of Electronic Science and Technology of China (CN)
- Corresponding author
- Wang, Luning
- Zhang, Xi
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 59
- Issue
- 2
- Pages
- 1429-1440
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2029132563
- Cited by
- 255
- References
- 89
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 4 · 2023: 11 · 2022: 18 · 2021: 26 · 2020: 22 · 2019: 19 · 2018: 19 · 2017: 20 · 2016: 32 · 2015: 22 · 2014: 19 · 2013: 10 · 2012: 10
- FWCI
- 6.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Science Foundation of China (60831004, 30900476)
- National Basic Research Program of China (2011CBA00408)
- National High Technology Research and Development Program of China (2009AA02Z302)
- International Cooperation and Exchanges Projects (2009DFB10500)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, mild cognitive impairment, regional homogeneity, spontaneous brain activity, resting-state functional connectivity, medial prefrontal cortex, posterior cingulate cortex, precuneus, inferior parietal lobule, compensatory mechanisms, memory impairment, clinical biomarkers
- MeSH terms
- Aged, Algorithms, Alzheimer Disease, Biological Clocks, Brain, Cognition Disorders, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Sensitivity and Specificity, Reproducibility of Results
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