Hippocampus-centred grey matter covariance networks predict the development and reversion of mild cognitive impairment
Open accessAbstract
Background: Mild cognitive impairment (MCI) has been thought of as the transitional stage between normal ageing and Alzheimer’s disease, involving substantial changes in brain grey matter structures. As most previous studies have focused on single regions (e.g. the hippocampus) and their changes during MCI development and reversion, the relationship between grey matter covariance among distributed brain regions and clinical development and reversion of MCI remains unclear. Methods: With samples from two independent studies (155 from the Beijing Aging Brain Rejuvenation Initiative and 286 from the Alzheimer’s Disease Neuroimaging Initiative), grey matter covariance of default, frontoparietal, and hippocampal networks were identified by seed-based partial least square analyses, and random forest models were applied to predict the progression from normal cognition to MCI (N-t-M) and the reversion from MCI to normal cognition (M-t-N). Results: With varying degrees, the grey matter covariance in the three networks could predict N-t-M progression (AUC = 0.692–0.792) and M-t-N reversion (AUC = 0.701–0.809). Further analyses indicated that the hippocampus has emerged as an important region in reversion prediction within all three brain networks, and even though the hippocampus itself could predict the clinical reversion of M-t-N, the grey matter covariance showed higher prediction accuracy for early progression of N-t-M. Conclusions: Our findings are the first to report grey matter covariance changes in MCI development and reversion and highlight the necessity of including grey matter covariance changes along with hippocampal degeneration in the early detection of MCI and Alzheimer’s disease.
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Integrating tau PET, FDG-PET, and amyloid-β achieved maximal performance (0.95 accuracy; representing a 7-14% improvement).
“integrating all three biomarkers achieved maximal performance (0.95 accuracy; representing a 7–14% improvement)”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Dang et al., 2023
The Methods section describes using baseline T1-weighted MRI, and Table 3 in the Results reports AUCs for grey-matter covariance networks. Searches for tau PET, FDG-PET, amyloid-β, and the stated performance figures found no passage reporting a three-biomarker model with 0.95 accuracy or a 7–14% improvement. Our copy does not address the attributed result. (LLM, conf=0.75)
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Mild Cognitive Impairment Is Not a Clinical Entity and Should Not Be Treated
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“Over the past few decades, much research has focused on the clinical and cognitive characteristics of MCI, and these studies have found that MCI is a heterogeneous at-risk state that does not necessarily lead to AD [9, 10].”
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“Furthermore, studies with longitudinal design also found that clinically defined MCI was not a stable population [11–14].”
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“However, another study found no differences in vascular risk factors between the MCI reversion, stabilization, and progression groups, with low white matter hyperintensity grades characteristic of MCI reversion [60].”
ResultThomas, K. R., Edmonds, E. C., Eppig, J. S., Wong, C. G., Weigand, A. J., Bangen, K. J., Jak, A. J., Delano‐Wood, L., Galasko, D. R., Salmon, D. P., et al. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.06.4948 · Cited by 65 · cited 2× · see citing works
“Furthermore, studies with longitudinal design also found that clinically defined MCI was not a stable population [11–14].”
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“Background Mild cognitive impairment (MCI) is considered to be the transitional stage between normal ageing and clinical dementia [1], and patients with MCI have substantial increases in the risk of progression to dementia [2].”
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“Sample characteristics The participants were from the Beijing Aging Brain Rejuvenation Initiative (BABRI) [27] and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [28].”
BackgroundSelf-citationGray matter structural covariance networks changes along the Alzheimer's disease continuum
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“Construction of the structural covariance network As cognitive impairment might cause changes in the correlation between seeds and other brain regions [33], we established standard structural covariance networks based on 65 sNC from the BABRI and 47 sNC from the ADNI (“Template sNC” in Table 1).”
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“Seed-PLS is a data-driven multivariate statistical technique that reveals the covariance patterns of the GM structure throughout the brain and is methodologically applicable to large-scale structural covariance networks [35].”
MethodBrain imaging of mild cognitive impairment and Alzheimer's disease.
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“For example, several studies have identified significant reductions in local GM volume in the hippocampus, entorhinal cortex, medial temporal lobe, insula, and thalamus in patients with MCI compared to cognitively normal older adults [17–19].”
ResultChung, S. J., Park, Y.-H., Yoo, H. S., Lee, Y. H., Ye, B. S., Sohn, Y. H., Lee, J., & Lee, P. H. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.02.023 · Cited by 30 · cited 2× · see citing works
“A longitudinal study of MCI reversion to cognitively normal status showed that MCI reverters exhibited less severely decreased functional connectivity in the DMN and executive control networks than non-reverters [51].”
BackgroundIs there an MCI reversion to cognitively normal? Analysis of Alzheimer's disease biomarkers profiles
Park, M. H., & Han, C. (2014) · International Psychogeriatrics · doi:10.1017/s1041610214002129 · Cited by 28 · cited 4× · see citing works
“Second, this study only focused on the measurement of structural covariance and lacked a comparison or combination with other biomarkers that have been proven to be related to the development or reversion of MCI, such as CSF amyloid-β 1 to 42 peptide and CSF total tau [11, 21, 46].”
BackgroundMethodResultInfluentialKatayama, O., Lee, S., Bae, S., Makino, K., Shinkai, Y., Chiba, I., Harada, K., & Shimada, H. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9041076 · Cited by 21 · cited 1× · see citing works
“Two other recent studies have reached the same conclusion that non-smoking is a beneficial factor in reversing from MCI to NC state [58, 59].”
BackgroundSha, F., Zhao, Z.-Y., Wei, C., Yang, Z., & Li, B. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215677 · Cited by 18 · cited 1× · see citing works
“Two other recent studies have reached the same conclusion that non-smoking is a beneficial factor in reversing from MCI to NC state [58, 59].”
BackgroundYang, C., Sun, X., Tao, W., Li, X., Zhang, J., Jia, J., Chen, K., & Zhang, Z. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00332 · Cited by 5 · cited 1× · see citing works
“T scanners (participants from BABRI and ADNI-GO&2); the acquisition parameters for each study have been published previously [27, 30–32].”
MethodSelf-citation
Flagged first, then by citations
3 of the 62 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
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- [34]Krishnan A Williams LJ McIntosh AR Abdi H Partial least squares (PLS) methods for neuroimaging: a tutorial and review Neuroimage. 2011 56 2 455 475 20656037
Cited by
Sorted by citations
Illakiya, T., & Karthik, R. (2023) · IEEE Access · doi:10.1109/access.2023.3332122 · Cited by 24 · see citing works
“Focusing on brain regions, such as hippocampal atrophy, is used in some of the research works [23], [24].”
MethodZhang, Q., Sheng, J., Zhang, Q., Wang, L., Yang, Z., & Xin, Y. (2023) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2023.107392 · Cited by 23 · see citing works
Wang, X., Peng, L., Zhan, S., Yin, X., Huang, L. T., Huang, J., Yang, J., Zhang, Y., Zeng, Y., & Liang, S. (2024) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2024.103921 · Cited by 20 · see citing works
Devignes, Q., Ren, B., Clancy, K. J., Howell, K., Pollmann, Y., Martinez-Sanchez, L., Beard, C., Kumar, P., & Rosso, I. M. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02795-1 · Cited by 8 · see citing works
“This approach is suitable for analyzing large-scale SCNs and has been used previously in multiple studies to examine structural network integrity [76, 77], including studies using aHPC and pHPC as seed regions [78, 79].”
MethodWei, X., Xiong, R., Xu, P., Zhang, T., Zhang, J., Jin, Z., & Li, L. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01752-4 · Cited by 8 · see citing works
“Nevertheless, these studies focused primarily on alterations in local anatomical regions and failed to consider the potential connections among distributed brain regions [27].”
Wang, W., Peng, J.-X., Hou, J., Yuan, Z.-Y., Xie, W., Mao, G., Pan, Y., Shao, Y., & Shu, Z.-Y. (2023) · Aging Clinical and Experimental Research · doi:10.1007/s40520-023-02456-1 · Cited by 11 · see citing works
Dang, M., Liu, B., Chen, Y., & Zhang, Z. (2026) · Psychoradiology · doi:10.1093/psyrad/kkag007 · Cited by 7 · see citing works
Self-citationPredictors of Transition from Mild Cognitive Impairment to Normal Cognition and Dementia
Gao, J., Liu, L., Yang, Z., Fan, J., & Initiative, A. D. N. (2025) · Behavioral Sciences · doi:10.3390/bs15111552 · Cited by 6 · see citing works
Zhan, S., Wang, J., Dong, J., Ji, X., Huang, L. T., Zhang, Q., Xu, D., Peng, L., Wang, X., Zhang, Y., et al. (2025) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2025.104532 · Cited by 6 · see citing works
Alzheimer’s disease detection through wavelet-based shifted elliptical local binary pattern
Garg, N., Choudhry, M. S., & Bodade, R. M. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107067 · Cited by 5 · see citing works
Lin, S., Xue, M., Sun, J., Xu, C., Wang, T., Lian, J., Lv, M., Yang, P., Sheng, C., Cheng, Z., et al. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.08.059 · Cited by 4 · see citing works
Zhao, X.-Y., Bai, H., & Li, Y.-P. (2026) · Proceedings of the 2026 2nd International Symposium on Bioinformatics and Computational Biology · doi:10.1145/3820664.3820667 · Cited by 3
“…coupling is organized at the level of intrinsic networks, the default mode network is a compelling target; an open question is whether DMN gray matter volume covaries with whole-brain functional activity in a network-structured way and whether this coupling systematically changes with age [13, 14].”
Kanoria, S., Veitch, D. P., Miller, M. J., Aisen, P. S. S., Beckett, L. A., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Lee, E. B., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71353 · Cited by 2 · see citing works
“The greater challenges of predicting CU to MCI progression, 87,88 MCI to CU reversion, 87 and MCI to dementia progression have also benefited from the application of ML methods to ADNI sMRI data to the point where reasonably accurate (AUCs in the range of 0.70–0.90) predictions are possible up to 7…”
InfluentialSun, B.-W., Wang, L., Gao, M., Fan, Z., & Zhang, T. (2025) · Symmetry · doi:10.3390/sym17101754 · Cited by 3
Sanami, S., Intzandt, B., Huck, J., Villeneuve, S., Iturria‐Medina, Y., & Gauthier, C. J. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.04.011 · Cited by 3 · see citing works
The impact of Alzheimer's disease on cortical complexity and its underlying biological mechanisms
Chen, L., Zhou, X., Qiao, Y., Wang, Y., Zhou, Z., Jia, S., Sun, Y., & Peng, D. (2025) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2025.111320
Yan, M., Liu, C., Zhang, Y., Pan, Y., Liu, T., Zhou, H., Li, Z., & Wang, Y. (2025) · GeroScience · doi:10.1007/s11357-025-01667-w · Cited by 2 · see citing works
- Authors
- Dang, M., Yang, C., Chen, K., Lu, P., Li, H., Zhang, Z., & for the Beijing Aging Brain Rejuvenation Initiative, F. T. A. D. N. I.
- Affiliations
- Beijing Normal University (CN)
- Banner Alzheimer’s Institute (US)
- China Academy of Chinese Medical Sciences (CN)
- Corresponding author
- Zhang, Zhanjun
- Venue
- Alzheimer's Research & Therapy
- Published
- 2023-02-02
- Volume
- 15
- Issue
- 1
- Pages
- 27-27
- ISSN
- 1758-9193 (ISSN-L)
- PMC
- PMC9893696
- OpenAlex
- W4319030137
- Cited by
- 16
- References
- 62
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 4 · 2023: 3
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Alzheimer s Research & Therapy (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- China Postdoctoral Science Foundation (2021M700485)
- State Key Program of National Natural Science of China (82130118)
- Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (81820108034)
- National Science Fund for Distinguished Young Scholars (81625025)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
- Keywords
- mild cognitive impairment, hippocampus, default mode network, frontoparietal network, Alzheimer's disease, early detection, brain network connectivity
- MeSH terms
- Alzheimer Disease, Brain, Hippocampus, Humans, Magnetic Resonance Imaging, Cognitive Dysfunction, Gray Matter
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