Early prediction of dementia using fMRI data with a graph convolutional network approach
Open accessAbstract
Objective . Alzheimer’s disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs). Approach . Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI. Main results . The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification. Significance . Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at: https://github.com/Shuning-Han/FC-based-GCN .
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Han's GCN framework of resting-state functional MRI achieved 80.3% mild cognitive impairment diagnosis accuracy while identifying weakened connectivity in the default mode and visual networks as early biomarkers.
“Han’s GCN framework of resting-state functional MRI (rs-fMRI) achieved 80.3% mild cognitive impairment (MCI) diagnosis accuracy while identifying weakened connectivity in the default mode and visual networks as early biomarkers”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Han et al., 2024
Section 3.2 reports 80.3% as the best average accuracy for the GCN’s MCI-versus-normal-control classification. The Discussion says default mode and visual network connectivity may be altered early and may matter for classification, but it does not establish weakened connectivity in both networks as identified early biomarkers. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881 · see citing works
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820 · see citing works
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304 · see citing works
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880 · see citing works
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358 · see citing works
Avants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,618 · see citing works
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106 · see citing works
fMRIPrep: A robust preprocessing pipeline for functional MRI
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0235-4 · Cited by 3,905 · see citing works
Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657 · see citing works
Accurate and robust brain image alignment using boundary-based registration
Greve, D. N., & Fischl, B. R. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.06.060 · Cited by 3,553 · see citing works
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260 · see citing works
Comprehensive Review on Alzheimer’s Disease: Causes and Treatment
Breijyeh, Z., & Karaman, R. (2020) · Molecules · doi:10.3390/molecules25245789 · Cited by 2,789 · see citing works
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.064 · Cited by 1,684 · see citing works
Identification of autism spectrum disorder using deep learning and the ABIDE dataset
Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., & Meneguzzi, F. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.08.017 · Cited by 1,016 · see citing works
LaMontagne, P. J., Benzinger, T. L., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A. G., et al. (2019) · medRxiv · doi:10.1101/2019.12.13.19014902 · Cited by 779 · see citing works
Neural Network for Graphs: A Contextual Constructive Approach
Micheli, A. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2010350 · Cited by 639 · see citing works
Dementia: timely diagnosis and early intervention
Robinson, L., Tang, E., & Taylor, J.-P. (2015) · BMJ · doi:10.1136/bmj.h3029 · Cited by 503 · see citing works
Alzheimer’s disease and its treatment by different approaches: A review
Srivastava, S., Ahmad, R., & Khare, S. K. (2021) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2021.113320 · Cited by 490 · see citing works
Alexander-Bloch, A. F., Gogtay, N., Meunier, D., Birn, R., Clasen, L., Lalonde, F., Lenroot, R., Giedd, J., & Bullmore, E. T. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00147 · Cited by 480 · see citing works
Deep Learning Approach for Early Detection of Alzheimer’s Disease
Helaly, H. A., Badawy, M., & Haikal, A. Y. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09946-2 · Cited by 397 · see citing works
Magnetic resonance imaging in Alzheimer’s disease and mild cognitive impairment
Initiative, A. D. N., Chandra, A., Dervenoulas, G., & Politis, M. (2018) · Journal of Neurology · doi:10.1007/s00415-018-9016-3 · Cited by 386 · see citing works
Metric learning with spectral graph convolutions on brain connectivity networks
Ktena, S. I., Parisot, S., Ferrante, E., Rajchl, M., Lee, M., Glocker, B., & Rueckert, D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.052 · Cited by 380 · see citing works
The trees and the forest: Characterization of complex brain networks with minimum spanning trees
Stam, C. J., Tewarie, P. K. B., van Dellen, E., van Straaten, E. C. W., Hillebrand, A., & Mieghem, P. V. (2014) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2014.04.001 · Cited by 354 · see citing works
Spectral Graph Convolutions for Population-Based Disease Prediction
Parisot, S., Ktena, S. I., Ferrante, E., Lee, M., Moreno, R. G., Glocker, B., & Rueckert, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_21 · Cited by 260 · see citing works
Li, H., Hou, X., Liu, H., Yue, C., He, Y., & Zuo, X. (2015) · Human Brain Mapping · doi:10.1002/hbm.22689 · Cited by 196 · see citing works
Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., & Grohe, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014602 · Cited by 172 · see citing works
Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review
Warren, S. L., & Moustafa, A. A. (2022) · Journal of Neuroimaging · doi:10.1111/jon.13063 · Cited by 162 · see citing works
Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders
Liu, S., Cai, W., Liu, S., Zhang, F., FULHAM, M., Feng, D., Pujol, S., & Kikinis, R. (2015) · Brain Informatics · doi:10.1007/s40708-015-0019-x · Cited by 150 · see citing works
Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
Chong-yaw Wee, A., Liu, C., Lee, A., Poh, J. S., Ji, H., & Qiu, A. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101929 · Cited by 146 · see citing works
Zhao, K., Duka, B., Xie, H., Oathes, D. J., Calhoun, V. D., & Zhang, Y. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118774 · Cited by 146 · see citing works
Self-citationEnactment effect in memory: evidence concerning the function of the supramarginal gyrus
Russ, M. O., Mack, W., Grama, C., Lanfermann, H., & Knopf, M. (2003) · Experimental Brain Research · doi:10.1007/s00221-003-1398-4 · Cited by 142 · see citing works
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13412 · Cited by 125 · see citing works
Using DeepGCN to identify the autism spectrum disorder from multi-site resting-state data
Cao, M., Yang, M., Qin, C., Zhu, X., Chen, Y., Wang, J., & Liu, T. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103015 · Cited by 114 · see citing works
Transfer Learning for Alzheimer's Disease Detection on MRI Images
Ghahnavieh, A. E., Luo, S., & Chiong, R. (2019) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iciaict.2019.8784845 · Cited by 109 · see citing works
Karakaya, T., Fußer, F., Schröder, J., & Pantel, J. (2013) · Current Neuropharmacology · doi:10.2174/157015913804999487 · Cited by 103 · see citing works
Multi-scale enhanced graph convolutional network for mild cognitive impairment detection
Lei, B., Zhu, Y., Yu, S., Hu, H., Xu, Y., Yue, G., Wang, T., Zhao, C., Chen, S., Yang, P., et al. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2022.109106 · Cited by 96 · see citing works
Zhang, Z., Hong, Z., Liang, K., Wang, H., Kong, S., Hu, J., Wu, F., & Sun, G. (2014) · Neuroscience Letters · doi:10.1016/j.neulet.2014.11.050 · Cited by 96 · see citing works
Bi, X., Zhao, X., Huang, H., Chen, D., & Ma, Y. (2019) · Cognitive Computation · doi:10.1007/s12559-019-09688-2 · Cited by 94 · see citing works
Graph convolutional network for fMRI analysis based on connectivity neighborhood
Wang, L., Li, K., & Hu, X. P. (2020) · Network Neuroscience · doi:10.1162/netn_a_00171 · Cited by 92 · see citing works
Illakiya, T., & Karthik, R. (2023) · Neuroinformatics · doi:10.1007/s12021-023-09625-7 · Cited by 78 · see citing works
Zhu, H., Zhou, P., Alcauter, S., Chen, Y., Cao, H., Tian, M., Ming, D., Qi, H., Wang, X., Zhao, X. W., et al. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/046008 · Cited by 69 · see citing works
Altered Functional Connectivity of Cognitive-Related Cerebellar Subregions in Alzheimer’s Disease
Zheng, W., Liu, X., Song, H., Li, K., & Wang, Z. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00143 · Cited by 68 · see citing works
Clinical symptoms in Alzheimer's disease
López, O. L., & DeKosky, S. T. (2008) · Handbook of clinical neurology · doi:10.1016/s0072-9752(07)01219-5 · Cited by 66 · see citing works
Influence of functional connectivity and structural MRI measures on episodic memory
He, J., Carmichael, O. T., Fletcher, E. M., Singh, B., Iosif, A., Martinez, O., Reed, B., Yonelinas, A., & DeCarli, C. S. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.12.029 · Cited by 58 · see citing works
Characterization of resting state activity in MCI individuals
Esposito, R., Mosca, A., Pieramico, V., Cieri, F., Cera, N., & Sensi, S. L. (2013) · PeerJ · doi:10.7717/peerj.135 · Cited by 50 · see citing works
Hidden Markov random field model for segmentation of brain MR image
Zhang, Y., Brady, J. M., & Smith, S. M. (2000) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.387617 · Cited by 42 · see citing works
Self-citationContrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
Tang, H., Ma, G., Guo, L., Fu, X., Huang, H., & Zhan, L. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3220220 · Cited by 40 · see citing works
Wu, Z., Xu, D., Potter, T. B. H., Zhang, Y., & Initiative, T. A. D. N. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00113 · Cited by 36 · see citing works
Self-citationDevika, K. N., & Murthy, O. V. R. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377173 · Cited by 20 · see citing works
Zhang, Y., Naparstek, S., Gordon, J. R., Watts, M., Shpigel, E., El-Said, D., Badami, F. S., Eisenberg, M. L., Toll, R. T., Gage, A. T., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00049-5 · Cited by 20 · see citing works
Self-citationPredicting cognitive scores with graph neural networks through sample selection learning
Hanik, M., Demirtaş, M. A., Gharsallaoui, M. A., & Rekik, I. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00585-7 · Cited by 19 · see citing works
Cerebral connectivity study by functional and diffusion MRI in intelligence
Suprano, I. (2019) · doi:10.70675/a155ad2fz25b1z4f0fza91dz446c09879ce2 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Niepert M, Ahmed M and Kutzkov K 2016 Learning convolutional neural networks for graphs Int. Conf. on Machine Learning (PMLR) pp 2014-23
- Wein S 2023 Applications of spatio-temporal graph neural network models for brain connectivity analysis PhD Thesis University of Regensburg
- Errica F, Podda M, Bacciu D and Micheli A 2020 A fair comparison of graph neural networks for graph classification Int. Conf. on Learning Representations (ICLR 2020)
- Fey M and Eric Lenssen J 2019 Fast graph representation learning with PyTorch geometric ICLR Workshop on Representation Learning on Graphs and Manifolds
Cited by
Sorted by citations
Dang, M., Liu, B., Chen, Y., & Zhang, Z. (2026) · Psychoradiology · doi:10.1093/psyrad/kkag007 · Cited by 7 · see citing works
- Authors
- Han, S., Sun, Z., Zhao, K., Duan, F., Caiafa, C. F., Zhang, Y., & Solé-Casals, J.
- Affiliations
- Universitat de Vic - Universitat Central de Catalunya (ES)
- Juntendo University (JP)
- Lehigh University (US)
- Nankai University (CN)
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Corresponding author
- Caiafa, Cesar F
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2024-01-29
- Volume
- 21
- Issue
- 1
- Pages
- 016013
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W4390822022
- Cited by
- 23
- References
- 56
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 6
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Conicet (submittedVersion, cc-by-nc-sa)
- Funders
- Tianjin Municipal Science and Technology Program (22PTZWHZ00040)
- Experimental Sciences and Technology at the University of Vic - Central University of Catalonia (doctoral programme)
- PICT (2020-SERIEA-00457)
- PIP (112202101 00284CO (Argentina))
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Epidemiology › Traumatic Brain Injury Research
- Keywords
- mild cognitive impairment, Alzheimer's disease, graph convolutional networks, resting-state fMRI, functional connectivity, brain network connectivity, dementia risk prediction, OASIS-3, default mode network, ventral attention network, sensorimotor network, visual network
- MeSH terms
- Alzheimer Disease, Brain, Brain Mapping, Dementia, Humans, Magnetic Resonance Imaging, Cognitive Dysfunction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
