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A 3D decoupling Alzheimer’s disease prediction network based on structural MRI

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Abstract

Purpose

This paper aims to develop a three-dimensional (3D) Alzheimer’s disease (AD) prediction method, thereby bettering current predictive methods, which struggle to fully harness the potential of structural magnetic resonance imaging (sMRI) data.

Methods

Traditional convolutional neural networks encounter pressing difficulties in accurately focusing on the AD lesion structure. To address this issue, a 3D decoupling, self-attention network for AD prediction is proposed. Firstly, a multi-scale decoupling block is designed to enhance the network’s ability to extract fine-grained features by segregating convolutional channels. Subsequently, a self-attention block is constructed to extract and adaptively fuse features from three directions (sagittal, coronal and axial), so that more attention is geared towards brain lesion areas. Finally, a clustering loss function is introduced and combined with the cross-entropy loss to form a joint loss function for enhancing the network’s ability to discriminate between different sample types.

Results

The accuracy of our model is 0.985 for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and 0.963 for the Australian Imaging, Biomarker & Lifestyle (AIBL) dataset, both of which are higher than the classification accuracy of similar tasks in this category. This demonstrates that our model can accurately distinguish between normal control (NC) and Alzheimer’s Disease (AD), as well as between stable mild cognitive impairment (sMCI) and progressive mild cognitive impairment (pMCI).

Conclusion

The proposed AD prediction network exhibits competitive performance when compared with state-of-the-art methods. The proposed model successfully addresses the challenges of dealing with 3D sMRI image data and the limitations stemming from inadequate information in 2D sections, advancing the utility of predictive methods for AD diagnosis and treatment.

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References

  1. Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, Cummings J, van der Flier WM. Alzheimer’s disease. Lancet. 2021;397(10284):1577–90.

    Article  Google Scholar 

  2. Nguyen-Ky T, Wen P, Li Y. Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals. IEEE Trans Biomed Eng. 2013;60(6):1488–98.

    Article MATH  Google Scholar 

  3. Schmierer T, Li T, Li Y. A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia. Health Inf Sci Syst. 2022;10(1):10.

    Article MATH  Google Scholar 

  4. Siuly S, Li Y, Wen P, Alcin OF. SchizoGoogLeNet: the GoogLeNet-based deep feature extraction design for automatic detection of schizophrenia. Comput Intell Neurosci. 2022;2022(1):1992596.

    Google Scholar 

  5. Izzo J, Andreassen OA, Westlye LT, van der Meer D. The association between hippocampal subfield volumes in mild cognitive impairment and conversion to Alzheimer’s disease. Brain Res. 2020;1728: 146591.

    Article  Google Scholar 

  6. Li Y, Wen P, Powers D, Clark CR. LSB neural network based segmentation of MR brain images. In: IEEE SMC’99 conference proceedings. 1999 IEEE international conference on systems, man, and cybernetics (Cat. No. 99CH37028), 199, vol 6. IEEE; 1999. p. 822–5.

  7. Bashar MR, Li Y, Wen P. Study of EEGs from somatosensory cortex and Alzheimer’s disease sources. Int J Biol Life Sci. 2011;8:2.

    MATH  Google Scholar 

  8. Li Y, Chi Z. MR brain image segmentation based on self-organizing map network. Int J Inf Technol. 2005;11(8).

  9. Brickman AM, Zahodne LB, Guzman VA, Narkhede A, Meier IB, Griffith EY, Provenzano FA, Schupf N, Manly JJ, Stern Y, et al. Reconsidering harbingers of dementia: progression of parietal lobe white matter hyperintensities predicts Alzheimer’s disease incidence. Neurobiol Aging. 2015;36(1):27–32.

    Article  Google Scholar 

  10. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

    Article MATH  Google Scholar 

  11. Sarki R, Ahmed K, Wang H, Zhang Y, Wang K. Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Trans Scalable Inf Syst. 2021.https://doi.org/10.4108/eai.16-12-2021.172436.

    Article MATH  Google Scholar 

  12. Pandey D, Wang H, Yin X, Wang K, Zhang Y, Shen J. Automatic breast lesion segmentation in phase preserved DCE-MRIs. Health Inf Sci Syst. 2022;10(1):9.

    Article MATH  Google Scholar 

  13. Wang S-H, Zhou Q, Yang M, Zhang Y-D. ADVIAN: Alzheimer’s disease VGG-inspired attention network based on convolutional block attention module and multiple way data augmentation. Front Aging Neurosci. 2021;13: 687456.

    Article  Google Scholar 

  14. Zhang Z, Gao L, Jin G, Guo L, Yao Y, Dong L, Han J, The Alzheimer’s Disease NeuroImaging Initiative. THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer’s disease. Quant Imaging Med Surg. 2021;11(7):3338–54.

    Article MATH  Google Scholar 

  15. Guan H, Liu Y, Yang E, Yap P-T, Shen D, Liu M. Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification. Med Image Anal. 2021;71: 102076.

    Article  Google Scholar 

  16. Hoang GM, Kim U-H, Kim JG. Vision transformers for the prediction of mild cognitive impairment to Alzheimer’s disease progression using mid-sagittal sMRI. Front Aging Neurosci. 2023;15:1102869.

    Article MATH  Google Scholar 

  17. Xing Y, Guan Y, Yang B, Liu J. Classification of sMRI images for Alzheimer’s disease by using neural networks. In: Yu S, Zhang Z, Yuen PC, Han J, Tan T, Guo Y, Lai J, Zhang J, editors. Pattern recognition and computer vision. Cham: Springer; 2022. p. 54–66.

    Chapter MATH  Google Scholar 

  18. Zhang X, Han L, Zhu W, Sun L, Zhang D. An explainable 3D residual self-attention deep neural network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE J Biomed Health Inform. 2022;26(11):5289–97.

    Article MATH  Google Scholar 

  19. Bakkouri I, Afdel K, Benois-Pineau J, Catheline G. Recognition of Alzheimer’s disease on sMRI based on 3D multi-scale CNN features and a gated recurrent fusion unit. In: 2019 International conference on content-based multimedia indexing (CBMI), 2019. IEEE; 2019. p. 1–6.

  20. Chen L, Qiao H, Zhu F. Alzheimer’s disease diagnosis with brain structural MRI using multiview-slice attention and 3D convolution neural network. Front Aging Neurosci. 2022;14: 871706.

    Article  Google Scholar 

  21. Liu F, Wang H, Liang S-N, Jin Z, Wei S, Li X. MPS-FFA: a multiplane and multiscale feature fusion attention network for Alzheimer’s disease prediction with structural MRI. Comput Biol Med. 2023;157: 106790.

    Article MATH  Google Scholar 

  22. Wei S, Li Y, Yang W. An adaptive feature fusion network for Alzheimer’s disease prediction. In: Health information science, lecture notes in computer science. Singapore: Springer; 2023. p. 271–82.

  23. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, Jack CR Jr, Jagust WJ, Shaw LM, Toga AW, Trojanowski JQ, Weiner MW. Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neurology. 2010;74(3):201–9.

    Article  Google Scholar 

  24. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations, 2015.

  25. Han R, Liu Z, Chen CLP. Multi-scale 3D convolution feature-based broad learning system for Alzheimer’s disease diagnosis via MRI images. Appl Soft Comput. 2022;120: 108660.

    Article MATH  Google Scholar 

  26. Hu K, Wang Y, Chen K, Hou L, Zhang X. Multi-scale features extraction from baseline structure MRI for MCI patient classification and ad early diagnosis. Neurocomputing. 2016;175:132–45.

    Article MATH  Google Scholar 

  27. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJ, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems. New York: Curran Associates Inc; 2012. p. 25.

    MATH  Google Scholar 

  28. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Advances in neural information processing systems, 2017, p. 30.

  29. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), 2016. IEEE; 2016. p. 770–778.

  30. Banerjee K, Prasad CV, Gupta RR, Vyas K, Anushree H, Mishra B. Exploring alternatives to Softmax function. In: Proceedings of the 2nd international conference on deep learning theory and applications (DeLTA 2021), 2020.

  31. Aljalbout E, Golkov V, Siddiqui Y, Strobel M, Cremers D. Clustering with deep learning: taxonomy and new methods. arXiv preprint; 2018.arXiv:1801.07648.

  32. Liang D, Lin L, Hu H, Zhang Q, Chen Q, lwamoto Y, Han X, Chen Y-W. Combining convolutional and recurrent neural networks for classification of focal liver lesions in multi-phase CT images. In: Medical image computing and computer assisted intervention—MICCAI 2018, 2018. Cham: Springer; 2018. p. 666–75.

  33. Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol. 2021;65(5):545–63.

    Article  Google Scholar 

  34. Prechelt L. Early stopping—but when? In: Neural Networks: tricks of the trade, 2022. Springer; 2002. p. 55–69.

  35. Bernerth JB, Aguinis H. A critical review and best-practice recommendations for control variable usage. Pers Psychol. 2016;69(1):229–83.

    Article  Google Scholar 

  36. Nanni L, Brahnam S, Salvatore C, Castiglioni I. Texture descriptors and voxels for the early diagnosis of Alzheimer’s disease. Artif Intell Med. 2019;97:19–26.

    Article MATH  Google Scholar 

  37. Gao X, Shi F, Shen D, Liu M. Task-induced pyramid and attention GAN for multimodal brain image imputation and classification in Alzheimer’s disease. IEEE J Biomed Health Inform. 2021;26(1):36–43.

    Article MATH  Google Scholar 

  38. Lian C, Liu M, Pan Y, Shen D. Attention-guided hybrid network for dementia diagnosis with structural MR images. IEEE Trans Cybern. 2020;52(4):1992–2003.

    Article MATH  Google Scholar 

  39. Guan H, Wang C, Cheng J, Jing J, Liu T. A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer’s disease. Hum Brain Mapp. 2022;43(2):760–72.

    Article MATH  Google Scholar 

  40. Zhu W, Sun L, Huang J, Han L, Zhang D. Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI. IEEE Trans Med Imaging. 2021;40(9):2354–66.

    Article MATH  Google Scholar 

  41. Salami F, Bozorgi-Amiri A, Hassan GM, Tavakkoli-Moghaddam R, Datta A. Designing a clinical decision support system for Alzheimer’s diagnosis on OASIS-3 data set. Biomed Signal Process Control. 2022;74: 103527.

    Article MATH  Google Scholar 

  42. Islam J, Zhang Y. Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inform. 2018;5:1–14.

    Article MATH  Google Scholar 

  43. Saratxaga CL, Moya I, Picón A, Acosta M, Moreno-Fernandez-de-Leceta A, Garrote E, Bereciartua-Perez A. MRI deep learning-based solution for Alzheimer’s disease prediction. J Pers Med. 2021;11(9):902.

    Article  Google Scholar 

  44. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, p. 770–8.

  45. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27(8):861–74.

    Article MathSciNet MATH  Google Scholar 

  46. Zheng B, Gao A, Huang X, Li Y, Liang D, Long X. A modified 3D EfficientNet for the classification of Alzheimer’s disease using structural magnetic resonance images. IET Image Process. 2023;17(1):77–87.

    Article MATH  Google Scholar 

  47. Mehmood A, Maqsood M, Bashir M, Shuyuan Y. A deep Siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sci. 2020;10(2):84.

    Article  Google Scholar 

  48. Tufail AB, Ma Y-K, Zhang Q-N. Binary classification of Alzheimer’s disease using sMRI imaging modality and deep learning. J Digit Imaging. 2020;33:1073–90.

    Article MATH  Google Scholar 

  49. Möller C, Vrenken H, Jiskoot L, Versteeg A, Barkhof F, Scheltens P, van der Flier WM. Different patterns of gray matter atrophy in early- and late-onset Alzheimer’s disease. Neurobiol Aging. 2013;34(8):2014–22.

    Article  Google Scholar 

  50. Yang J, Pan P, Song W, Huang R, Li J, Chen K, Gong Q, Zhong J, Shi H, Shang H. Voxelwise meta-analysis of gray matter anomalies in Alzheimer’s disease and mild cognitive impairment using anatomic likelihood estimation. J Neurol Sci. 2012;316(1–2):21–9.

    Article  Google Scholar 

  51. Xia M, Wang J, He Y. BrainNet viewer: a network visualization tool for human brain connectomics. PLoS ONE. 2013;8(7): e68910.

    Article  Google Scholar 

  52. Sadiq MT, Siuly S, Almogren A, Li Y, Wen P. Efficient novel network and index for alcoholism detection from EEGs. Health Inf Sci Syst. 2023;11(1):27.

    Article  Google Scholar 

  53. Li Y, Wen P, et al. Analysis and classification of EEG signals using a hybrid clustering technique. In: IEEE/ICME international conference on complex medical engineering, 2010. IEEE; 2010. p. 34–9.

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Author information

Authors and Affiliations

  1. School of Mathematics and Computing, University of Southern Queensland, 487-535 West Street, Toowoomba, QLD, 4350, Australia

    Shicheng Wei, Wencheng Yang & Yan Li

  2. Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia

    Eugene Wang

  3. Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia

    Eugene Wang

  4. Department of Engineering, La Trobe University, Bundoora, VIC, 3086, Australia

    Song Wang

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  1. Shicheng Wei

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  2. Wencheng Yang

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  3. Eugene Wang

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  4. Song Wang

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  5. Yan Li

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All authors contributed to the study conception, design, and manuscript writing. All authors read and approved the final manuscript.

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Correspondence toWencheng Yang orYan Li.

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Wei, S., Yang, W., Wang, E.et al. A 3D decoupling Alzheimer’s disease prediction network based on structural MRI.Health Inf Sci Syst13, 17 (2025). https://doi.org/10.1007/s13755-024-00333-3

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