Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Multimodal Neuroimaging Attention-Based architecture for Cognitive Decline Prediction (2401.06777v1)

Published 21 Dec 2023 in eess.IV and cs.AI

Abstract: The early detection of Alzheimer's Disease is imperative to ensure early treatment and improve patient outcomes. There has consequently been extenstive research into detecting AD and its intermediate phase, mild cognitive impairment (MCI). However, there is very small literature in predicting the conversion to AD and MCI from normal cognitive condition. Recently, multiple studies have applied convolutional neural networks (CNN) which integrate Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) to classify MCI and AD. However, in these works, the fusion of MRI and PET features are simply achieved through concatenation, resulting in a lack of cross-modal interactions. In this paper, we propose a novel multimodal neuroimaging attention-based CNN architecture, MNA-net, to predict whether cognitively normal (CN) individuals will develop MCI or AD within a period of 10 years. To address the lack of interactions across neuroimaging modalities seen in previous works, MNA-net utilises attention mechanisms to form shared representations of the MRI and PET images. The proposed MNA-net is tested in OASIS-3 dataset and is able to predict CN individuals who converted to MCI or AD with an accuracy of 83%, true negative rate of 80%, and true positive rate of 86%. The new state of the art results improved by 5% and 10% for accuracy and true negative rate by the use of attention mechanism. These results demonstrate the potential of the proposed model to predict cognitive impairment and attention based mechanisms in the fusion of different neuroimaging modalities to improve the prediction of cognitive decline.

View on arXiv
Definition Search Book Streamline Icon: https://streamlinehq.com
References (22)
  1. W. Jagust, “Vulnerable neural systems and the borderland of brain aging and neurodegeneration,” Neuron, vol. 77, no. 2, pp. 219–234, 2013.
  2. F. Salami, A. Bozorgi-Amiri, G. M. Hassan, R. Tavakkoli-Moghaddam, and A. Datta, “Designing a clinical decision support system for alzheimer’s diagnosis on oasis-3 data set,” Biomedical Signal Processing and Control, vol. 74, p. 103527, 2022.
  3. R. A. Sperling, P. S. Aisen, L. A. Beckett, D. A. Bennett, S. Craft, A. M. Fagan, T. Iwatsubo, C. R. Jack Jr, J. Kaye, T. J. Montine, et al., “Toward defining the preclinical stages of alzheimer’s disease: Recommendations from the national institute on aging-alzheimer’s association workgroups on diagnostic guidelines for alzheimer’s disease,” Alzheimer’s & dementia, vol. 7, no. 3, pp. 280–292, 2011.
  4. K. A. Johnson, N. C. Fox, R. A. Sperling, and W. E. Klunk, “Brain imaging in alzheimer disease,” Cold Spring Harbor perspectives in medicine, vol. 2, no. 4, p. a006213, 2012.
  5. G. Román and B. Pascual, “Contribution of neuroimaging to the diagnosis of alzheimer’s disease and vascular dementia,” Archives of medical research, vol. 43, no. 8, pp. 671–676, 2012.
  6. J. R. Petrella, R. E. Coleman, and P. M. Doraiswamy, “Neuroimaging and early diagnosis of alzheimer disease: a look to the future,” Radiology, vol. 226, no. 2, pp. 315–336, 2003.
  7. Y. Tu, S. Lin, J. Qiao, Y. Zhuang, and P. Zhang, “Alzheimer’s disease diagnosis via multimodal feature fusion,” Computers in Biology and Medicine, vol. 148, p. 105901, 2022.
  8. M. Liu, D. Cheng, K. Wang, Y. Wang, and A. D. N. Initiative, “Multi-modality cascaded convolutional neural networks for alzheimer’s disease diagnosis,” Neuroinformatics, vol. 16, pp. 295–308, 2018.
  9. M. Odusami, R. Maskeliūnas, R. Damaševičius, and S. Misra, “Machine learning with multimodal neuroimaging data to classify stages of alzheimer’s disease: a systematic review and meta-analysis,” Cognitive Neurodynamics, pp. 1–20, 2023.
  10. M. Velazquez and Y. Lee, “Multimodal ensemble model for alzheimer’s disease conversion prediction from early mild cognitive impairment subjects,” Computers in Biology and Medicine, vol. 151, p. 106201, 2022.
  11. L. R. Trambaiolli, A. C. Lorena, F. J. Fraga, and R. Anghinah, “Support vector machines in the diagnosis of alzheimer’s disease,” in Proceedings of the ISSNIP Biosignals and Biorobotics Conference, vol. 1, pp. 1–6, 2010.
  12. K. Vaithinathan, L. Parthiban, A. D. N. Initiative, et al., “A novel texture extraction technique with t1 weighted mri for the classification of alzheimer’s disease,” Journal of neuroscience methods, vol. 318, pp. 84–99, 2019.
  13. K. Gunawardena, R. Rajapakse, and N. Kodikara, “Applying convolutional neural networks for pre-detection of alzheimer’s disease from structural mri data,” in 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 1–7, IEEE, 2017.
  14. A. B. Tufail, Y. Ma, and Q.-N. Zhang, “Multiclass classification of initial stages of alzheimer’s disease through neuroimaging modalities and convolutional neural networks,” in 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 51–56, IEEE, 2020.
  15. J. Bardwell, G. M. Hassan, F. Salami, and N. Akhtar, “Cognitive impairment prediction by normal cognitive brain mri scans using deep learning,” in AI 2022: Advances in Artificial Intelligence: 35th Australasian Joint Conference, AI 2022, Perth, WA, Australia, December 5–8, 2022, Proceedings, pp. 571–584, Springer, 2022.
  16. C. Feng, A. Elazab, P. Yang, T. Wang, F. Zhou, H. Hu, X. Xiao, and B. Lei, “Deep learning framework for alzheimer’s disease diagnosis via 3d-cnn and fsbi-lstm,” IEEE Access, vol. 7, pp. 63605–63618, 2019.
  17. M. Golovanevsky, C. Eickhoff, and R. Singh, “Multimodal attention-based deep learning for alzheimer’s disease diagnosis,” Journal of the American Medical Informatics Association, vol. 29, no. 12, pp. 2014–2022, 2022.
  18. P. J. LaMontagne, T. L. Benzinger, J. C. Morris, S. Keefe, R. Hornbeck, C. Xiong, E. Grant, J. Hassenstab, K. Moulder, A. G. Vlassenko, et al., “Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease,” MedRxiv, pp. 2019–12, 2019.
  19. M. Jenkinson, M. Pechaud, S. Smith, et al., “Bet2: Mr-based estimation of brain, skull and scalp surfaces,” in Eleventh annual meeting of the organization for human brain mapping, vol. 17, p. 167, Toronto., 2005.
  20. A. Hoopes, J. S. Mora, A. V. Dalca, B. Fischl, and M. Hoffmann, “Synthstrip: skull-stripping for any brain image,” NeuroImage, vol. 260, p. 119474, 2022.
  21. M. Jenkinson, P. Bannister, M. Brady, and S. Smith, “Improved optimization for the robust and accurate linear registration and motion correction of brain images,” Neuroimage, vol. 17, no. 2, pp. 825–841, 2002.
  22. K. Hara, H. Kataoka, and Y. Satoh, “Learning spatio-temporal features with 3d residual networks for action recognition,” in Proceedings of the IEEE international conference on computer vision workshops, pp. 3154–3160, 2017.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Jamie Vo (1 paper)
  2. Naeha Sharif (4 papers)
  3. Ghulam Mubashar Hassan (22 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now