Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment (2312.09022v2)

Published 14 Dec 2023 in eess.IV, cs.CV, and q-bio.NC

Abstract: Effective connectivity estimation plays a crucial role in understanding the interactions and information flow between different brain regions. However, the functional time series used for estimating effective connectivity is derived from certain software, which may lead to large computing errors because of different parameter settings and degrade the ability to model complex causal relationships between brain regions. In this paper, a brain diffuser with hierarchical transformer (BDHT) is proposed to estimate effective connectivity for mild cognitive impairment (MCI) analysis. To our best knowledge, the proposed brain diffuser is the first generative model to apply diffusion models to the application of generating and analyzing multimodal brain networks. Specifically, the BDHT leverages structural connectivity to guide the reverse processes in an efficient way. It makes the denoising process more reliable and guarantees effective connectivity estimation accuracy. To improve denoising quality, the hierarchical denoising transformer is designed to learn multi-scale features in topological space. By stacking the multi-head attention and graph convolutional network, the graph convolutional transformer (GraphConformer) module is devised to enhance structure-function complementarity and improve the ability in noise estimation. Experimental evaluations of the denoising diffusion model demonstrate its effectiveness in estimating effective connectivity. The proposed model achieves superior performance in terms of accuracy and robustness compared to existing approaches. Moreover, the proposed model can identify altered directional connections and provide a comprehensive understanding of parthenogenesis for MCI treatment.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (53)
  1. R. Gupta and N. Sen, “Traumatic brain injury: a risk factor for neurodegenerative diseases,” Reviews in the Neurosciences, vol. 27, no. 1, pp. 93–100, 2016.
  2. B. Lei, E. Liang, M. Yang, P. Yang, F. Zhou, E.-L. Tan, Y. Lei, C.-M. Liu, T. Wang, X. Xiao et al., “Predicting clinical scores for alzheimer’s disease based on joint and deep learning,” Expert Systems with Applications, vol. 187, p. 115966, 2022.
  3. M. A. Myszczynska, P. N. Ojamies, A. M. Lacoste, D. Neil, A. Saffari, R. Mead, G. M. Hautbergue, J. D. Holbrook, and L. Ferraiuolo, “Applications of machine learning to diagnosis and treatment of neurodegenerative diseases,” Nature Reviews Neurology, vol. 16, no. 8, pp. 440–456, 2020.
  4. G. Deshpande, P. Santhanam, and X. Hu, “Instantaneous and causal connectivity in resting state brain networks derived from functional mri data,” Neuroimage, vol. 54, no. 2, pp. 1043–1052, 2011.
  5. H.-J. Park, K. J. Friston, C. Pae, B. Park, and A. Razi, “Dynamic effective connectivity in resting state fmri,” NeuroImage, vol. 180, pp. 594–608, 2018.
  6. Y. Zhong, L. Huang, S. Cai, Y. Zhang, K. M. von Deneen, A. Ren, J. Ren, A. D. N. Initiative et al., “Altered effective connectivity patterns of the default mode network in alzheimer’s disease: an fmri study,” Neuroscience letters, vol. 578, pp. 171–175, 2014.
  7. M. Scherr, L. Utz, M. Tahmasian, L. Pasquini, M. J. Grothe, J. P. Rauschecker, T. Grimmer, A. Drzezga, C. Sorg, and V. Riedl, “Effective connectivity in the default mode network is distinctively disrupted in alzheimer’s disease–simultaneous resting-state fdg-pet/fmri study,” Human brain mapping, vol. 42, no. 13, pp. 4134–4143, 2021.
  8. Z. Xia, T. Zhou, S. Mamoon, A. Alfakih, and J. Lu, “A structure-guided effective and temporal-lag connectivity network for revealing brain disorder mechanisms,” IEEE Journal of Biomedical and Health Informatics, 2023.
  9. B. Lei, S. Yu, X. Zhao, A. F. Frangi, E.-L. Tan, A. Elazab, T. Wang, and S. Wang, “Diagnosis of early alzheimer’s disease based on dynamic high order networks,” Brain imaging and behavior, vol. 15, pp. 276–287, 2021.
  10. G. Deshpande, X. Hu, R. Stilla, and K. Sathian, “Effective connectivity during haptic perception: a study using granger causality analysis of functional magnetic resonance imaging data,” Neuroimage, vol. 40, no. 4, pp. 1807–1814, 2008.
  11. K. Friston, “Causal modelling and brain connectivity in functional magnetic resonance imaging,” PLoS biology, vol. 7, no. 2, p. e1000033, 2009.
  12. R. Schlösser, T. Gesierich, B. Kaufmann, G. Vucurevic, S. Hunsche, J. Gawehn, and P. Stoeter, “Altered effective connectivity during working memory performance in schizophrenia: a study with fmri and structural equation modeling,” Neuroimage, vol. 19, no. 3, pp. 751–763, 2003.
  13. J. B. Rowe, L. E. Hughes, R. A. Barker, and A. M. Owen, “Dynamic causal modelling of effective connectivity from fmri: are results reproducible and sensitive to parkinson’s disease and its treatment?” Neuroimage, vol. 52, no. 3, pp. 1015–1026, 2010.
  14. Z. Liu, L. Bai, R. Dai, C. Zhong, H. Wang, Y. You, W. Wei, and J. Tian, “Exploring the effective connectivity of resting state networks in mild cognitive impairment: an fmri study combining ica and multivariate granger causality analysis,” in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.   IEEE, 2012, pp. 5454–5457.
  15. X. Wu, X. Wen, J. Li, and L. Yao, “A new dynamic bayesian network approach for determining effective connectivity from fmri data,” Neural Computing and Applications, vol. 24, pp. 91–97, 2014.
  16. N. Xu, R. N. Spreng, and P. C. Doerschuk, “Initial validation for the estimation of resting-state fmri effective connectivity by a generalization of the correlation approach,” Frontiers in neuroscience, vol. 11, p. 271, 2017.
  17. V. Riedl, L. Utz, G. Castrillón, T. Grimmer, J. P. Rauschecker, M. Ploner, K. J. Friston, A. Drzezga, and C. Sorg, “Metabolic connectivity mapping reveals effective connectivity in the resting human brain,” Proceedings of the National Academy of Sciences, vol. 113, no. 2, pp. 428–433, 2016.
  18. J. Ji, J. Liu, A. Zou, and A. Zhang, “Acoec-fd: Ant colony optimization for learning brain effective connectivity networks from functional mri and diffusion tensor imaging,” Frontiers in Neuroscience, vol. 13, p. 1290, 2019.
  19. S. Chiang, M. Guindani, H. J. Yeh, Z. Haneef, J. M. Stern, and M. Vannucci, “Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data,” Human brain mapping, vol. 38, no. 3, pp. 1311–1332, 2017.
  20. A. R. Anwar, M. Muthalib, S. Perrey, A. Galka, O. Granert, S. Wolff, U. Heute, G. Deuschl, J. Raethjen, and M. Muthuraman, “Effective connectivity of cortical sensorimotor networks during finger movement tasks: a simultaneous fnirs, fmri, eeg study,” Brain topography, vol. 29, pp. 645–660, 2016.
  21. S. Wang, X. Wang, Y. Shen, B. He, X. Zhao, P. W.-H. Cheung, J. P. Y. Cheung, K. D.-K. Luk, and Y. Hu, “An ensemble-based densely-connected deep learning system for assessment of skeletal maturity,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 1, pp. 426–437, 2020.
  22. S. Hu, W. Yu, Z. Chen, and S. Wang, “Medical image reconstruction using generative adversarial network for alzheimer disease assessment with class-imbalance problem,” in 2020 IEEE 6th international conference on computer and communications (ICCC).   IEEE, 2020, pp. 1323–1327.
  23. N. Talebi, A. M. Nasrabadi, and I. Mohammad-Rezazadeh, “Estimation of effective connectivity using multi-layer perceptron artificial neural network,” Cognitive neurodynamics, vol. 12, pp. 21–42, 2018.
  24. Y. Wang, Y. Wang, and Y. W. Lui, “Generalized recurrent neural network accommodating dynamic causal modeling for functional mri analysis,” NeuroImage, vol. 178, pp. 385–402, 2018.
  25. N. Talebi, A. M. Nasrabadi, I. Mohammad-Rezazadeh, and R. Coben, “Ncreann: Nonlinear causal relationship estimation by artificial neural network; applied for autism connectivity study,” IEEE transactions on medical imaging, vol. 38, no. 12, pp. 2883–2890, 2019.
  26. Z. Abbasvandi and A. M. Nasrabadi, “A self-organized recurrent neural network for estimating the effective connectivity and its application to eeg data,” Computers in biology and medicine, vol. 110, pp. 93–107, 2019.
  27. J. Liu, J. Ji, G. Xun, L. Yao, M. Huai, and A. Zhang, “Ec-gan: inferring brain effective connectivity via generative adversarial networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, 2020, pp. 4852–4859.
  28. J. Ji, J. Liu, L. Han, and F. Wang, “Estimating effective connectivity by recurrent generative adversarial networks,” IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3326–3336, 2021.
  29. A. Zou, J. Ji, M. Lei, J. Liu, and Y. Song, “Exploring brain effective connectivity networks through spatiotemporal graph convolutional models,” IEEE Transactions on Neural Networks and Learning Systems, 2022.
  30. S. Hu, Y. Shen, S. Wang, and B. Lei, “Brain mr to pet synthesis via bidirectional generative adversarial network,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II 23.   Springer, 2020, pp. 698–707.
  31. S. Wang, Z. Chen, S. You, B. Wang, Y. Shen, and B. Lei, “Brain stroke lesion segmentation using consistent perception generative adversarial network,” Neural Computing and Applications, vol. 34, no. 11, pp. 8657–8669, 2022.
  32. J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851, 2020.
  33. A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in International Conference on Machine Learning.   PMLR, 2021, pp. 8162–8171.
  34. A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, “Repaint: Inpainting using denoising diffusion probabilistic models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11 461–11 471.
  35. Y. Leng, Z. Chen, J. Guo, H. Liu, J. Chen, X. Tan, D. Mandic, L. He, X. Li, T. Qin et al., “Binauralgrad: A two-stage conditional diffusion probabilistic model for binaural audio synthesis,” Advances in Neural Information Processing Systems, vol. 35, pp. 23 689–23 700, 2022.
  36. H.-J. Park and K. Friston, “Structural and functional brain networks: from connections to cognition,” Science, vol. 342, no. 6158, p. 1238411, 2013.
  37. L. Guo, C. Wang, W. Yang, S. Huang, Y. Wang, H. Pfister, and B. Wen, “Shadowdiffusion: When degradation prior meets diffusion model for shadow removal,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 14 049–14 058.
  38. N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, “Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain,” Neuroimage, vol. 15, no. 1, pp. 273–289, 2002.
  39. J. Wang, X. Wang, M. Xia, X. Liao, A. Evans, and Y. He, “Gretna: a graph theoretical network analysis toolbox for imaging connectomics,” Frontiers in human neuroscience, vol. 9, p. 386, 2015.
  40. Q. Zuo, L. Lu, L. Wang, J. Zuo, and T. Ouyang, “Constructing brain functional network by adversarial temporal-spatial aligned transformer for early ad analysis,” Frontiers in Neuroscience, vol. 16, p. 1087176, 2022.
  41. Z. Cui, S. Zhong, P. Xu, Y. He, and G. Gong, “Panda: a pipeline toolbox for analyzing brain diffusion images,” Frontiers in human neuroscience, vol. 7, p. 42, 2013.
  42. J. Kawahara, C. J. Brown, S. P. Miller, B. G. Booth, V. Chau, R. E. Grunau, J. G. Zwicker, and G. Hamarneh, “Brainnetcnn: Convolutional neural networks for brain networks; towards predicting neurodevelopment,” NeuroImage, vol. 146, pp. 1038–1049, 2017.
  43. S. Yu, S. Wang, X. Xiao, J. Cao, G. Yue, D. Liu, T. Wang, Y. Xu, and B. Lei, “Multi-scale enhanced graph convolutional network for early mild cognitive impairment detection,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII 23.   Springer, 2020, pp. 228–237.
  44. L. Zhang, L. Wang, J. Gao, S. L. Risacher, J. Yan, G. Li, T. Liu, D. Zhu, A. D. N. Initiative et al., “Deep fusion of brain structure-function in mild cognitive impairment,” Medical image analysis, vol. 72, p. 102082, 2021.
  45. L. Liu, Y.-P. Wang, Y. Wang, P. Zhang, and S. Xiong, “An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders,” Medical Image Analysis, vol. 81, p. 102550, 2022.
  46. Q. Zuo, B. Lei, N. Zhong, Y. Pan, and S. Wang, “Brain structure-function fusing representation learning using adversarial decomposed-vae for analyzing mci,” arXiv preprint arXiv:2305.14404, 2023.
  47. B. Lei, N. Cheng, A. F. Frangi, E.-L. Tan, J. Cao, P. Yang, A. Elazab, J. Du, Y. Xu, and T. Wang, “Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early alzheimer’s disease,” Medical image analysis, vol. 61, p. 101652, 2020.
  48. X. Song, F. Zhou, A. F. Frangi, J. Cao, X. Xiao, Y. Lei, T. Wang, and B. Lei, “Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction,” Medical Image Analysis, vol. 69, p. 101947, 2021.
  49. Z. Qi, X. Wu, Z. Wang, N. Zhang, H. Dong, L. Yao, and K. Li, “Impairment and compensation coexist in amnestic mci default mode network,” Neuroimage, vol. 50, no. 1, pp. 48–55, 2010.
  50. X. Wang, X. Cui, C. Ding, D. Li, C. Cheng, B. Wang, and J. Xiang, “Deficit of cross-frequency integration in mild cognitive impairment and alzheimer’s disease: A multilayer network approach,” Journal of Magnetic Resonance Imaging, vol. 53, no. 5, pp. 1387–1398, 2021.
  51. S. J. Chung, Y. J. Kim, J. H. Jung, H. S. Lee, B. S. Ye, Y. H. Sohn, Y. Jeong, and P. H. Lee, “Association between white matter connectivity and early dementia in patients with parkinson disease,” Neurology, vol. 98, no. 18, pp. e1846–e1856, 2022.
  52. S.-Y. Lin, C.-P. Lin, T.-J. Hsieh, C.-F. Lin, S.-H. Chen, Y.-P. Chao, Y.-S. Chen, C.-C. Hsu, and L.-W. Kuo, “Multiparametric graph theoretical analysis reveals altered structural and functional network topology in alzheimer’s disease,” NeuroImage: Clinical, vol. 22, p. 101680, 2019.
  53. M. Bozzali, G. J. Parker, L. Serra, K. Embleton, T. Gili, R. Perri, C. Caltagirone, and M. Cercignani, “Anatomical connectivity mapping: a new tool to assess brain disconnection in alzheimer’s disease,” Neuroimage, vol. 54, no. 3, pp. 2045–2051, 2011.
Citations (2)
View on Semantic Scholar

Summary

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

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